区分配电网中发生的单相接地故障类型,能够有针对性地制定故障检修策略,提升故障处置效率。配电自动化设备作为配电网故障快速辨识与处理的重要载体,对故障分类的原理及效果差异性较大,准确率无法满足电力系统工作需求,为此提出一种基...区分配电网中发生的单相接地故障类型,能够有针对性地制定故障检修策略,提升故障处置效率。配电自动化设备作为配电网故障快速辨识与处理的重要载体,对故障分类的原理及效果差异性较大,准确率无法满足电力系统工作需求,为此提出一种基于分类回归树与多核残差网络(classfication and regression tree and multi-core ResNet, CART-MRN)的树状结构故障类型识别方法。首先,建立树状故障分类结构,利用Fourier变换、经验模态分解(empirical mode decompsition, EMD)分解等方法提取故障点电压电流的多域故障特征;其次,结合特征分析与信息增益建立适应不同小电流接地系统的融合算法模型,并引入粒子群算法优化网络超参数;最后,通过现场录波数据验证与对比实验,证明该方法能快速、有效地完成单相接地故障分类识别,且更具有适应性。展开更多
As it is crucial to protect the transmission line from inevitable faults consequences,intelligent scheme must be employed for immediate fault detection and classification.The application of Artificial Neural Network(A...As it is crucial to protect the transmission line from inevitable faults consequences,intelligent scheme must be employed for immediate fault detection and classification.The application of Artificial Neural Network(ANN)to detect the fault,identify it’s section,and classify the fault on transmission lines with improved zone reach setting is presented in this article.The fundamental voltage and current magnitudes obtained through Discrete Fourier Transform(DFT)are specified as the inputs to the ANN.The relay is placed at section-2 which is the prime section to be protected.The ANN was trained and tested using diverse fault datasets;obtained from the simulation of different fault scenarios like different types of fault at varying fault inception angles,fault locations and fault resistances in a 400 kV,216 km power transmission network of CSEB between Korba-Bhilai of Chhattisgarh state using MATLAB.The simulation outcomes illustrated that the entire shunt faults including forward and reverse fault,it’s section and phase can be accurately identified within a half cycle time.The advantage of this scheme is to provide a major protection up to 99.5%of total line length using single end data and furthermore backup protection to the forward and reverse line sections.This routine protection system is properly discriminatory,rapid,robust,enormously reliable and incredibly responsive to isolate targeted fault.展开更多
Existing power grid fault diagnosis methods relyon manual experience to design diagnosis models, lack theability to extract fault knowledge, and are difficult to adaptto complex and changeable engineering sites. Consi...Existing power grid fault diagnosis methods relyon manual experience to design diagnosis models, lack theability to extract fault knowledge, and are difficult to adaptto complex and changeable engineering sites. Considering thissituation, this paper proposes a power grid fault diagnosismethod based on a deep pyramid convolutional neural networkfor the alarm information set. This approach uses the deepfeature extraction ability of the network to extract fault featureknowledge from alarm information texts and achieve end-to-endfault classification and fault device identification. First, a deeppyramid convolutional neural network model for extracting theoverall characteristics of fault events is constructed to identifyfault types. Second, a deep pyramidal convolutional neuralnetwork model for alarm information text is constructed, thetext description characteristics associated with alarm informationtexts are extracted, the key information corresponding to faultsin the alarm information set is identified, and suspicious faultydevices are selected. Then, a fault device identification strategythat integrates fault-type and time sequence priorities is proposedto identify faulty devices. Finally, the actual fault cases and thefault cases generated by the simulation are studied, and theresults verify the effectiveness and practicability of the methodpresented in this paper.展开更多
In order to increase robustness of the AERS (Aero-engine Rotor System) and to solve the problem of lacking fault samples in fault diagnosis and the difficulty in identifying early weak fault, we proposed a new method ...In order to increase robustness of the AERS (Aero-engine Rotor System) and to solve the problem of lacking fault samples in fault diagnosis and the difficulty in identifying early weak fault, we proposed a new method that it not only can identify the early fault of AERS but also it can do self-recovery monitoring of fault. Our method is based on the analysis of the early fault features on AERS, and it combined the SVM (Support Vector Machine) with the stochastic resonance theory and the wavelet packet decomposition and fault self-recovery. First, we zoom the early fault feature signals by using the stochastic resonance theory. Second, we extract the feature vectors of early fault using the multi-resolution analysis of the wavelet packet. Third, we input the feature vectors to a fault classifier, which can be used to identify the early fault of AERS and carry out self-recovery monitoring of fault. In this paper, features of early fault on AERS, the zoom of early fault characteristics, the extraction method of early fault characteristics, the construction of multi-fault classifier and way of fault self-recovery monitoring are studied. Results show that our method can effectively identify the early fault of AERS, especially for identifying of fault with small samples, and it can carry on self-recovery monitoring of fault.展开更多
文摘区分配电网中发生的单相接地故障类型,能够有针对性地制定故障检修策略,提升故障处置效率。配电自动化设备作为配电网故障快速辨识与处理的重要载体,对故障分类的原理及效果差异性较大,准确率无法满足电力系统工作需求,为此提出一种基于分类回归树与多核残差网络(classfication and regression tree and multi-core ResNet, CART-MRN)的树状结构故障类型识别方法。首先,建立树状故障分类结构,利用Fourier变换、经验模态分解(empirical mode decompsition, EMD)分解等方法提取故障点电压电流的多域故障特征;其次,结合特征分析与信息增益建立适应不同小电流接地系统的融合算法模型,并引入粒子群算法优化网络超参数;最后,通过现场录波数据验证与对比实验,证明该方法能快速、有效地完成单相接地故障分类识别,且更具有适应性。
基金support of Chhattisgarh Council of Science&Technology(CGCOST)Raipur for funding the project No.8062/CGCOST/MRP/13,dtd.27.12.2013.
文摘As it is crucial to protect the transmission line from inevitable faults consequences,intelligent scheme must be employed for immediate fault detection and classification.The application of Artificial Neural Network(ANN)to detect the fault,identify it’s section,and classify the fault on transmission lines with improved zone reach setting is presented in this article.The fundamental voltage and current magnitudes obtained through Discrete Fourier Transform(DFT)are specified as the inputs to the ANN.The relay is placed at section-2 which is the prime section to be protected.The ANN was trained and tested using diverse fault datasets;obtained from the simulation of different fault scenarios like different types of fault at varying fault inception angles,fault locations and fault resistances in a 400 kV,216 km power transmission network of CSEB between Korba-Bhilai of Chhattisgarh state using MATLAB.The simulation outcomes illustrated that the entire shunt faults including forward and reverse fault,it’s section and phase can be accurately identified within a half cycle time.The advantage of this scheme is to provide a major protection up to 99.5%of total line length using single end data and furthermore backup protection to the forward and reverse line sections.This routine protection system is properly discriminatory,rapid,robust,enormously reliable and incredibly responsive to isolate targeted fault.
基金the National Natural Science Foundation of China(51877079).
文摘Existing power grid fault diagnosis methods relyon manual experience to design diagnosis models, lack theability to extract fault knowledge, and are difficult to adaptto complex and changeable engineering sites. Considering thissituation, this paper proposes a power grid fault diagnosismethod based on a deep pyramid convolutional neural networkfor the alarm information set. This approach uses the deepfeature extraction ability of the network to extract fault featureknowledge from alarm information texts and achieve end-to-endfault classification and fault device identification. First, a deeppyramid convolutional neural network model for extracting theoverall characteristics of fault events is constructed to identifyfault types. Second, a deep pyramidal convolutional neuralnetwork model for alarm information text is constructed, thetext description characteristics associated with alarm informationtexts are extracted, the key information corresponding to faultsin the alarm information set is identified, and suspicious faultydevices are selected. Then, a fault device identification strategythat integrates fault-type and time sequence priorities is proposedto identify faulty devices. Finally, the actual fault cases and thefault cases generated by the simulation are studied, and theresults verify the effectiveness and practicability of the methodpresented in this paper.
文摘In order to increase robustness of the AERS (Aero-engine Rotor System) and to solve the problem of lacking fault samples in fault diagnosis and the difficulty in identifying early weak fault, we proposed a new method that it not only can identify the early fault of AERS but also it can do self-recovery monitoring of fault. Our method is based on the analysis of the early fault features on AERS, and it combined the SVM (Support Vector Machine) with the stochastic resonance theory and the wavelet packet decomposition and fault self-recovery. First, we zoom the early fault feature signals by using the stochastic resonance theory. Second, we extract the feature vectors of early fault using the multi-resolution analysis of the wavelet packet. Third, we input the feature vectors to a fault classifier, which can be used to identify the early fault of AERS and carry out self-recovery monitoring of fault. In this paper, features of early fault on AERS, the zoom of early fault characteristics, the extraction method of early fault characteristics, the construction of multi-fault classifier and way of fault self-recovery monitoring are studied. Results show that our method can effectively identify the early fault of AERS, especially for identifying of fault with small samples, and it can carry on self-recovery monitoring of fault.
