Identification Of Weak Point In Power System Based On Probabilistic Risk Theory

Traditional power system risk assessment is based on the assumption of complete probability information,that is,the probability distribution and correlation of random variables are well known.The probability distribution of random variables is usually assumed empirically and parameters of failure statistics are used for parameter estimation.Completely independent or completely related decisions are obtained through failure mechanism analysis,and it is difficult to describe the exact correlation.Therefore,complete probability information can not be obtained.Nowadays,a large number of new components are widely used in power systems.Due to the lack of statistical data,the non-integrality of probability information is exacerbated,and the evaluation of weak points and traditional power system risk assessment faces great challenges.In view of the above problems,this paper carries out the following research on the identification of weak point in power system based on probabilistic risk theory:In the process of power system risk assessment,considering the higher-order uncertainties of power equipment failures,the interval values are used to represent the reliability parameters of power equipment,and the index is used as input data to calculate the interval value of power system risk assessment.The traditional method of using interval operation and state enumeration method ignores the correlation between variables,which leads to overestimation in interval operation and making the calculated interval value too wide.Therefore,this paper presents a method of using the combination of belief function theory and universal generating function,which can calculate the narrower and more accurate interval value of power system risk assessment index.And,compared with the traditional method,this method can be combined with the Monte Carlo simulation method,and has an advantage in calculating large grid risk assessment index.The example using IEEE-RTS 79 as the test system uses the method proposed in this paper to calculate the interval risk assessment index,which verifies the validity of the method.And the relationship between the interval width of the system risk assessment index and the system cognition uncertainty is calculated.Sensitivity analysis of power system risk assessment can identify the weak points in the power system and is significant to improve system reliability.In this paper,an improved method for the potential increase of sensitivity analysis is proposed.The method takes the reliability of the component to be evaluated into account and reduces the influence of calculation error.Considering the higher-order uncertainties of power equipment failure,fuzzy numbers are used to describe the reliability parameters.For the interval values of the same membership degree of fuzzy numbers,the belief universal generating function method proposed in this paper is used to calculate component fuzzy importance index.The methods of defocusing,such as gravity center method and distance method,are used to normalize the fuzzy importance index of components.The components that have significant influence on the system reliability are identified when the reliability parameters of the components are fuzzy numbers.The influence of fuzziness of component reliability parameters on system reliability index is revealed.Through the calculation and analysis of the component fuzzy importance index of IEEE-RTS79 system,the weak points of the system are identified and the correctness and effectiveness of the method are verified.The traditional way to identification of weak point in power system is to evaluate the sensitivity of the power system risk assessment index from the perspective of component capacity.However,the traditional sensitivity analysis method requires a large amount of computing resources.Based on this,this paper proposes a rapid evaluation method for identification of weak point of bulk power systems based on tight constraint identification method,which evaluates weak point from the perspective of component capacity and reduces the amount of calculation.Finally,this model is used to evaluate the generator nodes and transmission lines of the IEEE-RTS 79 system.Compared with the traditional way of sensitivity analysis,the results verify the correctness of this model.