上海交通大学学报

上海交通大学学报 >

2021 , Vol. 55 >Issue S2: 42 - 50

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2021.S2.007

基于极限学习机的双馈感应风力发电机综合自适应调频参数优化方法

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  • 1.国家电网有限公司华东分部,上海 200120
    2.上海电力大学 电气工程学院,上海 200090
金皓纯(1983-),男,浙江省绍兴市人,高级工程师,从事调度自动化、电力监控系统网络安全研究.

收稿日期: 2021-10-20

  网络出版日期: 2022-01-24

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Optimization of DFIG Comprehensive Adaptive Frequency Regulation Parameters Based on Extreme Learning Machine

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  • 1. East Branch of State Grid Corporation of China, Shanghai 200120, China
    2. School of Electric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Received date: 2021-10-20

  Online published: 2022-01-24

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摘要

针对新能源并网带来的系统频率稳定问题,双馈感应风力发电机(DFIG)多采用虚拟惯量及下垂控制参与电力系统的调频.为了能够充分发挥DFIG的调频能力,通过分析频率动态响应各阶段虚拟惯量及下垂系数的作用机理,提出对虚拟惯量及下垂系数的自适应控制.基于极限学习机预测不同等级风速下的各项调频指标,通过对调频指标建立目标函数对综合自适应调频控制参数的优化,并提出最优减载率有功备用控制方案.仿真结果表明了该方法的有效性.

关键词： 双馈感应风力发电机; 极限学习机; 综合自适应控制; 频率动态响应

本文引用格式

金皓纯, 葛敏辉, 徐波 . 基于极限学习机的双馈感应风力发电机综合自适应调频参数优化方法[J]. 上海交通大学学报, 2021 , 55(S2) : 42 -50 . DOI: 10.16183/j.cnki.jsjtu.2021.S2.007

Abstract

Aiming at the problem of insufficient system frequency regulation ability caused by wind turbines connected to the grid, doubly fed induction generators (DFIG) mostly use virtual inertia and droop control to participate the frequency regualtion of the power system. However, traditional control strategies cannot fully utilize the frequency regulation capability of DFIG. In order to further improve the frequency stability of the system, the adaptive control of the virtual inertia and the droop coefficient are realized by analyzing the effects of the virtual inertia and the droop coefficient in each stage of the frequency dynamic response. Then, based on the extreme learning machine to predict the various frequency regulation index at different levels of wind speed, the objective function of the frequency regulation index is established to achieve the optimization of the comprehensive adaptive frequency regulation parameters, and the variable load shedding rate active standby control scheme adapted to the wind speed is proposed. The simulation results show the effectiveness of the method.

Key words： doubly fed induction generator (DFIG); extreme learning machine; integrated adaptive control; frequency dynamic response

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