Uncertainty Description And Assessment Of Indicators For Voltagesags Evaluation

In recent years, with the frequency control equipment, a variety of automated production lines and computer systems which are highly sensitive to voltage sags widely used in industrial production, the voltage sag problem has aroused more and more attention. Voltage sag has become the main power quality problem affecting the normal operation of electrical equipment, and has caused huge economic losses. Evaluating the severity and the influence on sensitive equipment of voltage sag reasonably and accurately can not only measure the quality of power supply system, evaluate economic loss of voltage sag quantitatively, but also provide the basis for fixing prices according to the quality in electric power market.This paper in-depth analyses equipment tolerance and voltage sags characteristics the two most important factors in voltage sag severity assessment and points out the advantages and disadvantages of existing voltage sag severity indicators. Based on the theoretical foundation, this paper establishes evaluation indicators and assessment method for voltage sags.Against the uncertainty characteristics of voltage sags and equipment tolerance, this paper applies uncertain theory to establish new voltage sag severity assessment indicators and method. In this method, cloud model is used to describe voltage sag’s features and voltage tolerance of sensitive equipment and fuzzy security incidents membership function is used to describe the state of equipment failure subjected to voltage sag. Based on the new description, voltage sag incompatibility index is established to reflect voltage sags disturbance level and voltage sag influence index considering severity and range of voltage sag is established to reflect the consequences of voltage sags. In cloud model, the value of incompatibility index and influence index can express uncertain factors of voltage sag severity assessment. Compared with traditional methods, the proposed method can reflect differences between waveforms and can avoid over assessment of voltage sags. The simulation results validate the indicators and methods expressed and calculated by cloud model are feasible and reasonable. Assessment results are more reasonable and accurate.