Alternating-current/direct-current-system reactive power optimization method based on good-point set quantum particle swarm algorithm

• Main Authors: SU QINGMEI, HUANG DAOSHAN, HUANG TING, ZHANG JIAN, CAI ZHENCAI, LI HAIKUN, CAI BINGJUN, LIN YIN, LIU ZHINUAN, WU DANYUE
• Format: Patent
• Language: Chinese; English
• Published: 18.01.2017
• Subjects: CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER; ELECTRICITY; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERATION; REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGYGENERATION, TRANSMISSION OR DISTRIBUTION; SYSTEMS FOR STORING ELECTRIC ENERGY; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS; TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINSTCLIMATE CHANGE

The invention relates to an alternating-current/direct-current-system reactive power optimization method based on a good-point set quantum particle swarm algorithm. The method comprises the following steps of step1, establishing an alternating-current/direct-current-system reactive power optimization model; step2, improving a quantum genetic algorithm; and step3, using the improved quantum genetic algorithm to solve the alternating-current/direct-current-system reactive power optimization model. In the invention, the QPSO is improved and a problem of premature convergence generated because the QPSO is easy to fall into local optimum is solved. 本发明涉及种基于佳点集量子粒子群算法的交直流系统无功优化方法，包括以下步骤：步骤S1：建立交直流系统无功优化模型；步骤S2：对量子遗传算法进行改进；步骤S3：使用改进后的量子遗传算法对交直流系统无功优化模型进行求解。本发明对QPSO进行改进，解决了其容易陷入局部最优，导致早熟收敛的问题。