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Design of PSS for multi-machine system using extreme learning machine algorithm
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AI
DOI：10.1016/j.measurement.2024.116582.png)
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Abstract
En 中文
A novel Extreme Learning Machine algorithm is used to train the neural network for Power System Stabilizer (PSS) to minimize low-frequency oscillations. The use of rapid-acting exciters, the interconnection of various power systems, and disturbances like faults and load changes all contribute to the generation of low-frequency oscillations. If sufficient damping is not provided, these oscillations generate and sustain, and eventually cause the power system to shut down entirely. The lead-lag power system stabilizer is a conventional device used but it is slow in operation and can apply to linear systems only. Artificial intelligence techniques like, fuzzy and neural networks are used to overcome the bottlenecks. The neural networks are trained using backpropagation and extreme learning algorithms. The operation of the designed power system stabilizers is verified on a 7-machine, 29-bus system, 4-machine, 11-bus system, and SMIB System. The proposed controller has been providing better damping performance compared to other controllers in terms of Integral Time Squared Error (ITSE) and Integral Squared Error (ISE). The proposed system is designed and validated through the R2023b MATLAB/Simulink Software environment.
Keywords:
Power System Stabilizer
Power System Stability
Fuzzy Controller
Back Propagation Algorithm
Extreme Learning Machine Algorithm
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