Neural Network Control of Power Systems.
Patrick Avoke, Student Member, IEEE (Calvin College)
Abstract: Like most other re...
Figure 2: A Multi-layer Perceptron
Figure 3: Showing structure of recurrent neural network.
III. Problems with Neural Nets...
During the normal operation of the transformer, the
circuit breaker is closed at a peak source voltage of t = 1.25
such th...
VIII. Training Sets and Procedures
The concept of training in any neural network
greatly impacts the efficacy of the syste...
Network architecture, training data and training parameters
sections. Given the difficulty associated with simulating a
ne...
Network architecture, training data and training parameters
sections. Given the difficulty associated with simulating a
ne...
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  1. 1. 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, over- shedding 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 black- box computational methods for addressing basic Stimuli- Response 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 black- box 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. 1
  2. 2. Figure 2: A Multi-layer Perceptron 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 would guarantee both satisfactory performance and a cost- effective 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. 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 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. 2
  3. 3. 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 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. Choosing network topology b. Unit characteristics of each unit in the system. c. Training procedures and methods d. Training Sets/variables. e. 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 non- recurrent 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. 3
  4. 4. 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. 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. 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. 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 4
  5. 5. 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 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.   5
  6. 6. 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 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.   5

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