基于电能质量装置数据和改进克隆选择算法的电力动态负荷建模

电力负荷具有随机性、非线性、时变性和分散性等特点,且电网自然扰动提供有效数据不足,使得负荷建模难度很大。为了解决这一难题,利用广东电网电能质量监测管理系统后台数据库的大量非对称扰动数据进行负荷建模。采用BPA中三阶感应电动机并联ZIP负荷模型和改进的克隆选择算法,通过自适应调整高斯变异和定向进化机制来提高多维函数的全局寻优能力和辨识效率。基于实测数据的负荷建模结果表明,所提出的算法对提高辨识精度和克服模型参数的分散性具有显著作用,能够满足实际工程的应用需要。

Electrical Dynamic Load Modeling Based on the Power Quality Equipments Data and the Improved Clone Selection Algorithm

Because of the characteristics of randomness, non-linear, time-varying and dispersion etc. of power load, together with lack of valid data provided by the nature distribution of the power system, it is difficult to set up a load model. To solve this problem, based on a large number of asymmetric disturbance data stored in the database of Guangdong Power Grid Company's power quality monitoring and management system, an effective load model is established. Using third-order induction motor in parallel with ZIP load model in BPA and improved clonal selection algorithm, global optimization capability and identification efficiency of the multi-dimensional functions is increased by self-adoption adjustment Gaussian mutation and directed evolution mechanism. The result of the load modeling based on the real measurement data indicated that the proposed algorithm may significantly improve identification precision and overcome the dispersion of the model parameters, and satisfied the need of the practical engineering application.