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Introduction. This study investigates substation failures caused by lightning strikes, which significantly affect operational reliability. Given the random nature of lightning strikes, a robust statistical approach is essential for accurately assessing their effects. Method. The research develops a comprehensive procedure to analyse the random distribution of non-vertical lightning strikes on transmission lines using the Monte Carlo method, a widely recognized statistical simulation technique. The goal of this work is to evaluate the performance of air-insulated substations under various lightning strike scenarios affecting the connected transmission lines. This is assessed in terms of mean time between failures (MTBF), determined by the basic insulation level of the equipment. The study incorporates both vertical and non-vertical strikes to address a critical gap in the literature, offering practical insights to enhance the reliability and safety of air-insulated substations. By considering the angle of lightning strikes, the study improves the accuracy of evaluating lightning performance using precise modelling of system components. Results. MATLAB and EMTP software were used to simulate and analyse the substation’s response to lightning-induced surges at various strike angles. The results are more representative of real-world conditions and reveal that non-vertical lightning strikes significantly reduce MTBF, underscoring the importance of advanced protective measures. Practical value. The findings highlight the necessity of accounting for the angle of lightning strikes when assessing substation reliability. [ABSTRACT FROM AUTHOR]

The accurate recognition of the connection relationship of the substation secondary wiring drawings is a key link in the operation and maintenance of the power system, but the traditional manual recognition method has the problems of time-consuming, labor-intensive and error-prone. Therefore, this paper proposes an intelligent recognition algorithm for substation secondary wiring drawings based on D-LLE algorithm. The composition of the substation secondary layout diagram is analyzed, and the connection relationship feature image is preprocessed, including smoothing and contrast enhancement, to improve the image quality. D-LLE algorithm (Denoised Locally Linear Embedding) is used to effectively extract the connection features in the drawings, which improves the feature expression ability and recognition accuracy. The improved Canny operator is used to extract the feature edge of the connection relationship, and the feature recognition algorithm is used to realize the intelligent recognition of the connection relationship. The experimental results show that the completeness of connection information extraction of substation secondary wiring drawings obtained by the proposed algorithm is between 98.56% and 99.98%, and the mean absolute error of intelligent recognition drawings connection relationship is always lower than 0.01. It shows that the proposed algorithm can accurately and quickly identify the connection relationship in the substation secondary wiring drawings, significantly improve the recognition efficiency, and has strong robustness and versatility, which can provide strong support for the intelligent operation and maintenance of the power system.Article Highlights: The composition of substation secondary configuration diagram is analyzed, and the pretreatment technology is proposed. The D-LLE algorithm is introduced to extract features, which improves the recognition accuracy of connection relations. The Canny operator is improved for edge extraction, and intelligent recognition of connection relationship is realized. [ABSTRACT FROM AUTHOR]