Open AccessPower quality comprehensive evaluation method based on fuzzy mathematics and cloud theory
Author(s) -
Canran Shen,
Lanxin Hu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1684/1/012136
Subject(s) - fuzzy logic , cloud computing , membership function , mathematics , fuzzy set , variable (mathematics) , weight , fuzzy mathematics , construct (python library) , mathematical optimization , weight function , data mining , fuzzy number , computer science , artificial intelligence , statistics , lie algebra , pure mathematics , programming language , operating system , mathematical analysis
In the comprehensive evaluation of power quality, there exist problems such as the boundary of evaluation index classification and weight distribution which is too subjective or objective. Based on the theory of fuzzy mathematics and cloud, the power quality comprehensive evaluation method was proposed to solve the two problems. This method improves the traditional fuzzy comprehensive evaluation method through variable weight theory and cloud theory, which are fixed weight calculation and membership function construction. Firstly, the superposition variable weight construction method of excitation type in variable weight theory is selected to calculate the weight vector. Secondly, the normal cloud model in cloud theory is used to construct the membership function to comprehensively evaluate power quality. Compared with the traditional fuzzy comprehensive evaluation method, the improved fuzzy comprehensive evaluation method solves the influence of the actual situation of evaluation factors on the index weight and avoids the influence of subjective factors in the membership function construction process of the traditional fuzzy comprehensive evaluation method, making the evaluation results more reasonable and credible. Finally, a practical example is given to prove that the model is more accurate and reasonable.