Application of support vector machine in power system

2020 ◽  
Vol 2 (1) ◽  
pp. 30-37
Author(s):  
Li Minghui
Keyword(s):  
Support Vector Machine ◽  
Power System ◽  
Support Vector

scholarly journals Robust support vector machine-based zero-crossing detector for different power system applications

2019 ◽  
Vol 13 (1) ◽  
pp. 83-89
Author(s):  
Minati Ghosh ◽  
Chiranjib Koley ◽  
Nirmal Kumar Roy
Keyword(s):  
Support Vector Machine ◽  
Power System ◽  
Support Vector ◽  
Zero Crossing

Optimal Training of Artificial Neural Networks to Forecast Power System State Variables

2014 ◽  
Vol 3 (1) ◽  
pp. 65-82 ◽  
Author(s):  
Victor Kurbatsky ◽  
Denis Sidorov ◽  
Nikita Tomin ◽  
Vadim Spiryaev
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Simulated Annealing ◽  
Power System ◽  
Simulated Annealing Algorithm ◽  
Active Power ◽  
Support Vector ◽  
State Variables ◽  
Annealing Algorithm ◽  
Artificial Neural

The problem of forecasting state variables of electric power system is studied. The paper suggests data-driven adaptive approach based on hybrid-genetic algorithm which combines the advantages of genetic algorithm and simulated annealing algorithm. The proposed method has two stages. At the first stage the input signal is decomposed into orthogonal basis functions based on the Hilbert-Huang transform. The genetic algorithm and simulated annealing algorithm are applied to optimal training of the artificial neural network and support vector machine at the second stage. The results of applying the developed approach for the short-term forecasts of active power flows in the electric networks are presented. The best efficiency of proposed approach is demonstrated on real retrospective data of active power flow forecast using the hybrid-genetic support vector machine algorithm.

Power System Early Warning Mechanism based on Least Squares Support Vector Machine

2013 ◽  
Vol 8 (9) ◽  
pp. 577-584
Author(s):  
Luo Zhikun ◽  
Liu Xiaoxiao ◽  
Guo Xinze ◽  
Qi Wenhui ◽  
Lian Guohai ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Power System ◽  
Least Squares ◽  
Early Warning ◽  
Support Vector ◽  
Early Warning Mechanism

scholarly journals An Optimized Method of Wind Speed Prediction with Support Vector Machine and Genetic Algorithm

2021 ◽  
Vol 15 ◽  
pp. 212-217
Author(s):  
Guan-fa Li ◽  
Wen-sheng Zhu
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Support Vector Machine ◽  
Wind Speed ◽  
Power System ◽  
Prediction Models ◽  
Optimization Method ◽  
Support Vector ◽  
Wind Speed Prediction ◽  
Speed Prediction

Due to the randomness of wind speed and direction, the output power of wind turbine also has randomness. After large-scale wind power integration, it will bring a lot of adverse effects on the power quality of the power system, and also bring difficulties to the formulation of power system dispatching plan. In order to improve the prediction accuracy, an optimized method of wind speed prediction with support vector machine and genetic algorithm is put forward. Compared with other optimization methods, the simulation results show that the optimized genetic algorithm not only has good convergence speed, but also can find more suitable parameters for data samples. When the data is updated according to time series, the optimization range of vaccine and parameters is adaptively adjusted and updated. Therefore, as a new optimization method, the optimization method has certain theoretical significance and practical application value, and can be applied to other time series prediction models.

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