Fault isolation and reconfiguration are very important aspects of distribution network automation. When there is a fault in the distribution system, the loads connected with the fault zone should be separated and the operation pattern can be found by changing the status of the section switches or contact switches. A method of using different fault isolation and reconfiguration measures to deal with different zone faults is proposed. Different coding methods for different zone faults are introduced. Particle Swarm Optimization (PSO) algorithm is used to find the optimal result of the switch status. A distribution network in Dezhou grid in Shandong province is simulated with MATLAB and simulation results verify the effectiveness of the proposed method.

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The traditional distribution network operation risk assessment does not fully consider the intermittency and volatility of new energy sources, which has an adverse impact on the security management of the power grid. Therefore, combined with the probabilistic characteristics of new energy power generation, this paper establishes a distribution network operation risk assessment model and a comprehensive risk index system including new energy grid-connected, mainly including voltage over-limit, voltage collapse, line active power over-limit, system load loss, abandoning the wind and abandoning the light, exceeding the frequency limit, etc. On this basis, this paper formulates the weighting factors of each risk index, and comprehensively judges the weak points of the system. The example analysis shows that the risk assessment method proposed in this paper can quantitatively analyze the weak links after the new energy is connected to the distribution network. This verifies the effectiveness of the proposed method and provides a reference for the future development of new energy grid-connected security.

To settle the issue of conventional principal component analysis similarity factor cannot take advantage of higher-order statistics information of process variables, we presented an improved statistics principal analysis similarity factor (SPASF) to identify fault patterns in our work. In the improved SPASF approach, process data is first converted from original space into a new statistics space by the means of statistics pattern analysis (SPA) technology, and principal component analysis (PCA) is then employed to extract principal components in statistics space. At last, the pattern of snapshot dataset is recognized by measuring the similarity of principal components derived from statistics snapshot dataset and statistics historical fault dataset. The effectiveness of suggested SPASF based approach is verified through a case study on continuous stirring tank reactor (CSTR) by the means of recognizing fault patterns of snapshot datasets.