| 武晋明,王玮,徐丙垠,邹国锋.多元信息协同的光伏直流电弧故障检测及定位[J].电子测量与仪器学报,2025,39(9):277-287 |
| 多元信息协同的光伏直流电弧故障检测及定位 |
| Detection and localization of photovoltaic DC arc faults based onmulti-information collaboration |
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| DOI： |
| 中文关键词: 直流电弧故障 多元信息协同 电压电流特征 故障类型辨识 故障检测及定位 物联网 |
| 英文关键词:direct current arc faults multi-dimensional information cooperation voltage-current characteristics fault type identification fault detection and localization internet of things |
| 基金项目:国家自然科学基金（52077221）项目资助 |
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| Author | Institution |
| Wu Jinming | School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255049, China |
| Wang Wei | School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255049, China |
| Xu Bingyin | School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255049, China |
| Zou Guofeng | School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255049, China |
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| 摘要点击次数: 617 |
| 全文下载次数: 525 |
| 中文摘要: |
| 针对光伏阵列尤其是大型光伏系统中电弧故障的检测和定位问题，从构建不同场景下的电弧故障等值电路模型出发，分析了不同位置、不同类型电弧故障下的电压电流特征差异，根据组串和母线内故障的典型故障特征，分别针对组串侧故障检测构建了融合使用组串电流均差值、特征频段能量比的综合判据，针对母线侧故障检测则使用电压均值和均差值构建检测判据，融合使用组串电流与母线电压多元信息，据此提出基于物联网思想的光伏系统直流电弧故障检测和定位策略。所提方法在实现故障检测的同时，可准确辨识故障类型并确定故障区段，且具备良好的抗干扰能力，能够有效区分阴影遮挡等非故障工况。实验结果表明，该方法相较于传统单一特征量检测方法具有明显的优越性，是利用物联网技术开展电弧故障检测的新思路。 |
| 英文摘要: |
| Aiming at the issue of arc fault detection and localization in photovoltaic arrays, particularly in large-scale photovoltaic systems, this study constructs equivalent circuit models of arc faults under diverse operational scenarios to analyze voltage and current characteristic differences induced by arc faults of distinct locations and varying types. Based on the typical fault characteristics observed in Photovoltaic string-side operations, this study establishes a detection criterion integrating characteristic frequency band energy ratios and differences between the mean values. Furthermore, leveraging the distinct fault signatures of busbar-side faults, a detection criterion combining voltage mean values and differences from the means is established. Finally, by synergistically utilizing multivariate information from both string currents and busbar voltages, an IoT-based strategy for direct current arc fault detection and localization in photovoltaic systems is proposed. The method proposed in this paper not only achieves fault detection but also accurately identifies fault types and determines the fault segments. This method exhibits good anti-interference capabilities, effectively distinguishing between non-fault conditions such as shadow occlusion. Experimental results demonstrate that this method significantly outperforms traditional single-feature detection methods. It represents a novel approach to implementing arc fault detection through Internet of Things technologies. |
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