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A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

机译：基于PMU的广域电力系统监测的径向基函数神经网络主成分分析新方案

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A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method. (C) 2015 Elsevier B.V. All rights reserved.
机译：本文提出了一种新颖的基于模型的主成分分析（PCA）方法，用于广域电力系统监控，旨在解决常规PCA的关键缺陷之一，即无法处理非高斯分布变量。它是对原始PCA方法的重要扩展，该方法已经证明其性能优于传统方法，如频率变化率（ROCOF）。 ROCOF方法可以快速处理本地信息，但是其阈值很难确定，并且可能容易发生误跳闸现象。提出的基于模型的PCA方法使用径向基函数神经网络（RBFNN）模型来处理数据集中的非线性问题，以解决无高斯问题，然后再将PCA方法用于孤岛检测。为了建立有效的RBFNN模型，本文首先使用快速输入选择方法来去除不重要的神经输入。接下来，采用启发式优化技术，即基于教学学习的优化（TLBO）来调整RBF神经元中的非线性参数，以建立优化模型。然后，使用新颖的基于RBFNN的PCA监视方案，使用模型输出与实际PMU测量之间的残差进行广域监视。实验结果证实了该方法在监测一系列具有不同分布特征的过程变量中的有效性和有效性，表明该RBFNN PCA方法是对线性PCA方法的有效扩展，是一种可靠的方案。 （C）2015 Elsevier B.V.保留所有权利。

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