基于云边协同架构的断路器动作时间预测方法

冯鸫; 龙启; 杨旭; 黎卫国; 梁敬哲; 段雄英; 李劲松

冯鸫,龙启,杨旭,黎卫国,梁敬哲,段雄英,李劲松.基于云边协同架构的断路器动作时间预测方法[J].电测与仪表,2026,63(8):122-131.
fengdong,longqi,yangxu,liweiguo,liangjingzhe,duanxiongying,lijinsong.Prediction method for circuit breaker action time based on cloud edge collaborative architecture[J].Electrical Measurement & Instrumentation,2026,63(8):122-131.

基于云边协同架构的断路器动作时间预测方法

Prediction method for circuit breaker action time based on cloud edge collaborative architecture

DOI：10.19753 / j.issn1001-1390.2026.08.013

中文关键词: 断路器动作时间随机森林算法 Hermite插值选相投切鲸鱼优化算法云边协同

英文关键词:Circuit breaker action time, Random Forest Algorithm, Hermite interpolation, Choose compatible cutting, Whale Optimization Algorithm, Cloud edge collaboration

基金项目:中国南方电网有限责任公司重点科技项目(CGY KJXM20220346);国家自然科学基金资助项目(52177131)

作者中文名	作者英文名	单位
冯鸫	fengdong	中国南方电网有限责任公司超高压输电公司
龙启	longqi	中国南方电网有限责任公司超高压输电公司
杨旭	yangxu	中国南方电网有限责任公司超高压输电公司电力科研院
黎卫国	liweiguo	中国南方电网有限责任公司超高压输电公司电力科研院
梁敬哲	liangjingzhe	大连理工大学电气工程学院
段雄英	duanxiongying	大连理工大学电气工程学院
李劲松	lijinsong	大连理工大学电气工程学院

摘要点击次数: 8

中文摘要:

特高压断路器的动作时间具有较大的分散性。为了增强相控开关动作时间预测的准确性，本文针对550kV液压操动机构断路器进行了现场实验。随后针对外部参量与断路器实际动作时间分散性之间的关系，建立了以控制电压，环境温度和油压三种变量为输入的随机森林（RF）回归预测模型，利用改进后的鲸鱼优化算法（WOA）对随机森林算法（RF）进行了优化提高了其预测精度。由于神经网络算法难以部署在嵌入式装置内，而实际工程中针对断路器合闸时间采用的预测方法精准度较低，为了解决这一问题，本文提出一种基于云边协同计算架构的断路器动作时间预测补偿方法，即先在云端计算机内用改进后的WOA-RF模型进行大数据训练，利用搭建好的模型计算节点信息，之后根据节点信息利用Hermite插值在现场嵌入式装置内计算断路器的合闸时间并进行补偿，同时将历史合闸数据记录传递至云端计算机系统内不断完善数据库。研究结果表明，利用该方案可更精确的预测断路器的动作时间。

英文摘要:

The operating time of ultra-high voltage circuit breakers has significant dispersion. In order to enhance the accuracy of predicting the action time of phase controlled switches, this paper conducted on-site experiments on 550kV hydraulic operated circuit breakers. Subsequently, a random forest (RF) regression prediction model was established based on the relationship between external parameters and the actual operation time dispersion of circuit breakers, with three variables of control voltage, environmental temperature, and oil pressure as inputs. The improved whale optimization algorithm (WOA) was used to optimize the RF algorithm and improve its prediction accuracy. Due to the difficulty of deploying neural network algorithms in embedded devices and the low accuracy of prediction methods used for circuit breaker closing time in practical engineering, this paper proposes a compensation method for circuit breaker action time prediction based on cloud edge collaborative computing architecture. Firstly, an improved WOA-RF model is trained on big data in a cloud computer, and node information is calculated using the built model. Then, based on the node information, Hermite interpolation is used to calculate the closing time of the circuit breaker in the embedded device on site and compensate for it. At the same time, historical closing data records are transmitted to the cloud computer system to continuously improve the database. The research results indicate that using this scheme can more accurately predict the action time of circuit breakers.

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