| 引用本文: | 周建华,马国煜,陶 锴,等.基于GWO优化与BiLSTM-AM的配电网电能质量复合扰动自适应感知[J].电力系统保护与控制,2025,53(19):151-161.[点击复制] |
| ZHOU Jianhua,MA Guoyu,TAO Kai,et al.Adaptive sensing of power quality composite disturbances in distribution systems based on GWO optimization and BiLSTM-AM[J].Power System Protection and Control,2025,53(19):151-161[点击复制] |
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| 摘要: |
| 为有效应对高渗透率分布式电源并网引起的电压暂升、电压振荡等电能质量扰动(power quality disturbances, PQDs)问题，提出一种基于双向长短期记忆(bidirectional long short-term memory, BiLSTM)网络-注意力机制(attention mechanism, AM)的复合扰动自适应感知方法。首先，通过灰狼优化(grey wolf optimizer, GWO)算法优化改进的完全自适应噪声集合经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise, ICEEMDAN)参数，实现扰动信号模态分解与重构。其次，提取扰动信号的层次加权排列熵(hierarchical weighted permutation entropy, HWPE)特征。最后，构建BiLSTM-AM模型，利用多维特征长短期依赖关系实现电能质量复合扰动识别。在仿真与真实电网数据集上开展实验验证，结果表明所提方法对不同扰动均具有较好的识别效果。此外，与现有深度网络模型相比，所提模型具有更高的识别准确率。 |
| 关键词: 配电网 分布式电源 电能质量扰动 层次加权排列熵 双向长短期记忆网络 |
| DOI：10.19783/j.cnki.pspc.241545 |
| 投稿时间：2024-03-31修订日期：2025-03-13 |
| 基金项目:国家电网有限公司科技项目资助(J2024062)“基于扰动自愈控制的智能配电网电能质量耐受能力提升技术研究” |
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| Adaptive sensing of power quality composite disturbances in distribution systems based on GWO optimization and BiLSTM-AM |
| ZHOU Jianhua1,MA Guoyu2,TAO Kai2,XU Junjun2 |
| (1. Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China; 2. School of
Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China) |
| Abstract: |
| In order to effectively address power quality disturbances (PQDs) such as voltage swells and voltage oscillations caused by the high penetration grid connection of distributed generation, a composite disturbance adaptive sensing method based on a bidirectional long short-term memory (BiLSTM) network integrated with an attention mechanism (AM) is proposed. First, the grey wolf optimizer (GWO) algorithm is employed to optimize the parameters of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The modals of the disturbance signals are decomposed and reconstructed. Subsequently, the features of hierarchical weighted permutation entropy (HWPE) of the disturbance signals are extracted. Finally, a BiLSTM-AM model is constructed to capture the long- and short-term dependencies of multidimensional features for composite PQD recognition. Validation using both simulated and real-world power grid datasets demonstrates excellent identification performance on various disturbance types with the proposed method. Moreover, compared with other deep learning models, the proposed model further demonstrates higher recognition accuracy. |
| Key words: distribution network distributed generation power quality disturbance hierarchical weighted permutation entropy bidirectional long short-term memory |
