| 引用本文: | 王 灿,常文涵,张雪菲,等.基于SMA-Adaboost的单三相混联微网群孤岛检测方法[J].电力系统保护与控制,2025,53(21):1-14.[点击复制] |
| WANG Can,CHANG Wenhan,ZHANG Xuefei,et al.An islanding detection method for hybrid single- and three-phase microgrid clusters based on SMA-Adaboost[J].Power System Protection and Control,2025,53(21):1-14[点击复制] |
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| 摘要: |
| 非计划孤岛事件对单三相混联微网群的稳定运行存在较大威胁，及时检测出微网群的实际孤岛状况是确保其安全稳定运行的重要前提。然而，传统孤岛检测方法存在检测盲区，且较多弱相关或不相关电气特征量会对孤岛检测的准确性造成不利影响。为此，提出一种基于改进自适应增强(adaptive boosting, Adaboost)学习器的单三相混联微网群孤岛检测方法。首先，在Adaboost学习器中引入黏菌优化算法(slime mould algorithm, SMA)以改善分类能力、减弱扰动影响，并采用群组自适配归一化(group switchable normalization, GSN)权重学习方式缩短检测耗时。其次，基于所构建的SMA-Adaboost学习器建立孤岛检测模型。同时为提高孤岛检测模型的效率和准确度，基于偏最小二乘法(partial least squares, PLS)提取出与孤岛状态强相关电气特征量。最后，在基于改进IEEE37节点的单三相混联微网群中对所提方法的性能进行仿真验证。结果表明，所提孤岛检测方法能够不受扰动信号和系统三相不平衡度的影响进行准确的孤岛检测，与其他检测方法相比具有更强的准确性及泛化能力。 |
| 关键词: 非计划孤岛 微网群 孤岛检测 自适应增强学习器 |
| DOI：10.19783/j.cnki.pspc.241758 |
| 投稿时间：2024-12-30修订日期：2025-04-10 |
| 基金项目:国家自然科学基金项目资助(52107108) |
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| An islanding detection method for hybrid single- and three-phase microgrid clusters based on SMA-Adaboost |
| WANG Can1,2,CHANG Wenhan1,ZHANG Xuefei3,XI Lei1,YANG Nan1 |
| (1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China;
2. Hubei Provincial Engineering Technology Research Center for Microgrid (China Three Gorges University),
Yichang 443002, China; 3. State Grid Jingzhou Power Supply Company, Jingzhou 434000, China) |
| Abstract: |
| Unintentional islanding events pose a significant threat to the stable operation of hybrid single- and three-phase hybrid microgrid clusters. Timely and accurate detection of the actual islanding status is essential to ensure their secure and reliable operation. However, traditional islanding detection methods suffer from detection blind spots, and many weakly correlated or irrelevant electrical characteristics can adversely affect the accuracy of islanding detection. To address these issues, an islanding detection method for hybrid single- and three-phase microgrids based on the improved adaptive boosting (Adaboost) method is proposed. First, the slime mould algorithm (SMA) is introduced into the Adaboost learner to improve classification ability and reduce disturbance effects. Additionally, the weight learning method using group switchable normalization (GSN) is adopted to shorten the detection time. Then, an islanding detection model is established using the proposed SMA-Adaboost learner. To further enhance the efficiency and accuracy of the islanding detection model, electrical features strongly correlated with islanding status are extracted based on the partial least squares (PLS) method. Finally, the performance of the proposed method is validated through simulations on a hybrid single- and three-phase microgrid cluster based on the improved IEEE37-bus system. The results demonstrate that the proposed islanding detection method can accurately detect islanding without being affected by disturbance signals or three-phase system unbalance, and exhibits superior accuracy and generalization capability compared to existing detection methods. |
| Key words: unintentional islanding microgrid cluster islanding detection adaptive boosting learner |
