Graph-theory-based intelligent optimization method for failure recovery of smart distribution grid

Abstract

The invention discloses a graph-theory-based intelligent optimization method for failure recovery of a smart distribution grid. The method comprises the following steps of 1) inputting network parameters including an original structure of a distribution network, line parameters of each branch, load of each node, DG (Distributed Generation) data and other parameters; 2) inputting a number of the current faulty line and zeroing the corresponding switching state thereof; 3) setting dimensionality, iterations and corresponding parameter values of a quantum particle swarm optimization algorithm; 4) initializing a position value xk, a quantum bit, a rotation angle, a local optimal vector xp and a global optimal vector xg of each particle; 5) correcting the position value of each particle based on a graph theory; 6) updating a rotation angle guiding value, a quantum rotation angle and a bit of each quantum particle in sequence; 7) updating the position value xk of each quantum particle; 8) updating the local optimal vector and the global optimal vector of each particle; 9) carrying out convergence test; and 10) outputting an optimal particle position value x to obtain a corresponding failure recovery strategy.

Description

technical field [0001] The invention relates to an intelligent optimization method for fault restoration of an intelligent distribution network based on graph theory, and is especially aimed at a large number of infeasible solutions generated by correcting intelligent optimization algorithms in fault restoration problems. Background technique [0002] With the strengthening of distribution network construction and the maturity of micro-grid technology, distributed generation (distributed generation, DG) plays an increasingly important role in the power grid. The connection of DG to the distribution network makes the structure of the distribution network more and more complicated. Once the distribution network fails, if the corresponding failure recovery strategy cannot be formulated quickly, it may lead to the expansion of the power outage area, and it is difficult to give full play to the greatest advantage of DG. , causing huge economic losses. Therefore, the fault recove...

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Problems solved by technology

However, the intelligent algorithm adopts the method of random optimization, and it is easy to generate a large number of infeasible solutions that violate the radial constraints and power balance constraints of the distribution network during the optimization process. If no correction is made, the efficiency of the algorithm will decrease, and the algorithm will easily fall into local Optimal solution

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