| 江 兵,杨 春,杨雨亭,巢一帆.基于 ACO 优化 BP 神经网络的变压器热点温度预测[J].电子测量与仪器学报,2022,36(10):235-242 |
| 基于 ACO 优化 BP 神经网络的变压器热点温度预测 |
| Temperature prediction of transformer hot spot based onBP neural network optimized by ACO |
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| DOI： |
| 中文关键词: 热点温度 BP 神经网络 改进 PCA 蚁群算法 |
| 英文关键词:hot spot temperature BP neural network improved PCA ACO |
| 基金项目: |
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| 摘要点击次数: 714 |
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| 中文摘要: |
| 针对变压器热点温度预测精度问题,提出一种蚁群算法( ant colony algorithm,ACO)结合改进主成分分析法( improved
principal component analysis,IPCA)优化 BP 神经网络的热点温度预测模型。 首先采用 IPCA 去除数据冗余信息,并解决参数间
相关性问题,提高网络泛化能力。 为了避免 BP 神经网络容易陷入局部最优和收敛速度慢,利用 ACO 优化网络权值和与阈值,
加快算法速率,提高预测精度。 通过变压器温度实测数据验证,预测结果中的 mae、mse、mape 指标分别为 0. 065 7、0. 006 7、
0. 44%,预测精度和网络性能优于 IEEE、BP、IPCA-BP 模型,从而验证所提模型的有效性和可行性。 |
| 英文摘要: |
| Aiming at the prediction accuracy of transformer hot spot temperature, the ant colony algorithm ( ACO) combined with
improved principal component analysis ( IPCA) was proposed to optimize BP neural network model to predict hot spot temperature.
Firstly, IPCA is used to remove data redundancy information and solve the correlation between parameters to improve the ability of
network generalization. In order to avoid BP neural network that is easily falling into local optimum and slow convergence speed, ACO
was used to optimize the weights and thresholds of the network to speed up the algorithm and improve the prediction accuracy. Verified
by the measured transformer temperature data, the mae, mse and mape indexes in the predicted results are 0. 065 7, 0. 006 7 and
0. 44%, respectively. The prediction accuracy and network performance are better than those of IEEE, BP and IPCA-BP models, thus
verifying the validity and feasibility of the proposed model. |
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