| 摘要: |
| 针对目前变压器故障诊断准确率低的问题，提出一种多策略集成模型。首先通过等度量映射(isometric mapping, Isomap)对高维非线性不可分的变压器故障数据进行降维处理。其次，利用混合核极限学习机(hybrid kernel based extreme learning machine, HKELM)进行训练学习，考虑到HKELM模型易受参数影响，所以利用北方苍鹰优化算法(northern goshawk optimization, NGO)对其参数进行寻优。但由于NGO收敛速度较慢，易陷入局部最优，引入切比雪夫混沌映射、择优学习、自适应t分布联合策略对其进行改进。同时为了提高模型整体的准确率，通过结合Adaboost集成算法，构建Adaboost-INGO-HKELM变压器故障辨识模型。最后，将提出的Adaboost- INGO-HKELM模型与未进行降维处理的INGO-HKELM模型、Isomap-INGO-KELM模型、Adaboost-Isomap- GWO-SVM等7种模型的测试准确率进行对比。提出的Adaboost-INGO-HKELM模型的准确率可达96%，均高于其他模型，验证了该模型对变压器故障辨识具有很好的效果。 |
| 关键词: 故障诊断 油浸式变压器 Adaboost集成算法 切比雪夫混沌映射 混合核极限学习机 等度量映射 |
| DOI：10.19783/j.cnki.pspc.231055 |
| 投稿时间：2023-08-16修订日期：2023-11-06 |
| 基金项目:国家自然科学基金项目资助(51974151)；辽宁省教育厅重点实验室基金项目资助(LJZS003) |
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| Transformer fault identification based on Adaboost-INGO-HKELM |
| XIE Guomin,JIANG Haiyang |
| (Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China) |
| Abstract: |
| Aiming at the low accuracy of transformer fault diagnosis, a multi-strategy integrated model is proposed. Firstly, the high dimensional nonlinear indivisible transformer fault data is reduced by isometric mapping (Isomap). The hybrid kernel based extreme learning machine (HKELM) is used for training and learning. Considering that the HKELM model is easily affected by parameters, the northern Goshawk optimization (NGO) algorithm is used to optimize its parameters. However, due to the slow convergence rate of NGO, it is easy to fall into local optimal, and Chebyshev chaotic mapping, optimal learning, and adaptive T-distribution joint strategies are introduced to improve it. At the same time, in order to improve the overall accuracy of the model, the Adaboost-INGO-HKELM transformer fault identification model is constructed by combining the Adaboost integrated algorithm. Finally, the test accuracy of the proposed Adaboost-INGO-HKELM model is compared with that of INGO-HKELM model without dimensionality reduction, Isomap-INGO-KELM model, Adaboost-Isomap-GWO-SVM model. The Adaboost-INGO-HKELM model proposed in this paper can achieve an accuracy of 96%, which is higher than other models, which verifies that the model has a good effect on transformer fault identification. |
| Key words: fault diagnosis oil-immersed transformer Adaboost integration algorithm Chebyshev chaotic mapping hybrid kernel based extreme learning machine isometric mapping |
