| 引用本文: | 贺虎成,王承海,辛钟毓,等.一种组合重构的电能质量扰动特征提取方法[J].电力系统保护与控制,2023,51(10):34-44. |
| HE Hucheng,WANG Chenghai,XIN Zhongyu,et al.A feature extraction method for power quality disturbance based on reconstructed combination[J].Power System Protection and Control,2023,51(10):34-44 |
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| 摘要: |
| 电力系统中电能质量扰动类型较多、扰动特征表征复杂，特征提取的有效性直接影响识别精度。为了保证特征提取的有效性，通常以牺牲特征向量维度作为代价，但特征向量维度过高会增加识别模型的复杂度和降低识别的速度。基于以上考虑，提出了一种基于能量熵和功率谱熵的组合重构特征提取方法。首先根据电能质量扰动信号特性和改进集合经验模态分解(modified ensemble empirical mode decomposition, MEEMD)对电能质量扰动信号进行处理。其次利用能量熵和功率谱熵对扰动特征进行组合提取，构建高精度、低维度的特征向量。最后通过双层前馈神经网络(double-layer back propagation neural network, DBPNN)对扰动信号进行识别。仿真和实验结果表明，与单一特征提取方法相比，所提出的组合重构特征提取方法的特征向量维度、识别模型复杂度和识别难度降低，准确率较高，且具有一定的抗噪性。 |
| 关键词: 电能质量扰动 特征提取 经验模态分解 神经网络 |
| DOI：10.19783/j.cnki.pspc.221473 |
| 分类号: |
| 基金项目:陕西省自然科学基础研究计划-陕煤联合基金项目资助(2019JLM-51) |
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| A feature extraction method for power quality disturbance based on reconstructed combination |
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HE Hucheng1, WANG Chenghai1, XIN Zhongyu2, WEI Jiahui1, WANG Linke1
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1. School of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi’an 710054, China;
2. Shandong Guohua Shidai Investment Development Co., Ltd., Jinan 250002, China
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| Abstract: |
| There are many types of power quality disturbances in a power system, and the characteristics of the disturbances are complex. The validity of feature extraction directly affects the recognition accuracy. In order to ensure the validity of feature extraction, the dimension of the eigenvector is usually sacrificed, but the high dimension of the eigenvector will increase the complexity of the recognition model and reduce the speed of recognition. From the above considerations, a combined reconstruction feature extraction method is proposed based on energy entropy and power spectral entropy. First, the power quality disturbance signal is processed according to its characteristics and modified ensemble empirical mode decomposition (MEEMD). Second, the disturbance features are extracted by combining energy entropy and power spectrum entropy, so that a high-precision and low-dimension eigenvector is constructed. Finally, the disturbance signals are identified by a double-layer back propagation neural network (DBPNN). Simulation and experimental results show that, compared with the single feature extraction method, the dimension of the eigenvector, the complexity of the recognition model and the difficulty of recognition are reduced in the combined reconstruction feature extraction method proposed. The accuracy rate is higher and it has a certain degree of anti-noise. |
| Key words: power quality disturbance feature extraction empirical mode decomposition neural network |
