An EnGRFA control scheme based power system stabilizers (PSS) for the stability analysis with wind energy integration

Penchalaiah G.; Ramya R.

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An EnGRFA control scheme based power system stabilizers (PSS) for the stability analysis with wind energy integration

机译：An EnGRFA control scheme based power system stabilizers (PSS) for the stability analysis with wind energy integration

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In this paper presented a control scheme for optimization and adjustment of the parameters of the electrical system stabilizer for the stability analysis of integrated electrical network of wind energy. The proposed control system is the Garra Rufa fish optimization (GRFA) and artificial transgender longicorn algorithm (ATLA) called enhanced GRFA (EnGRFA). In the proposed control scheme, considering the minimum error objective function, the ATLA algorithm is used to improve the GRFA search process, which is why it is called the EnGRFA control scheme. The main idea of installing a power system stabilizer (PSS) is to give additional damping of synchronous motor for suppressing system oscillations by using an auxiliary stabilization signal to control the excitation of synchronous motor. The equivalent wind turbine generator parameters are optimized to use the EnGRFA control scheme to produce the smallest deviation from the detailed system response. To use eigenvalue analysis to adjust the controller parameters, the objective function is assessed against two sub-objective functions (SOF). The task of the first SOF is to minimize the real part of Eigen values, and second SOF objective is to exploit that damping ratio. Using this proposed control scheme, uncertainties and unbalance issues can be found accurately and quickly to eliminate stability issues under power system. The EnGRFA control scheme is employed to study that eigenvalue analysis of dynamic behavior of the integrated wind energy system under different natural frequencies. The proposed control scheme is accomplished on MATLAB/Simulink platform and its performance is estimated and compared with the existing techniques.

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来源
《Artificial Intelligence Review: An International Science and Engineering Journal》 |2023年第8期|8437-8468|共32页
作者
Penchalaiah G.; Ramya R.;
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作者单位

SRM Institute of Science and Technology (Kattankulathur Campus);

SRM Institute of Science and Technology;

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原文格式 PDF
正文语种英语
中图分类人工智能理论;
关键词
Power system stability; Wind-turbine generator; Garra Rufa fish optimization; Artificial transgender longicorn algorithm;

An EnGRFA control scheme based power system stabilizers (PSS) for the stability analysis with wind energy integration

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