HydroPower Plant Planning for Resilience Improvement of Power Systems using Fuzzy-Neural based Genetic Algorithm

• URL: http://arxiv.org/abs/2106.12042v1
• Date: Wed, 16 Jun 2021 21:08:01 GMT
• Title: HydroPower Plant Planning for Resilience Improvement of Power Systems using Fuzzy-Neural based Genetic Algorithm
• Authors: Akbal Rain, Mert Emre Saritac
• Abstract summary: This paper will propose a novel technique for optimize hydropower plant in small scale based on load frequency control (LFC) This technique use self-tuning fuzzy Proportional- Derivative (PD) method for estimation and prediction of planning. Deep Spiking Neural Network (SNN) used as the main deep learning techniques to optimize this load frequency control which turns into Deep Spiking Neural Network (DSNN)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper will propose a novel technique for optimize hydropower plant in small scale based on load frequency control (LFC) which use self-tuning fuzzy Proportional- Derivative (PD) method for estimation and prediction of planning. Due to frequency is not controlled by any dump load or something else, so this power plant is under dynamic frequency variations that will use PD controller which optimize by fuzzy rules and then with neural deep learning techniques and Genetic Algorithm optimization. The main purpose of this work is because to maintain frequency in small-hydropower plant at nominal value. So, proposed controller means Fuzzy PD optimization with Genetic Algorithm will be used for LFC in small scale of hydropower system. The proposed schema can be used in different designation of both diesel generator and mini-hydropower system at low stream flow. It is also possible to use diesel generator at the hydropower system which can be turn off when Consumer demand is higher than electricity generation. The simulation will be done in MATLAB/Simulink to represent and evaluate the performance of this control schema under dynamic frequency variations. Spiking Neural Network (SNN) used as the main deep learning techniques to optimizing this load frequency control which turns into Deep Spiking Neural Network (DSNN). Obtained results represented that the proposed schema has robust and high-performance frequency control in comparison to other methods.

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