Power System Operational Risk Assessment Based On Random Set Theory

The purpose of the power system operation is to transmit electricity to the users in accordance with the standard of power quality,take into account its reliability and economy.However,with the continuous deepening of the interconnection of power system,various internal and external factors will have an impact on the operational reliability of power system,resulting in frequent failures of the system.Therefore,in order to reduce the operational risk of power system,it is necessary to study the more rigorous theory and method for operational risk assessment.And the real-time operational conditions of the power system should be taken into consideration to realize the real-time evaluation and early warning of the power grid risks.In the process of operational risk assessment,the volatility of each node’s load needs to be taken into account,and a more precise load model is used to describe the uncertainty of the load.Because each prediction model has its advantages and disadvantages,in order to make full use of the information provided by various methods.this thesis proposes the random set theory as a unified representation framework of multi-source uncertain information.This theory makes full use of the information of the original data and each prediction models,and all the information from each method is transformed into a unified form of the random set.Based on the D-S combination rules,the multi-source uncertain information expressed by random set forms is fused,which makes up for the shortcomings of the single model method and improves the prediction accuracy.The failure rate of the components is the basic input data for operational risk assessment,so the real-time failure rate of the components should be solved in the case of the real-time operational conditions and the external environment.In this paper,a power transformer is taken as an example,and its real-time failure rate model based on BP neural network is established.Through the data preprocessing,the main monitoring variables closely related to the transformer operational state are extracted,and it is used as the input of the BP neural network.The corresponding failure rate calculated by the history database of transformer’s fault records is used as the output of the model.Based on the current real-time monitoring data,the real-time failure rate of the transformer can be predicted.According to the load model and the failure rate prediction model,the operational risk assessment algorithm for the power system is proposed.In this paper,the random set form is used to unify the uncertain information of power grid fault and load,and the probability distribution of component level risk indices such as nodes and branches is given under the comprehensive influence of uncertain parameters.All the system states are classified according to the number of faults,and the supporting degree of various types of system operational states to various risks is calculated,that is,the basic probability assignment(BPA).Then,using D-S evidence combination rules to make decision fusion for all the BPA,the comprehensive influence of various uncertain factors on system reliability can be obtained.Analysis on IEEE 33 system and IEEE 39 system show that the proposed method is reasonable.