A complex distribution network fault recovery system and method considering multiple objectives

Abstract

The invention discloses a complex distribution network fault recovery system and method considering multi-objectives. The invention performs fault recovery and island division on the distribution network network based on the improved bee colony algorithm. Cross-search strategy, bee colony perception radius and algorithm search cut-off conditions make the search purpose more clear, the convergence speed is further improved, the search range is increased, and the basic bee colony algorithm is easy to fall into local optimal solution and difficult to obtain accurate optimal solution. The shortcomings of the optimal solution improve its convergence in the calculation process and can effectively improve the fault recovery efficiency of the power grid.

Description

technical field [0001] The invention belongs to the technical field of distribution network fault recovery, and in particular relates to a complex distribution network fault recovery system and method considering multiple objectives. Background technique [0002] Distribution network fault restoration and reconstruction refers to that when a permanent fault occurs in the distribution network, by adjusting the state of the section switch and the contact switch, the fault area is isolated, and the premise of not making major changes to the grid structure and meeting the constraints Reconfigure the power grid in order to restore the power supply to non-fault blackout areas to the maximum and fastest speed. Due to the comprehensive consideration of factors such as the number of switching operations, the amount of load restoration, network constraints, and load levels, distribution network fault restoration is a multi-objective, multi-combination, and multi-constraint nonlinear c...

AI Technical Summary

Problems solved by technology

The mathematical optimization method is suitable for dealing with fault recovery problems with small system scale and low complexity. As long as there is an optimal solution for the objective function, the optimal solution can be found; but the power supply recovery problem is an NP-hard problem. Simply using traditional mathematics The optimization method has the problem of dimensionality disaster, and also has problems such as large amount of calculation, long calculation time, and poor real-time performance.

However, the artificial bee colony algorithm still has some shortcomings, mainly: premature convergence, low search accuracy, slow convergence speed in the later stage, easy to fall into local optimal solution, difficult to obtain accurate optimal solution, etc.

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