Abstract: A method and an apparatus for high-throughput preparation of a cement-based material are released and belong to the technical field of cement production. According to the technical solution of the present application, a single-mine, a single-phase or a unit components maintaining specific hydration hardening characteristics are used as structural units, and the method comprises the following steps that (1) the structural units are placed in storage tubes X1, X2, X3 . . . Xn respectively; (2) the materials in the storage pipes are put according to the composition design proportion of the cement-based material, and Y1, Y2, Y3 . . . Ym of mixed materials are prepared; (3) fully and uniformly mixing the Y1, Y2, Y3 . . . Ym of mixed materials through a uniform mixing device; and 4) respectively filling the Y1, Y2, Y3 . . . Ym of uniformly mixed materials into storage tanks Z1, Z2, Z3 . . . Zm to prepare m groups of cement-based material samples.

Abstract: The present disclosure provides a power unit, aiming to provide a four-in-one drive powertrain for an electric vehicle. Key points of the technical scheme include a shell body. One side of the shell body is formed with a chamber having a partition structure arranged in a middle part thereof, the partition structure separates the chamber into a control part and a drive part, and the other side of the shell body is provided with a deceleration part and a DC/DC part. The control part includes a containing cavity positioned on a left side of the chamber and integrally formed with the shell body to place a controller. The drive part includes an accommodating cavity positioned on a right side of the chamber and integrally formed with the shell body to install an electric motor.

Abstract: Systems and methods for generating a load model having composite load components for electric power systems are disclosed which include acquiring state information at a bus of the electric power system, acquiring a training event record, and generating the load model by sequentially executing steps of determining a final load component composition for a predetermined composite load model structure referencing to both the state information and the training event record, and determining a plurality of final load parameters corresponding to the final load component composition and the predetermined load model structure also referencing to both the state information and the training event record.

Abstract: A system comprising a first electroacoustic transducer connected to an interrogation unit and at least one second electroacoustic transducer connected to a resonator, wherein the first electroacoustic transducer and the second electroacoustic transducer form an acoustic channel and the second electroacoustic transducer forms with the resonator a passive cooperative target which, upon receiving an interrogation signal from the interrogation unit, transmits a response signal via the acoustic channel, and the interrogation signal has a higher energy than the response signal.

Abstract: A system with deep reinforcement learning based control determines optimal actions for major components in a commercial building to minimize operation costs while maximizing comprehensive comfort levels of occupants. An unsupervised deep Q-network method is introduced to handle the energy management problem by evaluating the influence of operation costs on comfort levels considering the environment factors at each time slot. An optimum control decision can be derived that targets both immediate and long-term goals, where exploration and exploitation are considered simultaneously.

Abstract: Systems and methods for processing measurement data in an electric power system include acquiring the measurement data by a phasor measurement unit (PMU) coupled to a line of the electric power system, and inputting a plurality of the measurement data within a predetermined time window into a K-nearest neighbor (KNN) for identifying bad data among the plurality of the measurement data, wherein when one of the plurality of measurement data contains a bad datum, the machine learning module sends the bad datum to a denoising autoencoder module for correcting the bad datum, wherein the denoising autoencoder module outputs a corrected part corresponding to the bad datum, and when one of the plurality of measurement data contains no bad datum, the machine learning module bypasses the denoising autoencoder module and outputs the one of the plurality of measurement data as an untouched part.

Abstract: A method to simulate a circuit includes receiving at least one circuit requirement and at least one Modular Multilevel Converter (MMC) parameter; determining an operating mode and switching states of an arm circuit; determining each capacitor current based on the switching states and an arm current; determining capacitor voltage and arm voltage; and generating an equivalent circuit model to simulate MMC-based HVDC systems and DC grids in a hybrid AC and DC power system.

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: State estimation in an electric power system includes acquiring electrical measurements from the electric power system at a reporting rate of the electrical measurements, processing the electrical measurements into sequence data including positive sequence data, processing positive sequence data by an observable state estimator to generate a plurality of estimated states including a plurality of estimated observable states, parameters of the observable state estimator being updated by a first training module in a first time thread, processing the plurality of estimated states by an unobservable state estimator to generate a plurality of estimated unobservable states, parameters of the unobservable state estimator being updated by a second training module in a second time thread independent of the first time thread, and outputting a plurality of final estimated states generated by concatenating the plurality of estimated observable states and the plurality of estimated unobservable states.

Abstract: A method is disclosed for identification of the mechanism of power system low-frequency oscillations and distinguish natural oscillations and forced oscillations using machine learning or neural network.

Abstract: Systems and methods manage electrical loads in a grid by applying Robust principal component analysis (R-PCA) to decompose annual load profiles into low-rank components and sparse components; extracting one or more predetermined features; constructing a similarity graph; selecting submodular cluster centers through the constructed similarity graph; determining a cluster assignment based on selected centers; and applying the clustering assignment for load analysis.

Abstract: A system with deep reinforcement learning based control determines optimal actions for major components in a commercial building to minimize operation costs while maximizing comprehensive comfort levels of occupants. An unsupervised deep Q-network method is introduced to handle the energy management problem by evaluating the influence of operation costs on comfort levels considering the environment factors at each time slot. An optimum control decision can be derived that targets both immediate and long-term goals, where exploration and exploitation are considered simultaneously.

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 method for managing a power load of a grid includes performing a statistic-based distribution estimation of a composite load model using static and dynamic models with Gibbs sampling; deriving a distribution estimation of load model coefficients; and controlling grid power based on a simulation, a prediction, a stability analysis or a reliability analysis with the load model coefficients.

Abstract: Systems and methods are disclosed for power management by estimating power contingency of a grid at a cloud control center; performing decentralized real-time measurement and making local decisions at one more computer controlled outlets connected to the grid; and aggregating distributed loads to provide emergency frequency support to the grid.

Abstract: The present disclosure provides a method for performing clustering on operation modes of a power system based on a sparse autoencoder. The method includes: obtaining related data of the power system; setting a training parameter, a number of hidden layers, and a number of neurons; training an autoencoder model using the related data and extracting a topological structure and a weight matrix from the model; performing cluster analysis to obtain a number of typical scenarios; and performing decoding to obtain original data at centers of respective scenarios. The present disclosure can achieve fast selection and dimensionality reduction of feature vectors representing operation modes of a power system. In view of this, the present disclosure provides a novel idea and method for selecting a feature vector representing an operation mode of a power system and generating a typical operation scenario.

Abstract: Systems and methods are disclosed to control the power grid frequency by capturing the frequency change using an extended Kalman filter method with the distributed smart outlet devices at the low-voltage distribution level; and locally control the relay that provides power to the appliance by comparing the captured frequency with the threshold sent from the cloud control center.

Abstract: A method for managing a power load of a grid includes performing a statistic-based distribution estimation of a composite load model using static and dynamic models with Gibbs sampling; deriving a distribution estimation of load model coefficients; and controlling grid power based on a simulation, a prediction, a stability analysis or a reliability analysis with the load model coefficients.

Abstract: Systems and method are disclosed for monitor long-term voltage stability in a power system by estimating post-contingency operating point(s) considering PV-PQ transitions based on the current operating point; predicting a sensitivity based Thevenin index (STI) for each contingency using the estimated operating condition; and determining a voltage stability assessment (VSA) considering N?1 contingency.