Research on project post-evaluation of wind power based on improved ANP and fuzzy comprehensive evaluation model of trapezoid subordinate function improved by interval number

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The paper aims at the post evaluation of wind power projects.

A new evaluation model is set up which is improved ANP and Fuzzy comprehensive evaluation model improved by interval number.

Improved ANP model by interval number is used to determine the weight of the index.

Improved fuzzy comprehensive evaluation model is based on trapezoid membership which is impoved by interval numbers.


The safety operation and economic benefits of wind farms are paid more attention by industry and society. Therefore, it’s necessary to evaluate the wind power projects to find the deviation between actual situation, forecast target and first-class level. The commonly used methods of post-evaluation are AHP and fuzzy comprehensive evaluation which have three problems to be solved. The first is AHP method can’t represent the correlation among the indexes. The second is the uncertainty of project data and experts’ judgment. The third is the rectangle membership function can’t realize data classification between adjacent levels. ANP can describe the relationship between indicators to eliminate deviation caused by independent calculation. The trapezoidal membership function is useful for rapid classification data between adjacent levels by maximum membership degree. And the interval can utilize imperfect information to solve the limitation of point estimation. So this paper proposes ANP model and fuzzy comprehensive evaluation model based on trapezoid membership which are all improved by interval numbers to evaluate projects. The paper makes a calculation of Pinglu wind farm, and the result shows new model is more stable with accuracy and applicability for post-evaluation which can solve the problems such as incomplete information, data fluctuation and subjective judgment.


Interval number


Fuzzy comprehensive evaluation

Project post-evaluation of wind power

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