With the continuous increase in user-side flexible controllable resources connected into a distribution system, the components of electrical load become too diverse and difficult to be accuracy forecasted. A short-term load forecast method that integrates variational modal decomposition (VMD), gated recurrent unit (GRU) and time convolutional network (TCN) into a hybrid network is proposed in this paper. Firstly, original electrical load sequence data with noise are decomposed into intrinsic IMF components with different frequencies and amplitudes based on the VMD method. Secondly, a combined load forecasting method based on the GRU and TCN network is proposed for the high and low-frequency load subsequent signals, respectively. Finally, the high and low-frequency signals forecasting results of the GRU and TCN network are rebuilt for the final load forecasting. The experiment results based on actual operation data (data set 1) and simulation data (data set 2), which show that the proposed method can reduce the forecasting error by 36.20% and 10.8%, respectively, in comparison with VMD-GRU. The reliability and accuracy of the proposed method is verified through the comparison with other methods such as LSTM, Prophet and XG Boost.
Since the attitude angle between the gripper and the roller of the robot arm is not adjusted during the autonomous obstacle surmounting process of the substation inspection robot, and the obstacle surmounting route found is not optimal, the path of the substation inspection robot for autonomous obstacle surmounting is not the optimal result. Therefore, the autonomous obstacle surmounting method of the substation inspection robot based on locust optimization algorithm is proposed. After calculating the vertical distance between the overhead line and the working ground and adjusting the attitude angle between the gripper and the roller of the manipulator, a two-dimensional Gaussian scale space is constructed to extract the key feature points of the obstacle image in the space. According to the feature extraction results, combined with the locust optimization algorithm, the autonomous obstacle path of the substation inspection robot is planned to obtain the most superior obstacle path. In the experiment, the obstacle surmounting effect of the proposed method is verified. The experimental results show that the error rate of the optimal solution is low, and the path planning performance is good when the proposed method is used for robot autonomous obstacle crossing control.
Energy storage batteries work under constantly changing operating conditions such as temperature, depth of discharge, and discharge rate, which will lead to serious energy loss and low utilization rate of the battery, resulting in a sharp attenuation of life, and the battery often fails before the end of its service life. Battery replacement leads to increasing energy storage costs, and in order to ensure the efficient, safe and reliable operation of batteries under complex working conditions of the power grid, effective management of batteries is required. The battery model is the theoretical basis of the management algorithm, and life prediction is the key technology to ensure battery safety. In view of the above practical application requirements, this paper studies the dynamic modeling of energy storage battery life based on multi-parameter information, and the results show that the proposed life model accurately reflects the battery life under multi-parameter information.
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