Abstract
Keyword
Introduction
Load frequency, voltage and tie line power are the essential parameters of an electrical power
system, which must be maintained within the prescribed limits. Power system sensitivity
analysis reveals that mismatch in real and reactive power demand affects the system
frequency and voltage respectively. As the real and reactive power demand varies throughout
a day, therefore the mismatch minimization can be possible by close loop control. The
preliminary work on LFC was carried out by O.I. Elgerd and C. Fosha. They presented
transfer function of speed governor and turbine control system for a single area nonreheat
thermal system [1]. Conventional controllers use integral control to minimize the frequency
and voltage deviation [2]. The disadvantage of using only integral control is that its high gain
value causes large oscillations and further lead to system instability. The dynamic
performance of system under various loading condition depends upon the gain selection of
controller parameters and it may deteriorate with unturned parameters. Various control
techniques have been found in literature for tuning controller parameters for LFC such as PI/
PID [3]–[5], Optimal Linear Control [6], IP control [7], GA & Linear Matrix [8] & for AVR
such as Artificial Bee Colony Algorithm [9], Adaptive Hybrid Learning Algorithm [10]. It
has been found that few literature have addressed combined control of AVR & LFC for a
non-reheat thermal power system [11]–[13]. A Nonlinear Threshold Accepting Algorithm
(NLTA) has been utilized for developing the gain parameters of PID controller for LVF-AVR
scheme for multiarea power system in reference [11]. A combination of Generalized Fuzzy
Model (GFM) and Gaussian Mixture Model (GMM) has been used for determining gain
parameters for LFC and AVR for a single area power system [13] However, none of the
above papers have focused upon hardware implementation of combined AVR-LFC scheme
for a synchronous generator while considering control system of governor, turbine, and
excitation system. This paper addresses combined LFC and AVR problem using PI
controllers for a single area power system with HIL implementation. The research work was
carried out to mimic practical non-reheat thermal area unit on a small scale. HIL is equipped
with a synchronous generator which power nonlinear loads. Also, various control system
dynamics such as turbine, speed governor and excitation were incorporated. The remainder of
this paper is organized as follows. SectionII contains system architecture; Section III explains
system modelling and its control. Results and various case studies are discussed in Section
IV. Finally, conclusions and future work represented in Sections V and VI, respectively.
SYSTEM ARCHITECTURE
The system architecture for LFC and AVR control for a single area power system is shown in
Fig.1. It consists of a synchronous machine coupled with a DC machine. The DC machine
acts as a turbine-governor system which consists of armature and field controls. In this work,
DC voltage applied to the field is kept constant and to incorporate speed control of alternator
(synchronous generator), speed of the DC machine is controlled. Speed control of DC
machine emulates steam input controller of turbine. This is achieved by controlling DC
voltage applied to the armature winding. Speed control of alternator helps in controlling load
frequency. Therefore, load frequency control is achieved by controlling armature voltage of
DC machine. Also, load voltage control is possible by controlling DC excitation voltage of
alternator. Load frequency and voltage are sensed by voltage and current sensor which are
fed to dSPACE controller. The dSPACE controller continuously controls the frequency and
voltage by controlling output voltage of armature and excitation control respectively. A three
phase L-C filter is connected in series to reduce harmonic distortion. A three-phase linear
load is connected at the output of alternator which consists of combination of resistor and
inductor (R-L) load.
LFC & AVR control for a single area power system
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Selection of Suitable Feed forward Neural Network Based Power System Stabilizer for
Robust Excitation Control System
Abstract—a reliable and economic power system is the key requirement for the development
of any country’s power sector. Electrical power system is quite complex due to its non-linear
behaviour and changing load conditions. In an excitation system, the Automatic voltage
regulator (AVR) and load frequency controller (LFC) are connected to maintain the terminal
voltage and load frequency. The high gain of AVR produces negative damping on rotor of
generator. This behaviour of AVR can be easily controlled by the introduction of power
system stabilizer (PSS). The major function of PSS is to inject supplementary stabilizing
signals to minimize the effect produced by AVR. For this purpose, various controllers as
power system stabilizers have been proposed by researchers to tune their parameters. In this
research work, three types of controller’s i.e.
 Probabilistic feed forward neural network (PFFNN)
 Multi-layer perceptron feed forward neural network (MLPFFNN) and
 Radial basis function feed forward neural network (RBFFN)
These are designed in Matlab / Simulink to tune the parameters of PSS. These controllers are
implemented as single machine connected with infinite bus (SMIB). The simulation results
for terminal voltage (Vt) and load frequency (f) of these PSSs are compared. The simulation
results for RBFFN based power system stabilizer show the promising improvement in
stability of synchronous generator than other types of FFNN controllers.
Keywords-Power System; Excitation System; Feed forward NN; Power System Stability
INTRODUCTION
A power system is subjected to various disturbances due to its dynamic and transient
behaviour [1]. A reliable and economic power system is responsible to generate and deliver
electric power with minimum disturbances. The voltage and frequency oscillations in a power
system badly affect its reliability [2]. In an excitation system, load frequency controller
(LFC) and Automatic Voltage Regulator (AVR) maintain the active power (P) and reactive
power (Q) respectively [2]. The high gain of AVR may produce a negative damping torque to
the rotor of synchronous generator. This problem can be solved by the introduction of power
system stabilizer (PSS). A PSS injects supplementary stabilizing signals at the input of
synchronous generator to remove the effect of AVR [3]. The synchronous generator is
subjected to various loading conditions. To cope with the changing load conditions these
parameters must be tuned accordingly [4].
Literature Review
A supervisory level power system stabilizer (SPSS) is capable to compensate non-linear
behaviour of power system [5]. A proportional integral gain controller provides minimum
overshoot and small settling time [6]. A proportional integral derivative (PID) and fuzzy logic
controller can better understand the transient behavior of synchronous generator [9]. A
particle swarm optimization (PSO) can efficiently reduce negative damping produced by
AVR [7]. An ANN based controller is suitable for controlling active and reactive power of
synchronous generator [7 - 8]. A multi-layer perceptron (MLP) based PSS can tune the
parameters of a power system (PS) effectively with minimum settling time and overshoot
value [9]. A PNN based PSS reduces negative damping effect of AVR and improves transient
and dynamic stability of power system [9]. The conventional PSS has fixed gain and these
systems operate at a particular operating condition. The power system is subjected to various
loading conditions, for this reason conventional controllers produce poor control of power
system [10]. The ANN possesses the ability to cope with changing load conditions and tune
the parameters of power system in an effective style [11]. The non-linear behaviour of a
power system can easily be handled by ANNs [12, 13]. For these reasons, in this research the
various types of feed forward neural network based controllers are designed as PSS. This
research compares the simulations results for
 PFFNN
 MLPFFN and,
 RBFFN based Power System Stabilizers (PSSs).
II. MATHEMATICAL MODELLING OF SYNCHRONOUS GENERATOR
For an excitation control system mathematical modelling plays a vital role. Here all models
are represented using their state space representations. For non-linear systems, state space
models are used [14]. In this connection, dynamic stability of power system an SMIB is
taken into consideration. State Space Description of a Synchronous Generator a linearized
state space model of a synchronous generator is given as under [14]:
.
that an RBF based PSS has less settling time and less overshoot value than all other types of
FFNN controllers. The results shown in Table.2 for terminal voltage show that there is slight
difference in settling time and over shoot value of all FFNN based PSSs. However in
transient stability nano seconds have also got significant importance. Therefore it is
concluded that an RBF based PSS has robust control over frequency deviation and terminal
voltage of synchronous generator. The results of RBFFN-PSS for terminal voltage show that
a RBFFN-PSS has less settling time and over shoot values. Such type of controller can
effectively control the terminal voltage of synchronous generator in transient conditions.
CONCLUSION
A reliable and economic power system can be achieved by controlling the parameter of
power system within permissible limits. An excitation control system is responsible for
reliable operation of power system. In an excitation system AVR produces negative damping
effect on the rotor of generator. To reduce the effect of AVR PSS in connected in conjunction
with AVR which partially or completely cancels out the negative damping effect of AVR
This research is focused on selection of suitable FFNN based PSS. For this purpose FFNN
based PSSs are designed and connected with SMIB in Matlab. The simulation results for
three types of FFNN i.e. PNN, MLP and RBF are compared. The RBF based PSS shows its
robustness over all other types of FFNN based PSSs. Hence it is concluded that an RBFFN
based PSS has superior control on the negative damping effect of AVR. Such type of PSS can
improve transient as well as dynamic stability of power system
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