Comprehensive Evaluation on Power Quality of Tidal Current Convertors

2019 ◽  
Vol 53 (3) ◽  
pp. 46-53
Author(s):  
Caixia Xue ◽  
Xiang-nan Wang ◽  
Ning Jia ◽  
Yuan-fei Zhang ◽  
Hai-nan Xia
Keyword(s):  
Power Generation ◽  
Support Vector Machine ◽  
Power Quality ◽  
Tidal Current ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Support Vector ◽  
Operational Data ◽  
Testing And Evaluation

AbstractWith the continuous development of testing and evaluation of tidal current convertors, power quality assessment is becoming more and more critical. According to the characteristics of Chinese tidal current power generation and power quality standards, this paper proposes a comprehensive evaluation method of power quality based on K-means clustering and a support vector machine. The fundamental purpose of the method is to automatically select the weights of various indicators in the comprehensive assessment of power quality, by which the influence of subjective factors can be eliminated. In order to achieve the above purpose, K-means clustering is used for automatically classifying the operational data into five different categories. Then, a support vector machine is used to study and estimate the relationship of the operational data and categories. Using the method proposed in the paper, the analysis of operational data of a tidal current power generation shows that calculation results can objectively reflect the power quality of the device, and the influence of subjective factors is eliminated. The method can provide a reference for the testing and evaluation of a large amount of tidal current convertors in the future.

scholarly journals Research on support vector machine method for comprehensive evaluation after compression

2020 ◽  
Vol 145 ◽  
pp. 02072
Author(s):  
Wang Chengwang ◽  
Chen Shuai ◽  
Chen Gaojie ◽  
Lu Han
Keyword(s):  
Support Vector Machine ◽  
Hydraulic Fracture ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Kernel Functions ◽  
Support Vector ◽  
Treatment Technology ◽  
Machine Method ◽  
Fracture Width ◽  
Fracture Closure

Hydraulic fracturing is one of the important measures to increase production of oil and gas reservoirs. Pressure after the assessment of the existing technology is usually divided into direct and indirect diagnosis of hydraulic fracture. The diversity of evaluation results is due to the uncertainty and immaturity of the technology itself. Up to now, we have not found an economical and accurate hydraulic fracture evaluation method in the development and application of fracturing technology. In this paper, the pressure treatment technology after the comprehensive evaluation of boundary conditions is studied. By introducing the support vector regression theory, an evaluation model and a solution for correcting fracturing parameters are proposed. Fracturing parameters include fracture length, fracture height, fracture width, integrated fracturing fluid leakage coefficient, fracture conductivity, fracture closure pressure, and so on. For the optimization of various parameters, objective and scientific comprehensive evaluation results can be obtained by selecting different kernel functions. The results show that the model and method based on support vector machine are effective and practical.

scholarly journals A New Comprehensive Evaluation Method for Water Quality: Improved Fuzzy Support Vector Machine

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1303 ◽  
Author(s):  
Wei Shan ◽  
Shensheng Cai ◽  
Chen Liu
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Membership Function ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Domestic Water ◽  
Machine Method ◽  
The Government

With the pressure of population growth and environmental pollution, it is particularly important to develop and utilize water resources more rationally, safely, and efficiently. Due to safety concerns, the government today adopts a pessimistic method, single factor assessment, for the evaluation of domestic water quality. At the same time, however, it is impossible to grasp the timely comprehensive pollution status of each area, so effective measures cannot be taken in time to reverse or at least alleviate its deterioration. Thus, the main propose of this paper is to establish a comprehensive evaluation model of water quality, which can provide the managers with timely information of water pollution in various regions. After considering various evaluation methods, this paper finally decided to use the fuzzy support vector machine method (FSVM) to establish the model that is mentioned above. The FSVM method is formed by applying the membership function to the support vector machine. However, the existing membership functions have some shortcomings, so after some improvements in these functions, a new membership function is finally formed. The model is then tested on the artificial data, UCI dataset, and water quality evaluation historical data. The results show that the improvement is meaningful, the improved fuzzy support vector machine has good performance, and it can deal with noise and outliers well. Thus, the model can completely solve the problem of comprehensive evaluation of water quality.

Incentive and Punishment Mechanism Based Method Study of Power Quality Dynamic Comprehensive Evaluation

2013 ◽  
Vol 325-326 ◽  
pp. 632-636 ◽  
Author(s):  
Jin Jing Yuan ◽  
Sen Ouyang ◽  
Jin Liang Jiang
Keyword(s):  
Time Series ◽  
Power Quality ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Comprehensive Evaluation Method ◽  
Mechanism Model ◽  
Time Period ◽  
The Index System ◽  
Punishment Mechanism

A dynamic comprehensive evaluation method of power quality based on incentive and punishment mechanism is proposed. At the beginning, the index system is designed and unified. Next, the incentive and punishment mechanism model of each index is established. Then, the vertical and horizontal scatter degree method and minimal variability theory are applied to calculate the comprehensive evaluation value of all the indices on every time period and on the whole time series respectively. Finally, the realistic example demonstrates that this method can easily differentiate the quality of different evaluation objects.

Power quality indexes prediction based on cluster analysis and support vector machine

2017 ◽  
Vol 2017 (1) ◽  
pp. 814-817 ◽  
Author(s):  
Jie Song ◽  
Zhenzhen Xie ◽  
Jian Zhou ◽  
Xiu Yang ◽  
Aiqiang Pan
Keyword(s):  
Cluster Analysis ◽  
Support Vector Machine ◽  
Power Quality ◽  
Support Vector ◽  
Quality Indexes
Sign in / Sign up

Export Citation Format

Share Document