| 引用本文: | 余传祥,潘傲然,毛文鹏,郭豪杰,余霖辉.基于ISCSO的智能电表误差和线损率联合评估模型[J].电力系统保护与控制,2025,53(13):117-127.[点击复制] |
| YU Chuanxiang,PAN Aoran,MAO Wenpeng,GUO Haojie,YU Linhui.Joint evaluation model of smart meter error and line loss rate based on ISCSO[J].Power System Protection and Control,2025,53(13):117-127[点击复制] |
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| 摘要: |
| 针对当前智能电表误差和线损率联合评估精度较低的问题，提出了一种基于改进沙猫群优化算法(improved sand cat swarm optimization algorithm, ISCSO)的智能电表误差和线损率联合评估模型。首先根据典型台区拓扑结构和电能量守恒定律确定了电表误差和线损率评估模型的适应度函数，并依据台区数据确定了参数范围。其次，采用变焦佳点集、威布尔最优值引导策略、蒲公英优化算法以及联想学习变异策略对沙猫群优化算法进行改进，并经测试函数验证了算法的优越性。最后，基于适应度函数和改进后的算法建立了智能电表误差和线损率联合评估模型，并通过算例验证了相比于带有遗忘因子递推最小二乘法的动态线损智能电表误差评估模型和智能电表误差与线损率联合评估的约束优化模型，所提方法在智能电表误差与线损率的评估精度上都有较大的提升。 |
| 关键词: 智能电表 线损率 沙猫群优化算法 误差评估 |
| DOI：10.19783/j.cnki.pspc.241298 |
| 投稿时间：2024-09-25修订日期：2025-03-25 |
| 基金项目:国家电网有限公司总部管理科技项目资助(5700-
202327261A-1-1-ZN) |
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| Joint evaluation model of smart meter error and line loss rate based on ISCSO |
| YU Chuanxiang,PAN Aoran,MAO Wenpeng,GUO Haojie,YU Linhui |
| (State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing 400044, China) |
| Abstract: |
| To address the issue of low accuracy in the joint evaluation of smart meter errors and line loss rates, a joint evaluation model based on the improved sand cat swarm optimization (ISCSO) algorithm is proposed. First, the fitness function of the model is determined using a typical station topology structure and the law of energy conservation, and parameter ranges are determined based on area data. Next, the original sand cat swarm optimization algorithm is improved using a zoomed good point set, a Weibull optimal value-guided strategy, the dandelion optimizer, and an associative learning mutation strategy. The superiority of the improved algorithm is verified using benchmark test functions. Finally, the joint evaluation model for smart meter errors and line loss rates is established based on the fitness function and the improved algorithm. Case studies verify that the proposed method significantly improves the evaluation accuracy of smart meter errors and line loss rates compared to the dynamic line loss smart meter error evaluation model based on recursive least squares with a forgetting factor, and the constrained optimization model for joint evaluation of smart meter error and line loss rate. |
| Key words: smart meter line loss rate sand cat swarm optimization algorithm error evaluation |
