Transmission Network Expansion Planning Based on Hybridization Model of Probabilistic Neural Networks and Harmony Search Algorithm

Transmission network expansion planning (TNEP) is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimizing problem in which the expansion purposes are optimized. The Artificial Intelligence (AI) tools such as Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS), Artificial neural networks (ANNs) and etc are various methods for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for the large-scale systems, which shows effectiveness utilizing of this models. Therefore, in this paper by a new approach, the hybridization model Probabilistic Neural Networks (PNNs) and Harmony Search Algorithm (HSA) was used to solve the TNEP problem. Finally, by considering uncertainty role in the load based on scenario technique, this proposed model is tested on the Garver’s 6-bus network.