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首页> 外文期刊>Swarm and Evolutionary Computation >Estimation of transformer parameters from nameplate data by imperialist competitive and gravitational search algorithms
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Estimation of transformer parameters from nameplate data by imperialist competitive and gravitational search algorithms

机译：帝国主义竞争力和引力搜索算法从铭牌数据估计变压器参数

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Accurate determination of parameters in power transformer equivalent circuit is important because it can influence the simulation results of condition monitoring on power transformers, such as analysis of frequency response. This is due to inaccurate simulation results will yield incorrect interpretation of the power transformer condition through its equivalent circuit. Works on development of transformer models have been widely developed since the past for transient and steady-state analyses. Estimating parameters of a transformer using nameplate data without performing a single experiment has been developed in the past. However, the average error between the actual and estimated parameter values in the past work using Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) is considerably large. This signifies that there is a room for improvement by using other optinfisation techniques, such as state of the art methods which include Heterogeneous Comprehensive Learning PSO (HCLPSO), LSHADE-EpSin, Imperialist Competitive Algorithm (ICA), Gravitational Search Algorithm (GSA) and others. Since ICA and GSA have advantages over GA and PSO, in this work, estimation of transformer parameters from its nameplate data was proposed using ICA and GSA. The results obtained using ICA and GSA was compared to those using GA and PSO to determine the parameters of transformer equivalent circuit. The results show that GSA performs the best as it gives the lowest average error compared to PSO, GA and ICA. Therefore, the proposed technique using GSA and ICA can give a better accuracy than PSO and GA in estimating the parameters of power transformers. The proposed method can also be applied to estimate parameters of three-phase transformers from their nameplate data without disconnecting them from the grid for testing.
机译：精确确定电力变压器等效电路中的参数非常重要，因为它可以影响电力变压器上的状态监测的仿真结果，例如频率响应分析。这是由于仿真结果不准确的结果，通过其等效电路产生对电力变压器条件的不正确解释。自过去的瞬态和稳态分析以来，变压器模型开发的开发工作已经广泛开发。过去已经开发了使用铭牌数据估计变压器的参数，而不在不执行单个实验的情况下开发。然而，使用粒子群优化（PSO）和遗传算法（GA）的过去的工作中实际和估计参数值之间的平均误差相当大。这使得通过使用其他OptinFisation技术，例如包括异质综合学习PSO（HCLPSO），LSHADE-EPSIN，帝国主义竞争算法（ICA），引力搜索算法（GSA）和其他。由于ICA和GSA具有GA和PSO的优势，因此，使用ICA和GSA提出了来自其铭牌数据的变压器参数的估计。将使用ICA和GSA获得的结果与使用GA和PSO的结果进行比较，以确定变压器等效电路的参数。结果表明，与PSO，GA和ICA相比，GSA更好地执行了最低的平均误差。因此，使用GSA和ICA的所提出的技术可以在估计电力变压器的参数时比PSO和GA提供更好的精度。所提出的方法也可以应用于从铭牌数据估计三相变压器的参数，而不会将它们与网格与电网连接进行测试。

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