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: 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 system for testing a Nakagami fading channel and a verification method thereof are provided. The testing system includes a signal generator, a Nakagami fading channel simulator, and a computer. The signal generator is used to output a sine wave signal whose frequency is f and transmit the sine wave signal to the Nakagami fading channel simulator and the computer. The Nakagami fading channel simulator is used to generate a Nakagami fading channel. The computer is used to perform data processing and analysis. In the verification method, time domain fading characteristics, first-order statistics characteristics, and second-order statistics characteristics of the Nakagami fading channel are respectively verified. Verifying the time domain fading characteristics is verifying a waveform fluctuation rate and a fluctuation range on a time domain under different Nakagami fading factors.

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: Systems and methods for autonomous line flow control in an electric power system are disclosed which includes acquiring state information at a line in the electric power system at a first time step, obtaining a flow data of the line at a next time step based on the acquired state information, generating an early warning signal when the obtained flow data is higher than a predetermined threshold, activating a deep reinforcement learning (DRL) agent to generate an action using a DRL algorithm based on the state information, and executing the action to adjust a topology of the electric power system.

Abstract: Systems and methods for autonomous voltage control in an electric power system are disclosed which include acquiring state information at buses of the electric power system, detecting a state violation from the state information, generating a first action setting based on the state violation using a deep reinforcement learning (DRL) algorithm by a first artificial intelligent (AI) agent assigned to a first region of the electric power system where the state violation occurs, and maintaining a second action setting by a second AI agent assigned to a second region of the electric power system where no substantial state violation is detected.

Abstract: Systems and methods are disclosed to manage a microgrid with a hybrid energy storage system (HESS) includes deriving a dynamic model of a bidirectional-power-converter (BPC)-interfaced HESS; applying a first neural network (NN) to estimate a system dynamic; and applying a second NN to calculate an optimal control input for the HESS through online learning based on the estimated system dynamics.

Abstract: The disclosure discloses a transformer DGA data prediction method based on multi-dimensional time sequence frame convolution LSTM, including the steps: firstly, collecting and dividing monitoring information of dissolved gas in transformer substation oil into a test set and a verification set; secondly, extracting characteristic parameters by adopting a non-coding ratio method, deleting data which are basically kept unchanged, and performing normalization, noise superposition etc.

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: 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 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: Systems and methods are disclosed for controlling a power system by formulating a voltage control problem using a deep reinforcement learning (DRL) method with a control objective of training a DRL-agent to regulate the bus voltages of a power grid within a predefined zone before and after a disturbance; performing offline training with historical data to train the DRL agent; performing online retraining of the DRL agent using live PMU data; and providing autonomous control of the power system below a sub-second after training.

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: A Deep Belief Network (DBN) feature extraction-based analogue circuit fault diagnosis method comprises the following steps: a time-domain response signal of a tested analogue circuit is acquired, where the acquired time-domain response signal is an output voltage signal of the tested analogue circuit; DBN-based feature extraction is performed on the acquired voltage signal, wherein learning rates of restricted Boltzmann machines in a DBN are optimized and acquired by virtue of a quantum-behaved particle swarm optimization (QPSO); a support vector machine (SVM)-based fault diagnosis model is constructed, wherein a penalty factor and a width factor of an SVM are optimized and acquired by virtue of the QPSO; and feature data of test data are input into the SVM-based fault diagnosis model, and a fault diagnosis result is output, where the feature data of the test data is generated by performing the DBN-based feature extraction on the test data.

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

Abstract: Systems and methods are disclosed to manage a microgrid with a hybrid energy storage system (HESS) includes deriving a dynamic model of a bidirectional-power-converter (BPC)-interfaced HESS; applying a first neural network (NN) to estimate a system dynamic; and applying a second NN to calculate an optimal control input for the HESS through online learning based on the estimated system dynamics.

Abstract: A space vector pulse width modulation (SVPWM) method for suppressing a common-mode voltage of a multiphase motor includes the following steps: (1) dividing all basic vectors of the multiphase motor into q types, and selecting therefrom x types having equal common-mode voltage magnitude of which an absolute value is smallest; (2) for each type in the x types of basic vectors, structuring y classes of auxiliary vectors according to an optimization model; (3) synthesizing reference vectors by virtue of the auxiliary vectors to obtain functioning time of basic vectors functioning in each switching period; and (4) obtaining an optimal functioning sequence of the basic vectors functioning in each switching period with fewest switching operations of a converter as a purpose. The present invention may effectively suppress a magnitude and frequency of the common-mode voltage of the multiphase motor without increasing calculation complexity or reducing other performance indexes.

Abstract: A system for testing a Nakagami fading channel and a verification method thereof are provided. The testing system includes a signal generator, a Nakagami fading channel simulator, and a computer. The signal generator is used to output a sine wave signal whose frequency is f and transmit the sine wave signal to the Nakagami fading channel simulator and the computer. The Nakagami fading channel simulator is used to generate a Nakagami fading channel. The computer is used to perform data processing and analysis. In the verification method, time domain fading characteristics, first-order statistics characteristics, and second-order statistics characteristics of the Nakagami fading channel are respectively verified. Verifying the time domain fading characteristics is verifying a waveform fluctuation rate and a fluctuation range on a time domain under different Nakagami fading factors.

Abstract: A space vector pulse width modulation (SVPWM) method for suppressing a common-mode voltage of a multiphase motor includes the following steps: (1) dividing all basic vectors of the multiphase motor into q types, and selecting therefrom x types having equal common-mode voltage magnitude of which an absolute value is smallest; (2) for each type in the x types of basic vectors, structuring y classes of auxiliary vectors according to an optimization model; (3) synthesizing reference vectors by virtue of the auxiliary vectors to obtain functioning time of basic vectors functioning in each switching period; and (4) obtaining an optimal functioning sequence of the basic vectors functioning in each switching period with fewest switching operations of a converter as a purpose.