摘要
为提高电能质量分级评估的客观性和准确性,提出了一种基于CRITIC和改进Grey-TOPSIS的电能质量分级评估方法。首先,基于压力-状态-响应模型中子系统间的逻辑关系,从压力、状态、响应多维度选取评价指标,建立科学的电能质量综合评估指标体系。由于指标间存在一定关联关系,综合指标间的差异因素,从两种信息角度采用CRITIC赋权法,保证指标赋权的客观性。然后,建立改进Grey-TOPSIS评估模型,引入灰色关联度改进单一的欧氏距离。将图形贴近度与空间位置融合,构建新距离测度,弥补原有判据的缺陷。同时,将电能质量等级矩阵加入评估矩阵中,通过计算实测和等级序列与正理想解间的贴近度,实现对电能质量等级客观的量化分级。最后,通过实际算例分析,验证了所提方法能够充分利用数据信息,降低主观因素影响,获得更准确、全面的电能质量分级评估结果。
To improve the objectivity and accuracy of power quality evaluation,a grading evaluation model of power quality based on criteria importance through intercriteria correlation(CRITIC)and an improved grey-technique for order preference by similarity to an ideal solution(Grey-TOPSIS)method is proposed.First,the evaluation indices are selected based on the interaction between subsystems in a pressure-state-response(PSR)model,and a comprehensive evaluation index system is established.Because there is a certain correlation between indices,and considering the differences between indices,the CRITIC method is used to ensure the objectivity of index weighting based on two aspects of information.Then,an improved Grey-TOPSIS evaluation model is established,and the Euclidean distance is improved by introducing grey correlation degree.The new distance measure is constructed by fusing the progress of graph pasting with spatial position to make up for the defect of the original criterion.Moreover,the power quality grade is added to the evaluation matrix,and the grading evaluation of power quality is realized by judging the distance between the grade sequence and the positive ideal solution.The case study indicates that the improved model makes full use of data information,reduces the impact of subjective factors,and can obtain more accurate and comprehensive power quality evaluation results.
作者
赵洪山
李静璇
米增强
蒲靓
崔阳阳
ZHAO Hongshan;LI Jingxuan;MI Zengqiang;PU Liang;CUI Yangyang(School of Electrical&Electronic Engineering,North China Electric Power University,Baoding 071003,China)
出处
《电力系统保护与控制》
CSCD
北大核心
2022年第3期1-8,共8页
Power System Protection and Control
基金
国家重点研发计划项目资助(2018YFE0122200)。
