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Power System Dynamics and Stability, 2nd Edition
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Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Prof. Dr. Yanchi Zhang
School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China
Prof. Dr. Dongdong Li
School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Dr. Chenghong Gu
Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
Department of Electrical Engineering, ESTIA Institute of Technology, 64210 Bidart, France
Dr. Nan Zhao
School of Engineering, Lancaster University, Lancaster LA1 4YW, UK
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Power System Dynamics and Stability, 2nd Edition

Abstract submission deadline
28 February 2026
Manuscript submission deadline
30 April 2026
Viewed by
2538

Topic Information

Dear Colleagues,

With the increase in power electronic components and equipment, the power electronization of new power systems will lead to fundamental changes in their structural characteristics, operating characteristics, and control mode, causing complex electromagnetic transient processes and dynamic stability problems. These will challenge the safe and stable operation of power systems. To ensure the safe and stable operation of power electronic systems, the goals of this Topic are to reveal the operation mechanism and establish a numerical simulation model of the power electronic system, analyze and study the theory of instantaneous electrical parameters and the electromagnetic transient stability theory, explore new control methods and new power equipment, and realize more accurate analysis models, reasonable and stable analysis ideas, control technologies, and intelligent management and control strategies for power electronic systems.

This second edition expands upon the successful foundation laid by the first edition, continuing to encourage the dissemination of new concepts, ideas, and novel methods to analyze the modeling and dynamic stability of power electronic systems. It aims to disseminate fundamental research, innovation, and information exchange in these related fields. Application papers are also highly welcome. Topics of interest include but are not limited to the following:

  1. A security and stability analysis of power electronic systems;
  2. Research on mechanism models of power electronic systems;
  3. Research on electromagnetic transient simulation models of power electronic systems;
  4. An analysis of the power electronic system simulation method;
  5. A power electronic system oscillation analysis and suppression measures;
  6. A power electronic system oscillation control method;
  7. Power electronic system stability and control based on cloud computing and artificial intelligence;
  8. A parameter optimization method for power electronic system control;
  9. Research on grid connection control strategies and methods;
  10. Mechanism analysis methods for power electronic systems;
  11. Power electronic oscillation suppression devices;
  12. Research on the operation mode of power electronic systems.

Prof. Dr. Da Xie
Prof. Dr. Yanchi Zhang
Prof. Dr. Dongdong Li
Dr. Chenghong Gu
Dr. Ignacio Hernando-Gil
Dr. Nan Zhao
Topic Editors

Keywords

  • power electronics
  • power system
  • modeling
  • dynamic stability analysis
  • mechanism analysis
  • simulation method
  • control strategy

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Electricity
electricity 		- 4.8 2020 27.9 Days CHF 1000 Submit
Electronics
electronics 		2.6 5.3 2012 16.4 Days CHF 2400 Submit
Energies
energies 		3.0 6.2 2008 16.8 Days CHF 2600 Submit
Processes
processes 		2.8 5.1 2013 14.9 Days CHF 2400 Submit
Eng
eng 		- 2.1 2020 21.5 Days CHF 1200 Submit

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Published Papers (2 papers)

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20 pages, 7943 KiB  
Article
Fault Classification and Precise Fault Location Detection in 400 kV High-Voltage Power Transmission Lines Using Machine Learning Algorithms
by Ömer Özdemir, Raşit Köker and Nihat Pamuk
Processes 2025, 13(2), 527; https://doi.org/10.3390/pr13020527 - 13 Feb 2025
Viewed by 1139
Abstract
Fault detection, classification, and precise location identification in power transmission lines are critical issues for energy transmission and power systems. Accurate fault diagnosis is essential for system stability and safety as it enables rapid problem resolution and minimizes interruptions in electrical energy supply. [...] Read more.
Fault detection, classification, and precise location identification in power transmission lines are critical issues for energy transmission and power systems. Accurate fault diagnosis is essential for system stability and safety as it enables rapid problem resolution and minimizes interruptions in electrical energy supply. The characteristic parameters of mixed-conductor power transmission lines connected to the grid were calculated using the relevant line data. Based on these parameters, a dataset was created with computer-derived values. This dataset included variations in arc resistance and the short circuit power of the corresponding bus, facilitating the performance testing of various machine learning algorithms. It was observed that the correct determination of the faulty phase was of high importance in the correct determination of the fault position. For this reason, a gradual structure was preferred. It was achieved with a 100 percent success rate in fault detection with the ensemble bagged algorithm. It was obtained with the neural network algorithm with a 99.97 percent success rate in faulty phase detection. The most successful location results were obtained with the interaction linear algorithm with 0.0066 MAE for phase-to-phase faults and the stepwise linear algorithm with 0.0308 MAE for phase ground faults. Using the proposed algorithm, fault locations were identified with a maximum error of 26 m for phase-to-ground faults and 110 m for phase-to-phase faults on a transmission line with a mixed conductor of approximately 178 km. Additionally, we compared the training and testing results of several machine learning algorithms metrics including the accuracy, total error, mean absolute error, root mean square, and root mean square error to provide informed recommendations based on their performance. The findings aim to guide users in selecting the most effective machine learning models for predicting failures in transmission lines. Full article
(This article belongs to the Topic Power System Dynamics and Stability, 2nd Edition)
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20 pages, 5644 KiB  
Article
Optimal Control of the Green Low-Carbon Base Station System Based on the Concept of Energy Router
by Guangyi Shao, Tong Liu, Yanjia Wang, Zongping Wang, Yuhui Wang and Qi Wang
Processes 2025, 13(1), 288; https://doi.org/10.3390/pr13010288 - 20 Jan 2025
Viewed by 902
Abstract
This paper establishes an energy router system for green and low-carbon base stations, a −48 V DC bus multi-source parallel system including photovoltaic, wind turbine, grid power, and energy storage batteries, and studies the control strategy managing system energy distribution. Firstly, from the [...] Read more.
This paper establishes an energy router system for green and low-carbon base stations, a −48 V DC bus multi-source parallel system including photovoltaic, wind turbine, grid power, and energy storage batteries, and studies the control strategy managing system energy distribution. Firstly, from the perspective of system physical layer design, we combine multiple power circuits to complete the design of the system’s modular power conversion circuits and linearize the power electronic converters for modeling and analyze their stability. Different control strategies are proposed for different power converters to ensure the stable operation of the system. Secondly, from the perspective of overall energy optimal control, we construct system operating states and control algorithms based on the switching strategy of the energy router between different operating states of the system and use a heuristic algorithm based on rolling optimization to achieve the optimal control of the system at the physical level. Finally, we use Simulink to simulate and verify the state switching of the multi-source system, analyze control results according to the actual typical working conditions, and conduct experiments on the overall system. Simulations demonstrate that the system can achieve smooth transitions among various modes. The results of actual experiments show that the established multi-source system can save 60.28% of energy utilization costs annually, and the bus voltage control strategy can be effectively implemented while maintaining an appropriate voltage deviation. Full article
(This article belongs to the Topic Power System Dynamics and Stability, 2nd Edition)
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