A hybrid artificial neural network/genetic algorithm approach to on-line switching operations for the optimization of electrical power systems

This paper is intended to present an approach to decision making in the operation of electrical power systems that will use a simple genetic algorithm as a teacher for the process of supervised learning of a feedforward, backpropagation artificial neural network. The fitness function used in the genetic algorithm is based on a load flow program and used to determine the optimal condition of the critical switches of the system. Reward and penalty functions are applied to it in order to emphasize environmental, economic, security, robustness, public policy and other considerations as they are predetermined by the philosophy of operation of the electric utility. These considerations (policies) become a part of the training set and operation of the neural network. The fitness function used by the genetic algorithm in order to rank the possible solutions is based on a load flow program. The binary nature of the genetic algorithm is particularly appropriate for the operation of switches. The result of the methodology is the equivalent of an online implicit load flow program used to redesign the configuration of the power system in real-time by opening and closing critical switches that are placed along the power system. Experiments leading towards the development of this methodology using real data from the Peninsular Control Area (The Yucatan Peninsula) of the National Mexican Interconnected Power Grid are also presented.