超级电容储能系统实时性能评估与剩余使用寿命预测研究

With the increasing penetration of new energy such as solar and wind energy power generation system into the distribution network, the disadvantages of random fluctuation, intermittency and inflexible output power will cause more and more negative effects on the economical operation and power quality of the distribution network. The supercapacitor energy storage system, which possesses the merits of improving the flexibility of power grid, optimizing generating efficiency, promoting the reliability of power supply and decreasing the greenhouse gas emission, has a promising application prospect. But, in practical applications, the performance degradation of each cell will aggravate under the comprehensive influence of temperature and current ratio, which will reduce the reliability and safety of the whole system and shorten its useful life. This project takes the supercapacitor as the research object and, in order to establish the accurate mathematical models, focuses on the basic scientific research on changing mechanism under multi-factor’s influence and degradation process of the supercapacitor’s performance characteristic parameters. On the basis of that and with comprehensive analysis of the performance characteristic parameters’ horizontal data under actual working conditions and the longitudinal degradation data from the full life cycle process, the accurate prediction of energy storage system’s real-time performance and remaining useful life can be fulfilled. The purpose of this project is to afford the scientific basis for realizing the condition-based measurement，which can provide theoretical and technological support for practical application of the supercapacitor energy storage system.

随着以太阳能和风能为代表的新能源发电系统对配电网渗透率的日益提升，其随机波动性、间歇性和输出功率不可调度等特征对配电网经济运行及电能质量所产生的负面影响越来越大。超级电容储能系统因具备提高电网柔性、优化发电效率、提升供电可靠性和降低温室气体排放等优点而拥有广阔的应用前景。但是在实际使用过程中，受温度和充放电倍率等因素的综合影响会加剧单体性能的退化，从而导致储能系统的使用寿命缩短并降低整个系统的可靠性和安全性。本课题以超级电容器为研究对象，对其性能特征参数在多影响因素交互作用下的变化机制和退化过程进行基础科学研究，以期建立准确的数学模型。在此基础上，综合利用性能特征参数在实际工况作用下的横向和全寿命周期过程中的纵向演进数据对储能系统的实时性能进行评估并对剩余使用寿命做出准确预测。本课题研究工作旨在为实现视情维护提供科学依据，并为推广超级电容储能系统的实际应用提供理论基础和技术保障。

为解决在实际应用中对超级电容进行性能估计和健康状态评价等问题。本课题主要围绕超级电容多工况实验方案设计、动态特性建模、电气特征参数辨识、健康状态评价和在线监测系统设计等内容进行深入研究。取得主要结果如下：（1）针对现有测试标准中偏重于超级电容静态性能测试的不足，提出以温度和电流作为影响因子并交互作用的实验方案，从而获取超级电容在多种工况条件下的真实性能表现，实验数据分析表明温度、电流等影响因子与超级电容电气特性存在极强的相关性，并以回归分析方法建立了性能预测模型；（2）从超级电容电气特性预测的应用角度出发，提出采用支持向量回归的方法建立超级电容的动态特性模型，模型泛化能力强，实验和仿真结果表明所建模型具有很高的预测精度；（3）在分析现有超级电容电气特征参数辨识方法的基础上，根据不同应用场合的精度要求和算法实现复杂度分别采用最小二乘和智能优化算法进行参数辨识，两种算法结构框架不同，在各自框架下通过数据预处理、数据采样频率的调整可以有效提高辨识精度；（4）以实现对超级电容进行视情维护的目的出发，提出一种超级电容健康状态评价方法，该方法以超级电容电气特征参数变化度作为健康状态评价指标，通过该指标可实时描述超级电容当前性能，并及时合理地采取针对性维护措施，确保系统工作的可靠性；（5）设计了由下位机和上位机组成的超级电容在线监测系统，下位机的控制核心采用嵌入式结构，实现数据采集和传输，上位机基于PC机实现超级电容建模、电气特征参数辨识和可视化功能。为实际应用中及时掌握超级电容电气性能提供硬件支撑。