A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring
A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring
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DOI：
10.1016/j.epsr.2015.06.002
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发表时间：
2015-10
影响因子：
3.9
通讯作者：
Yuanjun Guo;Kang Li;Zhile Yang;Jing Deng;D. Laverty
中科院分区：
文献类型：
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作者：
Yuanjun Guo;Kang Li;Zhile Yang;Jing Deng;D. Laverty
针对传统主元分析方法不能处理非高斯分布变量的缺点，提出了一种用于广域电力系统监测的基于模型的主元分析方法。它是原始的主成分分析方法的一个重要扩展，该方法已经被证明优于传统的频率变化率(ROCOF)方法。ROCOF方法处理局部信息速度快，但阈值难以确定，容易发生公害跳闸。提出的基于模型的主成分分析方法首先利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，以解决非高斯问题，然后再使用主成分分析方法进行孤岛检测。为了建立一个有效的RBFNN模型，本文首先使用一种快速的输入选择方法来剔除不重要的神经输入。其次，采用一种启发式优化技术，即基于教-学的优化方法(TLBO)，对RBF神经网络中的非线性参数进行整定，建立优化模型。然后利用模型输出与实际PMU测量值之间的残差，将基于径向基函数神经网络的PCA监测方案应用于广域监测。实验结果证实了该方法对一组不同分布特征的过程变量监测的有效性和有效性，表明该方法是对线性主元分析方法的有效扩展，是一种可靠的方法。
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.