基于GAN-DRNN的電力變壓器剩余壽命預測
李柯夫1，游欣2，徐椰烴3，錢泓江4
(1 成都雙流國際機場股份有限公司，四川 成都 610200； 2 國網(wǎng)四川省電力公司超高壓分公司，四川 成都 610095；
3 國網(wǎng)四川省電力公司彭州市供電分公司，四川 彭州 611930； 4 四川大學 空天科學與工程學院，四川 成都 610207)
摘 要 ：大型變壓器的可靠性關(guān)乎電力系統(tǒng)的穩(wěn)定運行。由于時間、成本的局限性，致使變壓器剩余壽命統(tǒng)計數(shù)據(jù)規(guī)模較小，難以充分發(fā)揮機器學習算法的最佳預測性能。提出了一種基于生成對抗網(wǎng)絡(luò) (GAN) 的數(shù)據(jù)增強手段，有效解決變壓器剩余壽命樣本稀疏問題；構(gòu)建了具有良好預測性能的動態(tài)遞歸神經(jīng)網(wǎng)絡(luò) (DRNN) 模型，并驗證了其高效性。試驗結(jié)果表明，經(jīng)GAN作用的增強數(shù)據(jù)集能有效激發(fā)DRNN模型的預測性能，其預測精度最大提高了7.16%，預測結(jié)果均在 2.0 倍誤差分散帶以內(nèi)，實現(xiàn)了小樣本 情形下變壓器剩余壽命的高精度預測，較大程度上壓縮了變壓器剩余壽命預測的時間和成本。
關(guān)鍵詞 ： 變壓器 ；生成對抗網(wǎng)絡(luò) ；數(shù)據(jù)增強 ；動態(tài)遞歸神經(jīng)網(wǎng)絡(luò) ；剩余壽命預測
中圖分類號 ：TM401 ；TM407 文獻標識碼 ：B 文章編號 ：1007-3175(2025)12-0036-06
Prediction of Remaining Life Power Transformers Based on GAN-DRNN
LI Ke-fu1 , YOU Xin2 , XU Ye-ting3 , QIAN Hong-jiang4
（1 Chengdu Shuangliu International Airport Co., Ltd, Chengdu 610200, China; 2 State Grid Sichuan Electric Power Company Extra-High Voltage Branch Company, Chengdu 610095, China;
3 State Grid Sichuan Electric Power Company Pengzhou Power Supply Branch Company, Pengzhou 611930, China;
4 School of Aerospace Science and Engineering, Sichuan University, Chengdu 610207, China）
Abstract: The reliability of large transformers is closely associated with the stable operation of the power system. Due to the limitations of time and cost, the scale of statistics on the remaining service life of transformers is relatively small, it is difficult to fully leverage the best predictive performance of machine learning algorithms. In order to solve the above problems, this paper proposes a data augmentation means based on generative adversarial network (GAN), which effectively solves the problem of sparse samples of the remaining life of transformers, constructs a dynamic recurrent neural network (DRNN) model with good prediction performance and its high efficiency is verified. The experimental results show that the enhanced dataset treated with GAN can effectively stimulate the prediction performance of the DRNN model. Its prediction accuracy has been increased by up to 7.16%, and the prediction results are all within the error dispersion band of 2.0 times. It has achieved high-precision prediction of the remaining life of transformers in the case of small samples, and has largely compressed the time and cost of predicting the remaining life of transformers.
Key words: transformer; generative adversarial network; data augmentation; dynamic recurrent neural network; prediction of remaining life
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