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Advanced Control and Monitoring of High Voltage Power Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F6: High Voltage".

Deadline for manuscript submissions: 10 August 2026 | Viewed by 4891

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Special Issue Editors

Dr. Su Zhao

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Guest Editor

Department of Electric Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: environmentally friendly insulating gas; high-voltage insulation materials; novle contact material
Special Issues, Collections and Topics in MDPI journals

Dr. Yalin Wang

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Guest Editor

1. Department of Electrical Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2. Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, Shanghai 200240, China
Interests: charge transport of insulating materials; package insulation and reliability
Special Issues, Collections and Topics in MDPI journals

Dr. Geng Chen

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Guest Editor

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Interests: dielectrics materials; gas insulation; gas-solid interface charging; high voltage engineering

Dr. Kangning Wu

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Guest Editor

State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, China
Interests: high voltage insulation; dielectric physics

Special Issue Information

Dear Colleagues,

The Guest Editors are inviting submissions to a Special Issue of Energies focused on "Advanced Control and Monitoring of High Voltage Power Systems". High-voltage power systems form the backbone of modern electrical infrastructure, enabling efficient long-distance transmission and integration of renewable energy sources. As power grids evolve with increasing complexity and decentralization, advanced control strategies and real-time monitoring technologies are critical to ensure grid stability, fault resilience, and operational efficiency. Emerging challenges include the degradation of aging power equipment, insulation failure risks in cables and transformers, and the demand for sustainable grid infrastructure compatible with carbon neutrality goals.

This Special Issue aims to address these challenges by exploring cutting-edge methodologies in equipment condition assessment, insulation material innovation, and green power technologies, alongside control theory and intelligent monitoring systems. Topics of interest include, but are not limited to:

AI/ML applications for fault prediction and diagnostics
Digital twin-driven condition monitoring of HV equipment
Advanced sensor technologies for HV power system detection
Evaluation of aging and lifespan of insulation materials in HV equipment
Advanced design and testing of eco-friendly insulation materials
Green power equipment design for HVDC and offshore wind integration
Adaptive protection schemes for hybrid AC/DC grids

We welcome original research articles, case studies, and comprehensive reviews that advance the state-of-the-art in this critical field.

Dr. Su Zhao
Dr. Yalin Wang
Dr. Geng Chen
Dr. Kangning Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

high-voltage power systems
green power equipment
insulation condition monitoring
digital twin for grid assets
sustainable grid infrastructure
aging and degradation
lifespan analysis

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Published Papers (6 papers)

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Research

19 pages, 3188 KB

Open AccessArticle

Investigation of Fatigue Failure and Electrical Insulation Properties of Glass Fiber-Reinforced Epoxy Resin (EPGF) Composites Under Different Temperatures

by Bowen Xu, Jinghan Wang, Chenglu Wang and Chen Cao

Energies 2026, 19(11), 2497; https://doi.org/10.3390/en19112497 - 22 May 2026

Abstract

This study investigates the influence of temperature on the bending properties, fatigue life, and breakdown voltage of glass fiber/epoxy composites (EPGF). The three-point bending tests were conducted at room temperature (RT) and 60 °C, and the bending fatigue tests were carried out under three displacement amplitudes (0.80, 0.75, 0.70). At the same time, fatigue life prediction was conducted using the Weibull distribution fitting, microscopic structure analysis by scanning electron microscopy (SEM), and breakdown voltage tests in accordance with the GB/T1408-2006 standard. The results show that at 60 °C, the ultimate bending strength and flexural modulus of EPGF decreased by 52.67% and 65.45%, respectively. At high displacement amplitudes (S = 0.80, 0.75), 60 °C leads to a sharp rise in data dispersion with the coefficient of variation (CV) surging by 1.56 and 2.32 times separately. S and temperature exert a significant synergistic degradation effect on fatigue life, and the two-parameter Weibull distribution (R² > 0.85) can well characterize the fatigue life of EPGF. In terms of dielectric properties, 60 °C reduces the initial breakdown voltage of EPGF by 4.23% (p < 0.05). Fatigue damage causes a continuous drop in breakdown voltage. At RT with 80% damage, the reduction rate increases from 16.28% to 26.95% as S rises, showing a synergistic characteristic between amplitude and fatigue damage. Moreover, 60 °C only affects the initial breakdown voltage and has no significant effect on the fatigue-induced decrease in breakdown voltage. SEM observations indicate that 60 °C induces matrix cracking, fiber curling and interfacial debonding in EPGF. This study provides key experimental data and theoretical support for the fatigue life prediction and insulation performance evaluation of EPGF under different temperature fatigue conditions. Full article

(This article belongs to the Special Issue Advanced Control and Monitoring of High Voltage Power Systems)

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14 pages, 9048 KB

Open AccessArticle

Evolution and Failure Mechanism of Moisture Absorption, Mechanical, and Electrical Insulation Properties of Glass Fiber/Epoxy Resin (GF/EP) Composites Under Hygrothermal Aging

by Bowen Xu, Chenglu Wang, Jinghan Wang and Chen Cao

Energies 2026, 19(10), 2436; https://doi.org/10.3390/en19102436 - 19 May 2026

Viewed by 154

Abstract

Glass fiber/epoxy (GF/EP) composites are widely used in high-voltage electrical equipment due to their excellent specific strength, durability and dielectric properties. However, long-term exposure to hygrothermal environments will lead to performance degradation of the material, which seriously threatens its service reliability. To solve this problem, accelerated aging tests were systematically carried out in this study by immersing GF/EP specimens in deionized water at room temperature and 80 °C. The performance evolution laws and failure mechanisms of the material were investigated through moisture absorption kinetic analysis, tensile property testing, scanning electron microscope (SEM) fracture observation and breakdown voltage testing. The results show that the initial moisture absorption behavior of the material follows the Fickian diffusion mechanism, and the water diffusion rate at 80 °C is 31.8 times that at room temperature. After 35 days of aging, the retention rate of the maximum tensile force is 86.6% for the room temperature group, while it decreases to 38.2% for the 80 °C group. SEM observations show that the failure mode of the material changes from ductile fracture to brittle fracture after aging at 80 °C, accompanied by serious interfacial debonding. Temperature is the dominant factor for insulation performance degradation: the breakdown voltage retention rate is above 91% at room temperature, while it decreases to about 37% at 80 °C, and the influence of 60% maximum tensile force (

F_{m a x}

) preloading is relatively small. This study provides experimental data and theoretical support for the performance evaluation and life prediction of GF/EP composites in harsh hygrothermal service environments of high-voltage electrical equipment. Full article

(This article belongs to the Special Issue Advanced Control and Monitoring of High Voltage Power Systems)

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23 pages, 4383 KB

Open AccessArticle

Motion Characteristics and Defect Diagnosis of Metallic Particles in GIS/GIL

by Long He, Chen Cao, Yongming Zhu, Baojun Ma, Huan Lei and Yan Hu

Energies 2026, 19(9), 2138; https://doi.org/10.3390/en19092138 - 29 Apr 2026

Viewed by 374

Abstract

The operational reliability of gas-insulated switchgear/gas-insulated transmission lines (GIS/GIL) is critically threatened by internal metallic particles, which serve as primary triggers for insulation degradation. Conventional partial discharge (PD) detection methods often lack sensitivity during the early stages of particle movement. To overcome these limitations, this study aims to develop a novel non-intrusive defect diagnosis methodology based on the analysis of mechanical vibration signals. The coupled particle motion model integrating the electrostatic field, particle tracking, and multibody dynamics has been established. This model reveals the dynamic law that metallic particles migrate toward the conductor and undergo charge polarity reversal after collision, with a maximum speed of 2.7 m/s. Meanwhile, the peak vibration acceleration excited by the collision is calculated as 0.02 m/s². Accordingly, the high-voltage experimental platform with the full-scale prototype is built to simulate the actual operating conditions of the power grid. With the particle defects set inside the prototype, vibration signals are collected by using an accelerometer, and the measured peak vibration acceleration is 0.017 m/s². Finally, a defect diagnosis method based on the Hilbert–Huang Transform (HHT) and correlation coefficient analysis is proposed. This method uses Empirical Mode Decomposition (EMD) to extract the IMF4 component of the signal in the vicinity of the 1000 Hz frequency band. When particle defects occur, the correlation coefficient between the IMF4 component and the original signal exceeds 0.7668. This vibration-based monitoring technique provides an alternative for the condition-based maintenance of GIS/GIL, offering significant engineering value for enhancing the safety and reliability of power transmission infrastructure. Full article

(This article belongs to the Special Issue Advanced Control and Monitoring of High Voltage Power Systems)

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24 pages, 7839 KB

Open AccessArticle

Power Transformer Breathing System Condition Monitoring Based on Pressure–Temperature Optical Sensing and Deep Learning Method

by Jiabi Liang, Jian Shao, Peng Wu, Qun Li, Yuncai Lu, Yalin Wang and Zhaokai Lei

Energies 2026, 19(5), 1130; https://doi.org/10.3390/en19051130 - 24 Feb 2026

Viewed by 465

Abstract

During long-term operation of power transformers, oil temperature and pressure exhibit strong non-stationarity and multi-scale coupling, which makes early-stage breathing system faults difficult to detect accurately. To address this issue, this paper proposes an integrated diagnosis and early-warning method for transformer breathing systems. It combines a multi-parameter optical sensor with a deep-learning algorithm. The pressure–temperature optical sensing system based on Fabry–Pérot (F–P) interferometry and fiber Bragg grating (FBG) technology is developed to achieve high-precision synchronous measurement of pressure and temperature. To handle the non-stationary and multi-scale characteristics of the measured signals, a swarm-intelligence-optimized variational mode decomposition (VMD) method is employed to adaptively decompose time series temperature and pressure data. On this basis, a joint forecasting model integrating a temporal convolutional network (TCN) and an inverted Transformer (iTransformer) is constructed to capture both local temporal dynamics and long-term dependencies. Furthermore, based on the pressure equilibrium mechanism of transformer breathing systems, oil temperature and equivalent oil level are inferred, and abnormality criteria suitable for both multi-point and single-point monitoring are established. Experimental and field tests on a 220 kV transformer demonstrate that the proposed method outperforms conventional models in prediction accuracy. Full article

(This article belongs to the Special Issue Advanced Control and Monitoring of High Voltage Power Systems)

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18 pages, 2172 KB

Open AccessArticle

A Prediction Method for the Surface Arc Inception Voltage of Epoxy Resin Based on an Electric Field Feature Set and GS-SVR

by Yihong Lin, Dengfeng Wei, Zhiwen Zhang, Zhaoping Ye, Wenhua Huang and Shengwen Shu

Energies 2025, 18(22), 5936; https://doi.org/10.3390/en18225936 - 11 Nov 2025

Viewed by 505

Abstract

To address the critical challenges posed by the complex coastal climate on the external insulation of electrical equipment, research into the prediction of the surface arc inception voltage of epoxy resin under multiple conditions is of great significance for preventing failures and guiding operations and maintenance. In this regard, we propose a prediction method for surface arc inception voltage based on grid search-optimized support vector regression (GS-SVR). Using a 21-dimensional electric field feature set along the shortest inter-electrode path as model input, high-accuracy prediction of surface arc inception voltage under complex conditions is achieved. The results demonstrate that the model accurately predicts surface arc inception voltage with limited samples, achieving a mean absolute percentage error (MAPE) of 6.24%. Furthermore, the non-uniform coefficient-based dataset partitioning method improves prediction accuracy compared to random partitioning, with the lowest MAPE of only 2.39%. The findings provide theoretical and technical support for improving the anti-pollution flashover and anti-condensation performance of epoxy resin insulating materials. Full article

(This article belongs to the Special Issue Advanced Control and Monitoring of High Voltage Power Systems)

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17 pages, 4494 KB

Open AccessArticle

A Fault Detection Method for Multi-Sensor Data of Spring Circuit Breakers Based on the RF-Adaboost Algorithm

by Chuang Wang, Peijie Cong, Sifan Yu, Jing Yuan, Nian Lv, Yu Ling, Zheng Peng, Haoyan Zhang and Hongwei Mei

Energies 2025, 18(14), 3890; https://doi.org/10.3390/en18143890 - 21 Jul 2025

Cited by 1 | Viewed by 2917

Abstract

In the context of increasing the complexity and intelligence of modern power systems, traditional maintenance approaches for circuit breakers have shown limitations in meeting both reliability and economic requirements. This paper proposes a multi-sensor data fusion fault detection method based on the RF-Adaboost algorithm for spring-operated circuit breakers. By integrating pressure, speed, coil current, and energy storage motor sensors into the mechanism, multi-source operational data are acquired and processed via denoising and feature extraction techniques. A fault detection model is then constructed using the RF-Adaboost classifier. The experimental results demonstrate that the proposed method achieves over 96% accuracy in identifying typical fault states such as coil voltage deviation, reset spring fatigue, and closing spring degradation, outperforming conventional approaches. These results validate the model’s effectiveness and robustness in diagnosing complex mechanical failures in circuit breakers. Full article

(This article belongs to the Special Issue Advanced Control and Monitoring of High Voltage Power Systems)

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