| 张彼德,陈广,廖其龙,邱杰,马俊梅,何恒志,阎铁生.基于IGJO-DHKELM的光伏阵列故障诊断[J].电子测量与仪器学报,2024,38(11):79-89 |
| 基于IGJO-DHKELM的光伏阵列故障诊断 |
| Fault diagnosis of photovoltaic arrays based on IGJO-DHKELM |
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| DOI： |
| 中文关键词: 光伏阵列 故障诊断 故障特征量 改进金豺算法 深度混合核极限学习机 |
| 英文关键词:photovoltaic array fault diagnosis fault feature set IGJO DHKELM |
| 基金项目:成都市科技攻关计划项目（2023-JB00-00014-GX）、太阳能技术集成及应用推广四川省高校重点实验室项目（SN240101）资助 |
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| Author | Institution |
| Zhang Bide | School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China |
| Chen Guang | School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China |
| Liao Qilong | School of Electrical and Information Engineering, Panzhihua University, Panzhihua 617000, China |
| Qiu Jie | School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China |
| Ma Junmei | School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China |
| He Hengzhi | School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China |
| Yan Tiesheng | School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China |
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| 中文摘要: |
| 为提高光伏阵列故障诊断精度，提出一种基于改进金豺算法（improved golden jackal optimization，IGJO）优化深度混合核极限学习机（deep hybrid kernel limit learning machine，DHKELM）的光伏阵列故障诊断方法。首先，在MATLAB/Simulink仿真平台模拟各种光伏阵列故障，在对故障特征进行了深入分析的基础上，提出了一个12维特征作为光伏阵列故障诊断的特征量；其次，通过引入透镜成像反向学习策略、正余弦算法策略和自适应T分布扰动策略对金豺算法进行改进，以提高其收敛速度和全局寻优能力，并将IGJO与其他优化算法通过测试函数进行对比；再次，将径向基核函数和多项式核函数引入极限学习机，并结合自编码器构成DHKELM模型。最后，通过IGJO对DHKELM模型的初始参数进行优化，建立了IGJO-DHKELM光伏阵列故障诊断模型。结果分析表明，与传统4维和5维故障特征量相比，利用所提12维故障特征量进行诊断时准确率更高；相较于其他故障诊断模型，基于IGJO-DHKELM的光伏阵列故障诊断方法具有更高的诊断精度。 |
| 英文摘要: |
| To enhance the accuracy of photovoltaic (PV) array fault diagnosis, this study proposes a novel method that utilizes an improved golden jackal optimization (IGJO) algorithm to optimize a deep hybrid kernel extreme learning machine (DHKELM) for PV array fault diagnosis. Initially, a range of PV array faults are simulated using the MATLAB/Simulink platform. Based on a comprehensive analysis of fault characteristics, a 12-dimensional feature set is proposed for fault diagnosis. Subsequently, the golden jackal algorithm is improved by introducing lens imaging reverse learning strategy, cosine and sine algorithm strategy, and adaptive T-distribution perturbation strategy to enhance its convergence speed and global optimization capability. Additionally, IGJO is compared with other optimization algorithms using test functions. Furthermore, radial basis kernel functions and polynomial kernel functions are incorporated into the extreme learning machine and combined with an autoencoder to form the DHKELM model. Finally, IGJO is employed to optimize the initial parameters of the DHKELM model, resulting in the establishment of the IGJO-DHKELM PV array fault diagnosis model. Analysis of the results indicates that the proposed 12-dimensional feature set provides higher diagnostic accuracy compared to traditional 4-dimensional and 5-dimensional feature sets. Moreover, the IGJO-DHKELM-based fault diagnosis method demonstrates superior diagnostic accuracy compared to other fault diagnosis models. |
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