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储能系统技术 储能系统 低电压穿越LVRT ★ 5.0

基于遗传算法优化风电场低电压穿越曲线的研究

Optimizing Low-Voltage Ride-Through (LVRT) Curves for Wind Farms Using Genetic Algorithms: A Case Study of Taiwan Power System

作者 Shiueder Lu · Chingsheng Chiu · Menghui Wang · Hwadong Liu
期刊 IEEE Transactions on Industry Applications
出版日期 2024年10月
技术分类 储能系统技术
技术标签 储能系统 低电压穿越LVRT
相关度评分 ★★★★★ 5.0 / 5.0
关键词 低电压穿越曲线 电力负荷预测 电力系统仿真 临界清除时间 三相短路故障
语言:

中文摘要

近年来,风力发电装机容量持续扩大,这就要求电力公司制定特定的低电压穿越(LVRT)曲线,以应对风电场引发的意外停电问题。截至目前,文献中缺乏关于LVRT曲线制定的相关报道,且国营的台湾电力公司(台电)也没有既定的指南来修订目前使用的LVRT曲线。本研究旨在根据台电预测的2025年电力负荷规划来制定LVRT曲线。研究使用配备GEWT 4.0兆瓦风力发电机模块的电力系统工程模拟器(PSSE)进行了仿真。定义了一个目标函数,在确保稳定性的同时将风力发电机的制造成本降至最低,并将临界清除时间(CCT)等条件作为约束条件。针对包括69千伏、69 - 161千伏和161千伏情况在内的五种场景,将公共连接点(PCC)处的三相短路故障模拟为最坏情况,以确定合适的LVRT曲线。CCT是LVRT制定过程中的关键参数,该过程还会考虑其他因素,如输电线路电压、电压骤降、骤降后故障恢复振荡的持续时间和幅度等。

English Abstract

Recently the capacity of installed wind energy has continued to expand, necessitating that power companies develop specified low-voltage ride-through (LVRT) curves to address unexpected power outages caused by wind farms. To date, the literature lacks reports on the specification of LVRT curves, and the state-operated Taiwan Power Company (Taipower) lacks established guidelines for revising the currently utilized LVRT curves. This study aims to specify LVRT curves based on a projected power load for 2025, as forecasted by Taipower. Simulations were conducted using the Power System Simulator for Engineering (PSSE) equipped with a GEWT 4.0 MW wind turbine module. An objective function was defined to minimize the manufacturing costs of wind turbines while ensuring stability and incorporating the critical clearing time (CCT) and other conditions as constraints. For each of the five scenarios, including 69, 69–161, and 161 kV cases, a three-phase short-circuit fault at a point of common coupling (PCC) was simulated as a worst-case scenario to determine an appropriate LVRT curve. The CCT emerged as a pivotal parameter in the LVRT specification process, which also considers additional factors such as transmission line voltage, voltage sag, and the duration and amplitude of fault recovery oscillations following the sag.
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SunView 深度解读

该LVRT优化技术对阳光电源风电变流器及储能系统具有重要应用价值。基于遗传算法的曲线优化方法可直接应用于SG系列风电变流器的控制策略设计,通过动态调整电压-时间响应曲线,提升故障穿越能力。对于PowerTitan储能系统,该技术可优化其电网支撑功能,在电压跌落时提供快速无功支撑,增强系统暂态稳定性。结合阳光电源的构网型GFM控制技术,可实现风储协同的智能LVRT响应,满足严苛并网标准。建议将遗传算法集成到iSolarCloud平台,实现不同电网环境下的LVRT曲线自适应优化,提升新能源电站的并网适应性与经济效益。