Voltage sag severity assessment based on multi-objective decision analytic hierarchy process

Voltage sag has gradually become one of the most important power quality problems with the large number of sensitive devices being put into use. In this paper, Monte Carlo simulation method is used to evaluate the risk of node voltage sag. Under different line fault probability models, the sag indicators are divided into the basic parameters of the network and the load side through the state performance and response mechanism. The subjective weight of each indicator of voltage sag is determined by fuzzy analytic hierarchy process (AHP). Then the various sags and target decision matrices are calculated through the characteristics of the temporary monitoring parameters. The objective decision matrix is transformed into a relative membership matrix by dimensionless processing and the objective weight of each index is determined by the method of maximizing deviation. Finally, comprehensive weights of each index can be obtained by combining subjective weights and objective weights. Weighting the comprehensive weight of the indicators obtained by each observation point and the severity of each indicator, the risk degree of the voltage sag at each observation point can be sorted. Four monitoring points were selected for actual measurement analysis and the results proved the feasibility of the method by using Matlab Simulation.