Nanocomposites that combine porous materials and a continuous conductive skeleton as a sulfur host can improve the performance of lithium–sulfur (Li-S) batteries. Herein, carbon nanotubes (CNTs) anchoring small-size (~40 nm) N-doped porous carbon polyhedrons (S-NCPs/CNTs) are designed and synthesized via annealing the precursor of zeolitic imidazolate framework-8 grown in situ on CNTs (ZIF-8/CNTs). In the nanocomposite, the S-NCPs serve as an efficient host for immobilizing polysulfides through physical adsorption and chemical bonding, while the interleaved CNT networks offer an efficient charge transport environment. Moreover, the S-NCP/CNT composite with great features of a large specific surface area, high pore volume, and short electronic/ion diffusion depth not only demonstrates a high trapping capacity for soluble lithium polysulfides but also offers an efficient charge/mass transport environment, and an effective buffering of volume changes during charge and discharge. As a result, the Li-S batteries based on a S/S-NCP/CNT cathode deliver a high initial capacity of 1213.8 mAh g⁻¹ at a current rate of 0.2 C and a substantial capacity of 1114.2 mAh g⁻¹ after 100 cycles, corresponding to a high-capacity retention of 91.7%. This approach provides a practical research direction for the design of MOF-derived carbon materials in the application of high-performance Li–S batteries.

High-voltage equipment imposes increasingly stringent demands on polymeric insulating materials, particularly in terms of dielectric strength, space charge suppression, thermo-electrical stability, and interfacial reliability. Conventional polymers are prone to critical failure modes under high electric fields, including electrical treeing, partial discharge, interfacial degradation, and thermo-oxidative aging. This review systematically summarizes recent advances in polymer modification strategies specifically designed for high-voltage applications, covering nanofiller reinforcement, plasma surface engineering, and the development of self-healing insulating polymers. Multi-scale structural control and interface engineering, aligned with the specific requirements of high-voltage environments, have emerged as pivotal approaches to enhance insulation performance. Moreover, the integration of artificial intelligence-driven materials design, digital characterization, and application-oriented modeling holds significant promise for accelerating the development of next-generation high-voltage polymeric systems, thereby offering robust materials solutions for the reliable long-term operation of high-voltage equipment.

Aiming at the integrated energy microgrid, an important part of the energy internet, this paper constructs a multi-energy storage system optimization configuration model of the integrated energy microgrid in an independent mode, and proposes a configuration method that includes the rated power and capacity of the storage system and the heat storage system. The storage system model includes the estimation of battery life during heating and non-heating periods. The model takes economy as the index, and considers the relevant constraints of thermoelectric coupling of thermoelectric units, including thermal and electrical balance, unit climb, energy storage system and self-sufficiency probability, etc., and uses a The bacterial colony chemotaxis (BCC) algorithm model based on unit output and energy storage system power distribution strategy to solve the problem. The operation characteristics of cogeneration units equipped with energy storage system are discussed. The results show that the proposed multi-energy storage system configuration method has significant economic and environmental benefits in both heating and non-heating periods, and promotes the uptake of wind power.

Turn-to-turn short circuit faults in the primary winding of generator terminal voltage transformers can lead to erroneous operation of stator grounding protection systems. This paper analyzes the fault characteristics associated with such failures and derives formulas for the fault phase current and zero-sequence voltage during a turn-to-turn short circuit in the primary winding. A 3D finite element model of the generator terminal voltage transformer is established by using Altair Flux 3D, and the accuracy of the model is verified. Based on this model, simulation tests were conducted to investigate turn-to-turn short circuits in the primary winding. The results reveal that as the number of shorted turns increases, the voltage of the fault phase decreases continuously while the voltages of the other two phases increase. The current in the short-circuited phase rises significantly, accompanied by an increase in zero-sequence voltage. Visualizations of magnetic field parameters indicate that as the number of shorted turns increases, the magnetic induction magnitude of the fault phase rises steadily and approaches saturation, resulting in heightened magnetic field intensity near the shorted turns. This analysis of fault characteristics through simulation contributes to the advancement of fault diagnosis systems for generator terminal voltage transformers.

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.

Accurate fault-type identification and fault-distance estimation are essential for the reliable operation of transmission lines, yet their simultaneous realization remains challenging under nonlinear transient responses and limited labeled data. This study proposes an IVY-optimized generalized additive model (IVY-GAM) framework that combines physically guided feature construction, interpretable nonlinear modeling, and adaptive hyperparameter optimization. A 200 km two-terminal transmission-line model is established in MATLAB/Simulink to generate 385 fault samples covering 11 fault categories. Twelve features are constructed from phase-mode transformation, db4 wavelet decomposition, transient modulus maxima, three-phase voltage statistics, and double-ended wavefront-arrival indices. IVY is then used to optimize the key GAM parameters for fault identification and localization. Under a fixed train–validation–test partition, IVY-GAM achieves an identification accuracy of 99.987% and a localization $$R^2$$ of 0.99674, with an MAE of 3.16 km, an RMSE of 3.91 km, and a maximum error of 11.84 km. Stratified five-fold out-of-fold evaluation further yields an identification accuracy of 99.22% and a macro-averaged F1-score of 0.992, while the localization task obtains a mean MAE of 7.08 km and a mean $$R^2$$ of 0.9520. Feature-group ablation, permutation importance, and GAM main-effect analyses show that three-phase voltage means primarily support fault-type discrimination, whereas double-ended arrival indices dominate distance estimation and modal maxima provide complementary information. The proposed framework therefore offers a balanced solution for accurate, interpretable, and computationally efficient transmission-line fault diagnosis.