Assessment method of distribution network health level based on multivariate information

Abstract

In order to enhance self-monitoring and self-diagnosis capabilities in smart distribution networks, this paper proposes a method for assessing the health level of the network based on multivariate information. First, we construct an evaluation indicator system for the health of the smart distribution network by integrating the smart distribution network information system. Next, we utilize the improved back propagation (BP) neural network and multivariate indicator information to calculate the health indexes of both the grid layer and equipment. We then solve the health index of the equipment layer based on network topology and goal-oriented methodology. Furthermore, by utilizing the health information of both the equipment and grid layer, we apply fuzzy evaluation and Dempster-Shafer (D-S) evidence theory to obtain the health level of the distribution network. We provide a comprehensive evaluation of the overall health status of the smart distribution network. Finally, the proposed method is validated using data from a regional distribution network. The results demonstrate its effectiveness in improving the smart distribution networks’ overall health and stability by enabling more effective self-monitoring and self-diagnosis.

Hydropower unit health assessment based on a combination weighting and improved fuzzy comprehensive evaluation method

“…The main elements of the comprehensive condition assessment of hydropower units combine the structural system of the equipment with the distribution of measurement points, Extracting and constructing correlation indicators that reflect the operational health state, and integrating the performance indicators to make a holistic and global evaluation of the system health state. In recent years, as the concept of condition maintenance continues to advance, The power industry has successively established comprehensive condition detection systems, A wealth of measurement point information provides data support for the overall evaluation of the health state of the equipment, Condition assessment theory and techniques have also developed considerably, Commonly used condition evaluation methods include hierarchical analysis (Ge et al, 2020), cluster analysis (Hu et al, 2019), fuzzy comprehensive evaluation (Fang et al, 2016), grey system theory (Huang et al, 2022), etc. In terms of calculating the weights, hierarchical analysis, as a classic system analysis method, is widely used in risk assessment, resource allocation, equipment evaluation and other fields due to its clear structure, hierarchical nature and adaptability to complex systems (Zhang H. et al, 2020;Liu et al, 2022;Cai et al, 2023;Zhu et al, 2023). Yucesan and Kahraman (2019) use hierarchical analysis for risk assessment of hydropower plants to help ensure grid security and prevent economic losses.…”