Parallel Multi-population Improved Brain Storm Optimization with Differential Evolution strategies for State Estimation in Distribution Systems using Just in Time Modeling and Correntropy

Daich Azuma, Y. Fukuyama, Akihiro Oi, Toru Jintsugawa, H. Fujimoto
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引用次数: 3

Abstract

This paper proposes parallel multi-population improved brain storm optimization with differential evolution strategies (PMP-IBSODE) for state estimation in distribution systems (SEDS) using just in time (JIT) modeling and correntropy. SEDS is a function which estimates system conditions such as voltage and current everywhere in the distribution system using limited measurement data. When outliers, which are not true values, are measured at the measurement points, JIT modeling and correntropy can be effective. Moreover, application of evolutionary computation techniques is necessary for the SEDS considering of a nonlinear characteristic of an objective function caused by equipment in distribution systems. Various evolutionary computation techniques including IBSODE have been applied to the SEDS. However, speed-up of calculation and high quality estimation results are required because of penetration of renewable energies. An evolutionary computation technique using multi-population and parallel distributed computing is one of solutions for the challenges. The proposed method is verified to speed up computation time and obtain higher quality estimation results than conventional methods.
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基于即时建模和相关熵的配电系统状态估计的并行多种群改进差分进化头脑风暴优化
本文提出了一种基于差分进化策略的并行多种群改进头脑风暴优化方法(PMP-IBSODE),该方法利用准时化(JIT)建模和相关熵对配电系统进行状态估计。SEDS是利用有限的测量数据估计配电系统各处的电压和电流等系统条件的函数。当在测量点测量非真值的离群值时,JIT建模和熵可以有效。此外,考虑到配电系统中由设备引起的目标函数的非线性特性,需要应用进化计算技术进行动态动态分析。包括IBSODE在内的各种进化计算技术已被应用于SEDS。然而,由于可再生能源的普及,对计算速度和估算结果的质量提出了更高的要求。采用多种群并行分布式计算的进化计算技术是解决这一难题的方法之一。结果表明,与传统的估计方法相比,该方法可以加快计算速度,获得更高质量的估计结果。
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