## 1 Introduction  
The gradual depletion of fossil fuels has not only led to tight energy supplies, but has also caused serious environmental problems [1; 2]. This has driven the global dependence on renewable energy. In addition, due to the fragility of the large power grid, a series of large power outages have occurred, and the distributed power generation system can improve the stability of the system to some extent. Therefore, these factors together lead to an increasing penetration of distributed generation in the power system. However, the distributed generation (DG) grid-connecting process generally requires a large number of power electronic interface devices for energy conversion [3], so the grid connection will have a potential impact on power quality (PQ). It may cause problems such as frequency deviation, voltage fluctuation, voltage flicker, voltage unbalance, harmonic distortion, and DC injection [4; 5]. As DG systems account for a larger proportion of power system capacity, DG has an increasingly greater impact on the PQ of distribution networks. Therefore, establishing a comprehensive PQ comprehensive evaluation model can not only accurately know the operation level of each DG system, but also provide a basis for DG grid-connected pricing, which is of great significance. Comprehensive evaluation of PQ is an important approach to quantify the overall PQ performance, and its results can be used as an important reference to distinguish PQ. The overall performance of PQ is usually derived from a comprehensive description of multiple indices [6]. In order to obtain more comprehensive and perfect power performance, it is necessary to comprehensively evaluate and analyze the PQ data, and its core task is to obtain a single quantitative index by means of weighting and merging of multiple PQ indices [7; 8]--that is, to sort the PQ samples that contain multiple power quality evaluation indices. However, the current PQ evaluation methods have not considered the correlation between the PQ indices, or there are some shortcomings such as the weighting methods being simple, a large amount of calculation, and a lack of practical engineering guidance [9; 10; 11]. The artificial neural network method proposed in [12] requires a large amount of sample data to train the network. The fuzzy comprehensive evaluation method proposed in [13; 14; 15] relies too much on experience and is too subjective in the process of determining membership function and obtaining weights. The traditional analytic hierarchy process (AHP) proposed in [16; 17] needs to construct a judgment matrix, and the calculation amount is too large. The entropy weight (EW) method and its combination method proposed in [18; 19] do not take subjective weights into account, which may result in inaccurate weights. In [20], a TOPSIS method based on entropy weight is proposed. The algorithm sorts the PQ samples by calculating the proximity to the ideal solution, where the proximity is calculated by the Euclidean distance between power performance of PQ sampling points and positive and negative ideal solutions. This method of adopting double reference values avoids the situation of juxtaposed evaluation results. However, after the correlation analysis of the measured data of PQ, it is found that each batch of indices participating in the evaluation shows a high correlation, which has not received attention in the previous PQ evaluation methods. If the Euclidean distance in the traditional TOPSIS method is replaced by the Mahalanobis distance, the correlation between the PQ indices can be considered, and the juxtaposition of the evaluation results can be avoided, making the evaluation results more scientific. Therefore, this paper proposes an improved TOPSIS method for comprehensive evaluation of power quality considering the correlation of indices.