CAJ | 학술논문

针对风电系统中，风力的不确定性导致粒子的适应度不稳定性较大、劣性粒子偏多，难以快速收敛到最优值，进而造成系统电压偏差较大，网损剧增的问题，提出了基于动态云进化粒子群算法对风电系统进行无功优化。首先以网损最小作为优化目标建立了风电系统无功优化模型。然后提出动态云进化粒子群算法。该算法根据粒子的适应度值，选取优秀个体进行进化，从而降低劣性粒子比例，增强搜索速度。再通过云发生器，使得优秀个体进化出的优秀种群趋于正态分布，从而达到改善粒子分布的目的。在此基础上，根据正态云的分布特点，动态改变飞行速度，进一步改善粒子分布、提高搜索精度。最后以风电系统的有功网损为优化目标，进行补偿容量的确定，仿真结果证明了该方法的有效性。
침대풍전계통중，풍력적불학정성도치입자적괄응도불은정성교대、렬성입자편다，난이쾌속수렴도최우치，진이조성계통전압편차교대，망손극증적문제，제출료기우동태운진화입자군산법대풍전계통진행무공우화。수선이망손최소작위우화목표건립료풍전계통무공우화모형。연후제출동태운진화입자군산법。해산법근거입자적괄응도치，선취우수개체진행진화，종이강저렬성입자비례，증강수색속도。재통과운발생기，사득우수개체진화출적우수충군추우정태분포，종이체도개선입자분포적목적。재차기출상，근거정태운적분포특점，동태개변비행속도，진일보개선입자분포、제고수색정도。최후이풍전계통적유공망손위우화목표，진행보상용량적학정，방진결과증명료해방법적유효성。
For wind power system, the uncertainty of wind leads to the instability of the particle' fitness and more pessimum particles, so it's difficult to quickly converge to the optimal value, which causes the large system voltage deviation and the sharp increase of network loss. A Dynamic Cloud Evolution Particle Swarm Optimization (DCEPSO) algorithm is proposed to realize the reactive power optimization of wind power system. Firstly, the minimum network loss is designed to be the optimization goal of reactive power optimization model of wind power system. Secondly, the DCEPSO algorithm is presented. According to the particle's fitness value, the algorithm selects excellent individuals to evolve, which reduces the proportion of pessimum particle and increases the search speed. Then through the cloud generator, excellent population evolved by excellent individuals tends to normal distribution, so as to improve the particle distribution. On this basis, according to the characteristics of the normal cloud distribution, dynamically changing speed can further improve the particle distribution and the search precision. Finally, the active network loss of wind power system is made to be the optimization goal to determine the capacity of compensation. The simulation results prove the effectiveness of the proposed method.