Cellular Multilayer Perceptron for Prediction of Voltages in a Power System

With the increase in renewable energy sources connected to the power grid, better identification tools are needed for power system voltage profile prediction. Current deregulation trends have led to voltages operating close to stability limits which increases the need for quick estimation tools for system security and contingency analysis. This paper presents a Cellular Multilayer Perceptron (CMLP) architecture for fast identification and prediction of bus voltages. The CMLP method is compared with a standard MLP neural network for bus voltage prediction on the 12-bus three-area test power system. CMLPs can represent a direct mapping of any power system simplifying the equations and allowing for easy scalability to large power systems. Keywords-Cellular Multilayer Perceptron (CMLP); Multilayer Perceptron (MLP); Small Population Particle Swarm Optimization (SPPSO) I. INTRODUCTION An increase in the trend for interconnections between power systems to reduce cost has led to more complexity in the voltage collapse problem mainly due to system voltages operating much closer to their stability limits. As the load demand on a system is increased, voltages that are already operating close the stability limits decrease causing major disturbances and blackouts (1). Early estimation of power system voltage instability is imperative for reduction of large blackouts. The current trend of deregulated and competitive power markets has also brought about a more complicated network topology due to frequent transactions between generation and distribution companies. Voltage instability can be alleviated by implementing adaptive solutions to improve stability margins and dynamic control. To achieve this goal, a tool needs to be developed to correctly identify power system voltage profiles under different operating conditions. Traditional methods for contingency analysis involve numerous load flow calculations requiring long computation time (2). Today's power systems require faster prediction tools for assessing voltage stability so that preventative action can be taken before blackouts due to voltage collapse occur. For effective voltage stability, a power system must maintain a steady and acceptable voltage level at normal operating conditions, after increase in load, during system configuration changes or when the system is being subjected to a disturbance. The motivation of this work is to decrease the distress caused by the rise in renewable energy sources along with increased load demands which cause power system voltage profiles to be constantly changing (3). It is necessary to adjust power system parameter settings quickly and efficiently in response to these changes. To achieve this, system changes should be ultimately predicted faster than real time so that the necessary controls can be implemented before disruptions occur which will help to reduce or even prevent system disturbances (4), (5). This requires a method for very fast and accurate power system modeling (6). Neural Networks are commonly used for prediction due to their pattern recognition capabilities and ability to approximate very complex input- output relationships learned from a set of training samples (3), (7). Multilayer Perceptron neural networks (MLP) have been frequently studied for use in voltage stability tracking, prediction, and monitoring (7)-(13). This paper describes how a Cellular Multilayer Perceptron neural network (CMLP) can be used to predict bus voltages. The CMLP architecture allows for more accurate system equivalent modeling and faster prediction. CMLPs use internal connection weights to learn the relationships between inputs and outputs consequently discovering the intricate characteristics of the system under study. The dynamics of a highly interconnected power system can be directly replicated by the connections between the cells of the CMLP with the cellular connections representing the transmission lines that connect the buses of the power system. Ideally a CMLP can be used to predict system voltages N steps ahead in time. For an online application, a CMLP predicting system voltages N steps ahead in time will provide the system operator more time to implement the necessary controls or run analysis on the system. Simulation results on the 12-bus multi- machine power system show the performance capabilities of this technique. The CMLP is compared with a single MLP network to show its superior voltage prediction capabilities over standard techniques.