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With the successful application of artificial intelligence technology in various fields, deep reinforcement learning (DRL) algorithms have applied in active corrective control in the power system to improve accuracy and efficiency. However, the "black-box" nature of deep reinforcement learning models reduces their reliability in practical applications, making it difficult for operators to comprehend the decision-making mechanism. process of these models, thus undermining their credibility. In this paper, a DRL model is constructed based on the Markov decision process (MDP) to effectively address active corrective control issues in a 36-bus system. Furthermore, a feature importance explainability method is proposed, validating that the proposed feature importance-based explainability method enhances the transparency and reliability of the DRL model for active corrective control. [ABSTRACT FROM AUTHOR]

With the advancement of power systems, the integration of a substantial portion of renewable energy often leads to frequent voltage surges and increased fluctuations in distribution networks (DNs), significantly affecting the safety of DNs. Active distribution networks (ADNs) can address voltage issues arising from a high proportion of renewable energy by regulating distributed controllable resources. However, the conventional mathematical optimization-based approach to voltage reactive power control has certain limitations. It heavily depends on precise DN parameters, and its online implementation requires iterative solutions, resulting in prolonged computation time. In this study, we propose a Volt-VAR control (VVC) framework in ADNs based on multi-agent reinforcement learning (MARL). To simplify the control of photovoltaic (PV) inverters, the ADNs are initially divided into several distributed autonomous sub-networks based on the electrical distance of reactive voltage sensitivity. Subsequently, the Multi-Agent Soft Actor-Critic (MASAC) algorithm is employed to address the partitioned cooperative voltage control problem. During online deployment, the agents execute distributed cooperative control based on local observations. Comparative tests involving various methods are conducted on IEEE 33-bus and IEEE 141-bus medium-voltage DNs. The results demonstrate the effectiveness and versatility of this method in managing voltage fluctuations and mitigating reactive power loss. [ABSTRACT FROM AUTHOR]

The blast furnace tuyere is a crucial component for blast furnace production. The frequent damage of the copper tuyere impedes the smooth operation of the blast furnace. Welding a wear‐resistant layer on the tuyere can extend its service life, but it still falls short of the expectation in a large blast furnace. In this article, it is aimed to elucidate the erosion mechanism of the wear‐resistant layer. A scanning electron microscope, an energy dispersive spectrometer, and an optical microscope are employed to examine the morphology, composition, and metallographic structure of the samples obtained from a 5800 m3 blast furnace tuyere in China, as well as to calculate the frequency of daily slag crust shedding from cooling staves. The results indicate that the main cause of the erosion of the wear‐resistant layer is the shedding of slag crust due to the temperature fluctuation of the cooling staves. Finally, the erosion mechanism of the wear‐resistant layer is proposed. It is suggested that the formation and propagation of cracks caused by erosion of slag and hot metal are the primary failure mode of the wear‐resistant layer. [ABSTRACT FROM AUTHOR]