Research on Classification Method of Voltage Sag Based on Scenario Analysis
Yunzhao Wang, Yuzhen Zhao
Abstract
Yunzhao Wang, Yuzhen Zhao
Abstract
In order to better study the impact of voltage sags on power systems, this article combines scenario analysis to classify and study voltage sag events. The causes of each type of voltage sag event are summarized, and the voltage sag failure data obtained from the voltage sag monitoring system can be used to understand the situation of the power grid failure, which has assisted the voltage sag management and power quality improvement. This paper uses PSCAD / EMTDC to build a model of voltage sag event by Monte Carlo method. It mainly simulates IEEE-30 nodes through random number generation module combined with probability density distribution such as fault type, fault duration, and location of fault occurrence. A large number of fault data generated by the voltage sag model are clustered by the K-means algorithm using MATLAB simulation software. Finally, a new classification method of voltage sag is formed.
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In order to better study the impact of voltage sags on power systems, this article combines scenario analysis to classify and study voltage sag events. The causes of each type of voltage sag event are summarized, and the voltage sag failure data obtained from the voltage sag monitoring system can be used to understand the situation of the power grid failure, which has assisted the voltage sag management and power quality improvement. This paper uses PSCAD / EMTDC to build a model of voltage sag event by Monte Carlo method. It mainly simulates IEEE-30 nodes through random number generation module combined with probability density distribution such as fault type, fault duration, and location of fault occurrence. A large number of fault data generated by the voltage sag model are clustered by the K-means algorithm using MATLAB simulation software. Finally, a new classification method of voltage sag is formed.
Key concepts: Voltage sag, Fault (geology), MATLAB, Voltage, Computer science, Monte Carlo method, Power (physics), Reliability engineering