Suzhou Electric Appliance Research Institute
期刊號(hào): CN32-1800/TM| ISSN1007-3175

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基于改進(jìn)海鷗優(yōu)化算法的有源配電網(wǎng)故障定位

來源:電工電氣發(fā)布時(shí)間:2025-07-24 13:24 瀏覽次數(shù):10

基于改進(jìn)海鷗優(yōu)化算法的有源配電網(wǎng)故障定位

朱謝琰
(揚(yáng)州三新供電服務(wù)有限公司寶應(yīng)分公司,江蘇 揚(yáng)州 225800)
 
    摘 要:分布式電源接入配電網(wǎng)后,故障電流特性發(fā)生改變,使得故障定位難度增加。針對(duì)有源配電網(wǎng)故障定位問題,提出一種基于改進(jìn)海鷗優(yōu)化算法的有源配電網(wǎng)故障定位方法。對(duì)傳統(tǒng)配電網(wǎng)故障電流編碼方式進(jìn)行改進(jìn)使其適用于有源配電網(wǎng),同時(shí)提出有源配電網(wǎng)故障定位的開關(guān)函數(shù);對(duì)基本海鷗優(yōu)化算法進(jìn)行改進(jìn),通過改進(jìn)型 Logistics 混沌映射豐富初始化海鷗種群的多樣性;對(duì)線性參數(shù) H 進(jìn)行非線性化處理,使其與收斂過程更匹配;引入正弦、余弦算子使算法在全局搜索和局部開發(fā)之間達(dá)到平衡,以提升尋優(yōu)質(zhì)量,并實(shí)施自適應(yīng) t 分布變異以提升尋優(yōu)速度。通過在 IEEE 33 節(jié)點(diǎn)配電網(wǎng)算例中進(jìn)行仿真驗(yàn)證,經(jīng)與其他智能優(yōu)化算法對(duì)比,結(jié)果表明改進(jìn)后的海鷗優(yōu)化算法在定位速度、迭代次數(shù)、容錯(cuò)性能方面具有顯著優(yōu)勢(shì)。
    關(guān)鍵詞: 分布式電源;配電網(wǎng);故障定位;改進(jìn)海鷗優(yōu)化算法;容錯(cuò)性能
    中圖分類號(hào):TM711 ;TM727     文獻(xiàn)標(biāo)識(shí)碼:B     文章編號(hào):1007-3175(2025)07-0052-08
 
The Fault Location of Active Distribution Network Based on
Improved Seagull Optimization Algorithm
 
ZHU Xie-yan
(Baoying Branch of Yangzhou Sanxin Power Supply Service Co., Ltd, Yangzhou 225800, China)
 
    Abstract: After distributed power sources are connected to the distribution network, the characteristics of fault currents change, increasing the difficulty of fault location. Aiming at the problem of fault location in active distribution networks, a fault location method for active distribution networks based on the improved seagull optimization algorithm is proposed. This paper improves the traditional fault current coding method of distribution networks to make it applicable to active distribution networks, and at the same time proposes the switch function for fault location in active distribution networks, then, the basic seagull optimization algorithm is improved, and the diversity of the initialized seagull population is enriched through the improved Logistics chaotic mapping. Perform nonlinearization processing on the linear parameter H to make it more compatible with the convergence process; the sinusoidal and cosine operators are introduced to achieve a balance between global search and local development of the algorithm, so as to improve the optimization quality, and adaptive t-distribution variation is implemented to enhance the optimization speed. Through simulation verification in the IEEE 33-node distribution network example and comparison with other intelligent optimization algorithms, the results show that the improved seagull optimization algorithm has significant advantages in positioning speed, the number of iterations, and fault-tolerant capability.
    Key words: distributed generation; distribution network; fault location; improved seagull optimization algorithm; fault-tolerant performance
 
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