Identification Method for Voltage Sags Based on K-means-Singular Value Decomposition and Least Squares Support Vector Machine

Abstract

Voltage sag is one of the most serious problems in power quality. The occurrence of voltage sag will lead to a huge loss in the social economy and have a serious effect on people’s daily life. The identification of sag types is the basis for solving the problem and ensuring the safe grid operation. Therefore, with the measured data uploaded by the sag monitoring system, this paper proposes a sag type identification algorithm based on K-means-Singular Value Decomposition (K-SVD) and Least Squares Support Vector Machine (LS-SVM). Firstly; each phase of the sag sample RMS data is sparsely coded by the K-SVD algorithm and the sparse coding information of each phase data is used as the feature matrix of the sag sample. Then the LS-SVM classifier is used to identify the sag type. This method not only works without any dependence on the sag data feature extraction by artificial ways, but can also judge the short-circuit fault phase, providing more effective information for the repair of grid faults. Finally, based on a comparison with existing methods, the accuracy advantages of the proposed algorithm with be presented.

Carbon Price Forecasting Based on Multi-Resolution Singular Value Decomposition and Extreme Learning Machine Optimized by the Moth–Flame Optimization Algorithm Considering Energy and Economic Factors

“…corresponds to the small singular, reflecting the detailed component of H.; Continue to perform SVD on A 1 , and the decomposition process is shown in Figure 1 [35]. MRSVD for one-dimensional discrete signals shows that there is a certain difference between MRSVD and WT.…”

Conceptual Framework of Antecedents to Trends on Permanent Magnet Synchronous Generators for Wind Energy Conversion Systems

“…By 2019, [108] proposed an identification method on K-means-singular value decomposition and least squares support vector machine which the simulations were proposed for voltage sags based upon an annealing algorithm for multi-objective optimization. To gain the pareto solutions in a significant manner.…”

“…The novel method proposed in this study questions the stop criterion, new rank formula, fitness sharing functions, and other such enhancements. For the purpose of validating, the proposed method's robustness, the study validated two numerical examples [108].…”

Recognition of Voltage Sag Sources Based on Phase Space Reconstruction and Improved VGG Transfer Learning

“…At present, the research on the recognition of voltage sag sources falls into two categories: direct methods [ 2 , 3 , 4 , 5 , 6 , 7 ] and indirect methods [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. The direct methods include the RMS method [ 2 , 3 ] and the deep learning method [ 4 , 5 , 6 , 7 ].…”

“…Common methods for feature extraction include wavelet transform [ 8 , 9 ], Fourier transform [ 10 , 11 ], S transform [ 12 ], Hilbert transform [ 13 ], and empirical mode decomposition [ 14 ], etc. The main methods of pattern recognition include neural network [ 15 , 16 ], support vector machine [ 17 ], principal component analysis [ 18 ], fuzzy comprehensive evaluation [ 19 ], and so on. Among them, the RMS method [ 2 , 3 ] is simple and easy to implement, but it is easy to produce misjudgment for complex sag situations; the deep learning method [ 4 , 5 , 6 , 7 ] does not rely on manual extraction of features, but the model training efficiency is low.…”