Intelligent Control of Power System Stabilizer Based on Archimedes Optimization Algorithm – Feed Forward Neural Network
Universitas Negeri Surabaya, Widi Aribowo, Supari Muslim, Universitas Negeri Surabaya, Bambang Suprianto, Universitas Negeri Surabaya, Subuh Isnur Haryudo, Universitas Negeri Surabaya, Aditya Chandra Hermawan, Universitas Negeri Surabaya
Abstract
A power system is a collection of equipment that has characteristics. Power system stability is maintaining how the system can return to its position. Power system stabilizer (PSS) is a device used to maintain the stability of the power system due to load changes. The archimedes optimization algorithm (AOA) is a metaheuristic method based on the force that occurs in the fluid due to a load. The method is based on the laws of physics. A neural network is a concept to duplicate the work function of the human brain. In this study, the archimedes optimization algorithm (AOA) will be proposed to improve the performance of the feed forward neural network (FFNN). This hybrid method is called AOA-NN. The hybrid method is used to improve the performance of power system stabilizers. To test the ability and effectiveness of the AOA-NN method, a comparison with the conventional PSS, feed-forward neural network (FFNN), Cascade-forward neural network (CFNN), Distributed time-delay neural network (DTDNN) and Sine Tree-Seed Algorithm -Feed-forward Neural network (STSA-NN) method are applied. From the research, it can be concluded that the method proposed by AOA-NN has the best ability. The AOA-NN method has the ability to reduce the overshoot speed with an average value of 85.43972% and the overshoot rotor angle with an average value of 38.9278%.