基于退火粒子群和内点法的改进最优潮流算法  

Improved Optimal Power Flow Algorithm Based on Annealing Particle Swarm and Interior Point Method

作　　者：陈丽光[1] 文波[1] 聂一雄[2] 

机构地区：[1]广东电网公司河源供电局,广东河源517000 [2]广东工业大学,广东广州510006

出　　处：《广东电力》2013年第9期32-35,103,共5页Guangdong Electric Power

基　　金：广东省自然科学基金研究项目(10151009001000045)

摘　　要：针对一般智能算法计算最优潮流(optimal power flow,OPF)问题收敛速度慢、精度低等问题,提出一种退火粒子群(simulated annealing based particle swarm optimization,SA-PSO)和预测-校正原对偶内点法(predictor-corrector primal-dual interior point method,PCPDIPM)结合的改进最优潮流算法。该算法采用SA-PSO优化待求系统的离散变量,而在SA-PSO的每一次迭代过程中,通过PCPDIPM优化待求系统的连续变量,并对粒子进行适应度评估。这种求解模式将SA-PSO求解离散变量方便和PCPDIPM优化速度快的优点结合在一起,发挥了两种算法的优势。多个算例结果表明,与SA-PSO算法相比,该算法具有寻优能力强,收敛速度快,计算精度高的优点。Aiming at problems of slow convergence speed and low precision of general intelligent algorithms in calculating op-timal power flow, this paper proposes a new improved optimal power flow algorithm by combining simulated annealing par-ticle swarm optimization and predict-interior-point method. This algorithm uses SA-PSO to optimize disperse variables of the system waited for being solved while during each iteration process of SA-PSO, it uses PCPDIPM to optimize continuous vari-ables and estimate fitness of the particle. This kind of solve mode combines merits of convenient solution of disperse variables by using SA-PSO and fast optimizing speed of IPM which plays advantages of these two algorithms. Multiple results show that this algorithm is provided with strong optimizing performance, fast convergence speed and high calculating precision compa- ring with SA-PSO algorithm.

关 键 词：模拟退火算法 粒子群算法 预测-校正内点法 最优潮流 改进算法 

分 类 号：TM744[电气工程—电力系统及自动化]