摘要
传统微电网孤岛检测方法中,被动法存在检测盲区大、阈值设定难的问题,主动法存在干扰电能质量的问题。因此,提出一种基于自适应噪声的完全集成经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)-Teager-Kaiser能量算子(Teager-Kaiser energy operator,TKEO)和优化深度置信网络(deep belief network,DBN)的微电网孤岛检测方法。首先,使用CEEMDAN算法分解公共耦合点处的电压和电流信号,得到一系列本征模态函数(intrinsic mode function,IMF),并计算相关系数,确定有效IMF;其次,对有效IMF进行乘积融合,采用TKEO计算融合后的IMF的能量序列,得到重构的孤岛特征;最后,利用粒子群优化算法优化DBN,将提取的特征输入优化后的DBN中进行训练与测试。实验结果表明,所提方法能有效区分不同工况下的孤岛和非孤岛状态,检测准确率可达到99.52%,检测时间为25.326 ms,且抗噪声能力较强。
In the conventional islanding detection methods of microgrids,the passive method has problems such as large detection blind zones and difficult threshold setting,while the active method has the problem of disturbance in power quality.Therefore,an islanding detection method based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)-Teager-Kaiser energy operator(TKEO)and optimized deep belief network(DBN)is proposed.Firstly,the voltage and current signals at the common coupling points are decomposed by the CEEMDAN algorithm to obtain a series of intrinsic mode functions(IMF),and the correlation coefficients are calculated to determine the effective IMF.Secondly,the effective IMF is subjected to product fusion,and the energy sequence of the fused IMF is calculated by TKEO to obtain the reconstructed island features.Finally,the DBN is optimized by the particle swarm optimization algorithm,and the extracted features are input into the optimized DBN for training and testing.The experimental results show that the proposed method can effectively distinguish the islanding and non-islanding states under different working conditions,the detection accuracy can reach 99.52%,the detection time is 25.326 ms,and it has a strong anti-noise ability.
作者
余飞鸿
吴杰
夏岩
常政威
熊兴中
陈仁钊
YU Feihong;WU Jie;XIA Yan;CHANG Zhengwei;XIONG Xingzhong;CHEN Renzhao(School of Automation and Information Engineering,Sichuan University of Science and Engineering,Yibin 644000,China;Artificial Intelligence Key Laboratory of Sichuan Province,Sichuan University of Science and Engineering,Yibin 644000,China;Power Internet of Things Key Laboratory of Sichuan Province,Chengdu 610041,China;Zonergy Co.,Ltd.,Zigong 643000,China)
出处
《控制工程》
北大核心
2025年第7期1300-1310,共11页
Control Engineering of China
基金
电力物联网四川省重点实验室开放课题(PIT-F-202209)
四川轻化工大学研究生创新基金资助项目(y2021061)。
