[1]韦雪梅.变电领域移动便捷式直流电源车及核容检测技术分析[J].大众科技,2023,25(3):77-79.
　Analysis of Portable DC Power Supply Vehicle and Capacity Detection Technology in the Field of Power Transformation[J].Popular Science & Technology,2023,25(3):77-79.
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变电领域移动便捷式直流电源车及核容检测技术分析()
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《大众科技》[ISSN:1008-1151/CN:45-1235/N]

卷:
25
期数:
2023年3
页码:
77-79
栏目:
电力与机械
出版日期:
2023-03-20

文章信息/Info

Title:
Analysis of Portable DC Power Supply Vehicle and Capacity Detection Technology in the Field of Power Transformation
作者:
韦雪梅　
（广西电网有限责任公司河池供电局，广西 河池 547000）
关键词:
变电站; 直流电源; 移动便捷式; 剩余容量; 检测系统
Keywords:
substation;  DC power supply;  portable;  residual capacity;  detecting system
文献标志码:
A
摘要:
针对35 kV变电站只配置一套直流系统而存在运行安全风险的情况，设计一种移动便捷式直流电源车，用于变电站直流系统故障快速响应，尤其单套直流系统运行35 kV变电站，也可作为变电站直流定检临时备用电源，提升变电站运行可靠性。针对核容检测准确度不高的问题，文章选择与蓄电池剩余容量密切相关的开路电压、内阻、环境温度三个关键参数，采用以内阻法和开路电压法为核心的物理建模法，及以最小二乘支持向量机法为核心的辨识估计法联合检测，引入自动寻优的粒子群算法和启发性变异的遗传算法，提升蓄电池剩余容量检测精度，其平均误差可控制在3%以内，具有较强的实用性。
Abstract:
In view of the operation safety risk of only configuring one set of DC system in 35 kV substation, this paper designs a mobile and convenient DC power vehicle, which is used as rapid response to DC system faults in the substation. In particular, a single set of DC system running 35 kV substation can also be used as a temporary standby power supply for DC regular inspection of the substation to improve the reliability of substation operation. In view of the low accuracy of capacity detection, this paper selects three key parameters, namely, open circuit voltage, internal resistance and ambient temperature, which are closely related to the residual capacity of the battery. The physical modeling method with internal resistance method and open circuit voltage method as the core and the identification and estimation method with least squares support vector machine method as the core are used for joint detection, and the particle swarm optimization algorithm with automatic optimization and genetic algorithm with heuristic mutation are introduced, improve the detection accuracy of battery residual capacity, and its average error can be controlled within 3%, which has strong practicability.

参考文献/References:

[1]　董博，李永东. 蓄电池容量均衡方法概述[J]. 电源学报，2011(5): 32-36.[2] XU J, MI C C, CAO B. The state of charge estimation of lithiumion batteries based on a proportional-integral observer [J]. IEEE Transactions on Vehicular Technology, 2014, 63(4): 1614-1621.[3] RAHIMI-EICHI H, BARONTI F, CHOW M Y. Online adaptive parameter identification and state-of-charge coestimation for lithium-polymer battery cells[J]. IEEE Transactions on Industrial Electronics, 2014, 61(4): 2053-2061.[4] LEE S J, KIM J H, LEE J M, et al. The state and parameter estimation of an Li-ion battery using a new OCV-SOC concept[C]. Power Electronics Specialists Conference, 2007: 2799-2803.[5] AUNG H, SOON L K, TING G S. State-of-charge estimation of lithium-ion battery using square root spherical unscented Kalman filter (Sqrt-UKFST) in nanosatellite [J]. IEEE Transactions on Power Electronics, 2015, 30(9): 4774-4783.[6] CHARKHGARD M, FARROKHI M. State-of-charge estimation for lithium-ion batteries using neural networks and EKF[J]. IEEE Transactions on Industrial Electronics, 2010, 57(12): 4178-4187.[7] ANTON A, CARLOS J, GARCIA N P J, et al. Support vector machines used to estimate the battery state of charge[J]. IEEE Transactions on Power Electronics, 2013, 28(12): 5919-5926.[8] 张旭辉，林海军，刘明珠，等. 基于蚁群粒子群优化的卡尔曼滤波算法模型参数辨识[J]. 电力系统自动化，2014，38(4): 44-50.[9] 顾燕萍，赵文杰，吴占松. 最小二乘支持向量机的算法研究[J]. 清华大学学报，2010，50(7): 1063-1066.[10] 黄平. 粒子群算法改进及其在电力系统的应用[D]. 广州: 华南理工大学，2012.

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备注/Memo

备注/Memo:
【收稿日期】2022-09-20【作者简介】韦雪梅（1994－），女（壮族），广西电网有限责任公司河池供电局工程师，研究方向为变电领域移动便捷式直流电源车及核容检测技术分析。
更新日期/Last Update: 2023-05-30