Abstract: A method for making a spatio-temporal combined optimal scheduling strategy of a mobile energy storage (MES) system includes: inputting data of a power system, a traffic system, and an MES system; setting a time interval, and initializing a time interval counter; inputting real-time fault, traffic, and MES data; and performing rolling optimization and solving, and delivering regulation decision instructions of the MES system, till a fault is removed. The core of the present disclosure is to propose a spatio-temporal combined optimal model of the MES system to describe spatio-temporal coupling statuses of an energy storage vehicle, a traffic network, and a power distribution network. The present disclosure provides guidance for an optimal scheduling decision of the MES system by properly regulating a traveling path and charging and discharging power of the MES system, thereby supporting high-reliability operation of the power distribution network.

Abstract: A power transformer fault diagnosis method based on a stacked time series network, includes: collecting gas-in-oil data of a transformer in each substation; performing z-score normalization on the collected data to obtain a normalized matrix; dividing the normalized matrix into a training set and a test set in proportion; constructing a stacked time series network based on Xgboost and a bidirectional gated neural network, and inputting the training set and the test set to perform network training; and normalizing real-time collected data to obtain trainable data to predict a fault and update network parameters. The gas-in-oil data is predicted by using Xgboost and a gated neural network, obtains prediction data of a power transformer from two time series networks by using a meta learner, and obtains a fault diagnosis result of the transformer by using a Softmax layer. The neural network has accurate fault diagnosis performance and stable robustness.

Abstract: A method for diagnosing transformer fault based on a deep coupled dense convolutional neural network, includes: obtaining datasets of dissolved gas in oil of a transformer in normal and fault states; expanding the datasets by using an adaptive synthetic oversampling method; performing, in a form of a two-dimensional matrix, feature reconstruction on characteristic gas dissolved in the oil; building a transformer fault diagnosis model based on a deep coupled dense convolutional neural network; and dividing an expanded dataset into a training set and a test set, and taking the two-dimensional matrix as an input of the deep coupled dense convolutional neural network and a set label as an output to train the network to obtain a fault diagnosis model. The present disclosure can resolve a problem that a fault diagnosis accuracy rate of the transformer is low due to insufficient and unbalanced fault samples in the dissolved gas in the oil.

Abstract: A control method and system for a distribution network with distributed mobile energy storage systems is disclosed which relates to the power field. The present invention manages the multiple distributed mobile energy storage systems in the distribution network in a unified way, to improve the flexibility of the distribution network, and enable the distributed mobile energy storage systems to fully smooth new energy generation fluctuations, implement peak cut, facilitate grid auxiliary services, and improve the power quality. The control method includes: acquiring, by a sub-station coordination system, data of the distributed mobile energy storage systems; receiving, by a master-station dispatching system, the data of the distributed mobile energy storage systems, and obtaining external data; generating, by the master-station dispatching system, a control instruction; and controlling, by the sub-station coordination system, the distributed mobile energy storage systems.

Abstract: A control method and system for a distribution network with distributed mobile energy storage systems is disclosed which relates to the power field. The present invention manages the multiple distributed mobile energy storage systems in the distribution network in a unified way, to improve the flexibility of the distribution network, and enable the distributed mobile energy storage systems to fully smooth new energy generation fluctuations, implement peak cut, facilitate grid auxiliary services, and improve the power quality. The control method includes: acquiring, by a sub-station coordination system, data of the distributed mobile energy storage systems; receiving, by a master-station dispatching system, the data of the distributed mobile energy storage systems, and obtaining external data; generating, by the master-station dispatching system, a control instruction; and controlling, by the sub-station coordination system, the distributed mobile energy storage systems.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for acoustic echo cancellation and suppression are provided. An exemplary method comprises receiving a far-end acoustic signal and a corrupted near-end acoustic signal, wherein the corrupted near-end acoustic signal is generated based on (1) an echo of the far-end acoustic signal and (2) a near-end acoustic signal; feeding the far-end acoustic signal and the corrupted near-end acoustic signal into a neural network as an input to output a time-frequency (TF) mask that suppresses the echo and retains the near-end acoustic signal, and generating an enhanced version of the corrupted near-end acoustic signal by applying the obtained TF mask to the corrupted near-end acoustic signal.

Abstract: A method of echo path delay destination and echo cancellation is described in this disclosure. The method includes: obtaining a reference signal, a microphone signal, and a trained multi-task deep neural network, wherein the multi-task deep neural network comprises a first neural network and a second neural network; generating, using the first neural network of the multi-task deep neural network, an estimated echo path delay based on the reference signal and the microphone signal; updating the reference signal based on the estimated echo path delay; and generating, using the second neural network of the multi-task deep neural network, an enhanced microphone signal based on the microphone signal and the updated reference signal.

Abstract: A method of echo path delay destination and echo cancellation is described in this disclosure. The method includes: obtaining a reference signal, a microphone signal, and a trained multi-task deep neural network, wherein the multi-task deep neural network comprises a first neural network and a second neural network; generating, using the first neural network of the multi-task deep neural network, an estimated echo path delay based on the reference signal and the microphone signal; updating the reference signal based on the estimated echo path delay; and generating, using the second neural network of the multi-task deep neural network, an enhanced microphone signal based on the microphone signal and the updated reference signal.

Abstract: A method for making a spatio-temporal combined optimal scheduling strategy of a mobile energy storage (MES) system includes: inputting data of a power system, a traffic system, and an MES system; setting a time interval, and initializing a time interval counter; inputting real-time fault, traffic, and MES data; and performing rolling optimization and solving, and delivering regulation decision instructions of the MES system, till a fault is removed. The core of the present disclosure is to propose a spatio-temporal combined optimal model of the MES system to describe spatio-temporal coupling statuses of an energy storage vehicle, a traffic network, and a power distribution network. The present disclosure provides guidance for an optimal scheduling decision of the MES system by properly regulating a traveling path and charging and discharging power of the MES system, thereby supporting high-reliability operation of the power distribution network.

Abstract: Provided are a method and a device for identifying a distribution network topology error. The method includes: calculating a voltage of a coupling node to which each load belongs and obtaining a voltage sample space of coupling nodes to which all loads belong; calculating a current of a branch to which each load belongs and obtaining a current sample space of branches to which all loads belong; calculating a voltage correlation coefficient and a current correlation coefficient respectively between different loads according to the obtained voltage sample space and the current sample space; and completing verification and correction of the distribution network topology.

Abstract: The invention relates to a method for real-time scheduling of multi-energy complementary micro-grids based on a Rollout algorithm, which is technically characterized by comprising the following steps of: Step 1, setting up a moving-horizon Markov decision process model for the real-time scheduling of the multi-energy complementary micro-grids with random new-energy outputs, and establishing constraint conditions for the real-time scheduling; Step 2, establishing a target function of the real-time scheduling; Step 3, dividing a single complete scheduling cycle into a plurality of scheduling intervals, and finding one basic feasible solution meeting the constraint conditions for the real-time scheduling based on a greedy algorithm; and Step 4, finding a solution to the moving-horizon Markov decision process model for the real-time scheduling of the multi-energy complementary micro-grids by using the Rollout algorithm based on the basic feasible solution from Step 3.

Abstract: Provided are a method and a device for identifying a distribution network topology error. The method includes: calculating a voltage of a coupling node to which each load belongs and obtaining a voltage sample space of coupling nodes to which all loads belong; calculating a current of a branch to which each load belongs and obtaining a current sample space of branches to which all loads belong; calculating a voltage correlation coefficient and a current correlation coefficient respectively between different loads according to the obtained voltage sample space and the current sample space; and completing verification and correction of the distribution network topology.

Abstract: The invention relates to a method for real-time scheduling of multi-energy complementary micro-grids based on a Rollout algorithm, which is technically characterized by comprising the following steps of: Step 1, setting up a moving-horizon Markov decision process model for the real-time scheduling of the multi-energy complementary micro-grids with random new-energy outputs, and establishing constraint conditions for the real-time scheduling; Step 2, establishing a target function of the real-time scheduling; Step 3, dividing a single complete scheduling cycle into a plurality of scheduling intervals, and finding one basic feasible solution meeting the constraint conditions for the real-time scheduling based on a greedy algorithm; and Step 4, finding a solution to the moving-horizon Markov decision process model for the real-time scheduling of the multi-energy complementary micro-grids by using the Rollout algorithm based on the basic feasible solution from Step 3.

Abstract: A method of compressed sensing imaging includes acquiring a sparse digital image b, said image comprising a plurality of intensities corresponding to an I-dimensional grid of points, initializing points (x(k), y(k)), wherein x(k) is an element of a first expanded image x defined by b=R??1 x, wherein R is a Fourier transform matrix, ? is a wavelet transform matrix, y(k) is a point in ? ( ? i = 1 l ? ( ? i ? ? - 1 ? x ( k ) ) 2 ) 1 / 2 , ?i is a forward finite difference operator for a ith coordinate, and k is an iteration counter; calculating a first auxiliary variable s(k) from x ( k ) - ? 1 ( ?? ? ? n ? L n * ? y n ( k ) + ? ? ? R * ( R ? ? ? - 1 ? x ( k ) - b ) ) , wherein ?1,? are predetermined positive scalar constants, the sum is over all points n in x, and L* is an adjoint of operator L=(?1, . . .

Abstract: A method of compressed sensing imaging includes acquiring a sparse digital image b, said image comprising a plurality of intensities corresponding to an I-dimensional grid of points, initializing points (x(k), y(k)), wherein x(k) is an element of a first expanded image x defined by b=R??1 x, wherein R is a Fourier transform matrix, ? is a wavelet transform matrix, y(k) is a point in ? ( ? i = 1 l ? ( ? i ? ? - 1 ? x ( k ) ) 2 ) 1 / 2 , ?i is a forward finite difference operator for a ith coordinate, and k is an iteration counter; calculating a first auxiliary variable s(k) from x ( k ) - ? 1 ( ?? ? ? n ? L n * ? y n ( k ) + ? ? ? R * ( R ? ? ? - 1 ? x ( k ) - b ) ) , wherein ?1,? are predetermined positive scalar constants, the sum is over all points n in x, and L* is an adjoint of operator L=(?1, . . .