KEYWORDS: Power grids, Machine learning, Process modeling, Power supplies, Mathematical modeling, Education and training, Decision making, Performance modeling, Mathematical optimization, Computation time
Fault localization is more difficult during islanding black start in distribution grids containing distributed power sources due to the lack of local measurement devices and communication systems. This may lead to inability to accurately determine the fault location, thus affecting the restoration process. In this regard, this paper proposes a load partitioning self-recovery algorithm for islanded black-starting of distribution grids with distributed power sources. First, the distribution network black-start process is modeled as a Markov decision process, and the corresponding states, actions of intelligences and rewards are designed. On this basis, an algorithm for solving the self-healing problem of distribution network based on improved deep Q-network is proposed. The power system topology is connected to a graph convolutional network to capture the complex mechanism of power system black-start. The potential features generated in the grid topology are utilized to learn a control strategy for grid self-healing using deep reinforcement learning. Finally, the effectiveness and practicality of the proposed algorithm is verified by case studies.
The current conventional source-load intelligent tracking algorithm of distribution network mainly realizes active power control by calculating the regulation amount of output power, which leads to poor tracking effect due to the lack of intra-day scheduling optimization of distribution network. In this regard, the fuzzy prediction-based distribution grid source-load intelligent tracking algorithm is proposed. The multi-scenario technology is used to model the power output of distribution network power devices and the power load, and build the tracking scenario model; the intra-day optimization model is built, and the MPC control method is combined to realize the control of the power output and load situation of the distribution network; finally, the power fluctuation index is introduced to characterize the source-load tracking situation. In the experiments, the power control performance of the proposed method is verified. The experimental results show that the maximum power fluctuation value is low when the proposed method is used for source-load tracking, and it has a better power control performance.
The traditional energy-saving and load matching strategies for distribution networks have the problem of low accuracy in predicting the capacity of power equipment. Therefore, a new intelligent energy load matching strategy is proposed, which uses deep learning algorithms and K-means clustering algorithms to process and standardize power data, extract data features, and construct a capacity prediction model for energy storage devices in distribution networks. By finetuning the model structure network, the load condition of the distribution device is predicted, and the dynamic matching of source and load is achieved. Experimental verification shows that the matching effect of this strategy is superior to traditional methods, with a significant reduction in unit output and a high source load matching rate. This method has good application prospects in improving the energy utilization efficiency and reliability of distribution networks.
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