ARTIFICIAL NEURAL NETWORK APPLICATION FOR CASCADING FAILURE MITIGATION IN RENEWABLE POWER GENERATION PLANTS

Abstract: The lack of adequate fault detection and mitigation strategy has resulted to constant fault cascade on the power generation plants which has led to power plant shutdown and black outs, causing bottlenecks to the economic development of nations. The paper centered on the utilization of artificial intelligent models for the detection and mitigation of machine and inverter faults in the hydro and photovoltaic power plant that was operational in Kaiji power plant and Kaduna Solar power plant, Northern Nigeria. The artificial intelligent models utilized were artificial neural network (ANN) and Fuzzy Logic. The mathematical model for the generation plant was generated and represented in SIMULINK with current signal, voltage signal and speed being the parameters utilized for the measurement of the conditions in the gas plant. From the results presented, it was seen that the current signal, voltage signal and speed, For the fault occurrence in Hydro power plant, Fuzzy logic had 5.998mins, ANN mitigated fault time is 13.4992mins. For the fault occurrence in solar PV system, Fuzzy logic had 5.9988mins mitigation time, ANN mitigated fault for 13.2697 mins. The ANN model had better fault mitigation time. Keywords: Hydro plant, Solar plant (PV), faults, ANN, Fuzzy, Detection, Mitigation. Title: ARTIFICIAL NEURAL NETWORK APPLICATION FOR CASCADING FAILURE MITIGATION IN RENEWABLE POWER GENERATION PLANTS Author: S.N EMECHETA, C.B MBACHU, I OKONKWO International Journal of Novel Research in Electrical and Mechanical Engineering ISSN 2394-9678 Vol. 12, Issue 1, September 2024 - August 2025 Page No: 65-78 Novelty Journals Website: www.noveltyjournals.com Published Date: 11-August-2025 DOI: https://doi.org/10.5281/zenodo.16793697 Paper Download Link (Source) https://www.noveltyjournals.com/upload/paper/ARTIFICIAL%20NEURAL%20NETWORK%20APPLICATION-11082025-6.pdf