Distribution network optimization algorithm suitable for large-scale nonlinearity
A distribution network optimization, non-linear technology, applied in the direction of calculation, data processing application, prediction, etc., can solve the problems of increasing the amount of calculation, reducing the efficiency of algorithm space search, etc., to achieve the effect of improving the efficiency of space search
Pending Publication Date: 2022-05-17
STATE GRID JIANGSU ELECTRIC POWER CO ZHENJIANG POWER SUPPLY CO
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However, PGA also has certain limitations, that is, PGA will generate a considerable number of repeated solutions in genetic operations, which increases the amount of calculation and reduces the space search efficiency of the algorithm.
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[0051] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.
[0052] When solving distribution network optimization problems, the common genetic algorithm (Genetic Algorithm, GA) cannot guarantee the feasibility of the solution, while the partheno-genetic algorithm (PGA) avoids the infeasible solution, but it will produce Solve repeated new solutions, so both algorithms will add additional workload. In order to reduce the extra calculation amount and improve the search efficiency of the algorithm, the present invention proposes No Revisit Partheno-Genetic Algorithm (NRPGA), which combines the No Revisit Partheno-Genetic Algorithm with the Partheno-Genetic Algorithm: using the Partheno-Genetic Algorithm to ensure that all new The solution conforms to the relevant constraints of the distribution network, and then each new solution is checked using the non-revisit algorithm to ensure the uniqueness of the new ...
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The invention discloses a distribution network optimization algorithm suitable for large-scale nonlinearity. The distribution network optimization algorithm comprises the following steps: inputting line parameters; generating an initial net rack, and storing the initial net rack into a known structure array; performing revisit-free single parent genetic operation on the initial net rack to generate an initial population, storing the initial population into a known solution structure array and an excellent population structure array, and establishing a multi-way tree storage structure for a known solution structure; performing revisit-free single parent genetic manipulation on each gene of the excellent population to generate a filial generation gene, and storing the filial generation gene into a known structure array; carrying out fitness calculation on the excellent population structure array and the offspring genes, and arranging the excellent population structure array and the offspring genes from small to large according to a fitness function; adopting an elitist retention strategy, and replacing the existing excellent population structure array with the structure array of the gene ranked in the front; and judging whether t is greater than the number of iterations, and outputting an optimal solution if t is greater than the number of iterations. According to the non-revisit single parent genetic algorithm NRPGA, unnecessary calculation is avoided, and the space search efficiency can be effectively improved.
Description
technical field [0001] The invention relates to a large-scale nonlinear distribution network optimization algorithm, which belongs to the technical field of distribution network planning. Background technique [0002] Distribution network planning is an important content in the field of distribution system research, which is essentially a nonlinear, high-dimensional complex combinatorial optimization problem. At present, the solution methods can be generally divided into analytical algorithms and heuristic algorithms. The analytical algorithm needs to analyze the relationship between the elements in the problem, express it as a function expression and then calculate the solution. This method can obtain the optimal solution, but the calculation requires a lot of time, and it is only suitable for optimization problems with a small solution space. Compared with the analytical algorithm, the heuristic algorithm can solve the optimal value in a short time, and this algorithm can...
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IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06Y04S10/50
Inventor 胡宇行任立志赵辉李烁群杨虎
Owner STATE GRID JIANGSU ELECTRIC POWER CO ZHENJIANG POWER SUPPLY CO