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To enhance the flexibility of the home load optimization dispatching strategy and ensure the safe operation of the energy storage system, an optimization dispatching strategy for home energy management system (HEMS) based on time-of-use pricing and real-time energy storage system control is proposed. Firstly, a HEMS dispatching model is established with the constraints of dispatchable load and energy storage system working status, aiming to minimize the total cost for home users. This dispatching strategy controls the energy storage system charging and discharging behavior based on time-of-use pricing and real-time battery state of charge, which helps to reduce the power cost for home users and ensure the safe operation of the battery. Then, the optimal dispatching problem of HEMS is modeled as a Markov decision process (MDP) and solved by a deep reinforcement learning algorithm called soft actor-critic (SAC). The example results verify the effectiveness and superiority of the proposed method compared with other benchmark methods, which the system cost can be reduced by 15.87% at least.

Harmonics are important signatures in motor current indicating motor operating conditions and faults in motor maintenance. However, harmonics of motor faults are generally very weak and with frequency-dependent magnitudes, making it difficult to extract harmonics from noisy measurements, especially when the motor speed is varying. This paper introduces a novel weighted sparsity-driven and graph-model-based method to extract harmonics in the time-frequency domain. The method utilizes sparsity of harmonics in the frequency domain and their smoothness in the time domain due speed variation, and uses a frequency-dependent weight to leverage the magnitude of different harmonics. We formulate the harmonic signature extraction as a regularized optimization problem, and solve it us an alternating direction method of multipliers (ADMM). Results on experimental data show that the proposed method can effectively extract weak harmonic components in noisy measurements with improved performance over other existing methods.

In this paper, we propose an off-the-grid compressive sensing based method to detect broken-bar fault in squirrel-cage induction motors. To validate our method, we first build a dynamic model of squirrel-cage induction motor using multi-loop equivalent circuit to simulate motor current under fault conditions. We then develop an off-the-grid compressive sensing algorithm to extract the fault characteristic frequency from the simulated motor current by solving an atomic norm minimization problem. Comparing to other fault detection methods via motor current signature analysis, our method yields high resolution in extracting low-magnitude fault characteristic frequency with only 0.7 second measurements. Simulation results validate our proposed method.