Department of the Built Environment
Unit Building Physics and Services
Masterproject 2
Author
T.J. Baas BSc
0862373
Supervisors
Ir. R.P. Kramer
Dr. ir. A.W.M. van Schijndel
Date
April 2015
Place
Eindhoven
Modelling the behaviour of
controllers in an HVAC system
- a case study -
Masterproject 2
Modelling the behaviour of
controllers in an HVAC system
- a case study -
A research of Eindhoven University of Technology
In collaboration with Strukton Worksphere bv
Masterproject 2
Eindhoven, April 2015
Author:
T.J. Baas BSc
ID 0862373
Supervisors:
Ir. R.P. Kramer
Dr. ir. A.W.M. van Schijndel
Eindhoven University of Technology
Den Dolech 2
5612 AZ Eindhoven
The Netherlands
Tel: +31 (0)40 247 9111
E-mail: cec@tue.nl
All rights reserved.
Nothing out this paper may be copied, multiplied and/or may be published by print, photocopy,
microfilm or any other way without prior permission of the Eindhoven University of Technology.
© Copyright 2015 Eindhoven University of Technology
Abstract
At Eindhoven University of Technology (TU/e), practical buildings and systems are often modelled for
research purposes. These simulations are often based on Matlab oriented software. With the HAMBase
building model, developed by the university itself, one is able to model the heat and vapour flow in a building
very accurate. Because HAMBase is based on Matlab, system models can easily be imported from Simulink.
One is also able to build accurate system models with Simulink, but many difficulties are experienced while
modelling the control behaviour in the systems. Therefore, a new research project was set up, which
focusses solely on the modelling of the control behaviour in HVAC systems.
A research is set up in collaboration with Strukton Worksphere bv. Strukton can offer a large amount of
measurement data from HVAC systems and is always interested in potential energy savings within HVAC
systems. The HVAC system of the Strukton office itself is used as case-system for the simulation study. This
case system is, like most HVAC systems in the Netherlands, controlled by the Priva building management
system (BMS). The simulation study focusses on the modelling of the controllers in this BMS. With the
combination of these controller models and a system model of the AHU, a test model for control strategy
studies can be created.
Conventional PID controllers are widely applied for the control of HVAC systems. Although the basic
structure of a PID controller is rather simple, a lot of different configurations exist. With different PID
structures and features as anti-windup, many different configurations can be created. Eventually, the exact
algorithm of the controller in Priva is found and a model could be built. The controller model, built in Simulink,
shows very accurate simulation results compared to the measurement data. With the controller model
available, a sensitivity study is performed in order to test the influence of some controller specifications on
the model output. It was found that, based on stationary feedback signals, the influence of the data logfrequency and the anti-windup method is significantly large.
The performance of the different controllers in the case system is evaluated using the measurement history
of 2014. Performance indicators on long term performance and smoothness are calculated for each
controller and compared afterwards. It was found that the controllers of the heating- and cooling coil
performed significantly worse compared to the heat exchanger controller. This is probably caused by the
dead time in the system, but further research may conclude if energy savings can be achieved by loop
tuning.
With this research, a model is built which can accurately simulate the control behaviour in an HVAC system.
Further research is proposed to link the controller model to a process model, in order to allow studies on the
robustness of the controller. With the model of the controller, also a starting point for control strategy studies
is made. When the controller models are linked to simulation models of the components in the case system,
a perfect case model for control strategy studies is created. This model can be used for studies on
performance tuning and energy consumption reduction of HVAC systems. The modelling of the system
components and linking to a control loop model is therefore highly recommended.
Table of contents
Title
Modelling the behaviour of
controllers in an HVAC system
Abstract
3
1
1.1
1.2
1.3
Introduction
Background
Previous research of the TU/e
Research setup
6
6
6
6
2
2.1
2.2
2.3
2.4
2.5
Theory
PID controllers
PID algorithms
Integrator windup
Control loop performance tuning
Control loop performance evaluation
8
8
9
9
10
11
3
3.1
3.2
3.3
3.4
Method
Case building
Measurement data
Controller modelling
Sensitivity and performance evaluation
12
12
13
13
14
4
4.1
4.2
4.3
Results
Controller configuration
Model sensitivity
Controller performance
15
15
17
20
5
5.1
5.2
5.3
5.4
Discussion and recommendations
Controller model
Sensitivity study
Performance evaluation
Recommendations
21
21
21
21
21
6
Conclusions
23
7
7.1
7.2
7.3
References
Literature
Software
Model references
24
24
25
25
Table of contents
Title
Modelling the behaviour of
controllers in an HVAC system
Appendices:
I.
System diagrams Strukton Son
26
II.
Control schemes
28
III.
Monitoring overview
30
IV.
Specifications air handling unit
31
Technische Universiteit Eindhoven University of Technology
Introduction
1
In the first part of this chapter, some relevant background information is given, followed by the problem
definition and research setup in the second part.
1.1
Background
At Eindhoven University of Technology (TU/e), practical problems in buildings are often modelled for
research purposes. For the modelling, different software tools are used. Heat and vapour flows within the
building are modelled with HAMBase. HAMBase is a multi-zone building model based on Matlab and is
developed by the TU/e itself. Using HAMBase, one is able to perform accurate simulations on practical
issues.
In addition to the building model, Simulink is often used to simulate the (HVAC) systems in the building. Both
HAMBase and Simulink are based on Matlab, which makes it easy to integrate a Simulink model into a
HAMBase model. Using Simulink, one is able to build an accurate model of the building’s systems. However,
many difficulties are experienced during simulation of the controllers in the system. A lack of knowledge on
the exact algorithm and behaviour of the controllers makes it hard to perform an accurate simulation.
Besides the lack of knowledge, time is also a limiting factor while simulating the control systems. Therefore,
specific research on the modelling of such controllers is needed.
As designing-, developing- and exploitation firm in building services, Strukton Worksphere manages about
300 buildings within the Netherlands. The systems managed by Strukton are all controlled by a third party
building management system (BMS). Control signals sent by the controllers in the software are shown by the
BMS, but the actual control strategy of the controllers itself is often shielded. Knowledge on the actual
behaviour of the controllers provides the key access to performance studies for the company. Most of the
monitored data of the buildings managed by Strukton Worksphere is stored by the building management
system. Therefore, appropriate data to validate a software model is largely available.
Priva is an international developing and supplying firm of control systems for the built environment and
horticulture. Priva is a world leading company in control systems for horticulture and is with its Top-Control
building management system also market leading company in the Netherlands, with a market share of about
50%. From the buildings managed by Strukton Worksphere, is 85% equipped with Priva Top-Control. All
these management systems of Priva are built up with the same (PID) controllers. Simulating control systems
like Priva Top-Control are a big issue for the TU/e in performance studies on for example museums.
Understanding the control behaviour of the BMS is also very important for ATES balancing studies, which
are major issues in the Netherlands nowadays (Hoving, 2015).
1.2
Previous research of the TU/e
Previous to this research project, another research of the TU/e is performed on controllers in HVAC systems.
The master’s student M. Niesen built up a Simulink model of a test setup HVAC system (Niesen, 2010), built
at the Colorado State University. The test setup was part of an academic research on the Colorado State
University (Anderson, et al., 2007). The objective of the research project of Niesen was to build up a model
of an Air-Handling-Unit (AHU), in order to analyse different controllers and controlling strategies. The model
build by Niesen consisted of multiple SISO (Single Input Single Output) PI controllers. The Simulink-model of
the AHU can be used as reference model to test and analyse different controllers and control strategies in
further research.
1.3
Research setup
As explained in section 1.1, research on the behaviour of controllers in an HVAC system is needed. A
research project is started in order to get insight in the actual behaviour of controllers in such system. The
simulation model is also sufficient for future work control strategy studies. For this research, a case building
of Strukton is used to validate the simulation model. As case building, the office building of Strukton
Worksphere in Son is used. The air-handling-unit (AHU) of the building is modelled using the Simulink
software and validated with real-time monitored data of the BMS.
6 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
Objective
The objective of this research is to build up a
Simulink-model of an HVAC system, including its
controllers, and being able to perform accurate
simulations on the behaviour of the system and its
controllers. In order to validate the model, detailed
measurement data of a case-building is used.
The following research question is used as guidance
for the research project:
“Can the actual behaviour of an HVAC system,
determined by its controllers, be accurately
simulated, according to data from real-time
measurements?”
Figure 1.1 shows the structure of the research
project used to answer the research question. All
stages in the structure could, unfortunately, not be
accomplished in this project. This is mainly due to
drawbacks in controller simulation and a limited
amount of time available for the project. Further
research is needed for the remaining stages in the
structure. The grey-hatched boxes in the structure
represent the stages accomplished in this research
project. Further research is proposed to fulfil the
remaining part of the research structure. These
research proposals can be found in chapter 5.4.
This first part of the research project provides
answers to the following research questions:
1. What are the state of the art techniques used in
PID-control of HVAC components?
2. Can the behaviour of individual controllers in an
appropriate case system be accurately modelled
with Simulink?
3. Which tuning methods for PID controllers can be
used for the controllers in the case system?
4. What is the sensitivity of the controller’s
simulation model to the different components of
the controller?
5. Is it possible to give an indication of the
performance of the controllers in the case
system?
Figure 1.1; Structure of the research
The report is build up as follows:
Chapter one describes the problem definition, the research setup and some background information of the
project. In the second chapter, a brief overview is given of the some relevant theory found during the
literature review. Chapter three describes the research method, followed by the results in chapter four. The
results are discussed and further research is proposed in chapter five. The conclusions are drawn in chapter
six and the references to literature and corresponding modelling files can be found in chapter seven. Finally,
some additional relevant information can be found in the appendices.
7 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
Theory
2
Below, a brief overview is given of the theory on PID controllers, controller algorithms, anti-windup schemes,
control loop tuning and finally on evaluation techniques.
2.1
PID controllers
Automatic controllers are a key issue in most automatic processes. The controllers can be used to regulate
for example flow rates, temperatures, pressures etc. in automated processes. The most common controller
used in (industrial) processes is the PID-controller. This controller is named after the Proportional, Integral
and Derivative mode in the controller. The controller is based on feedback-control, it compares the measured
process variable with its setpoint. The calculated difference between the measured value and the setpoint is
called the error. Based on the error and the input settings, the PID controller sends an output to keep the
process variable at its setpoint.
Proportional control
The proportional mode in the controller changes the controller output in proportion to the input, the error. The
proportional factor is called the ‘controller gain’ or ‘proportional gain’. By increasing the proportional gain, the
controller will react faster to an error. However, when the proportional gain is set too high, the system will
begin oscillating. If the proportional gain is set too low, the controller won’t react adequately on errors. A
major drawback when applying proportional control only is offset. When for example the load on a heating
system suddenly changes, an error will occur. The control signal will respond proportional to this error.
However, at one point the control signal will become equal to the error. The measured error will then become
zero and the control signal will stay constant. Therefore, using proportional control only will always lead to an
offset.
Integral control
One way to eliminate the offset due to proportional control is implementing integral control. The integral
mode integrates the error over time. Therefore, it will increase or decrease the control signal as long as there
is an error present. The speed of the integral action depends on the magnitude of the error and the integral
time, which is an input setting for the integral control mode. The integral mode of a PID controller is switched
off by selecting an integral time of zero.
Derivative control
The third control mode in PID controllers is the derivative control. By differentiation of the error, the control
signal will respond more rapid on increments and decrements of the error. However, derivative control is
rarely used for HVAC systems, mostly since such quick response is not really necessary for HVAC systems
and derivative control makes the tuning of the controller more complex. By setting the derivative time to zero,
the derivative mode in the PID controller is switched off.
Figure 2.1 shows the recovery of the heating temperature after a sudden change in load. The recovery is
shown for proportional (P), proportional-integral (PI) and proportional-integral-derivative (PID) control.
Figure 2.1; System recovery for P, PI and PID control (Smuts J. , PID Controllers Explained, 2011)
8 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
2.2
PID algorithms
In total, three different PID algorithms are arranged by manufacturers (Smuts J. , 2010). The three algorithms
are called interactive algorithm, non-interactive algorithm and parallel algorithm. Figure 2.2 shows a
schematic diagram of the different algorithms.
Interactive
The oldest PID algorithm is the interactive
algorithm, also known as series algorithm. It is
represented by equation 2.1.
𝑢(𝑡) = 𝐾𝑐 ∙ (𝑒(𝑡) +
1
𝜏𝑖
𝑑
∫ 𝑒(𝑡) ∙ 𝑑𝑡) ∙ (1 + 𝜏𝑑 ∙ 𝑑𝑡) [2.1]
The second algorithm shown in figure 2.2 is
the non-interactive algorithm, also known as
ideal algorithm. If no derivative control is used
(τd = 0), the non-interactive algorithm will be
equal to the interactive algorithm. The noninteractive algorithm is represented by:
𝑢(𝑡) = 𝐾𝑐 ∙ (𝑒(𝑡) +
1
𝜏𝑖
∫ 𝑒(𝑡) ∙ 𝑑𝑡 + 𝜏𝑑 ∙
𝑑𝑒(𝑡)
)
𝑑𝑡
Non-interactive
[2.2]
The last PID algorithm is the parallel
algorithm. This is the only algorithm using a
proportional gain (Kp) instead of a controller
gain (Kc). The proportional gain doesn’t
influence the integral and derivative control,
where the controller gain does. The parallel
form of the algorithm makes tuning of the
controller much more complex. This algorithm
is therefore rarely applied. Equation 2.3 gives
a representation of the algorithm.
𝑢(𝑡) = 𝐾𝑝 ∙ 𝑒(𝑡) + 𝐾𝑖 ∙ ∫ 𝑒(𝑡) ∙ 𝑑𝑡 + 𝐾𝑑 ∙
2.3
𝑑𝑒(𝑡)
𝑑𝑡
Parallel
[2.3]
Figure 2.2; Schematic diagram different PID algorithms
Integrator windup
A major drawback of PID controllers is integrator windup. This occurs when the actuator saturates, which
causes an error in the feedback signal. The integrator will continue integrating while the actuator is fully
saturated and the integral value will become very large. Even when the error decreases the actuator still
saturates, which leads to large overshoots and settling times. This integration error is known as ‘integrator
windup’. A lot of PID-configurations exist to avoid integrator windup. In a document by Warsaw University of
Technology alone, thirteen different configurations were shown (Warsaw University of Technology). The
configurations can basically be divided in two different approaches, conditional integration and back
calculation. Also combinations of the two different approaches exist.
Back calculation
Back calculation is an approach which is used to avoid
integrator windup. Back calculation is first proposed by
Fertik and Ross, already in 1967. The approach is based
on tracking. Basically, a controller provided with back
calculation knows two different operation modes. In case
the actuator is not saturated, the controller acts as an
ordinary controller. Once the actuator saturates the
integral is recomputed so its new value gives an output at
the saturation level. Figure 2.3 shows a block diagram of
a PID controller provided with back calculation. The rate
of resetting the integral can be adjusted by the so called
‘tracking time constant 𝜏𝑡 .
9 Modelling the behaviour of controllers in an HVAC system
Figure 2.3; Block diagram of a PID controller provided
with back calculation
Technische Universiteit Eindhoven University of Technology
Conditional integration
Another approach to avoid integrator windup is conditional integration, also called integrator clamping. With
this approach the integral action is only used when certain conditions are met (no saturation for example).
Visioli defined four different forms of conditional integration (Visioli, 2003):
1. The integral term is limited to selected limits.
2. The integration is stopped when the system error is large, i.e. when |e| > ē, where ē is a selected
value.
3. The integration is stopped when the controller saturates, i.e. when u ≠ us.
4. The integration is stopped when the controller saturates and the system error and the manipulated
variable have the same sign, i.e. when u ≠ us and e∙u >0.
Also an alternate version of scheme 3 exists, where the integral term is kept at a prescribed value during
controller saturation. This approach is also called preloading. Previous research has shown that scheme 4
performs best (Hansson, Gruber, & Tödtli, 1994) (Rundqwist, 1991).
Combination of approaches
Several researches have proposed new anti-windup configurations, based on a combination of the two
approaches mentioned. Configurations proposed can for example be found in (Bohn & Atherton, 1995),
(Hodel & Hall, 2001) and (Visioli, 2003).
2.4
Control loop performance tuning
In order to optimize control loop performance, several hundreds of tuning methods exist for the PID
parameters, most of them recorded since 1992 (O'Dwyer, PI and PID controller tuning rules: an overview
and personal perspective, 2006). The most well-known and widely applied tuning rules are the ZieglerNichols rules, recorded already in 1942 (Ziegler & Nichols, 1942). Eleven years after the Ziegler-Nichols
tuning rules were published, Cohen and Coon published their tuning rules. The Cohen-Coon rules are based
on the same tuning objective as the Ziegler-Nichols rules, but are applicable for a wider variety of processes
and therefore also widely applied. The Cohen-Coon rules work well for processes with L<2τ, while the
Ziegler-Nichols rules work well for processes with L<0.5τ (where L is the dead time and τ is the time constant
of the process) (Smuts J. , Cohen-Coon Tuning Rules, 2011). Finally, the IMC tuning rules (also known as
Lambda rules) are also widely used (Rivera, Morari, & Skogestad, 1986). The IMC rules are more based on
robustness instead of fast step response, in contradiction to the Ziegler-Nichols and Cohen-Coon rules. But
as mentioned before, there are many different tuning methods for PID controllers.
Tuning objectives
In order to find the most suitable tuning rules for a process, it is important to define the tuning objective.
O’Dwyer defined five different classifications of tuning rules (O'Dwyer, PI and PID controller tuning rules: an
overview and personal perspective, 2006):
 Tuning rules based on a measured step response
 Tuning rules based on minimising an appropriate performance criterion
 Tuning rules that give a specified closed loop response
 Robust tuning rules, with an explicit robust stability and robust performance criterion built in to the
design process
 Tuning rules based on recording appropriate parameters at the ultimate frequency
One of the tuning objectives based on step response is called Quarter Amplitude Decay (QAD). QAD is
based on fast step response, which leads to relatively large overshoots. Like the name of the objective
suggests, the amplitude of the oscillation cycles decays with factor 4. Ziegler-Nichols, Cohen-Coon and
many other tuning rules are based on QAD. The IMC tuning rules are robust tuning rules, giving good
response on setpoint changes but give bad performances for integrating processes (Horn, Arulandu,
Gombas, VanAntwerp, & Braatz, 1996). Many other tuning rules are based on error-integral, for eample
minimising the Integral of Absolute Error (IAE) (O'Dwyer, Handbook of PI and PID Controller Tuning Rules
(2nd Edition), 2006).
10 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
Adaptive techniques
Adaptive tuning techniques are developed in order to maintain the optimal PID settings throughout dynamic
processes. Adaptive techniques were first introduced in 1983 and can generally be divided in three
categories (Äström, Hägglund, Hang, & Ho, 1993):
 Automatic tuning
 Gain scheduling
 Adaptive control
With automatic tuning (auto-tuning), a controller is tuned automatically. This tuning is however on demand of
the user, which can be manually or pre-programmed. Using gain scheduling, the parameters are changed
depending on a measured value (for example the system input or system output). Strategies where the
parameters are continuously adjusted on the process dynamics and disturbances are called adaptive control.
The on/off and traditional PID control techniques are still used in many HVAC systems (Afram & JanabiSharifi, 2014). Although it is multiple times shown that adaptive PI control has superior performance to that of
classical PI control (Seem, 1998) (Zhang & Bai, 2007). Also research projects on combinations of PID control
with Fuzzy control or neuron control for HVAC-control have proven to be successful, for example (Bai, 2013)
(Soyguder, Karakose, & Alli, 2009) (Wang & Jing, 2006).
2.5
Control loop performance evaluation
The feedback control loop of an HVAC system can be
described by the schematic diagram shown in figure 2.4.
Several indicators exist to evaluate the performance of
control loops in general. One of the most commonly used
performance evaluation techniques is the sensitivity
function (Rivera, Morari, & Skogestad, 1986):
𝑆=
𝑒
[2.4]
𝑦𝑠 −𝑑
Figure 2.4; Feedback control loop HVAC system
The variable ‘d’ in the sensitivity function refers to the load disturbances, which are often hard to measure.
Using the sensitivity function, it is preferable to keep the function as small as possible over a broad errorfrequency range. In order to evaluate step responses and the effect of load disturbances changes, Rivera et
al. introduced the Integral Absolute Error (IAE) and the Integral Squared Error (ISE):
∞
𝐼𝑆𝐸 = ∫0 (𝑦 − 𝑦𝑠 )2 𝑑𝑡
∞
𝐼𝐴𝐸 = ∫0 |𝑦 − 𝑦𝑠 | 𝑑𝑡
[2.5]
[2.6]
In order to evaluate the input performance, Skogestad introduced the Total Variation (TV) of the input
(Skogestad, 2003), which is a good indicator for the smoothness of the process. The total variation is the
sum of all the increments and decrements of the process input u(t), assuming a discretized signal:
𝑇𝑉 = ∑∞
𝑖=1|𝑢𝑖+1 − 𝑢𝑖 |
[2.7]
11 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
Method
3
In this chapter the case building is described, as well as its HVAC system and the monitoring techniques of
the system. Further, the research methods used for controller modelling and evaluation are discussed.
3.1
Case building
As mentioned in chapter 1, the office building of Strukton Worksphere in Son is used as case building for the
research project. The building has six floors and is provided with a parking lot, a restaurant and a total of
7500 square meter offices. The building is equipped with two air-handling-units, one providing the restaurant
of fresh conditioned air, the other supplying fresh air for the offices. Since the AHU’s are quite different, only
the unit supplying air to the offices is modelled. The building is provided with a Priva Top-Control building
management system. The volume- and energy flows in the HVAC system are broadly monitored by the
building management system and additional sensors placed by the company itself. Figure 3.1 shows a
schematic diagram of the AHU, detailed schematic diagrams of the building’s services are enclosed as
appendix I.
Figure 3.1; Schematic diagram air-handling-unit case building
The upper section of the AHU in figure 3.1 represents the flow of extracted air from the building (from right to
left). The air is extracted by a fixed speed fan and flows through the rotary heat exchanger to the ambient. At
the bottom section of the AHU, ambient air is extracted by a similar fixed speed fan. The supply air flows
through respectively the rotary heat exchanger, heating coil and cooling coil. Reversible heatpumps are
installed to supply conditioned water for the heating- and cooling coil and for additional space heating.
The HVAC system is controlled by the BMS. The system control is built up from a master-slave control with
four PI-controllers. The master loop controls the required temperature of the supply air and the activation of
the different components in the system. The amount of power supplied by the heat exchanger, heating coil
and cooling coil is controlled by individual PI-controllers.
Figure 3.2 below gives a schematic overview of the control system. Detailed process control schemes are
enclosed as appendix II.
Figure 3.2; Control loop air handling unit
12 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
3.2
Measurement data
The AHU from the case building is broadly measured, both by the BMS and by external transmitters. The
data from the BMS and the external transmitters is logged respectively per 4 and per 1 minute. Figure 3.3
gives an indication of the type and position of the measurements in the AHU (orange and purple circles). The
orange circles in the figure indicate measurement point (Moisture [%RH] and Temperature [°C]), the purple
circles indicate calculated values of the BMS (PI control signal [-] and Temperature [°C]).
Figure 3.3; Schematic diagram AHU monitoring
The data important for the modelling of the controller is the error signal (input of the controller) and the
control signal (output of the controller). The input of the controller is the difference between the air supply
temperature reached (T9) and the air supply temperature calculated (T10). The different control signals (PI1,
PI2, PI3, and PI4) are outputs of the controllers and these values are used for validation of the simulation
model. Remaining measurement points in the AHU are not directly of influence for the controller’s behaviour,
they are mostly relevant for the validation of the process models. An overview of all sensors in the AHU with
its description is enclosed as appendix III.
3.3
Controller modelling
To get insight in the behaviour of (PI-) controllers, a simulation model of the PI-controllers in the case system
is built with Matlab/Simulink. The R2013a version of Mathworks Matlab/Simulink was used for the simulation
study. The simulation model is fully built up in Simulink, using blocks from the basic Simulink library in the
software.
As explained in chapter 2, many different algorithms of PID controllers exist (different anti-windup schemes
etc.). Knowledge on the algorithm of the controller is therefore essential when building a simulation model of
the controller. Initially, the algorithm is tried to be found by trial and error and using the BMS-data for
validation. Using trial and error, an approximation of the controller’s behaviour could be made in Simulink. It
was however not possible to reproduce the measured control signal of the validation data. Eventually, a
meeting with the manufacturer of the BMS was set up, in order to trace the exact algorithm of the PIcontroller.
13 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
3.4
Sensitivity and performance evaluation
With the configuration of the controller known, a sensitivity study can be performed. Besides the sensitivity,
also the performance of the controllers in the case system is evaluated. This chapter describes the method
used for both evaluation studies.
Sensitivity
A sensitivity study shows the required knowledge level on the controller’s specifications, when a controller is
modelled. The influence on the controller’s output is calculated for variations in:
 Integrator method (Backward Euler, Forward Euler and Trapezoidal)
 Sample time (1 second, 10 seconds and 60 seconds)
 Data frequency (1 second, 4 minutes and 10 minutes)
 Anti-windup approach (Back-calculation and without anti-windup)
For each variation mentioned above, the simulation is performed and the controller’s output logged. In order
to compare the different sensitivities, agreements are calculated in mean values of the relative deviations,
compared to the measurement data.
Performance evaluation
With the large amount of measurement data available, a performance evaluation of the controllers in the
case system can be made. The controllers are evaluated based on their control hours, the hours the
temperature is controlled by that specific controller. As performance indicator, the Integral Absolute Error and
Total variation (equations 2.6 and 2.7) are used. After each performance is calculated, they are compared
with each other for evaluation.
14 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
Results
4
The results of the simulation study on the PID controller of the case building are presented below. The plots
that can be found in this chapter are results of the simulations with Matlab and Simulink. A list with the
corresponding simulation files for each figure can be found in chapter 7.3. The used Matlab- and Simulink
files are digitally enclosed with this report.
4.1
Controller configuration
In order to trace the exact algorithm of the controller, a meeting was set up with the manufacturing firm of the
BMS. This meeting provided more knowledge on the configuration of the controller, a practical approximation
of the controller’s configuration could be defined. This configuration is shown in figure 4.1.
Figure 4.1; Schematic diagram PI-controller with clamping
The configuration of the controller, according to Priva, has the interactive PID-form as main structure. For the
case building, this form is similar to the non-interactive form, since no derivative control is used. The
interactive PI-form is extended with a conditional integration anti windup feature. The anti-windup scheme
used is the fourth form mentioned by Visoli: “The integration is stopped when the controller saturates and the
system error and the manipulated variable have the same sign, i.e. when u ≠ us and e∙u >0”. The values of
Kc, Ti and the saturation limits of the controller can be defined by the user in the Priva software. Besides
these variables, also an offset, dead time and T d can be defined in the software, but these are all set at zero
for the case building.
However, simulation results of a back-calculation type show better agreement with the validation data. The
PID controller with back calculation used for the simulations is similar to the one discussed in chapter 2.3,
but without derivative control and a tracking time constant 𝜏𝑡 of 1. A schematic diagram of the PI controller
with back-calculation which shows good agreement is shown in figure 4.2. The values of Kc, Ti and the
saturation limits of the controller are similar to that used for the controller equipped with clamping control.
Figure 4.2; Schematic diagram PI-controller with back-calculation
15 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
The model of the PI controller is validated for two controllers of the case system, the heating coil (HC) and
the heat exchanger in heating mode (HEH). Only differences between these controllers are the values of Kc
and Ti, and the fact that the output of the HC-controller is rounded to integers. Figure 4.3 shows the control
th
signals of the heat exchanger and the heating coil for Tuesday 31 of April.
Figure 4.3; Control signals April 31th 2015
The control signal for the heat exchanger directly increases to 100% when the air handling units starts up.
Additional heating power is provided by the heating coil in the morning, this signal is then also used to
control the air temperature. In the afternoon, obviously less heating power is required. The control signal of
the heating coil decreases to zero and the control signal of the heat exchanger is used to adequately control
the air temperature. During start-up of the system, the control signal of the heating coil directly rises at 6:00
AM, while the signal of the heat exchanger starts rising a few minutes after 6:00 AM. This can be explained
by the starting procedure of the system, as entered in the BMS. This standard starting-sequence of the BMS
seems however not very convenient.
During the project, it became clear that the interval of the measurement data is very important. The
measurement data in the database of the case system has a measurement frequency of 4 minutes. This
data was interpolated to seconds for the simulations. It however became clear that with measurement data of
a higher frequency (measurements per second), much better agreement can be obtained for the simulation.
But since all data was logged per 4 minutes, the data in the database of the case building became useless
for further simulation purposes. The validation of the proposed controller model is therefore limited. The
controller is validated for 3 days of measurement data, only the heating coil (HC) and the heat exchanger
heating (HEH) were active during these days. Examples on the sensitivity of the data frequency can be found
in chapter 4.2 and proposals on further research are given in chapter 5.4.
The Simulink model with back calculation shows good agreement with the validation data, as can be seen in
figure 4.3. Providing the model with clamping (conditional integrating) instead of back-calculation shows less
agreement, the same holds for the standard PID-controller block in the Simulink library. In the meeting, Priva
indicated that the controller may react inaccurate to errors of low magnitude (± 0.1 °C). However, validation
of the model, without considering inaccuracies for small errors, shows good agreement. Further validation of
the controller is needed to investigate the accuracy for small errors and to determine the actual anti-windup
method in the controller (clamping or back-calculation).
16 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
4.2
Model sensitivity
Chapter 4.2 shows the sensitivity of some relevant factors to the simulation agreement. Very important to
note is that the controller model is not connected to a dynamic process, the feedback signal of the validation
data is used as input of the controller model. This means that when the simulated signal deviates, the model
will continue calculating from that deviation in the control signal instead of correcting itself. The sensitivities
presented below will therefore only apply for cases where the feedback signal from the measurements is
used.
Integrator method
The integration loop in the controller can be performed by use of different integration methods. The standard
Simulink integrator block provides three different approaches: Forward-Euler, Backward-Euler and
Trapezoidal integration. The model-configuration used for the plot in figure 4.3 makes use of Backward-Euler
integration. This integration approach shows thus good agreement with the measurement data. Figures 4.4
and 4.5, show the model output for respectively the Forward-Euler and Trapezoidal integration approach.
These both approaches show also good agreement with the measurements.
Figure 4.4; Control signal April 31th 2015 - Forward Euler integration
Figure 4.5; Control signal April 31th 2015 - Trapezoidal integration
Sample time
Besides integration method, also the sensitivity to sample time is tested. The only distinction between the
two different controllers in Priva is the sample time (1 and 10 seconds). The controller used in the case
building works on a sample time of 10 seconds. The model used for figure 4.3 is also based on a 10 seconds
sample time. Figures 4.6 and 4.7 show the output signal for sample times of respectively 1 and 60 seconds.
Only for the 60 seconds sample time, shown in figure 4.7, a slightly divergent control signal can be obtained.
This deviation is however very small.
17 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
Figure 4.6; Control signal April 31th 2015 - 1 second sample time
Figure 4.7; Control signal April 31th 2015 - 60 seconds sample time
Data frequency
As mentioned in chapter 4.1, the frequency of the measurement data is very important. For the output signal
in figure 4.3, a data interval of 1 second was used. Mainly since the interval of 4 minutes had shown bad
performance earlier. Figure 4.8 shows output of the same simulation model, but now based on a data interval
of 4 minutes. As expected, a significant deviation in output signal can be obtained. The deviation also
increases over time, this is because the controller continues calculating from the deviated output signal, as
explained before. Figure 4.9 shows the simulated control signal for a 10 minutes data interval, which
obviously amplifies the effect.
Figure 4.8; Control signal April 31th 2015 - 4 minutes data interval
18 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
Figure 4.9; Control signal April 31th 2015 - 10 minutes data interval
Anti-windup method
The final sensitivity discussed is that of the anti-windup method. As mentioned before, an anti-windup
method is applied in the controller model. Figure 4.10 shows the importance of taking the anti-windup feature
into account when building the simulation model. The figure shows a comparison between the measurement
th
data, and the output signal of the model without the anti-windup feature for April 20 . It shows a significantly
large difference, which is again mainly caused by the fact that the controller continues calculating with a
‘wrong’ input signal.
Figure 4.10; Control signal April 20th 2015 - Without anti-windup
Sensitivity overview
The differences between the measurement data
and the different model outputs are converted to
percentages mean absolute deviation. This
deviation during operation hours is calculated in
percentages for both the heating coil and the
heat exchanger. These percentages are shown in
table 4.1, to make a comparison between the
sensitivities easier. Looking at the percentages, it
can be seen that the controller is especially
sensitive for the data frequency and the antiwindup approach. From experiments with the
measurement data, a squared valve curve was
found for the heating and cooling coil. This
means that the deviation of 129.15% for the
heating coil, can lead to a deviation of 443% (!) in
energy consumption for this particular example.
19 Modelling the behaviour of controllers in an HVAC system
Table 4.1; Sensitivity overview controller
Sensitivity overview controller
Mean abs
deviation
Controller features
HEH
HC
Backward Euler
0,26%
6,49%
Integration
Forward Euler
0,38%
6,67%
method
Trapezoidal
0,28%
6,57%
1 second
0,35% 18,70%
Sample time 10 seconds
0,26%
6,49%
60 seconds
0,87% 17,90%
1 second
0,26%
6,49%
Data log
4
minutes
2,61%
37,82%
frequency
10 minutes
5,03% 52,59%
Back-calculation
0,26%
6,49%
Anti-windup
approach
None
0,00% 129,15%
Technische Universiteit Eindhoven University of Technology
4.3
Controller performance
Using the measurement data of 2014, a comparison can be made of the
controller’s performance. Table 4.2 shows the operation hours of each
controller in 2014. However, when multiple controllers are active (for
instance HEH and HC), only one controller is actively controlling the
temperature and the second one is at a fixed level. The control hours in
table 4.2 denote the hours the controller is actively controlling the air
temperature. A comparison in share of the control hours between the
four controllers in the system is shown in figure 4.11.
Table 4.2; Operation hours 2014
Operation hours 2014
Operation Control
Controller
hours
hours
PID HEH
PID HEC
PID HC
PID CC
2067.7
297
769.8
1287
1367.2
0
769.8
1287
To evaluate the controller’s performance, the Integral Absolute Error
(IAE) and Total Variation (TV) of each controller are calculated over
2014. In addition to the IAE and TV, also the mean IAE and mean TV
are calculated to make a performance-comparison of the different
controllers easier. Results of these performance indicators are shown in
table 4.3. Figure 4.12 shows a comparison between the performance
indicators of each controller.
The control hours of the HEC are zero over 2014, which means the
performance indicators (IAE and TV) are also zero and are therefore left
out of the plots. It can be noticed from the results that the heat
exchanger (HEH) performs clearly better on IAE, compared to the coils
(HC and CC). This is probably caused by the large dead time in the coilsystem, compared to the heat exchanger. The heat exchanger performs
also better on the process smoothness, expressed in TV, but for TV the
differences are smaller.
Figure 4.11; Control hour distribution
Table 4.3; Performance evaluation per controller
Performance evaluation per controller
Different PID controllers
Performance
criteria
PID HEH
PID HEC
PID HC
Operation hours [h]
2067.7
297
769.8
Control hours [h]
1367.2
0
769.8
IAE total [°C]
1127.9
0
3814.1
IAE mean [°C]
0.14
0
0.83
TV total [°C]
790.9
0
1213.9
TV mean [°C]
0.10
0
0.27
20 Modelling the behaviour of controllers in an HVAC system
PIC CC
1287
1287
7299.3
0.95
2655.5
0.35
Figure 4.12; Performance evaluation 2014:
IAE and TV
Technische Universiteit Eindhoven University of Technology
Discussion and recommendations
5
In the first part of this chapter, results of the project are discussed (5.1 t/m 5.3). In the last section of the
chapter, section 5.4, recommendations are given for further research.
5.1
Controller model
A model of the controller is built in Simulink, based on the measurement data from the case building.
Although a large amount of data-history is available, only few could be used for validation, due to the low
frequency of data-logging. The final controller model is therefore based on little validation data (3 days),
validation with additional data may be useful to guarantee its accuracy.
5.2
Sensitivity study
With the final configuration of the controller known, a sensitivity study is performed. The sensitivity study
showed the influence of individual controller specifications on the simulation outcome. The influences shown
are however only valid for cases without a process-model, like this project. In process-modelling and in
practical appliances, with interaction between controller and process, the controller would correct itself since
it will receive a different feedback signal. But the results of the sensitivity study can be used for controlleronly modelling.
5.3
Performance evaluation
For the performance evaluation, described in chapter 3.4 and 4.3, the data-history of 2014 is used. But like
also mentioned before, the log frequency of this data is of significant influence on the modelling accuracy.
The results of the performance evaluation have therefore to be taken with care. The results show however a
clear pattern, which is most important to know.
5.4
Recommendations
The model of the PI-controller is, as mentioned in section 5.1, based on relatively few validation data. Further
research should determine if the proposed algorithm gives accurate results for a larger set of measurement
data. Focus for these tests have to be on the anti-windup approach which is applied and the possible
inaccuracy for small errors (±0.1 K). Further research is also needed to model the dead time, offset and
derivative control functions of the Priva controller. This however, requires measurement data of a different
case system, since the specific case system of the Strukton building doesn’t use these functions.
Within this project, the sensitivity of the Priva controller to different factors is tested. This sensitivity study
shows the sensitivity of the controller model when the feedback control signal of the measurement data is
used. This however doesn’t show the dynamic behaviour of the controller. Further research is needed to
combine the controller model with a process model. Such combined model would allow studies on the
sensitivity, robustness and performance of the controller in dynamic situations.
The performance evaluation study on the controller in the case study showed a significant difference in
performance, between the controllers for the heat exchanger and the coils. This difference is probably
caused by the larger amount of dead time for the coils. But the behaviour of the heating and cooling coil has
high influence on the energy consumption, in contradiction to the heat exchanger. Further research on the
performance of the controllers may therefore lead to improvements on energy consumption and thermal
comfort.
During the project was found that the data log-frequency of the case building was too low for accurate
modelling. The large database, containing measurement data of multiple years, became more or less
useless. It is therefore recommended for Strukton to start logging the important variables of the system per
second, or per ten seconds, in order to build up a new database. Such database is preferable for the
recommended further research.
21 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
Control strategy studies
With this research on the behaviour of
controllers in an HVAC system, a solid
start is made towards control strategy
studies. The behaviour of the controllers
can successfully be simulated, but the
coupling with a system model is needed to
get insight in the dynamic behaviour and
performance of the control loop.
Figure 5.1 shows the project stages
needed to accomplish before control
strategy studies can be performed. Within
this project, a Simulink-model is built
which can perform accurate dynamic
simulations on the behaviour of the
separate PI-controllers in the case-system.
With this model, each slave control-loop
(see figure 3.2) can be simulated. Further
research is needed to build up an
interactive control model which can be
used to communicate between the
separate PI-controllers and simulate the
master control loop.
Besides the control loop modelling, also
system modelling is needed to build up an
appropriate test model for control strategy
studies. The AHU of the Strukton office in
Son can be used as case-system for the
system modelling. Further research is
needed to build up accurate models of
each component in the system, which can
thereafter be combined into one single
system model of the AHU (the track on the
left side in figure 5.1). When both the
control- and the system-model are built,
they can be merged into one single
Simulink model. With this model, a perfect
test environment is created for control
strategy studies on HVAC systems.
Figure 5.1; Research structure
Within this current project, a lot of background research is already performed on the system components and
its monitoring. The monitoring overview and the specifications of the system’s components which are already
found are enclosed as respectively appendix III and IV.
22 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
6
Conclusions
This chapter evaluates the results of the research project, based on the research questions which are
defined in chapter 1. The main research question for this project is:
“Can the actual behaviour of an HVAC system, determined by its controllers, be accurately simulated,
according to data from real-time measurements?”
The answer on the main research question of the project is given in the last section of this chapter. First,
answers are provided to the subquestions defined for the project.
What are the state of the art techniques used in PID-control of HVAC components?
The most widely applied control techniques in HVAC control are the conventional PID control strategies,
often applied with for example anti-windup techniques. Research has shown potentials for automatic PIDtuning and adaptive control. More recent research also shows potentials for combinations of PID- and Fuzzyor Neuron control for HVAC systems. Such control techniques are however still rarely applied within the field
of HVAC control.
Can the behaviour of individual controllers in an appropriate case system be accurately modelled
with Simulink?
For this project, an accurate controller model is built for the PID controller used in leading building
management software in the Netherlands. It is thus possible to accurately model the behaviour of individual
controllers in an HVAC system. Important to note is that data with a high log frequency is needed for
validation of the model.
Which tuning methods for PID controllers can be used for the controllers in the case system?
Practically, all tuning rules designed for PID controllers with the interactive and non-interactive form can be
applied at the PI controllers in the case system. Defining a tuning objective would help selecting the best
applicable tuning method. Looking at HVAC control, minimising error-integral based tuning could be a
suitable objective. Besides manual tuning methods, also automatic- and adaptive tuning can be applied to
the controllers in the case system.
What is the sensitivity of the controller’s simulation model to the different components of the
controller?
A sensitivity study was performed, in order to get an indication of the sensitivity of different controller
components. Results of this study showed that the model can significantly be influenced by the data log
frequency and the appliance of an anti-windup approach. It was also showed that the sensitivity to
integration methods was low and the sensitivity to the controller’s sample time was in between. If have to be
noted that the sensitivity study is based on a non-dynamic feedback signal.
Is it possible to give an indication of the performance of the controllers in the case system?
With the large database of the case system available, a performance evaluation could be made of the
different controllers in the case system. This performance evaluation showed that the performance of the
controllers which are controlling the heating- and cooling coil was significantly lower than that of the
controller controlling the heat exchanger. This difference in performance is probably caused by the dead time
in the coil-system, further research is proposed to examine possible performance improvements.
Based on the answers to the subquestions above, an answer can be derived for the main research question.
With this project, a model of the Priva controller is built in Simulink. This model is shown to be very accurate,
looking at the plots in chapter 4. In order to model the total (control) behaviour of the HVAC system, the
master loop has to be modelled to connect the different controllers. First tests of the interaction between two
controllers have proven to be successful and schematic diagrams of the master loop are made as well. So in
answer to the research question, it is possible to build accurate simulation models for the actual control
behaviour in an HVAC system. This project is a start towards detailed HVAC behaviour simulations and
performance optimization studies.
23 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
References
7
7.1
Literature
Afram, A., & Janabi-Sharifi, F. (2014). Theory and applications of HVAC control systems - a review of model
predictive control (MPC). Building and Environment 72, 343-355.
Anderson, M., Buehner, M., P., Y., Hittle, D., Anderson, C., Tu, J., et al. (2007). An experimental system for
advanced heating, ventilating and air conditioning (HVAC) control. Energy and Buildings 39, 136147.
Äström, K., Hägglund, T., Hang, C., & Ho, W. (1993). Automatic tuning and adaptation for PID controllers - a
survey. Control Eng. Practice (Vol.1 No.4), 699-714.
Bai, J. (2013). Development an Adaptive Incremental Fuzzy PI Controller for a HVAC system. INT J Comput
Commun 8, 654-661.
Bohn, C., & Atherton, D. (1995). An analysis package comparing PID anti-windup strategies. IEEE Control
Systems (Vol. 15), 34-40.
Chen, Y., & Treado, S. (2013). Development of a simulation platform based on dynamic models for HVAC
control analysis. Energy and Buildings.
Hansson, A., Gruber, P., & Tödtli, J. (1994). Fuzzy anti-reset windup for PID controllers. Control Eng.
Practice Vol.2 No.3, 398-396.
Hodel, A., & Hall, C. (2001). Variable-structure PID control to prevent integrator windup. IEEE transactions
on industrial electronics (Vol. 48, No. 2), 442-451.
Horn, I., Arulandu, J., Gombas, C., VanAntwerp, J., & Braatz, R. (1996). Improved filter design in Internal
Model Control. Ind. Eng. Chem. Res. 35, 3437-3441.
Hoving, J. (2015). Maintaining ATES balance using continuous commissioning and model predictive control.
Eindhoven: Eindhoven University of Technology.
Niesen, M. (2010). Optimalisatie regelsystemen. Eindhoven: Eindhoven University of Technology.
O'Dwyer, A. (2006). Handbook of PI and PID Controller Tuning Rules (2nd Edition). London: Imperial
College Press.
O'Dwyer, A. (2006). PI and PID controller tuning rules: an overview and personal perspective. Dublin: Dublin
Institute of Technology.
Rivera, D., Morari, M., & Skogestad, S. (1986). Internal Model Control. 4. PID Controller Design. Ind. Eng.
Chem. Process Des. Dev. 25, 252-265.
Rundqwist, L. (1991). Anti-reset windup for PID controllers (PhD thesis). Lund: Studentlitteratur AB.
Seem, J. (1998). A new Pattern Recognition Adaptive Controller with application to HVAC systems.
Automatica (Vol.34 No.8), 969-982.
Skogestad, S. (2003). Simple analytic rules for model reduction and PID controller tuning. Journal of Process
Control 13, 291-309.
Smuts, J. (2010, March 30). PID Controller Algorithms. Retrieved February 20, 2015, from Opticontrols:
http://blog.opticontrols.com/archives/124
Smuts, J. (2011, March 24). Cohen-Coon Tuning Rules. Retrieved March 3, 2015, from Opticontrols:
http://blog.opticontrols.com/archives/383
Smuts, J. (2011, March 07). PID Controllers Explained. Retrieved February 20, 2015, from Opticontrols:
http://blog.opticontrols.com/archives/344
Soyguder, S., Karakose, M., & Alli, H. (2009). Design and simulation of self-tuning PID-type fuzzy adaptive
control for an expert HVAC system. Expert Systems with Applications 36, 4566-4573.
Visioli, A. (2003). Modified anti-windup scheme for PID controllers. IEE Proc.-Control Theory Appl. Vol. 150,
49-54.
Wang, J., & Jing, Y. (2006). Study of neuron adaptive PID controller in a single-zone HVAC system.
Conference of: Innovative Computing, Information and Control, 142-145.
Warsaw University of Technology, I. (n.d.). Integral Anti-Windup for PI Controllers. Retrieved February 13,
2015, from Warsaw University of Technology: http://www.isep.pw.edu.pl/ZakladNapedu/lab-ane/antiwindup.pdf
Zhang, X., & Bai, J. (2007). A new adaptive PI controller and its application in HVAC systems. Energy
Conversion and Management 48, 1043-1054.
Ziegler, J., & Nichols, N. (1942). Optimum settings for automatic controllers. Transactions of the A.S.M.E.
(November 1942), 759-768.
24 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
7.2
Software
Simulations were performed, using the following software:
- Mathworks MATLAB R2013a
- Mathworks Simulink 8.1 (R2013a)
7.3
Model references
Some figures in the report contain plots from the Matlab- and/or Simulink simulations. These simulation files
used for the project are all digitally enclosed by this report. Below, a reference is made between the figures
in the report containing those plots and their corresponding simulation files.
Figure nr:
Figure 4.3;
Figure 4.4;
Figure 4.5;
Figure 4.6;
Figure 4.7;
Figure 4.8;
Figure 4.9;
Figure 4.10;
Figure 4.11;
Figure 4.12;
Control signals April 31th 2015
Matlab:
DATA20150331.m
Simulink: PI_controller_interaction.slx
Control signal April 31th 2015 - Forward Euler integration
Matlab:
DATA20150331.m
Simulink: PI_controller_interaction_ForwardEuler.slx
Control signal April 31th 2015 - Trapezoidal integration
Matlab:
DATA20150331.m
Simulink: PI_controller_interaction_Trapezoidal.slx
Control signal April 31th 2015 - 1 second sample time
Matlab:
DATA20150331_sample1.m
Simulink: PI_controller_interaction_sample1.slx
Control signal April 31th 2015 - 60 seconds sample time
Matlab:
DATA20150331_sample60.m
Simulink: PI_controller_interaction_sample60.slx
Control signal April 31th 2015 - 4 minutes data interval
Matlab:
DATA20150331_data4min.m
Simulink: PI_controller_interaction.slx
Control signal April 31th 2015 - 10 minutes data interval
Matlab:
DATA20150331_data10min.m
Simulink: PI_controller_interaction.slx
Control signal April 20th 2015 - Without anti-windup
Matlab:
DATA20150320.m
Simulink: PI_controller_interaction_withoutantiwindup.slx
Control hour distribution
Matlab:
Performance_2014.m
Performance evaluation 2014: IAE and TV
Matlab:
Performance_2014.m
25 Modelling the behaviour of controllers in an HVAC system
Page
16
17
17
18
18
18
19
19
20
20
Technische Universiteit Eindhoven University of Technology
I.
System diagrams Strukton Son
The figure below indicates the schematic diagram of the heating and cooling circuit of the case building.
26 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
The figure below indicates the schematic diagram of the air handling circuit of the case building.
27 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
II.
Control schemes
Below, a schematic diagram of the starting loop of the AHU at the case building is shown.
28 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
Below, a schematic diagram is shown of the control loop which determines the supply temperature setpoint.
29 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
III.
Monitoring overview
Below a schematic diagram is shown which indicates the positions of the sensors used for the project. The
sensors are listed in the tables beneath the figure. At the moment of writing, the sensors T4 and M3 are
being replaced due to bad performances.
Wisensys measurements
Nr.
ID
Code Strukton
Description
Unit
639
Base_5 - 570101TT_007 - Humidity
Measurement - Relative humidity extraction air after HE
% RH
M3
1903
Base_5 - 570101TT_012a - Humidity
Measurement - Relative humidity supply air after HE
% RH
T1
640
Base_5 - 570101TT_007 - Temperature
Measurement - Temperature extraction air after HE
°C
T4
1904
Base_5 - 570101TT_012a - Temperature
Measurement - Temperature supply air after HE
°C
1905
Base_5 - 570101TT_012 - Temperature
Measurement - Temperature supply air after HE
°C
665
Base_5 - dP_cooling_ind - Voltage
Measurement - Pressure difference cooling circuit
kPa
664
Base_5 - dP_heating_ind - Voltage
Measurement - Pressure difference heating circuit
kPa
62
Base_5 - pyranometer - Low Voltage
Measurement - Solar irradiation
Nr.
ID
Code Strukton
M2
282
SWS_PAND_OS1_GRFMET_72
357UT1
Measurement - Relative humidity extraction air
% RH
PI1
294
SWS_PAND_OS1_GRFPID_4
207M1
Calculated value - PID heat exchanger heating
%
PI2
295
SWS_PAND_OS1_GRFPID_5
207M1
Calculated value - PID heat exchanger cooling
%
PI3
292
SWS_PAND_OS1_GRFPID_6
329CV1
Calculated value - PID heating coil
%
PI4
325
SWS_PAND_OS1_GRFPID_8
329CV2
Calculated value - PID cooling coil
%
T2
281
255
357UT1
310TT1
Measurement - Temperature extraction air
Measurement - Ambient air temperature
°C
T3
SWS_PAND_OS1_GRFMET_47
SWS_PAND_OS1_GRFMET_1
T5
293
SWS_PAND_OS1_GRFMET_36
332TT7
Measurement - Incoming water temperature heating coil
T6
290
SWS_PAND_OS1_GRFMET_37
356TT1
Measurement - Outgoing water temperature heating coil
Not used
M1
W/m²
PRIVA History
Code Vision
Description
Measurement - Incoming water temperature cooling coil
Unit
°C
°C
°C
T7
288
SWS_PAND_OS1_GRFMET_40
356TT2
T8
289
SWS_PAND_OS1_GRFMET_41
356TT3
T9
287
356TT4
356TT4
Calculated value - Temperature air supply
°C
684
SWS_PAND_OS1_GRFMET_46
SWS_PAND_OS1_GRFSYS_79
Measurement - Temperature air supply
°C
283
SWS_PAND_OS1_GRFMET_28
356TT6
Measurement - Room temperature 4th floor
°C
284
SWS_PAND_OS1_GRFMET_29
356TT7
Measurement - Room temperature 3rd floor
°C
285
SWS_PAND_OS1_GRFMET_30
356TT8
Measurement - Room temperature 2nd floor
°C
286
SWS_PAND_OS1_GRFMET_31
357TT1
Measurement - Room temperature 1st floor
°C
Not used
T10
30 Modelling the behaviour of controllers in an HVAC system
Measurement - Outgoing water temperature cooling coil
°C
°C
Technische Universiteit Eindhoven University of Technology
IV.
Specifications air handling unit
This document describes the specifications of the AHU which were, during this project, found for modelling
purposes. Some specifications, like the flow quantities, may change over time.
The product specifications below are based on the product specifications of the AHU-manufacturer (Carrier
Airovision – 2009).
Heat exchanger:
Type:
Material rotor:
Nominal power:
Efficiency latent:
Efficiency dry:
Air flow resistance:
Hygroscopic
Aluminium
310.63 kW
47
%
60
%
188
Pa
Heating coil:
Type:
Nominal power:
Air flow resistance:
Water flow resistance:
Tube diameter:
Tube thickness:
Fin distance:
Fin thickness:
Water to air
149.47 kW
47
Pa
6
kPa
12.45
mm
0.35
mm
2
mm
0.12
mm
Cooling coil:
Type:
Nominal power:
Air flow resistance (wet):
Air flow resistance (drip):
Water flow resistance:
Tube diameter:
Tube thickness:
Fin distance:
Fin thickness:
Supply fan:
Manufacturer:
Type:
Nominal power:
Efficiency:
Rotational speed (fan):
Air flow rate:
Air supply velocity:
System pressure:
External pressure:
Dynamic pressure:
Total pressure:
Water to air
235
kW
235
Pa
27
Pa
15
kPa
16.5
mm
0.4
mm
2
mm
0.13
mm
Nicotra Gebhardt
RZR 11-0900
Fixed speed fan
15.55
kW
82
%
950
rpm
10.28
m³/s
8.11
m/s
78
Pa
300
Pa
39
Pa
1234
Pa
31 Modelling the behaviour of controllers in an HVAC system
Design conditions:
Inlet:
Outlet:
Design conditions:
Air inlet:
Air outlet:
Water inlet:
Water outlet:
Air flow rate:
Water flow rate:
In-coil air velocity
Design conditions:
Air inlet (dry):
Air inlet (wet):
Air outlet (dry):
Air outlet (wet):
Water inlet:
Water outlet:
Air flow rate:
Water flow rate:
In-coil air velocity:
-10
90
9.1
54
°C
% RH
°C
% RH
9
21
45
37
10.28
4.49
2.95
°C
°C
°C
°C
m³/s
l/s
m/s
27
21.1
60
15
14.9
99
9
15
10.28
9.36
2.97
°C
°C
% RH
°C
°C
% RH
°C
°C
m³/s
l/s
m/s
Technische Universiteit Eindhoven University of Technology
Extraction fan:
Manufacturer:
Type:
Nominal power:
Efficiency:
Rotational speed (fan):
Air flow rate:
Air supply velocity:
System pressure:
External pressure:
Dynamic pressure:
Total pressure:
Nicotra Gebhardt
RZR 11-0900
Fixed speed fan
11
kW
83
%
834
rpm
9.72
m³/s
7.67
m/s
70
Pa
350
Pa
35
Pa
935
Pa
Performance curve
The figure below shows the performance curve of both the extraction and supply fan. The figure originates
from the product specifications of the manufacturer (Nicotra Gehardt – Centrifugal Fans RZR, belt driven
(2013).
32 Modelling the behaviour of controllers in an HVAC system
Technische Universiteit Eindhoven University of Technology
Flow measurements
The air flow specifications below are based on two tuning-reports of LuWat Inregeltechniek bv.
Report 1:
July 2010
Air
Supply AHU offices:
Extract AHU offices:
Design:
26236 m³/h
29801 m³/h
Measurement 1:
32037 m³/h
33659 m³/h
Water:
Heating coil AHU offices
Cooling coil AHU offices:
Design:
4.180 l/s
unknown
Measurement 1:
4.117 m³/h
unknown
Report 2:
January 2012
Air
Supply AHU offices:
Extract AHU offices:
Design:
34216 m³/h
32261 m³/h
Measurement 1:
32385 m³/h
31826 m³/h
33 Modelling the behaviour of controllers in an HVAC system
Measurement 2:
4.354 l/s
unknown