【摘要】：風(fēng)電場出力具有隨機(jī)性,將其接入配電網(wǎng)會(huì)引起無功補(bǔ)償設(shè)備的動(dòng)作頻繁。為此,提出一種先對風(fēng)電場有功輸出曲線進(jìn)行分段,再對各個(gè)時(shí)段進(jìn)行整體動(dòng)態(tài)無功優(yōu)化的方法。針對直接對24h風(fēng)力發(fā)電曲線采用整體動(dòng)態(tài)無功優(yōu)化會(huì)引起維數(shù)災(zāi)導(dǎo)致算法難以收斂的問題,采用基于譜系聚類思想的風(fēng)電場出力曲線的分段法。針對傳統(tǒng)粒子群算法收斂到全局尋優(yōu)能力較差且易陷入局部最優(yōu)解的確定,采用云模型對粒子群算法的權(quán)值進(jìn)行動(dòng)態(tài)調(diào)整。最后,將上述算法運(yùn)用于改進(jìn)的IEEE 33節(jié)點(diǎn)系統(tǒng),仿真結(jié)果表明提出的動(dòng)態(tài)優(yōu)化方法大大縮小了優(yōu)化的時(shí)間,并減少了控制設(shè)備的動(dòng)作次數(shù),從而延長了設(shè)備的壽命。
[Abstract]:Wind farm output is random, and connecting it to distribution network will cause frequent action of reactive power compensation equipment. For this reason, a method is proposed to segment the active power output curve of wind farm first, and then to optimize the overall dynamic reactive power of each period. Aiming at the problem that the global dynamic reactive power optimization for 24 h wind power generation curve will lead to the difficulty of convergence of dimensionality disaster algorithm, a piecewise method of wind farm output curve based on pedigree clustering is adopted. The traditional particle swarm optimization (PSO) algorithm has poor convergence to global optimization and is easy to fall into local optimal solution. A cloud model is used to dynamically adjust the weight of PSO. Finally, the proposed algorithm is applied to the improved IEEE 33-bus system. The simulation results show that the proposed dynamic optimization method can greatly reduce the optimization time and reduce the number of actions of the control equipment, thus prolonging the life of the equipment.
【作者單位】： 國網(wǎng)江蘇省電力公司經(jīng)濟(jì)技術(shù)研究院;
【分類號】：TM614;TM714.3

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本文編號：2310957