Artificial Neural Networks: Multilayer Perceptron and Radial Basis to Obtain Post-Contingency Loading Margin in Electrical Power Systems

• Autor(a) principal: Bonini Neto, Alfredo [UNESP]
• Data de Publicação: 2022
• Outros Autores: Alves, Dilson Amancio [UNESP], Minussi, Carlos Roberto [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.3390/en15217939; http://hdl.handle.net/11449/249377
• topic: artificial intelligence; contingency analysis; continuation methods; load flow; maximum loading point; voltage collapse; voltage stability margin

This paper presents the ANN (Artificial Neural Networks) approach to obtaining complete P-V curves of electrical power systems subjected to contingency. Two networks were presented: the MLP (multilayer perceptron) and the RBF (radial basis function) networks. The differential of our methodology consisted in the speed of obtaining all the P-V curves of the system. The great advantage of using ANN models is that they can capture the nonlinear characteristics of the studied system to avoid iterative procedures. The applicability and effectiveness of the proposed methodology have been investigated on IEEE test systems (14 buses) and compared with the continuation power flow, which obtains the post-contingency loading margin starting from the base case solution. From the results, the ANN performed well, with a mean squared error (MSE) in training below the specified value. The network was able to estimate 98.4% of the voltage magnitude values within the established range, with residues around 10−4 and a percentage of success between the desired and obtained output of approximately 98%, with better result for the RBF (radial basis function) network compared to MLP (multilayer perceptron).