| 引用本文: | 王 燕,曹浩敏,刘世龙,等.一种电能质量混合扰动检测与识别新方法[J].电力系统保护与控制,2025,53(14):152-165.[点击复制] |
| WANG Yan,CAO Haomin,LIU Shilong,et al.A novel method for combined power quality disturbances detection and identification[J].Power System Protection and Control,2025,53(14):152-165[点击复制] |
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| 摘要: |
| 随着高碳电力系统向新型电力系统的快速转型和发展，风电、光伏等新能源及电力电子设备大规模接入电网，导致电力系统产生更为复杂、多变的电能质量扰动问题。为快速、准确地检测与捕捉扰动数据，并针对传统扰动识别方法对复杂混合扰动适用性降低、人工选取特征困难等不足，提出一种电能质量混合扰动检测与识别新方法。该方法首先采用所提出的峰差引导局部差和累加扰动检测方法，以快速、准确地检测与捕捉扰动数据。其次，采用改进迭代自适应核回归滤波方法对捕捉到的含噪扰动数据进行预处理，达到有效抑制噪声干扰、保留扰动突变等细节特征的目的。最后采用所提出的改进可视化轨迹圆方法把一维扰动数据变换为形状特征更明显、更利于辨识的二维轨迹圆图像，并输入卷积神经网络进行自动特征提取与分类。实验结果表明，新方法不仅具有较强的抗噪性和较高的扰动检测准确率，且对单一及复杂混合扰动具有较高的识别准确率。 |
| 关键词: 电能质量扰动检测与识别 可视化轨迹圆 迭代自适应核回归 卷积神经网络 |
| DOI：10.19783/j.cnki.pspc.240802 |
| 投稿时间：2024-06-24 |
| 基金项目:国家自然科学基金项目资助(52477198)；西南民族大学中央高校基本科研业务费专项资金项目资助(ZYN2025047) |
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| A novel method for combined power quality disturbances detection and identification |
| WANG Yan1,CAO Haomin2,LIU Shilong1,LUO Yushen1,BIAN Anji1 |
| (1. College of Electrical Engineering, Southwest Minzu University, Chengdu 610041, China;
2. Guangzhou Power Electrical Technology Co., Ltd., Guangzhou 510700, China) |
| Abstract: |
| With the rapid transformation of high-carbon power systems to new power systems, the large scale integration of renewable energy sources such as wind and solar, along with the widespread use of power electronic devices, has led to increasing complex and variable power quality disturbances (PQDs). To quickly and accurately detect and capture PQDs and to overcome limitations of traditional disturbance identification methods, such as reduced applicability to complex hybrid PQDs and difficulty in manually selecting features, this paper propose a novel approach for PQDs detection and identification. The proposed approach first employs peak difference guided local difference accumulation to rapidly and accurately detect and capture the PQDs. Then, the improved iterative adaptive kernel regression (IIAKR) method is used for preprocess the captured noisy PQDs, effectively suppressing noise while preserving detailed disturbance features. Finally, the improved visual trajectory circle (IVTC) method transforms the 1-D PQDs into 2-D trajectory circle images with more prominent shape and easier identification features, which are then input to convolutional neural networks (CNN) for autonomous feature extraction and classification. Experimental results show that the proposed approach offers strong noise immunity, high detection rate and classification accuracy for both single and complex PQDs. |
| Key words: PQD detection and identification visual trajectory circle iterative adaptive kernel regression CNN |
