CAJ | 학술논문

粒子群算法存在着早熟的现象，易陷入局部最小点，为了克服这个缺点，文章首先将云模型引入粒子群算法，将粒子分成2部分，靠近最优粒子和远离最优粒子的部分，其中靠近最优粒子种群的惯性权重由云模型的X-条件发生器自适应调整，提出了云自适应粒子群算法（cloud adaptive particle swarm optimization，CAPSO），然后引入梯度的思想，提出云自适应梯度粒子群算法（cloud adaptive gradien tpanicle swarm optimization，CAGPSO）。以网损最小为目标函数，对标准IEEE14和IEEE30节点系统进行仿真计算，结果表明改进后的CAGPSO算法能够获得更好的优化解。
입자군산법존재착조숙적현상，역함입국부최소점，위료극복저개결점，문장수선장운모형인입입자군산법，장입자분성2부분，고근최우입자화원리최우입자적부분，기중고근최우입자충군적관성권중유운모형적X-조건발생기자괄응조정，제출료운자괄응입자군산법（cloud adaptive particle swarm optimization，CAPSO），연후인입제도적사상，제출운자괄응제도입자군산법（cloud adaptive gradien tpanicle swarm optimization，CAGPSO）。이망손최소위목표함수，대표준IEEE14화IEEE30절점계통진행방진계산，결과표명개진후적CAGPSO산법능구획득경호적우화해。
Power system reactive power optimisation is regarded as a typical high-dimesional, nonlinear and discontinuous problem. Swarm optimization （PSO） algorithm converges rapidly and is easy to implement, how ever it has the defect of prematurity during the optimisation process and it makes the PSO easy to fall into the local minimum. To cope with this defect, firstly the cloud model is led into PSO, and the particles are divided into two parts, i.e., the part adjacent to the optimal particle and the part distant from the optimal particle, in which the inertia weight of the population adjacent to the optimal particle is adaptatively adjusted by the X-condition generator of cloud model; then the idea of gradient is led in and an algorithm named as cloud adaptive gradient particle swarm optimization, CAGPSO） algorithm is proposed. Taking the minimum network loss as objective function, simulation for the proposed CAGPSO algorithm by standard IEEE 14-bus system and IEEE 30-bus system are performed, simulation results show that a better optimal solution can be attained by the proposed CAGPSO algorithm.