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