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首页> 外文期刊>American journal of applied sciences >Vulnerability Assessment of Power System Using Radial Basis Function Neural Network and a New Feature Extraction Method | Science Publications
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Vulnerability Assessment of Power System Using Radial Basis Function Neural Network and a New Feature Extraction Method | Science Publications

机译：基于径向基函数神经网络和特征提取的电力系统脆弱性评估科学出版物

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> Vulnerability assessment in power systems is important so as to determine how vulnerable a power system in case of any unforeseen catastrophic events. This paper presents the application of Radial Basis Function Neural Network (RBFNN) for vulnerability assessment of power system incorporating a new proposed feature extraction method named as the Neural Network Weight Extraction (NNWE) for dimensionality reduction of input data. The performance of the RBFNN is compared with the Multi Layer Perceptron Neural Network (MLPNN) so as to evaluate the effectiveness of the RBFNN in assessing the vulnerability of a power system based on the indices, power system loss and possible loss of load. In this study, vulnerability analysis simulations were carried out on the IEEE 300 bus test system using the Power System Analysis Toolbox and the development of neural network models were implemented in MATLAB version 7. Test results prove that the RBFNN give better vulnerability assessment performance than the multilayer perceptron neural network in terms of accuracy and training time. The proposed feature extraction method decreases the training time drastically from hours to less than seconds, this bound to influence the vulnerability classification and increase the speed of convergence. It is also concluded that the reduction in error is achieved by using PSL as an output variable of ANN, in all the cases the error of RBFNN output by PSL is less than 4.87% which is well within tolerable limits.
机译： >电力系统中的漏洞评估很重要，以便确定在发生任何不可预见的灾难性事件时电力系统的脆弱性。本文介绍了径向基函数神经网络（RBFNN）在电力系统脆弱性评估中​​的应用，该方法结合了一种新的拟议特征提取方法，称为神经网络权重提取（NNWE），用于减少输入数据的维数。将RBFNN的性能与多层感知器神经网络（MLPNN）进行比较，以便基于指标，电力系统损耗和可能的负载损耗评估RBFNN在评估电力系统脆弱性方面的有效性。在这项研究中，使用Power System Analysis Toolbox在IEEE 300总线测试系统上进行了漏洞分析仿真，并在MATLAB版本7中实现了神经网络模型的开发。测试结果证明，RBFNN较之RBFNN具有更好的漏洞评估性能。多层感知器神经网络的准确性和训练时间。所提出的特征提取方法将训练时间从数小时显着减少到不到几秒钟，这势必会影响脆弱性分类并提高收敛速度。还可以得出结论，通过使用PSL作为ANN的输出变量，可以减少误差，在所有情况下，PSL输出的RBFNN的误差均小于4.87％，这完全在容许范围内。

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