期刊
ELECTRIC POWER SYSTEMS RESEARCH
卷 127, 期 -, 页码 197-205出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.epsr.2015.06.002
关键词
Wide-area monitoring; Synchrophasors; Model-based PCA; RBF neural network; Input selection; Teaching-learning-based-optimization
资金
- Chinese Scholarship Council
- RCUK/EPSRC [EP/L001063/1, EP/G042594/1]
- EPSRC [EP/G042594/1, EP/L001063/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [1283156, EP/L001063/1, EP/G042594/1] Funding Source: researchfish
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.
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