Abstract: A transformer fault diagnosis method and system using induced ordered weighted evidence reasoning is provided. The method includes the following steps. A typical data sample of transformer sweep frequency response analysis is loaded and a diagnostic label is set as an identification framework. Test data of a device to be diagnosed is loaded. Basic probability assignment is calculated and a reliability decision matrix is constructed. An induced ordered weighted averaging operator and its induction vector are calculated according to a sample source of the data. An index weight vector is calculated. All evidence is fused by the induced ordered weighted evidence theory and reliability of comprehensive evaluation is calculated, so as to determine a diagnosis result. The disclosure realizes fault identification, fault type distinction and fault position of power equipment by interpreting detection waveforms.

Abstract: A method and a system for fault diagnosis with small samples of power equipment based on virtual and real twin spaces are disclosed, which belong to the field of fault diagnosis of power equipment. The method includes: test samples containing different locations, types and severity levels of fault of power equipment are acquired to form a real physical space; a virtual mirror space is acquired by simulation according to a simulation model of the equipment to be diagnosed; the training set in the real physical space is spatially integrated with the sample set in the virtual mirror space to obtain a training sample set in the twin spaces; the training sample set in the twin spaces serves as the supplement to the training set in the real physical space, and the fault type and fault location serve as diagnostic labels to be input to the deep neural network for training.

Abstract: A method and a system of predicting an electric system load based on wavelet noise reduction and empirical mode decomposition-autoregressive integrated moving average (EMD-ARIMA) are provided. The method and the system belong to a field of electric system load prediction. The method includes the following steps. Raw load data of an electric system is obtained first. Next, noise reduction processing is performed on the load data through wavelet analysis. The noise-reduced load data is further processed through an EMD method to obtain different load components. Finally, ARIMA models corresponding to the different load components are built. Further, the ARIMA models are optimized through an Akaike information criterion (AIC) and a Bayesian information criterion (BIC). The load components obtained through predicting the different ARIMA models are reconstructed to obtain a final prediction result, and accuracy of load prediction is therefore effectively improved.

Abstract: The invention discloses a failure diagnosis method for a power transformer winding based on a GSMallat-NIN-CNN network.

Abstract: The disclosure provides a deep parallel fault diagnosis method and system for dissolved gas in transformer oil, which relate to the field of power transformer fault diagnosis. The deep parallel fault diagnosis method includes: collecting monitoring information of dissolved gas in each transformer substation and performing a normalizing processing on the data; using the dissolved gas in the oil to build feature parameters as the input of the LSTM diagnosis model, and performing image processing on the data as the input of the CNN diagnosis model; building the LSTM diagnosis model and the CNN diagnosis model, respectively, and using the data set to train and verify the diagnosis models according to the proportion; and using the DS evidence theory calculation to perform a deep parallel fusion of the outputs of the softmax layers of the two deep learning models.

Abstract: A transformer failure identification and location diagnosis method based on a multi-stage transfer learning theory is provided. Simulation is set up first, a winding parameter of a transformer to be tested is calculated, and a winding equivalent circuit is accordingly built. Different failures are configured for the equivalent circuit, and simulation is performed to obtain a large number of sample data sets. A sweep frequency response test is performed on the transformer to be tested, and detection data sets are obtained. Initial network training is performed on simulation data sets by using the transfer learning method, and the detection data sets are further trained accordingly. A failure support matrix obtained through diagnosis is finally fused. The multi-stage transfer learning theory is provided by the disclosure.

Abstract: A method includes steps: 1) obtaining monitoring information of different monitoring points in normal state of power equipment; 2) setting faults and obtaining monitoring information of different fault types, positions, monitoring points of the equipment; 3) taking the monitoring information obtained in steps 1) to 2) as training dataset, taking the fault types and positions as labels, inputting the training dataset and the labels to deep CNN for training; 4) collecting monitoring data, performing verification and classification using step 3), obtaining probability values corresponding to each of the labels; 5) taking classification results of different labels as basic probability assignment values, with respect to a monitoring system composed of multiple sensors, taking different sensors as different evidences for decision fusion, performing fusion processing using the DS evidence theory to obtain fault diagnosis result.

Abstract: The disclosure provides an internal thermal fault diagnosing method for an oil-immersed transformer based on DCNN and image segmentation, including: 1) dividing an internal area of a transformer, and using fault areas and normal status as labels of DCNN; 2) through lattice Boltzmann simulation, randomly obtaining multiple feature images of the internal temperature field distribution of the transformer under normal and various fault state modes, and the fault area serves as a label to form the underlying training sample set; 3) obtaining historical monitoring information of the infrared camera or temperature sensor, and forming its corresponding fault diagnosis results into labels; 4) combining all monitoring information contained in each sample into one image, and then extracting the same monitoring information from the samples in the sample set to form a new image; 5) segmenting image sample and then inputting the same into DCNN for training to obtain diagnosis results.

Abstract: A fault diagnosis method for power electronic circuits based on optimizing a deep belief network, including steps. (1) Use RT-LAB hardware-in-the-loop simulator to set up fault experiments and collect DC-link output voltage signals in different fault types. (2) Use empirical mode decomposition to extract the intrinsic function components of the output voltage signal and its envelope spectrum and calculate various statistical features to construct the original fault feature data set. (3) Based on the feature selection method of extreme learning machine, remove the redundancy and interference features, as fault sensitive feature data set. (4) Divide the fault sensitive feature set into training samples and test samples, and primitively determine the structure of the deep belief network. (5) Use the crow search algorithm to optimize the deep belief network. (6) Obtain the fault diagnosis result.

Abstract: A method and a system for power equipment diagnosis based on windowed feature and Hilbert visualization are provided, which belong to the field of power equipment fault diagnosis. The method includes: obtaining an original data set of monitoring data containing power equipment fault features; introducing windowed feature calculation considering logarithmic constraints to process data to obtain a feature sequence; using Hilbert visualization method for further processing to obtain a Hilbert image data set used to train and verify a convolutional neural network; and finally directly inputting newly obtained test sample data after windowed feature calculation and Hilbert visualization processing into the trained network for fault diagnosis and location.

Abstract: The disclosure discloses a power transformer winding fault positioning method based on deep convolutional neural network integrated with visual identification, including 1) a winding equivalent circuit is established, and a transfer function thereof is calculated; 2) a sine wave excitation source is set at one end of the power transformer winding to obtain the amplitude-frequency characteristic curve of each winding node; 3) circuits under various fault statuses are subjected to scanning frequency response analysis to extract amplitude-frequency characteristics; 4) a feature matrix is established based on the obtained amplitude-frequency characteristics; 5) scanning frequency response analysis is performed on the diagnosed power transformer to form a feature matrix; 6) the feature matrix is converted into an image, simulation and historical detection data are used as a training set, and a deep convolutional neural network is input for training; 7) diagnosed transformer is subjected to fault classification and

Abstract: A transformer fault diagnosis method and system using induced ordered weighted evidence reasoning is provided. The method includes the following steps. A typical data sample of transformer sweep frequency response analysis is loaded and a diagnostic label is set as an identification framework. Test data of a device to be diagnosed is loaded. Basic probability assignment is calculated and a reliability decision matrix is constructed. An induced ordered weighted averaging operator and its induction vector are calculated according to a sample source of the data. An index weight vector is calculated. All evidence is fused by the induced ordered weighted evidence theory and reliability of comprehensive evaluation is calculated, so as to determine a diagnosis result. The disclosure realizes fault identification, fault type distinction and fault position of power equipment by interpreting detection waveforms.

Abstract: A method for diagnosing an open-circuit fault of a switching transistor of a single-phase half-bridge five-level inverter is provided. It includes the following steps. A semi-physical experiment platform with a DSP controller and an RT-LAB real-time simulator as its core constructed, and an output side voltage is selected as a fault signal variable. Empirical mode decomposition is used to extract a fault feature vector, and then a HHT time-frequency diagram of the fault feature vector is extracted, a voltage signal is converted into spectrum data, and time-frequency diagram fuzzy sets corresponding to different fault types are obtained. Fusion of the time-frequency diagram fuzzy sets of the same fault type is performed to obtain a fusion image that contains more fault features. The fusion images corresponding to all fault types are inputted into the deep convolutional neural network for training and testing, and a fault diagnosis result is obtained.

Abstract: Systems and methods are disclosed for control voltage profiles, line flows and transmission losses of a power grid by forming an autonomous multi-objective control model with one or more neural networks as a Deep Reinforcement Learning (DRL) agent; training the DRL agent to provide data-driven, real-time and autonomous grid control strategies; and coordinating and optimizing power controllers to regulate voltage profiles, line flows and transmission losses in the power grid with a Markov decision process (MDP) operating with reinforcement learning to control problems in dynamic and stochastic environments.

Abstract: A transformer fault diagnosis and positioning system based on a digital twin is provided and includes the following. A communication sensing module, which is configured to transmit bottom-level monitoring data obtained from a device entity to a system support module. The system support module, which is configured to receive and preprocess bottom-level monitoring data, and store various data, models and expert systems. A dynamic twin module, which is configured to analyze a multi-dimensional probability status of a device fault, construct a dynamic degradation model of different health states, and realize model correction through human-computer interaction, real-time measurement, and dynamic update subsequently. A decision-making diagnosis module, which is configured to construct a digital twin of data to be diagnosed, to realize diagnosis and positioning of the health state.

Abstract: A method and a system of predicting an electric system load based on wavelet noise reduction and empirical mode decomposition-autoregressive integrated moving average (EMD-ARIMA) are provided. The method and the system belong to a field of electric system load prediction. The method includes the following steps. Raw load data of an electric system is obtained first. Next, noise reduction processing is performed on the load data through wavelet analysis. The noise-reduced load data is further processed through an EMD method to obtain different load components. Finally, ARIMA models corresponding to the different load components are built. Further, the ARIMA models are optimized through an Akaike information criterion (AIC) and a Bayesian information criterion (BIC). The load components obtained through predicting the different ARIMA models are reconstructed to obtain a final prediction result, and accuracy of load prediction is therefore effectively improved.

Abstract: The disclosure provides a silicon photovoltaic cell scanning eddy current thermography detection platform and a defect classification method. The technical solution adopted by the disclosure is: firstly, fixing the position of the electromagnetic inductive coil and the thermal imager, and using the main conveyor belt to carry the silicon photovoltaic cell to move forward on the production line to form a scanning eddy current heating of the silicon photovoltaic cell. Secondly, the defect temperature information is obtained through the thermal imager in terms of thermal image sequences. Thirdly, the feature extraction algorithms are used to extract the silicon photovoltaic cell defect features. Finally, the image classification algorithms are used to classify the silicon photovoltaic cell defects, and the sorting conveyor belts are used to realize the automatic sorting of silicon photovoltaic cells with different types of defects on the production line.

Abstract: The invention discloses a failure diagnosis method for a power transformer winding based on a GSMallat-NIN-CNN network.

Abstract: A method and a system for power equipment diagnosis based on windowed feature and Hilbert visualization are provided, which belong to the field of power equipment fault diagnosis. The method includes: obtaining an original data set of monitoring data containing power equipment fault features; introducing windowed feature calculation considering logarithmic constraints to process data to obtain a feature sequence; using Hilbert visualization method for further processing to obtain a Hilbert image data set used to train and verify a convolutional neural network; and finally directly inputting newly obtained test sample data after windowed feature calculation and Hilbert visualization processing into the trained network for fault diagnosis and location.

Abstract: Systems and methods are disclosed for control voltage profiles, line flows and transmission losses of a power grid by forming an autonomous multi-objective control model with one or more neural networks as a Deep Reinforcement Learning (DRL) agent; training the DRL agent to provide data-driven, real-time and autonomous grid control strategies; and coordinating and optimizing power controllers to regulate voltage profiles, line flows and transmission losses in the power grid with a Markov decision process (MDP) operating with reinforcement learning to control problems in dynamic and stochastic environments.