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A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring.
- Source :
-
Electric Power Systems Research . Oct2015, Vol. 127, p197-205. 9p. - Publication Year :
- 2015
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03787796
- Volume :
- 127
- Database :
- Academic Search Index
- Journal :
- Electric Power Systems Research
- Publication Type :
- Academic Journal
- Accession number :
- 108293217
- Full Text :
- https://doi.org/10.1016/j.epsr.2015.06.002