Editoral: Advancements in Power System Condition Monitoring, Fault Diagnosis and Environmental Compatibility

Feng Liu^1*Weicheng Yu¹Jun Chen¹Hui Wan²Shuai Yao³

• ¹Nanjing Tech University, Nanjing, China
• ²Department of Mechanical and Aerospace Engineering, University of Colorado, Colorado Springs, United States
• ³Cardiff University School of Engineering, Cardiff, United Kingdom

The growing demand for electricity and its rising consumption levels are imposing stricter demands on the safety and operational stability of power systems. Monitoring the operational status and diagnosing faults in these systems are crucial to maintaining the reliability, safety, and efficiency of power transmission and distribution. This process entails gathering and analyzing data from a variety of sensors and measuring devices installed across power system components. By evaluating equipment conditions in real time, potential failures can be identified early-before they escalate-and overall system performance can be enhanced. Over recent decades, advancements in data analytics, machine learning, and artificial intelligence have significantly improved the capabilities of power system monitoring and fault diagnosis. Integrating data from multiple sources enables more accurate detection of anomalies, which helps minimize operational interruptions, enhance grid reliability, and reduce maintenance expenses. At the same time, the adoption of these advanced monitoring and diagnostic technologies has also contributed to improved environmental compatibility of power systems. This Special Issue, entitled "Advancement in Power System Condition Monitor, Fault Diagnosis and Environmental Compatibility", collected 16 articles.The first area of the Special Issue is related with the power system condition monitor and optimization, which collected 8 articles. Dai et al. present a dynamic state awareness method for distribution networks. This novel solution leverages the core computational principle of the cubature Kalman filter (CKF) and establishes a robust noise statistical estimator (NSE). Li et al. presents a topology identification framework based on user profiling to navigate the challenges of scale and complexity in low-voltage distribution substations. Yao et al. in their paper developed a generative adversarial network (GAN) and convolutional neural network (CNN) based real-time processing technology for filtering images of underwater cables used in power systems. Lian et al. examined how the integration of distributed generators (DG) influences the structural characteristics of line and zero modes in distribution networks. The analysis specifically contrasted sequence network diagrams for DGs located upstream versus downstream of fault points. In addition, a novel method for identifying voltage sags in time-variant power distribution networks is introduced by Li et al. The proposed method is founded on the concept of inheritance, which is bifurcated into breadth and depth inheritance strategies. Yang et al. proposed a comprehensive weighting methodfor evaluating indicators, combining the analytic hierarchy process (AHP) and entropy weighting method, while considering the structure and operational status of the power system grid. With the VSSESP-DBP algorithm, Yan et al. gave a new approach for high-order harmonic monitoring, which can make great advance in accurately monitoring superharmonics Finally, Qin et al. studied monitoring modeling and analysis of HVDC transmission lines. They used the finite element method to study the real ±800 kV transmission line and builds a monitoring model.The second area is about the power system fault diagnosis. One of them is a review paper presented by Cao et al., in which they summarized the advancement in transformer fault diagnosis technologies, which include the traditional transformer fault diagnosis methods (polarization and depolarization current, sweep frequency-response analysis, dissolved gas analysis and vibration analysis techniques), and intelligent diagnosis methods (neural networks, deep learning, support vector machine, intelligent diagnosis and multi-source information fusion). Yu et al. investigated transformer partial discharge (PD) and given a method to determine the PD location in transformer based on gradient oil temperature, which gives technical supports for locating practical PD in transformer during operation. The last area is related with the environmental compatibility of the advanced power system technologies. Zhang et al. presented a research on a remaining life prediction technique for eco-friendly gas switchgear, which utilizes approximate dynamic programming. The findings indicate that their approach outperforms existing methods in both accuracy and computational efficiency, demonstrating its scalability and practical applicability. This research aims to systematically review and present the latest advancements in condition monitoring and fault diagnosis technologies of power system, as well as environmental compatibility. We firmly believe that the relevant findings will provide important references for the power system protection community, thereby contributing to enhanced safety and reliability of grid operation, promoting innovation in fault diagnosis technologies, and laying the foundation for building a sustainable power system in harmony with the environment.In the future, with the deepening of intelligent and digital transformation, power system condition monitoring and fault diagnosis will increasingly rely on multi-source data fusion and artificial intelligence technologies, gradually evolving from "perception and early warning" to "autonomous decision-making." Meanwhile, the deep integration of environmental protection and grid operation will become a key direction for the development of next-generation power systems, driving the power industry toward a cleaner, more efficient, and more resilient future.