Research on Transformer Health Status Assessment and Lifespan Prediction Based on Multidimensional Deterioration Fusion
1. Jubang Group Co., Ltd., Yueqing 325603, China
2. China Energy Engineering Group Anhui Electric Power Design Institute Co., Ltd., Hefei 230601, China
3. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| Abstract: | Power transformer is the core equipment in the power system, and its health status concerns the safe and stable operation of power system. Accurately evaluating and reliably predicting on the health status and lifespan of the transformer can provide important information for transformer operation and maintenance, consequently reduce the cost and fault risk. This work proposes a multidimensional degradation fusion method for evaluating the health status and predict the lifespan of transformers. Firstly, the multidimensional indicators that affect the health status of transformers are analyzed, and thus an indicator system is established. The degradation of each indicator is quantified by calculating the deviation between the measurement value and the initial value. Secondly, we analyzed impacts of various indicators on the health of transformers, hence we obtained the contribution weights of each dimension of deterioration on the health of transformers. Furthermore, we considered the nonlinear impact of indicator deterioration changes on transformer health. Consequently, we established the transformer health assessment model and the lifespan prediction model based on multidimensional deterioration nonlinear fusion mapping. Finally, we took an example to evaluate the health status of a transformer by utilizing the established mathematical model. The results demonstrated that the evaluation results of this method were consistent with reality, and the model was effective. |
| Keywords: | Transformer; Health Status Evaluation; Lifespan Prediction; Multidimensional Deterioration |
| DOI: | 10.57237/j.jest.2024.04.002 |
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Received: 5 August 2024
Accepted: 30 August 2024
Published Online: 9 September 2024
References
| [1] | 黄正勇, 赵晨, 王超凡, 等.油浸式变压器复合酯类环保绝缘油介电与老化特性 [J]. 变压器, 2024, 61(07): 13-21. |
| [2] | 叶开迪, 万前宏, 张师. 基于振动信号的变压器故障分析 [J]. 电气开关, 2024, 62(03): 85-87. |
| [3] | 刘永, 杨在葆, 韩克俊, 等. 基于电容耦合传递关系的局放故障定位技术研究及工程应用 [J]. 变压器, 2023, 60(02): 54-58. |
| [4] | 李喜桂, 常燕, 罗运柏, 等.基于健康指数的变压器剩余寿命评估 [J]. 高压电器, 2012, 48(12): 80-85. |
| [5] | 杨玥, 康琪, 杨军, 等. 基于贝叶斯网络的变压器健康状况分析 [J]. 测控技术, 2021, 40(09): 38-47. |
| [6] | 陈长征, 刘洋. 基于隐马尔科夫模型的变压器剩余寿命分析 [J]. 沈阳工业大学学报, 2020, 42(04): 412-416. |
| [7] | 石宜金, 谭贵生, 赵波, 等. 基于模糊综合评估模型与信息融合的电力变压器状态评估方法 [J]. 电力系统保护与控制, 2022, 50(21): 167-176. |
| [8] | 于丹, 王昭雷, 林博伟, 等. 基于云理论的电力变压器故障诊断研究 [J]. 工业控制计算机, 2024, 37(06): 147-149. |
| [9] | 滕杰, 孙学武, 贺志强. 基于集对分析方法和证据融合的变压器状态评估方法研究 [J]. 自动化应用, 2020, (10): 93-96. |
| [10] | 李洁珊, 王朝硕, 章禹, 等. 基于历史信息挖掘的变压器健康状态聚类方法 [J]. 电力系统保护与控制, 2018, 46(14): 94-99. |
| [11] | 孟繁津. 基于健康状态的电力变压器可靠性和经济性寿命评估研究 [D]. 重庆大学, 2014. |
| [12] | 张良勇, 董晓芳. 排序集抽样下Weibull分布的产品可靠度估计[J]. 运筹与管理, 2024, 33(06): 100-105. |
| [13] | 张学萍, 王娜. 基于遗传算法的汽车底盘件疲劳寿命威布尔分布参数估计[J].浙江水利水电学院学报, 2024, 36(02): 77-80+90. |
| [14] | DL/T 596-1996. 电力设备预防性试验规程 [S]. |
| [15] | DL/T 722-2000. 变压器油中溶解气体分析和判断导则 [S]. |
| [16] | GB/T 1094.7-2008. 电力变压器第7部分: 油浸式电力变压器负载导则 [S]. |
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