变电站阀控密封式铅酸蓄电池状态监测算法研究

发布时间：2018-10-10 14:44

【摘要】：阀控密封式铅酸(Valve-Regulated Lead Acid,VRLA)蓄电池是变电站电力系统的重要组成部分,其可靠性关系到变电站相关电力设备能否安全与稳定的运行。由于蓄电池本身是一个复杂的电化学系统,影响它健康状态(State of Health,SOH)的因素很多,因此在线、准确的估计电池的SOH值逐渐成为业界研究的热点和难点。本文研究蓄电池工作在放电状态和浮充状态下SOH在线监测算法。论文主要研究内容有:从分析蓄电池失效机理着手,归纳影响电池SOH的几个主要因素,比较基于模糊神经网络、基于归一化放电电压曲线和基于剩余容量域放电电压曲线三种SOH估计方法的优缺点;针对蓄电池工作在放电状态下的SOH估计问题,设计了蓄电池放电状态SOH估计方案,包括针对Shepherd等效电路模型中变量难以测量的问题,推导出该模型适用于工程计算的近似计算数学模型,针对五点滑动平均方法处理电池数据中奇异值效果差的缺点,提出数据预处理的改进算法,针对运用放电数据对电池组中的健康电池以及落后电池进行分类的问题,设计了放电状态落后电池筛选模块,针对电池的SOH估计问题,分别设计了落后电池的SOH估计模块和基于递归最小二乘算法的健康电池的SOH估计模块;针对蓄电池工作在浮充状态下的SOH估计问题,论文设计了VRLA蓄电池浮充状态SOH估计方案,针对运用浮充数据对电池组的健康电池和落后电池进行分类的问题,设计浮充状态落后电池筛选模块,针对电池的SOH估计问题,分别设计落后电池的SOH估计模块和基于威布尔分布的健康电池的SOH估计模块。经过在蓄电池状态监测管理系统中评测,结果表明论文研究的蓄电池SOH估计算法满足实际工程精度要求。
[Abstract]:Valve-controlled lead-acid (Valve-Regulated Lead Acid,VRLA) battery is an important part of substation power system. Its reliability relates to the safe and stable operation of substation related power equipment. As the battery itself is a complex electrochemical system, there are many factors that affect the healthy state of (State of Health,SOH). Therefore, online and accurate estimation of the SOH value of the battery has gradually become a hot and difficult issue in the industry. In this paper, the SOH online monitoring algorithm for battery working in discharge and floating state is studied. The main contents of this paper are as follows: starting with the analysis of the failure mechanism of battery, several main factors affecting battery SOH are summarized and compared based on fuzzy neural network. The advantages and disadvantages of three SOH estimation methods based on normalized discharge voltage curve and residual capacity domain discharge voltage curve are discussed. Aiming at the problem of SOH estimation of battery working in discharge state, a scheme of SOH estimation for battery discharge state is designed. Aiming at the difficulty of variable measurement in Shepherd equivalent circuit model, an approximate mathematical model for engineering calculation is derived, and the disadvantage of the five-point moving average method in dealing with the singular value in battery data is discussed. An improved algorithm for data preprocessing is proposed. Aiming at the problem of classifying healthy and backward batteries in battery pack by using discharge data, a battery screening module is designed to estimate the SOH of the battery. The SOH estimation module of backward battery and the SOH estimation module of healthy battery based on recursive least square algorithm are designed respectively. Aiming at the problem of SOH estimation of battery working in floating state, this paper designs a SOH estimation scheme for VRLA battery floating state. Aiming at the problem of classifying the healthy and backward batteries in the battery pack by using the floating charge data, the filter module of the backward battery in the floating state is designed, and the SOH estimation problem of the battery is also discussed. The SOH estimation module of backward battery and the SOH estimation module of healthy battery based on Weibull distribution are designed respectively. The results show that the SOH estimation algorithm of battery studied in this paper can meet the requirements of practical engineering precision.
【学位授予单位】：太原科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM912;TM63

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