摘要
为了解决低压台区线损计算在理论上因线路复杂、用户众多以及数据获取困难等带来计算难度与精度不足的问题,提出了一种结合改进K-Means^(++)算法与Elman神经网络的创新计算方法。深入分析了低压台区线损的决定因素,并依据相关性分析构建了线损的关键特征指标集。采纳主成分分析方法实施数据降维,简化数据结构。通过改进的K-Means^(++)算法对数据集进行有效聚类,优化模型训练过程。同时,整合粒子群优化算法进一步提升Elman神经网络的性能。通过对实际数据进行仿真验证,结果证实所提出的方法在训练效率和计算精度方面表现优异。
To address theoretical challenges and accuracy limitations in estimating line losses for low-voltage substations,arising from complex transmission lines,multiple users,and data acquisition difficulties,we devised an innovative calculation approach in this study.Our method merges an enhanced K-means^(++)algorithm with an Elman neural network.We initially conducted an in-depth analysis of factors influencing line losses in low-voltage substations and identified key indicators through correlation analysis.Employing principal component analysis(PCA),we reduced data dimensionality and complexity.Utilizing an enhanced K-means^(++)algorithm,we efficiently clustered the dataset and optimized model training.Integration of particle swarm optimization algorithms further boosted the Elman neural networksperformance.Simulation verification using actual data affirmed the method's superior performance in training efficiency and computational accuracy.
作者
张林山
廖耀华
王恩
李波
朱梦梦
王毅
ZHANG Linshan;LIAO Yaohua;WANG En;LI Bo;ZHU Mengmeng;WANG Yi(Electric Power Science Research Institute of Yunnan Power Grid Co.,Ltd.,Kunming 650217,CHN;Key Laboratory of Green Energy and Digital Power Measurement and Control in Yunnan Provinces,Kunming 650217,CHN;School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,CHN)
出处
《半导体光电》
CAS
北大核心
2024年第3期477-484,共8页
Semiconductor Optoelectronics
基金
中国南方电网有限责任公司科技项目(YNKJXM20220166).
作者简介
张林山(1980-),男,云南省昆明市人,硕士,高级工程师,研究方向:电能计量、智能量测、科技成果转化及应用;通信作者:廖耀华(1992-),男,湖南省衡阳市人,硕士,工程师,研究方向:高电压计量、智能量测、电力计量故障检测与处理等。
