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This paper introduces a comprehensive performance evaluation algorithm explicitly designed for secondary equipment in substations, specifically targeting the relay protection system. In contrast to the current evaluation systems, this novel method navigates the complex internal interconnections and mechanisms inherent within secondary system equipment. Such complications have previously impeded the accuracy and breadth of evaluations, thereby limiting the degree of precision and innovation attainable within substations. The proposed approach effectively integrates the improved Analytic Hierarchy Process entropy weight (IAHP‐EW) method with the Learning Vector Quantization (LVQ) neural network. Initially, the IAHP‐EW method identified the comprehensive evaluation indicators and their corresponding weights for relay protection devices. Following weight allocation, these evaluation indicators are scrutinized and computed utilizing the multivariate regression analysis algorithm, resulting in performance evaluation outcomes for the relay protection system. These outcomes are subsequently classified and utilized in training the LVQ neural network, promoting the network's capacity to autonomously evaluate the performance status of the relay protection system. To corroborate the viability and effectiveness of this proposed performance evaluation and prediction algorithm, empirical operating data from a local substation is used. The results suggest a significant improvement in the evaluation accuracy of secondary equipment performance, indicating potential for practical application and a valuable contribution to the field through the introduction of a novel approach to performance assessment of substation relay protection systems. [ABSTRACT FROM AUTHOR]

In view of the frequent water intake blockage caused by marine biological invasion in many nuclear power plants, which affects the safety of cold source in nuclear power plants, a defense system in depth including marine biological identification, detection, early warning and hierarchical response is established through investigation and analysis of the causes of the event. Underwater acoustic high-resolution multi beam detection method is used to detect jellyfish, hairy shrimp and other marine organisms, The echo characteristics are obtained, the distribution density in sensitive sea area is analyzed, and the effective early warning judgment is given. Among them, detection and early warning is the front-end disposal measure, which can provide accurate and timely marine biological information for the subsequent hierarchical response. At the same time, it can replace the underwater inspection work of divers, reduce the risk of divers, reduce the risk of industrial safety, and improve the reliability of cold source.

Capacitive deionization is an electrochemical ion removal technique that has broad application prospects for the efficient desalination of brackish water. The current research mainly focuses on the improvement of the electrode material performance and device structure for capacitive deionization. However, the capacitive deionization process is subject to the coupling effect of multiple physical parameters inside the porous electrode, the dominant order and optimization of multiple physical parameters in the capacitive deionization process are needed to be further investigated. In this study, the ion transport in porous electrode of the capacitive deionization is investigated via numerical simulation and physical similarity analysis. Fourteen dimensional physical parameters involved in ion transport are unified into eight dimensionless parameters based on the nondimensionalization of Navier–Stokes and Poisson–Nernst–Planck equations, and the dimensionless parameters are summarized into four categories, including ion adsorption capability, ion transport characteristics, ion motion driving force, and ion adsorption equilibrium time. Analysis based on the numerical models with an irregular porous electrode structure shows that the dimensionless parameters can explain a series of similar physical phenomena in the capacitive deionization processes. Moreover, a parametric sensibility analysis is performed to reveal that the concentration and velocity of inlet solution are dominant factors for optimizing the salt adsorption capacity per unit area and the average salt removal rate. In addition, the inlet solution concentration and electrode potential are critical factors for improving the removal efficiency. The physical similarity and parametric sensitivity analysis in this study provide theoretical guidance for performance improvement in the practical application of capacitive deionization. [ABSTRACT FROM AUTHOR]