Neurofuzzy system with GA-based algorithm for knowledge management in network planning

This paper presents the necessity of power system reinforcements in today´s electricity supply industry. The principal outcome of the paper is two-fold. First, the paper offers a dynamic market modeling that primarily considers power system economics, reliability, security, and operation constraints. The network planning model is formulated by using stochastic modeling. Afterward neurofuzzy system with GA-based algorithm is proposed as a methodological approach. The fuzzy inference system is also presented. It mainly prioritizes all power system constraints throughout the model and evaluates a proper decision making towards the fuzzy if-then rules. Within the paper moreover, genetic algorithm (GA) is applied for optimizing the objective function whilst the recurrent neural network (RNN) technique is chosen for knowledge acquisition by training and learning all data under different power system control and operation scenarios. Eventually, some conclusions on whether or not the network reinforcements are necessarily required within a competitive marketplace are drawn.