Measurement-based Load Modeling using Genetic Algorithms
Measurement-based Load Modeling using Genetic Algorithms
复制标题
使用遗传算法进行基于测量的负载建模
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
D. Hill
中科院分区：
文献类型：
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作者：
Jin Ma;Zhao Yang Dong;R. He;D. Hill
负荷建模对于电力系统的运行和控制非常重要。近年来，基于测量的负载建模得到了广泛的应用。从数学上讲，基于测量的负载建模问题与参数识别领域密切相关。因此，需要一种有效的优化方法来基于测量和模型输出之间的估计误差的反馈来推导负载模型参数。本文报告了我们将遗传算法应用于基于测量的负载建模研究的工作。由于遗传算法对负载模型参数的初始猜测具有鲁棒性，因此非常适合负载模型参数识别。本文研究了电站实际测量和数字仿真两种情况。为了进行比较，还应用经典的非线性最小二乘估计方法来寻找负载模型参数。负载模型的模拟输出证实了遗传算法在基于测量的负载建模分析中的效率。未来的工作将集中于加快遗传算法的收敛速度，和/或利用更有效的进化计算方法。
Load modeling is very important to power system operation and control. Measurement-based load modeling has been widely practiced in recent years. Mathematically, measurement-based load modeling problem are closely related to the parameter identification area. Consequently, an efficient optimization method is needed to derive the load model parameters based on the feedback of estimation errors between the measurements and model outputs. This paper reports our work on applying genetic algorithms on measurement-based load modeling research. Due to its robustness to the initial guesses on the load model parameters, genetic algorithms are very suitable for load model parameter identification. Two cases including both the real measurement in a power station and the digital simulation are studied in the paper. For comparison purpose, the classical nonlinear least square estimation method is also applied to find the load model parameters. The simulated outputs from the load model confirm the efficiency of genetic algorithms in measurement-based load modeling analysis. Future work will focus on fastening the converging speed of the genetic algorithms, and/or utilizing more efficient evolutionary computation methods.