scholarly journals Cost prediction on fabricated substation considering support vector machine via optimized quantum particle swarm optimization

2020 ◽  
Vol 24 (5 Part A) ◽  
pp. 2773-2780
Author(s):  
Yi Xiong ◽  
Yue Ming ◽  
Xiaohong Liao ◽  
Chuanyu Xiong ◽  
Wu Wen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Life Cycle ◽  
Least Squares ◽  
Life Cycle Cost ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Support Vector ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

At present, the prediction of the life cycle cost of fabricated substation is of great significance for the construction of fabricated substation. An enhanced prediction model based on quantum particle swarm optimization (QPSO) via least squares support vector machine is established. The relevant characteristic index of the life cycle of the fabricated substation is used as the input of the model, and the output is the life cycle cost. The simulation results are compared with the prediction results of QPSO optimized least squares support vector machine (LS-SVM), PSO optimized LS-SVM, traditional LS-SVM, and backpropagation neural network, which shows that the QPSO optimized LS-SVM model has better prediction accuracy, can predict and evaluate the life cycle cost more quickly, and can improve the benefits of fabricated substation construction.

Application of the Least Squares Support Vector Machine Based on Quantum Particle Swarm Optimization for Data Fitting of Small Samples

2014 ◽  
Vol 472 ◽  
pp. 485-489 ◽  
Author(s):  
Hong Kai Wang ◽  
Ji Sheng Ma ◽  
Li Qing Fang ◽  
Yan Feng Yang ◽  
Hai Ping Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Least Squares ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Data Fitting ◽  
Small Sample ◽  
Support Vector ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

In order to better observe the trend of small sample data, this paper based on that the least squares support vector machine (LS-SVM) algorithm has an outstanding performance in the data processing of small sample, presents a data fitting method for small sample. The quantum particle swarm optimization (QPSO) that has better global search ability is used to optimize the parameters of the least squares support vector machine, and establish the curve fitting model. According to error analysis, show that the method presented in this paper has a good application value.

Power Short-Term Load Forecasting Based on QPSO-SVM

2012 ◽  
Vol 591-593 ◽  
pp. 1311-1314
Author(s):  
Xing Tong Zhu ◽  
Bo Xu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Quantum Particle ◽  
Close Contact ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Short Term Load Forecasting

The values of parameters of support vector machine have close contact with its forecast accuracy. In order to accurately forecast power short-term load,we presented a power short-term load forecasting method based on quantum-behaved particle swarm optimization and support vector machine.First,cauchy distribution was used to improve the quantum particle swarm algorithm.Secondly,the improved quantum particle swarm optimization algorithm was used to optimize the parameter of support vector machine.Finally, the support vector machine was used for power short-term load forecasting. In the proposed method such factors impacting loads as meteorology,weather and date types are comprehensively considered. The experimental results show that the root-mean-square relative error of the proposed method is only 1.90%, which is less than those of SVM and PSO-SVM model by 2.29% and 2.80%, respectively.

Power Transformer Fault Diagnosis Based on Least Squares Support Vector Machine and Particle Swarm Optimization

2011 ◽  
Vol 50-51 ◽  
pp. 624-628
Author(s):  
Xin Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Power Transformer ◽  
Particle Swarm ◽  
Support Vector ◽  
Risk Minimization ◽  
Swarm Optimization ◽  
Transformer Fault

Dissolved gas analysis (DGA) is an important method to diagnose the fault of power t ransformer. Least squares support vector machine (LS-SVM) has excellent learning, classification ability and generalization ability, which use structural risk minimization instead of traditional empirical risk minimization based on large sample. LS-SVM is widely used in pattern recognition and function fitting. Kernel parameter selection is very important and decides the precision of power transformer fault diagnosis. In order to enhance fault diagnosis precision, a new fault diagnosis method is proposed by combining particle swarm optimization (PSO) and LS-SVM algorithm. It is presented to choose σ parameter of kernel function on dynamic, which enhances precision rate of fault diagnosis and efficiency. The experiments show that the algorithm can efficiently find the suitable kernel parameters which result in good classification purpose.

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