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The intelligent, robust and fast multi-class classification of power system disturbances is very important to improve control algorithms for ensuring power system security and reliability, an essential function for smart grid infrastructure. Moreover, in a future power system mostly consisting of distributed generators and renewable energy resources on which the disturbance has more impact, the analysis...
Power system load was effected by many factors such as weather conditions, holidays, day types, so that the build of short-term load forecasting model is very important. The author analyzed the theory of support vector machine, studied the learning discipline of minimize the structural risk, solved the problem of insufficient training samples better. At the base of support vector machine, The author...
Short-term load forecasting has been viewed as an important problem for its wide application. Grey forecasting model is tested by using electric load data sampled from SA for short-term load forecasting in this paper. Then by regarding the electric load residual series obtained from grey forecasting model as the original data, the grey forecasting model and the support vector machine (SVM) are applied...
The paper presents an intelligent technique for High Impedance Fault (HIF) detection using combined Extended Kalman Filter and Support Vector Machine. The proposed approach uses magnitude and phase change of fundamental, 3rd, 5th, 7th, 11th and 13th harmonic component as feature inputs to the SVM. The Gaussian kernel based SVM is trained with input sets each consists of '12' features with corresponding...
The present case study focusing on TNB, Malaysia's largest power utility, concentrates on load profiles as manifestations of customer behaviour. The main objective here is to base the investigation on comparing the efficacy of the Support Vector Machine (SVM) technique with the newly emerging techniques of Extreme Learning Machine (ELM) and its OS-ELM variant as means of classification and prediction...
With the development of electronic industry, accurate load forecasting of the future electricity demand plays an important role in regional or national power system strategy management. Electricity load forecasting is complex to conduct due to its nonlinearity of influence factors. Support vector machine (SVM) is a novel type of learning machine, which has been successfully employed to solve nonlinear...
Power quality disturbances identification is the important procedure for improving power quality, and online application has actual value. An efficient method for power quality disturbances identification is presented in this paper. Wavelet decomposition is used for extracting the features of various disturbances, and support vector machine in data mining is used for classifying the disturbances....
In this paper, the operation of differential relay for power transformer was presented using support vector machine. An SVM subroutine was used to discriminate internal faults from other situations, replacing the traditional Fourier method for harmonic restraint. The proposed methods was extensively tested and then compared to the traditional differential protection algorithm showing promising results...
Short-term electricity load forecasting is a difficult work as the load at a given point is dependent not only on the load at the previous hour but also on the load at the same hour on the previous day, and on the load at the same hour on the day with the same denomination in the previous week. So the accuracy of forecasting is influenced by many unpredicted factors. Support vector machine (SVM) is...
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