Research On Cascading Failure Simulation Modeling And Critical Line Identification In Large Power Grid

The power industry occupies an important position in the development of the national economy.At present,the scale of the power system is expanding.While improving the reliability and economic benefits of power supply,it also increases the risk of blackout accidents.In recent years,there have been several massive blackout accidents worldwide,causing huge losses to society.Studies have found that most massive blackout accidents are caused by cascading failures.Therefore,discuss the mechanism of cascading failures,establish a cascading failure simulation model that takes into account both authenticity and rapidity,efficiently and accurately identify the critical lines in the occurrence and development of cascading failures,so as to formulate corresponding prevention measures of cascading failures.The above research is of great significance for preventing blackout accidents and ensuring the stable operation of the power system.In order to solve the problem of the ambiguity of the physical meaning of line outageing model in the original DCSS(DC power flow Simulator of power system Separation)model,the line outageing process is mathematically described through the heat accumulation effect,and an improved DCSS cascading failure simulation model is established.which can not only simulate the line outageing process more reasonably,and timing sequence information of the generated fault chain is rich.The Polish 2383 nodes system,a large scale of power grid,is introduced,and the improved DCSS model is applied to this example.The simulation results show that the model proposed in this paper can better simulate the characteristics of different stages in the development of cascading failures,and the obtained fault chains clearly show the evolution path of cascading failures.Aiming at the problem of insufficient efficiency in cascading failure simulation,a Random Chemistry unbiased sampling method based on group testing is established.Without introducing prior probability or indicators,the generation speed of fault chains set with N-k(k≥ 2)as the initial failure is accelerated.A risk assessment method based on fault chains set is proposed.With the sampling process of RC method,the overall risk of power system chain failure is gradually and accurately measured.An example of Polish 2383 node system shows that the model proposed in this paper can effectively improve the speed of overall risk assessment.The statistical characteristics of the sampled fault chains set is studied,and the influence of simulation parameters on the overall risk of the system is discussed.Considering that some lines have important influence in different cascading failure evolution modes,a fault chains clustering and critical line identification algorithm based on edit distance is proposed.Edit distance is introduced to measure the similarity between fault chains.According to the Calinski-Harabaz index,the optimal number k of clusters is selected,and the hierarchical clustering of the fault chains is realized through the AGNES algorithm,and the classification assessment of the importance of critical line is further realized on the basis of the clustering,and the identification accuracy and efficiency of the critical line are further improved The critical lines identified in the Polish 2383 node system were expanded.Based on the risk level of cascading failures before and after the expansion,the importance of the critical lines identified by various algorithms was quantitatively compared,which further proved the effectiveness of the model and algorithm proposed in this paper.