## 1 Introduction  
In the current world, with the development of various undertakings, the demand for electric power is growing. Renewable energy will become the main energy source in the future due to the depleting nature of fossil fuels, cost, and environmental issues. Massive application of renewable distributed generation is an effective way to solve global energy and environmental problems [1,2]. China has achieved fruitful results in photovoltaic power generation and other renewable energy fields [3]. However, these renewable sources power generation systems may cause power quality problems such as frequency deviation, voltage fluctuation, voltage flicker, and harmonic distortion when they are integrated into a power system. At present, China has established national standards and specific requirements for an individual index of power quality, but they cannot reflect the comprehensive level. Therefore, it is imperative to establish a comprehensive assessment method of power quality in order to ensure the reliability and safety of the power grid. At the same time, it also has great significance for the reasonable formulation of electricity price in the power market. To date, some methods have been proposed for comprehensively evaluating the power quality of systems, including distributed power generation such as photovoltaic. However, existing methods have some shortcomings when applied. In [4, 5, 6], the probabilistic evaluation model is established to evaluate the power quality. However, in [4], computing probabilistic power flows requires very complicated programming. When the methods in [5,6] use the fuzzy evaluation method to determine the membership function, the results are greatly affected by human factors. The evaluation results will vary with the change of membership functions. In [7], the combination of improved analytic hierarchy process (IAHP) and entropy method is proposed to obtain the weights of the indices, adopting the gray correlation analysis method to evaluate the photovoltaic grid-connected power station, but different people's process of parameters such as resolution coefficients in the gray correlation method may be inconsistent. Catastrophe decision theory is applied in [8] to evaluate the power quality; the method does not need to determine the weight of indices, but it relies too much on the establishment of the index system. The artificial neural network method needs to collect many samples to train the network. If the sample size is not large enough, it will reduce the accuracy of the evaluation results [9]. In [10; 11; 12], the authors used accelerated genetic algorithms to improve the Shepard interpolation theory and projection pursuit method, obtaining reasonable evaluation results. However, these methods are complex and unsuitable for unified evaluation of many evaluation objects [13]. In addition, in [14], the classified evaluation method is applied, adopting the least squares support vector machine (LSSVM) theory to evaluate power quality. An improved principal component analysis method combined with the combination weight is proposed in [15] to evaluate power quality of the steady-state photovoltaic grid connection and to draw conclusions. There are also some publications using fuzzy theory to assess the steady-state power quality [16; 17; 18], and an adaptive fuzzy neural theory is proposed in [19] to evaluate the power quality. However, there is still no universally recognized and authoritative comprehensive assessment method of power quality in the world. To achieve a reasonable approach for setting comprehensive assessment of steady-state power quality, two problems need to be solved. One is to determine the weight of each index, and the other is to process the power quality indicator data. Most existing studies have used crisp numbers to determine the weights of various indicators. This may have produced different results due to the different experience and preferences of decision-makers. This situation also occurs in the use of membership functions.