Research On Discrimination Method Of The Key Transmission Sections Based On Community Detection Theory

Repeated blackouts occur frequently,which makes electric power workers pay more attention to the stable operation monitoring of power grid.In this context,the research on the transmission section of power grid came into being.The reason is that the scale of power system is expanding and the structure is more complex,so it is difficult to monitor all the components of the power grid.Therefore,it is necessary to implement the partition of large-scale power system,which can effectively monitor the key equipment in the power grid.The transmission section responsible for transmitting power among the subareas,as one of the most important characteristics of power grid operation,the safety and stability of the transmission section directly affect the safe operation of the whole system.The integration of large-scale wind power into the power grid poses a great challenge to the stable operation of power grid.The volatility of wind power output will have a significant impact on the transmission capacity between interconnected power grids across regions.Therefore,based on the research on the static safety transmission section of the power grid,this paper proposes a dynamic key transmission section identification method which considering the correlation of wind farms.The paper put forward a method of identifying the critical transmission section based on community discovery theory,to deep division of power grid by GN splitting algorithm in community discovery theory,and the power flow betweenness index is used to identify the key lines in the system as the inter regional tie line.This index not only emphasize account the direction of power transmission,but also quantifies the importance of transmission lines in power transmission,overcomes the lack of the network topology betweenness which transmission only considering the shortest path.,it is more in line with the characteristics of power flow distribution.Finally,uses the security margin to identify the key sections of the grid.Through safety margin index to measure the vulnerability of transmission section,not only can reflect the power transmission margin of the transmission section,at the same time embodies the motor output adjustment's influence on the power flow transmission,highlighting the weak links of the network.The validity and rationality of the proposed method are proved by the samples of IEEE39 system and a real power grid.This thesis is based on the Latin Hypercube Sampling method to generate correlated input variables by random sample,the stochastic load flow based on the cumulant method is used to approximate fitting cumulative distribution function of branch power flow and considering the uncertainty distribution of wind power output and the active power,the dynamic trend of the flow distribution characteristics is analysied effectively.Finally,for the dynamic key section identification considering correlation of wind farms,based on the probability distribution of branch current to modify betweenness and security margin,so as to identify the dynamic key section of power grid under the condition of considering correlation of the wind farm and randomness of power grid.The effectiveness of the proposed algorithm is verified by a simulation example of IEEE39 node system,and compared with the analysis results of the static transmission section in third chapter.The influence of wind power integration on transmission section identification is summarized.