基于深度学习融合网络的含噪电能质量扰动识别方法OA北大核心CSTPCD
Identification of power quality disturbance with noises based on an integrated deep learning network
针对强噪声环境下电能质量扰动识别精度不高的问题,提出一种自适应小波降噪和深度学习相结合的电能质量扰动识别方法.首先,通过改进峰和比分层自适应阈值和能量优化的阈值函数算法对含噪扰动信号进行降噪处理.然后,通过残差神经网络对降噪后的扰动信号进行深层特征提取,在此基础上融入多头注意力机制下的双向长短时记忆网络,建立时序特征依赖关系,构成适用于噪声环境下的扰动识别框架.最后,在不同强度噪声环境下对20类扰动信号进行仿真实验.由仿真结果可知,该方法具有良好…查看全部>>
A novel method combined with adaptive wavelet threshold noise reduction and deep learning is proposed to improve the accuracy of identifying power quality disturbances in strong-noise environments.First,the noise-containing disturbance signals are noise-reduced by a threshold function algorithm based on an improved peak and score level adaptive thresholding and energy optimization.Then,the residual network is used to extract deep features from the noise-redu…查看全部>>
王海东;程杉;徐其平;刘烨;王灿
三峡大学电气与新能源学院,湖北 宜昌 443002三峡大学电气与新能源学院,湖北 宜昌 443002三峡大学电气与新能源学院,湖北 宜昌 443002国网浙江省电力有限公司嘉兴供电公司,浙江 嘉兴 314000三峡大学电气与新能源学院,湖北 宜昌 443002
电能质量扰动自适应小波降噪残差神经网络多头注意力双向长短时记忆网络
power quality disturbancesadaptive wavelet thresholdresidual neural networkmulti-headed attentionbidirectional long-short term memory network
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This work is supported by the National Natural Science Foundation of China(No.52107108). 国家自然科学基金项目资助(52107108)
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