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Energy, Electrical and Power Engineering: 5th Edition
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Energy, Electrical and Power Engineering: 5th Edition

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 25 March 2026 | Viewed by 719

Special Issue Editors

Prof. Dr. Jian Zhang

E-Mail Website
Guest Editor
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: motor design; deep-sea driving system; system reliability
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaoyan Huang

E-Mail Website
Guest Editor
Department of Electrical Machine, Zhejiang University, Hangzhou 310024, China
Interests: permanent magnet motor; high speed train traction system; high efficiency motor drive system for EV; fault tolerant motor drives for aerospace; PMSM motor intelligent control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy and power are playing an increasingly pivotal role in our modern life and are transforming the way we utilize energy and the way we live. This Special Issue will bring together the latest innovations and knowledge in energy and power engineering such as new and renewable energy, power electronics and electric motor drives, distributed generation and multi-energy systems, data analytics, and artificial intelligence. You are cordially invited to contribute to the Special Issue and present your new work.

Topics of interest include, but are not limited to, the following:

  • Analog and Digital Signal Processing
  • Artificial Intelligence
  • Big Data and Data Processing
  • Bioenergy and Utilization
  • Communication Systems
  • Control Theory and Optimization
  • Diagnosis and Sensing Systems
  • Distributed Generation
  • Electrical Generators
  • Electrical Motor Drives
  • Electromagnetic and Applied Superconductivity
  • Electronics, Information and Control Systems
  • Energy Market and Power System Economics
  • Energy Storage
  • Engineering Materials and Process
  • Fuel Cells and Applications
  • Industrial Process Control and Automation
  • Intelligent Control Systems
  • Mechatronics and Robotics
  • Modeling, Simulation, and Analysis
  • Nuclear Energy
  • Power Electronic Converters
  • Power Generation and Sustainable Environment
  • Power Quality and Electromagnetic Compatibility
  • Power Planning and Scheduling
  • Power Semiconductors
  • Predictive Control
  • Protection, Operation, and Control
  • Real-Time Control
  • Reliability and Security
  • Renewable Energy
  • Sensors, Instruments, and Measuring Technologies
  • Smart Cities and Smart Grids
  • Solar Energy and Photovoltaics
  • Transmission and Distribution Systems
  • Wind Energy

Thank you very much for your participation!

Prof. Dr. Jian Zhang
Prof. Dr. Xiaoyan Huang
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

  • power converters
  • motor drives
  • electrified vehicles
  • wind power generation
  • measurement techniques

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Related Special Issue

Published Papers (3 papers)

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Research

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26 pages, 2755 KB  
Article
Fault Diagnosis Method for High-Voltage Direct Current Transmission System Based on Multimodal Sensor Feature-LightGBM Algorithm: A Case Study in China
by Qiang Li, Yingfei Li, Shihong Zhang, Yue Ma, Yinan Qiu, Xiaohang Luo and Bo Yang
Energies 2025, 18(23), 6253; https://doi.org/10.3390/en18236253 - 28 Nov 2025
Viewed by 144
Abstract
To improve/enhance the intelligence and accuracy of fault diagnosis in high-voltage direct current (HVDC) systems, this paper proposes a fault diagnosis model for HVDC systems based on the multimodal sensor feature-light gradient boosting machine (MSF-LightGBM) algorithm. First, a sample set encompassing four typical [...] Read more.
To improve/enhance the intelligence and accuracy of fault diagnosis in high-voltage direct current (HVDC) systems, this paper proposes a fault diagnosis model for HVDC systems based on the multimodal sensor feature-light gradient boosting machine (MSF-LightGBM) algorithm. First, a sample set encompassing four typical types of faults, namely alternating current (AC) faults, direct current (DC) faults, inverter commutation failures, and converter valve faults, was constructed based on the actual HVDC transmission data from China. Second, considering the issues of imbalanced sample classes and a relatively small sample size in the original dataset, a data augmentation method incorporating multiple types of noise is introduced to improve the diversity and practical representativeness of the samples. Then, time-series features in the time domain, frequency domain, and wavelet domain, along with Pearson correlation features among 15 sensors, are extracted to form a comprehensive feature vector. On this basis, automatic feature selection is performed using recursive feature elimination (RFE) to screen out the key features. Finally, the paper builds an optimized LightGBM classification model is built using the key features. Through comparative experiments with five machine learning methods, the results indicate that the accuracy of the proposed method on the test set reaches 0.9583, significantly outperforming the other comparison models. The receiver operating characteristic (ROC) curve analysis reveals that the average area under the curve (AUC) for all four types of faults is 0.975, validating the stability and accuracy of the proposed model in multi-class fault identification. Full article
(This article belongs to the Special Issue Energy, Electrical and Power Engineering: 5th Edition)
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25 pages, 5551 KB  
Article
Improved Polar Lights Optimizer Based Optimal Power Flow for ADNs with Renewable Energy and EVs
by Peng Zhang, Yifan Zhou, Fuyou Zhao, Xuan Ruan, Wei Huang, Yang He and Bo Yang
Energies 2025, 18(20), 5403; https://doi.org/10.3390/en18205403 - 14 Oct 2025
Viewed by 379
Abstract
With the large-scale integration of renewable energy sources such as wind and photovoltaic (PV) power, along with the increasing use of electric vehicle (EV), the operation of active distribution network (ADN) faces challenges, including bidirectional power flows, voltage fluctuations, and increased network losses. [...] Read more.
With the large-scale integration of renewable energy sources such as wind and photovoltaic (PV) power, along with the increasing use of electric vehicle (EV), the operation of active distribution network (ADN) faces challenges, including bidirectional power flows, voltage fluctuations, and increased network losses. To address these issues, this study develops a multi-objective optimal power flow (MOOPF) model that simultaneously considers wind and PV generation, battery energy storage systems (BESSs), and EV charging loads. The proposed model aims to simultaneously optimize operating cost, node voltage deviation, and network losses, while ensuring voltage quality and system reliability. An improved polar lights optimizer (IPLO) is introduced to solve the MOOPF problem, enhancing global search capability and convergence efficiency without increasing computational complexity. Simulation results on the improved IEEE-33 bus test system show that compared with conventional algorithms such as GA, ABC, PSO and WOA, the IPLO optimizer achieves superior performance. Specifically, IPLO significantly reduces voltage deviation and network losses, while maintaining an average voltage level close to unity, thereby improving both voltage quality and energy efficiency. Furthermore, when compared with the original PLO, IPLO also demonstrates a reduction in operating cost. These results validate the effectiveness and applicability of the proposed IPLO-based MOOPF framework in ADNs with high use of renewable energy and EVs. Full article
(This article belongs to the Special Issue Energy, Electrical and Power Engineering: 5th Edition)
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Review

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34 pages, 3744 KB  
Review
Intelligent Fault Diagnosis for HVDC Systems Based on Knowledge Graph and Pre-Trained Models: A Critical and Comprehensive Review
by Qiang Li, Yue Ma, Jinyun Yu, Shenghui Cao, Shihong Zhang, Pengwang Zhang and Bo Yang
Energies 2025, 18(24), 6438; https://doi.org/10.3390/en18246438 - 9 Dec 2025
Abstract
High-voltage direct-current (HVDC) systems are essential for large-scale renewable integration and asynchronous interconnection, yet their complex topologies and multi-type faults expose the limits of threshold- and signal-based diagnostics. These methods degrade under noisy, heterogeneous measurements acquired under dynamic operating conditions, resulting in poor [...] Read more.
High-voltage direct-current (HVDC) systems are essential for large-scale renewable integration and asynchronous interconnection, yet their complex topologies and multi-type faults expose the limits of threshold- and signal-based diagnostics. These methods degrade under noisy, heterogeneous measurements acquired under dynamic operating conditions, resulting in poor adaptability, reduced accuracy, and high latency. To overcome these shortcomings, the synergistic use of knowledge graphs (KGs) and pre-trained models (PTMs) is emerging as a next-generation paradigm. KGs encode equipment parameters, protection logic, and fault propagation paths in an explicit, human-readable structure, while PTMs provide transferable representations that remain effective under label scarcity and data diversity. Coupled within a perception–cognition–decision loop, PTMs first extract latent fault signatures from multi-modal records; KGs then enable interpretable causal inference, yielding both precise localization and transparent explanations. This work systematically reviews the theoretical foundations, fusion strategies, and implementation pipelines of KG-PTM frameworks tailored to HVDC systems, benchmarking them against traditional diagnostic schemes. The paradigm demonstrates superior noise robustness, few-shot generalization, and decision explainability. However, open challenges remain, such as automated, conflict-free knowledge updating; principled integration of electro-magnetic physical constraints; real-time, resource-constrained deployment; and quantifiable trustworthiness. Future research should therefore advance autonomous knowledge engineering, physics-informed pre-training, lightweight model compression, and standardized evaluation platforms to translate KG-PTM prototypes into dependable industrial tools for intelligent HVDC operation and maintenance. Full article
(This article belongs to the Special Issue Energy, Electrical and Power Engineering: 5th Edition)
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