基于灰色关联和麻雀搜索算法的阶段性线损自适应快速预测方法
投稿时间:2024-01-04  修订日期:2024-04-09  点此下载全文
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陈俏玲* 广东电网有限责任公司阳江供电局 529500
中文摘要:由于输电线路内外环境特征冗余,若忽略了外环境特征对线损的影响,难以获取影响因子间的关联度,导致线损预测结果偏差较大。因此,提出基于灰色关联和麻雀搜索算法的阶段性线损自适应快速预测方法。构建内环境以及外环境两种线损影响因子,采用灰色关联分析方法,通过计算数据列形态的灰色关联系数,得到每个影响因子的关联度,聚类解析阶段性的线损特征,引入麻雀搜索算法,实现自适应的快速线损预测。实验结果表明:所提方法应用后,线损电量与实际结果高度一致,预测结果准确度较高,满足了资源优化配置的现实需求。
中文关键词:线损预测  灰色关联  麻雀搜索算法  阶段性预测  自适应快速预测  预测方法  
 
A stage based adaptive fast prediction method for line loss based on grey correlation and sparrow search algorithm
Abstract:Due to the redundancy of internal and external environmental characteristics of transmission lines, if the influence of external environmental characteristics on line losses is ignored, it is difficult to obtain the correlation between influencing factors, resulting in significant deviation in line loss prediction results. Therefore, a phased adaptive fast prediction method for line loss based on grey correlation and sparrow search algorithm is proposed. Construct two types of line loss influencing factors, internal and external, using grey correlation analysis method. By calculating the grey correlation coefficient of the data column form, obtain the correlation degree of each influencing factor, cluster and analyze the phased line loss characteristics, and introduce sparrow search algorithm to achieve self adaptive and fast line loss prediction. The experimental results show that after the application of the proposed method, the line loss electricity is highly consistent with the actual results, and the accuracy of the predicted results is high, meeting the practical needs of resource optimization allocation.
keywords:Line loss prediction  Grey correlation  Sparrow search algorithm  Periodic prediction  Adaptive fast prediction  Prediction methods  
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