Neural Network Control of Power Systems.
Patrick Avoke, Student Member, IEEE (Calvin College)
Abstract: Like most other real world dynamical systems, power
systems are non-linear hence require a convenient method of
controlling the activities of the system. The approach to this problem
often involves linearization of the system and then the application of
various methods of linear systems controls to manage the system.
Needless to say, the efficacy of the linearization step would
determine how effective a selected control method would have on
the chosen system.
With the emergence of neural networks design, modern methods of
controlling nonlinear system have been more accurate and
convenient for the engineer to work with. In effect, it is possible to
“train” neural networks to monitor a system for any irregularities or
disturbances and initiate a process to restore “normal” operational
conditions within the system based on forecasted results.
I. Introduction.
Selecting a control measure is often influenced by
economic factors, speed of system, and state of the system as
well as its sensitivity to other controls systems. Typical
emergency conditions in a power installation involve
overloading in the power lines. The primary measures for
relieving overloaded lines are phase shifting, load shedding,
tie line scheduling, generation shifting and controlled power
system generation. Load shedding as a fix for overloaded
lines in the long term has a correlation with overload levels,
implementation of controlled separation and re-establishing
power balance. Some adverse effects of uncontrolled load
shedding include an increase in the system voltage, overshedding as well as some undesired increases in line flow.
Adibi and Thorne were one of the many sources of
proposed controls solution for large power systems. They
proposed a real-time control scheme for load-shedding in
underground transmission networks. This brilliant scheme
used approximate calculations to accelerate the solution time.
Despite the cleverness of this system, it was observe that
large interconnected power systems were very difficult to
incorporate in any such schemes. A big part of the failure of
the system to adequately address the standing problem was
the lack of computer or communication support at the local
control levels at the time. With the overwhelming
preponderance of computer technology today, many more
sophisticated control measures have hitherto been developed
and tested successfully as a remedy.
II. Artificial Neural Network Controls (ANN).
Artificial neural networks were first developed in
the early nineteen forties when a neurophysiologist, Warren
McCulloch and a mathematician, Walter Pitts, wrote a paper
on how neutrons might work by modeling a simple electrical
circuit to describe the process. The idea with this model was
to investigate the activity of neurons in the thinking process.
In modern times, questions about incorporating neural
networks to drive state of the art power systems grids have
precipitated growing interest in ways to simulate and control
the power system.
Figure 1: General role of Neural Network.
The artificial neural network as defined by Schalkoff
(Schalkoff, 2), is a network composed of a number of
interconnected units with each unit having input or output
characteristics that implements a local computation or
function. Typical neural networks operate in parallel nodes
whose function is determined by the network structure, the
connection strengths and the function in each node. Neural
networks have the unit ability to “learn”. In other words, the
human does not necessarily have to be able to explain the
“problem” to the system. Designing neural network solutions
for systems often starts with a series of questions regarding
the system such as: “What sort of problem does one seek to
solve?”, “Can the network be trained to solve the problem?”
and “What would be the best network structure to solve the
problem?”(Schalkoff, 11). Once these questions are
addressed, parameters for designing the system can be define
to include the network structure, training procedure, testing
and input/output parameters of the ship.
Neural nets can be conveniently described as blackbox computational methods for addressing basic StimuliResponse processes (S-R). On each side of the black-box
(ANN) is a known set of inputs corresponding to their
respective output set hence any distortion in the input of the
system would employ algorithms and codes within the blackbox to produce a unique output for that stimulus. It is through
this process that the “new” output is added to the already
existing set of standard neural network responses for known
stimuli. It is important to note that the standard S-R pairs
encoded into the artificial neural network ought to represent
the stable states of the system during normal operation. The
approach to “learning” by ANN’s could take the form of
deterministic methods like back-propagation and Hebbian
approaches or could involve the stochastic approach such as
genetic algorithms or simulated annealing.
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would guarantee both satisfactory performance and a costeffective solution to the problem.
Satisfactory performance can be best achieved after
a long time of “learning” by the system. In other words, the
performance of any neural network is directly proportional to
amount of operational time since installation of the network.
Figure 2: A Multi-layer Perceptron
Neural Network Controls in Load Shedding
In practice, an operational load shedding scheme for
bulk power systems should be able to incorporate an “infinite
number of possible system states that would be mapped to a
finite number of actions.” (King et al, 426). The training set
should contain both the “standard” normal operational
conditions and the state of parameters necessary to implement
the appropriate action for system stability. Once the training
process is completed, the benefits of the systems are
immediately evident in the speed of response to faults and the
seamless integration with existing power system controls.
Load shedding neural networks are composed of an
input layer, two hidden layers and one output layer. The input
layer comprises the incoming voltage (usually a bus voltage)
composed of many active line flows that are channeled
through one output that triggers shedding of a chosen load at
the bus level. Training sets of all the neural networks are
often extracted from identical emergency states to ensure that
responses are consistent.
IV. Faulty System
Figure 3: Showing structure of recurrent neural network.
III. Problems with Neural Nets.
Neural networks work quite well with predicting
outcomes of non-linear systems in the event of a fault but
would obviously need an “initial standard” called the training
set to compare any fault signals to. This standard would
basically indicate to the system whether parameters coming
through fall within normal operational condition. It is almost
safe to assume therefore that the efficacy of a neural network
within a power installation is premised on the quality of the
initial training set. The concern with neural nets in this
respect is that composing training sets is a non-trivial task to
say the least and is very expensive to develop.
Another problem with artificial neural networks is
that because of its poor ability to communicate exact
prediction steps to the user, it is difficult to determine the
choice of the number of hidden layers and neurons per hidden
layer that exist in the system. With this constraint, the
designer must be careful to have enough training set nodes
within the system to achieve the best results while noting that
too many neurons (or nodes) a memorization of the training
sets with the risk of losing the networks ability to generalize.
The choice of a neural network structure, number of nodes
and training sets heavily depend on the actual problem at
hand. Experts however often recommend that the “minimum
required topology” of the network is implementation as it
Emergencies that contribute a great deal to service
interruption, system degradation and ultimately loss in
revenue, are rife in the power systems industry. In order to
alleviate the impact of power interruptions, corrective and
emergency responses have to be readily available to restore
normal operational conditions of the power installation. There
are however a finite number of measures that can be applied
to ameliorate the problem. As the emergency progresses, less
desirable fixes such as load shedding may be necessary to
control the unstable bulk power system.
For modeling the workings of an ideal neural
network in some power system, we would simulate the
operation of a current transformer model (from Matlab
demonstration library) using normal operating parameters and
extreme values that would cause a saturation of the
transformer.
The current transformer is used to measure the
current levels in the shunt indicator connected to a 120kV
network. The transformer is rated at 2000A/5A, 5VA with a
primary winding consisting of a single turn passing through
the toroidal core connected in series with the shunt indicator
(69.3KV, 1kA RMS). The secondary winding, on the other
hand, has 400 turns and is short circuited through a 1ohm
load resistor. A voltage sensor connected at the secondary
coil reads a voltage that should be proportional to the primary
current. 2.5 Amps current flows through the secondary coil in
steady state.
During the normal operation of the transformer, the
circuit breaker is closed at a peak source voltage of t = 1.25
such that the current levels stay below 10pu saturation value
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for normal operation of the transformer. With this modeling,
there is no current asymmetry hence minimal error due to
reactance of the current transformer (figure 6).
Once the breaker closing time is reducing from 1.25/50s to
1/50s, a fault is introduced into the system causing the
transformer to quickly reach saturation. (Figure 7). The
change in this breaker value causes the current asymmetry in
the shunt reactor. Clearly, the first three cycles show the flux
contained under the 10pu saturation value hence primary
current and secondary voltage remain superimposed on each
other. After the third cycle, flux asymmetry caused by the
primary current tends to saturate the current transformer. The
effect is a distortion in the secondary voltage.
Using inappropriate switching parameters for the
secondary switch could also result in an unstable system.
Figure 8 demonstrates the effect of changing the secondary
switching time from 99 to 0.1 seconds. One can quickly
observe the clipping effect at the saturation point (10pu) as
the voltage spikes to about 250V as a result of dramatic
changes in flux.
The above described faults easily depict the
challenge involved in maintaining a transformer and the need
for a more intelligent system to monitor, ameliorate and
possible prevent future occurrences of such faults within the
system
V. Model Reference Control Solution
In modern times, neural network systems have been
the ideal remedy for most of the above mentioned challenges
in the power systems industry. Neural network controls of
power systems basically allow the configured system to learn
the pattern of undesirable voltage and current levels and
respond appropriately to restore stability in the system.
Although the initial set-up costs of implementation are very
expensive, the long term benefits and efficiency of the
system. Setting up an ideal artificial neural network involves
extensive planning of the network topology-number of input,
hidden and output nodes to implement and the training sets
used in the process. An important task with setting up the
system is interfacing the neural network with the “outside”
world. Designing a functional neural network for any given
power system would involve five major design parameters
that ought to be considered during implementation:
a.
b.
c.
d.
e.
Choosing network topology
Unit characteristics of each unit in the system.
Training procedures and methods
Training Sets/variables.
Input/Output representations and post-processing.
The basic design process of the neural network would
typically follow the following steps:
i.
Study system under consideration.
ii. Determine the availability of measurable inputs.
iii. Consider constraints on desired system performance
and computational resources.
iv. Consider the availability and quality of training and
test sets.
v. Consider the availability of suitable Artificial
Neural Network (ANN) systems.
vi. Develop ANN simulations.
vii. Train the ANN system
viii. Simulate system performance using the test sets.
ix. Iterate among preceding steps until desired
performance is reached.
VI. Choosing Network Topology.
In viewing various neural networks, about four
different network topology concepts are apparent-recurrent
networks, on-recurrent networks, Layered networks as well
as Competitive interconnect structures. Recurrent and nonrecurrent networks are mutually exclusive whilst the other
two topologies could be either recurrent or non-recurrent.
The selection of any particular topology would largely
depend on ones system requirement and cost restrictions.
For this project, the layered network model would
be used to demonstrate the efficacy of an ideal neural
network within a given power system. With the layered
model, the implementer specifies the number of nodes in the
input, hidden and output layers of the neural network. This
decision would depend on the desired complexity of the
system.
VII. Unit Characteristics of Inputs and Outputs
Here, the engineer has the opportunity to select input
and output nodes of the network to meet the needs of the
power system. For the power system model, our desired
inputs would be current and voltage parameters or quantized
data that would be compared with incoming quantized values
to check for consistency. This activity can be best likened to
pattern recognition by the human brain. Although the exact
method of pattern recognition by the brain is hitherto
unknown, it is obvious that humans can easily recognize
printed and handwritten patterns in various colors, styles and
font sizes. In the same way, any variance in data values of the
incoming signal can be compared to an existing bank of
values for similarities. Once a match is found, the system (via
the perceptrons) drives the power system to respond with the
correct mapping to an output value to restore the stability of
the system. In the event of a non-matching value, the neural
network would note and store the unrecognized signal and
respond with some appropriate output. This would mean that
for any subsequent occurrence of this signal would be easily
identified and solved. With these general inputs, the neural
network can begin the process of pattern recognition of the
incoming signals.
Representing unit characteristics as inputs and outputs often
has a number of associated challenges as inputs may be
continuous over an interval, discreet, coded, etc. For effective
performance of the network, the implementer must ensure
that the inputs are properly specified.
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VIII. Training Sets and Procedures
The concept of training in any neural network
greatly impacts the efficacy of the system. Once a system is
properly trained and tested with appropriate input and output
values, the performance in the event of a fault is often
remarkable. Training a neural network is probably the most
expensive and most tasking aspect of the process.
Neural networks can be trained for function approximations
by nonlinear regression (pattern association), pattern
association or pattern classification. The process of training
the network involves set of “training sets” that show the
proper network behavior and target outputs. For the analysis
of neural networks, there are different training algorithms that
could be implemented for a power systems model. These
algorithms include Backpropagation, conjugate gradient
algorithm, Quasi-Newton algorithm as well as Line Search
algorithms.
In this document, the batch training backpropagation method
would be used to analyze and train the neural network. This
process updates the weights and biases after the entire
training set has been applied to the network.
Needless to say, the neural network design is only as good as
the test sets applied to the model. An excellent test set would
always produce excellent results in the event of an
unexpected fault. The costs involved in this step alone often
stems from getting an accurate mathematical model to
simulate as many faults conditions as the designer can
anticipate. It is also vital to note that the availability of some
system memory would also determine the extent of success
that is achieved. Once the appropriate test sets are identified
and programmed into the system, the “learning” process
always demands more memory to store every new
information about the system and its behavior.
v. Availability of ANN Systems
For this model, the Model Reference Control tool
within SimPowersystems software was used as to simulate
the workings of a transformer. This tool serves as the most
appropriate model since it affords the designer some
opportunity to design the plant neural network model after
some reference model. This reference control tool comprises
two neural networks, the controller network and the plant
model network as can be seen in figure 4.
i. System under Consideration
The system in focus for neural network
implementation is a simple transformer. Specifically, one is
often interested in feasible methods of identifying and
containing electrical faults in transmission to improve the
performance of the system, reduce the risks associated with
an unstable electrical system and ultimately reduce long term
costs of running the transformer. With the use of neural
networks in the design of the transformer model, we hereby
explore the feasibility of a neural network implementation
within an operational transformer.
ii. Availability of Measurable Inputs
With the system in question, there are definitely
measurable inputs that can be identified within the
transformer-voltage and current levels, load capacities, etc.
Furthermore, it is relatively easy to measure the input
variables of the system at any point in time.
iii. Constraints on desired System performance
The biggest constraints on the performance of the
transformer could come from a number of factors. For
instance, power surges as a result of lightning strikes or any
general system imbalance due a snapped transmission lines
could destabilize the smooth operation of the transformer.
iv. Availability and quality of test sets.
This tends to be a very important step in the
construction of the artificial neural network system for the
transformer because of the costs involved in building and
testing training sets necessary to ensure the proper
functioning of the neural network transformer model. The
implementer of the neural network must have as a top priority
acquiring viable test sets to “set the tone” for the system to
emulate in the event of a fault. These test sets often act as the
standard by which the rest of the neural network operates.
Figure 4: Model Reference Control Plant Model.
As seen in the block diagram above, the plant model
of the system is first identified before the controller network
is trained such that the output from the plant follows the
reference model output of the system. This configuration of
the model reference tool allows the plant network to “learn”
by linking command inputs with desired output processes and
passing the results through both the plant and the neural
networks plant model.
vi. System Modeling and Network Training.
As mentioned earlier, using the model reference
control tool in neural networks, it is possible to include a
network control with the transformer model to monitor the
performance of the system, identify and remedy problems
within the system by “learning”. A typical Model Reference
control box as seen in figure 10 comprises three parts-the
Network architecture, training data and training parameters
sections. Given the difficulty associated with simulating a
neural network within the current transformer, we would
consider an example system that shows how the model
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reference control network would tailor the output of any
random signal to conform to a desired standard output for a
robotic arm (simulink demo).These allow the engineer to
specify training parameters and values necessary to ensure a
working system. In this block, the user can select the size and
characteristics of the input, hidden and output layers of the
system. The Plant identification block holds the
characteristics of the system in question and prompts the user
to specify the system variables as well as the number of
layers to be used for analyses of the Plant.
Training the neural network is often done in epochs
or cycles that are based largely on the inputs into the system.
So for various inputs, the neural network basically compares
each input to other known inputs and plots a graph of the
error gradient between these two values. The process of
comparison is carried out for as many random inputs as
possible while each result and the corresponding response of
the Plant are stored for future reference.
For a current transformer, the obvious challenges in
modeling the neural network would be the cost of the initial
setup. In the long term however, researchers in this area have
confirmed that neural networks would soon emerge as the
primary method for controlling and safeguarding modern
power systems.
Avoke, Patrick (Calvin College, ’05) is currently completing
undergraduate work in Calvin College, Grand Rapids, MI. He
hopes to continue on to Graduate school for a Masters
program in electrical engineering.
References
Looney, Carl. “Pattern Recognition using Neural
Networks-theory and Algorithms for Engineers
and Scientists.” University of Nevada, Oxford
University Press, 1997
Beale, Hagan Demuth. “Neural Network Design”,
International Thomson, 1996
Haykin, Simon. “Neural Networks: A
comprehensive foundation.”McMaster University,
McMillan, 1994
Schalkoff, Robert. “Artificial Neural Networks”
Clemson University, McGraw-Hill, 1997.
King,Roger and Novosel Damir. “Using artificial
neural networks for load shedding to alleviate
overloading in lines.” IEEE Transactions of Power
Delivery, Vol 9, no 1, January, 1994.
Software: Matlab simulink, Simpower Systems
demonstrations.
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