Advances in Power Science and Technology, 2nd Edition

Topic Information

Dear Colleagues,

With the continuous increase in renewable energy in the power system, technologies such as grid control and optimization, energy storage planning, wind power forecasting, and renewable power to ammonia have become increasingly important. These technologies can help us realize the sustainable development of the power system and improve the security, stability, and reliability of the power grid.

The purpose of power grid control and optimization is to ensure the stability and reliability of the power system through real-time monitoring and to adjust the operation of the power grid. The purpose of energy storage planning is to optimize the energy storage capacity and distribution of the power system to meet the load demand and respond to emergencies such as pre-deployment and dynamic scheduling of mobile energy storage. The purpose of wind power prediction is to predict the future wind speed and wind energy using meteorology, statistics, and machine learning methods, so as to optimize the planning and scheduling of wind power generation. The purpose of converting renewable power to ammonia is to utilize surplus wind and solar resources, promote the integration of power-to-hydrogen and ammonia, and facilitate the green and low-carbon transformation of the chemical industry.

Research within this topic involves many fields, including power systems, power electronics, energy storage technology, meteorology, statistics, and machine learning. Through relevant research, the challenges faced by the power system can be effectively solved, and the sustainable development of the power industry can be promoted. This second edition expands upon the successful foundation laid by the first edition, continuing to explore and innovate within the dynamic and evolving landscape of renewable energy integration and power system management.

Prof. Dr. Bo Yang
Prof. Dr. Lin Jiang
Topic Editors

Keywords

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
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit
Solar solar	-	-	2021	23.4 Days	CHF 1000	Submit

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Published Papers (26 papers)

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17 pages, 3545 KiB  

Open AccessArticle

Optimal Scheduling of Active Distribution Networks with Hybrid Energy Storage Systems Under Real Road Network Topology

by Ling Miao, Li Di, Jian Zhao, Hao Liu, Yurong Hu and Xiaozhao Wei

Processes 2025, 13(5), 1492; https://doi.org/10.3390/pr13051492 - 13 May 2025

Viewed by 202

Abstract

With the increasing proportion of renewable energy in power systems, the applications of mobile energy storage systems (MESSs) with better flexibility and controllability are becoming more widespread. To further explore the hybrid ESS optimization scheduling problem of MESS and SESS, this paper first [...] Read more.

With the increasing proportion of renewable energy in power systems, the applications of mobile energy storage systems (MESSs) with better flexibility and controllability are becoming more widespread. To further explore the hybrid ESS optimization scheduling problem of MESS and SESS, this paper first quantifies parts of actual road topologies in Dali City, China, and combines the Dijkstra algorithm to obtain an MESS path optimization framework. Subsequently, a hybrid ESS optimization scheduling model combining MESS and SESS is constructed with the objective functions of maximizing the scheduling benefits of the hybrid ESS and minimizing system voltage deviation. Finally, the non-dominated sorting genetic algorithm III (NSGA-III) is used to solve the hybrid ESS optimization scheduling model. To verify the effectiveness of the proposed method, this paper selected typical daily load and renewable energy output data in winter in Dali for the case study. The final result shows that the total profit of the optimized scheduling of the hybrid ESS is CNY 578, of which the arbitrage income is CNY 1119.2 and the total cost is CNY 540.44. Meanwhile, the voltage distribution range and total power loss are optimized from [0.9616, 1.0105] to [0.9723, 1.0008] and 0.963 MW to 0.9134 MW, which indicates that the coordinated scheduling of hybrid ESS is the key to improving the reliability of distribution network operation, and the path optimization of MESS is crucial for enhancing its profitability. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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22 pages, 6913 KiB  

Open AccessArticle

Coordinated Interaction Strategy of User-Side EV Charging Piles for Distribution Network Power Stability

by Juan Zhan, Mei Huang, Xiaojia Sun, Zuowei Chen, Zhihan Zhang, Yang Li, Yubo Zhang and Qian Ai

Energies 2025, 18(8), 1944; https://doi.org/10.3390/en18081944 - 10 Apr 2025

Viewed by 246

Abstract

In response to the challenges of imbalanced economic efficiency of charging stations caused by disorderly charging of large-scale electric vehicles (EVs), rising electricity expenditure of users, and increased risk of stable operation of the power grid, this study designs a user-side vehicle pile [...] Read more.

In response to the challenges of imbalanced economic efficiency of charging stations caused by disorderly charging of large-scale electric vehicles (EVs), rising electricity expenditure of users, and increased risk of stable operation of the power grid, this study designs a user-side vehicle pile resource interaction strategy considering source load clustering to enhance the economy and safety of electric vehicle energy management. Firstly, by constructing a dynamic traffic flow distribution network coupling architecture, a bidirectional interaction model between charging facilities and transportation/power systems is established to analyze the dynamic correlation between charging demand and road network status. Next, an EV charging and discharging electricity price response model is established to quantify the load regulation potential under different scenarios. Secondly, by combining urban transportation big data and prediction networks, high-precision inference of the spatiotemporal distribution of charging loads can be achieved. Then, a multidimensional optimization objective function covering operator revenue, user economy, and grid power quality is constructed, and a collaborative decision-making model is established. Finally, the IEEE69 node system is validated through joint simulation with actual urban areas, and the non-dominated sorting genetic algorithm II (NSGA-II) based on reference points is used for the solution. The results show that the optimization strategy proposed by NSGA-II can increase the operating revenue of charging stations by 33.43% while reducing user energy costs and grid voltage deviations by 18.9% and 68.89%, respectively. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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16 pages, 1427 KiB  

Open AccessArticle

InvMOE: MOEs Based Invariant Representation Learning for Fault Detection in Converter Stations

by Hao Sun, Shaosen Li, Hao Li, Jianxiang Huang, Zhuqiao Qiao, Jialei Wang and Xincui Tian

Energies 2025, 18(7), 1783; https://doi.org/10.3390/en18071783 - 2 Apr 2025

Viewed by 311

Abstract

Converter stations are pivotal in high-voltage direct current (HVDC) systems, enabling power conversion between an alternating current (AC) and a direct current (DC) while ensuring efficient and stable energy transmission. Fault detection in converter stations is crucial for maintaining their reliability and operational [...] Read more.

Converter stations are pivotal in high-voltage direct current (HVDC) systems, enabling power conversion between an alternating current (AC) and a direct current (DC) while ensuring efficient and stable energy transmission. Fault detection in converter stations is crucial for maintaining their reliability and operational safety. This paper focuses on image-based detection of five common faults: metal corrosion, discoloration of desiccant in breathers, insulator breakage, hanging foreign objects, and valve cooling water leakage. Despite advancements in deep learning, existing detection methods face two major challenges: limited model generalization due to diverse and complex backgrounds in converter station environments and sparse supervision signals caused by the high cost of collecting labeled images for certain faults. To overcome these issues, we propose InvMOE, a novel fault detection algorithm with two core components: (1) invariant representation learning, which captures task-relevant features and mitigates background noise interference, and (2) multi-task training using a mixture of experts (MOE) framework to adaptively optimize feature learning across tasks and address label sparsity. Experimental results on real-world datasets demonstrate that InvMOE achieves superior generalization performance and significantly improves detection accuracy for tasks with limited samples, such as valve cooling water leakage. This work provides a robust and scalable approach for enhancing fault detection in converter stations. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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15 pages, 2423 KiB  

Open AccessArticle

Dual-Branch Discriminative Transmission Line Bolt Image Classification Based on Contrastive Learning

by Yan-Peng Ji, Jian-Li Zhao, Liang-Shuai Liu, Hai-Yan Feng, Jia-Qi Du and Xia Fang

Processes 2025, 13(3), 898; https://doi.org/10.3390/pr13030898 - 19 Mar 2025

Viewed by 243

Abstract

The classification of transmission tower bolt images faces challenges such as class imbalance, sample scarcity, and the low pixel proportion of pins. Traditional classification methods exhibit poor performance in identifying key categories with small proportions, fail to leverage the correlation between transmission line [...] Read more.

The classification of transmission tower bolt images faces challenges such as class imbalance, sample scarcity, and the low pixel proportion of pins. Traditional classification methods exhibit poor performance in identifying key categories with small proportions, fail to leverage the correlation between transmission line fittings and bolts, and suffer from severe false positive issues. This study proposes a novel approach that dynamically integrates two sampling strategies to address the class imbalance problem while incorporating contrastive learning and category labels to enhance the discrimination of easily confused samples. Additionally, an auxiliary branch discrimination mechanism effectively exploits the correlation between fittings and bolts and, combined with a threshold-based decision process, significantly reduces the false positive rate (by 3.74%). The experimental results demonstrate that, compared to the baseline SimCLR framework with ResNet18, the proposed method improves accuracy (Acc) by 10.22%, reduces the false alarm rate by 5%, and significantly enhances classification reliability in transmission line inspections, thereby mitigating unnecessary human resource consumption. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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14 pages, 6968 KiB  

Open AccessArticle

A Small-Sample Target Detection Method for Transmission Line Hill Fires Based on Meta-Learning YOLOv11

by Yaoran Huo, Yang Zhang, Jian Xu, Xu Dai, Luocheng Shen, Conghong Liu and Xia Fang

Energies 2025, 18(6), 1511; https://doi.org/10.3390/en18061511 - 19 Mar 2025

Viewed by 446

Abstract

China has a large number of transmission lines laid in the mountains and forests and other regions, and these transmission lines enable national strategic projects such as the west-east power transmission project. However, the occurrence of mountain fires in the corresponding areas will [...] Read more.

China has a large number of transmission lines laid in the mountains and forests and other regions, and these transmission lines enable national strategic projects such as the west-east power transmission project. However, the occurrence of mountain fires in the corresponding areas will seriously affect these transmission projects. At the same time, these mountain fires yield fewer image samples and complex backgrounds. Based on this, this paper proposes a transmission line hill fire detection model with YOLOv11 as the basic framework, named meta-learning attention YOLO (MA-YOLO). Firstly, the feature extraction module in it is replaced with a meta-feature extraction module, and the scale of the detection head is adjusted to detect smaller-sized hill fire targets. After this, the re-weighting module learns class-specific re-weighting vectors from the support set samples and uses them to recalibrate the mapping of meta-features. To enhance the model’s ability to learn target hill fire features from complex backgrounds, adaptive feature fusion (AFF) is integrated into the feature extraction process of YOLOv11 to improve the model’s feature fusion capabilities, filter out useless information in the features, and reduce the interference of complex backgrounds in detection. The experimental results show that the accuracy of MA-YOLO is improved by 10.8% in few-shot scenarios. MA-YOLO misses fewer hill fire targets in different scenarios and is less likely to be affected by complex backgrounds. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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27 pages, 2851 KiB  

Open AccessArticle

The Multi-Objective Distributed Robust Optimization Scheduling of Integrated Energy Systems Considering Green Hydrogen Certificates and Low-Carbon Demand Response

by Yulong Yang, Han Yan and Jiaqi Wang

Processes 2025, 13(3), 703; https://doi.org/10.3390/pr13030703 - 28 Feb 2025

Viewed by 737

Abstract

To address the issues of energy wastage and uncertainty impacts associated with high levels of renewable energy integration, a multi-objective distributed robust low-carbon optimization scheduling strategy for hydrogen-integrated Integrated Energy Systems (IES) is proposed. This strategy incorporates a green hydrogen trading mechanism and [...] Read more.

To address the issues of energy wastage and uncertainty impacts associated with high levels of renewable energy integration, a multi-objective distributed robust low-carbon optimization scheduling strategy for hydrogen-integrated Integrated Energy Systems (IES) is proposed. This strategy incorporates a green hydrogen trading mechanism and low-carbon demand response. Firstly, to leverage the low-carbon and clean characteristics of hydrogen energy, an efficient hydrogen utilization model was constructed, consisting of electricity-based hydrogen production, waste heat recovery, multi-stage hydrogen use, hydrogen blending in gas, and hydrogen storage. This significantly enhanced the system’s renewable energy consumption and carbon reduction. Secondly, to improve the consumption of green hydrogen, a novel reward–punishment green hydrogen certificate trading mechanism was proposed. The impact of green hydrogen trading prices on system operation was discussed, promoting the synergistic operation of green hydrogen and green electricity. Based on the traditional demand-response model, a novel low-carbon demand-response strategy is proposed, with carbon emission factors serving as guiding signals. Finally, considering the uncertainty of renewable energy, an innovative optimal trade-off multi-objective distributed robust model was proposed, which simultaneously considered low-carbon, economic, and robustness aspects. The model was solved using an improved adaptive particle swarm optimization algorithm. Case study results show that, after introducing the reward–punishment green hydrogen trading mechanism and low-carbon demand response, the system’s total cost was reduced by approximately 5.16% and 4.37%, and carbon emissions were reduced by approximately 7.84% and 6.72%, respectively. Moreover, the proposed multi-objective distributed robust model not only considers the system’s economy, low-carbon, and robustness but also offers higher solving efficiency and optimization performance compared to multi-objective optimization methods. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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20 pages, 4536 KiB  

Open AccessArticle

Optimal Scheduling of Integrated Energy System Based on Carbon Capture–Power to Gas Combined Low-Carbon Operation

by Shumin Sun, Jiawei Xing, Yan Cheng, Peng Yu, Yuejiao Wang, Song Yang and Qian Ai

Processes 2025, 13(2), 540; https://doi.org/10.3390/pr13020540 - 14 Feb 2025

Viewed by 594

Abstract

In this paper, an IES optimal cooperative scheduling method based on a master–slave game is proposed considering a carbon emission trading (CET) and carbon capture system (CCS) combined operation with power to gas (P2G). We analysed the behaviour of integrated energy system operators [...] Read more.

In this paper, an IES optimal cooperative scheduling method based on a master–slave game is proposed considering a carbon emission trading (CET) and carbon capture system (CCS) combined operation with power to gas (P2G). We analysed the behaviour of integrated energy system operators (IESO) and energy suppliers (ES) when the system is operating in different states. This paper first introduces the structure of IES and the mathematical model of the game frame. Secondly, mixed integer linear programming and particle swarm optimization (MILP–PSO) are used. The final simulation results show that in the main scenario, IESO and ES have an income of CNY 181,900 and CNY 279,400, respectively, and the actual carbon emission is 106.75 tons. The overall income is balanced, and the carbon emission is in the middle. The results provide a reference value for operators and users to make decisions. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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16 pages, 1763 KiB  

Open AccessArticle

Optimal Dispatch for Electric-Heat-Gas Coupling Multi-Park Integrated Energy Systems via Nash Bargaining Game

by Xuesong Shao, Yixuan Huang, Meimei Duan, Kaijie Fang and Xing He

Processes 2025, 13(2), 534; https://doi.org/10.3390/pr13020534 - 14 Feb 2025

Viewed by 456

Abstract

To improve the energy utilization rate and realize the low-carbon emission of a park integrated energy system (PIES), this paper proposes an optimal operation strategy for multiple PIESs. Firstly, the electrical power cooperative trading framework of multiple PIESs is constructed. Secondly, the hydrogen [...] Read more.

To improve the energy utilization rate and realize the low-carbon emission of a park integrated energy system (PIES), this paper proposes an optimal operation strategy for multiple PIESs. Firstly, the electrical power cooperative trading framework of multiple PIESs is constructed. Secondly, the hydrogen blending mechanism and carbon capture system and power-to-gas system joint operation model are introduced to establish the model of each PIES. Then, based on the Nash bargaining game theory, a multi-PIES cooperative trading and operation model with electrical power cooperative trading is constructed. Then, the alternating direction method of multipliers algorithm is used to solve the two subproblems. Finally, case studies analysis based on scene analysis is performed. The results show that the cooperative operation model reduces the total cost of a PIES more effectively compared with independent operation. Meanwhile, the efficient utilization and production of hydrogen are the keys to achieve carbon reduction and an efficiency increase in a PIES. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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23 pages, 22016 KiB  

Open AccessArticle

An Armature Defect Self-Adaptation Quantitative Assessment System Based on Improved YOLO11 and the Segment Anything Model

by Yuntong Dai and Xia Fang

Processes 2025, 13(2), 532; https://doi.org/10.3390/pr13020532 - 14 Feb 2025

Cited by 1 | Viewed by 866

Abstract

There is a need to address challenges faced in detecting and segmenting defects in micro-vibration motor armatures, which are crucial components used in digital devices. Due to their complex structure and tiny size, quality control during assembly is difficult. In this paper, an [...] Read more.

There is a need to address challenges faced in detecting and segmenting defects in micro-vibration motor armatures, which are crucial components used in digital devices. Due to their complex structure and tiny size, quality control during assembly is difficult. In this paper, an adaptive segmentation quantization (ASQ) system based on YOLO 11 and SAM is proposed to address the issue above. The system consists of a target detection (TD) unit, shape segmentation (SS) unit, and quantitative assessment (AS) unit, and introduces a practical combination of YOLO11 for defect detection and SAM for segmentation, integrating this with a novel quantitative assessment framework to measure defect severity and occurrence. This approach is efficient and cost-effective, supporting real-time industrial applications by allowing for automated, rapid analysis and improvement identification. Finally, a quantitative evaluation standard with more than 90% accuracy was achieved. Additionally, a hardware system was developed to implement this framework in industrial settings. The proposed framework adopts a strategy of intelligent morphological feature extraction and computation, focusing on pixel-level segmentation and quantitative assessment. This research makes a significant step forward in automating quality control processes for micro-scale components, providing a robust and adaptive solution for the enhancement of manufacturing efficiency and product quality. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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22 pages, 8198 KiB  

Open AccessArticle

The Intrinsic Mechanism and Suppression Strategy of Transient Current Imbalance Among Parallel Converters

by Mingjie Fu, Huafeng Cai and Xinchun Lin

Electronics 2025, 14(4), 714; https://doi.org/10.3390/electronics14040714 - 12 Feb 2025

Viewed by 450

Abstract

Due to the difficulty in achieving a high-power output with a single converter, parallel converters are widely used in high-power applications. However, inconsistency in the output voltage feedback coefficients of individual converters and the associated dispersion in parallel systems often lead to unbalanced [...] Read more.

Due to the difficulty in achieving a high-power output with a single converter, parallel converters are widely used in high-power applications. However, inconsistency in the output voltage feedback coefficients of individual converters and the associated dispersion in parallel systems often lead to unbalanced current sharing during transient processes, such as load disturbances. This imbalance can result in certain converters being overloaded during transients, leading to premature shutdown. Subsequently, the load on the remaining converters increases, further aggravating the imbalance and triggering additional shutdowns in a domino effect, which may, ultimately, cause the entire parallel converter system to shut down. To address this issue, this study focuses on parallel phase-shift full-bridge systems, analyzing the intrinsic mechanism by which feedback coefficient dispersion affects transient current sharing. A droop control strategy with improved transient virtual impedance is proposed to enhance the current sharing during transients. A simulation and experimental results demonstrate that the proposed strategy significantly improves current sharing during transient processes, effectively enhancing the dynamic performance and reliability of the system. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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24 pages, 4148 KiB  

Open AccessArticle

System Optimization Scheduling Considering the Full Process of Electrolytic Aluminum Production and the Integration of Thermal Power and Energy Storage

by Yulong Yang, Han Yan, Jiaqi Wang, Weiyang Liu and Zhongwen Yan

Energies 2025, 18(3), 598; https://doi.org/10.3390/en18030598 - 27 Jan 2025

Viewed by 669

Abstract

To address the curtailment phenomenon caused by the high penetration of renewable energy in the system, an optimization scheduling strategy is proposed, considering the full process of electrolytic aluminum production and the integration of thermal power and energy storage. Firstly, to explore the [...] Read more.

To address the curtailment phenomenon caused by the high penetration of renewable energy in the system, an optimization scheduling strategy is proposed, considering the full process of electrolytic aluminum production and the integration of thermal power and energy storage. Firstly, to explore the differentiated response capabilities of various devices such as high-energy-consuming electrolytic aluminum units, thermal power units, and energy storage devices to effectively address uncertain variables in the power system, a Variational Mode Decomposition method is introduced to construct differentiated response methods for its low-frequency, medium-frequency, and high-frequency components. Secondly, based on the real production regulation characteristics of the high-energy-consuming electrolytic aluminum load, and considering various influencing factors such as current, temperature, and output, a scheduling model involving electrolytic aluminum load is established. Then, the power generation characteristics in other processes of electrolytic aluminum production are fully exploited to achieve energy storage conversion, replacing the energy storage batteries that respond to high-frequency components. Finally, by combining the deep peak-shaving model of thermal power units, an optimization scheduling model is established for the joint operation of the full electrolytic aluminum production load and thermal-power-storage systems, with the goal of minimizing system operating costs. The case study results show that the proposed model can significantly enhance the system’s renewable energy absorption capacity, reduce energy storage installations, and enhance the economic efficiency of the system’s peak-shaving operation. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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20 pages, 4477 KiB  

Open AccessArticle

A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning

by Yan Chen, Miaolin Yu, Haochong Wei, Huanxing Qi, Yiming Qin, Xiaochun Hu and Rongxing Jiang

Energies 2025, 18(3), 580; https://doi.org/10.3390/en18030580 - 26 Jan 2025

Cited by 1 | Viewed by 911

Abstract

Accurate wind power forecasting is crucial for optimizing grid scheduling and improving wind power utilization. However, real-world wind power time series exhibit dynamic statistical properties, such as changing mean and variance over time, which make it difficult for models to apply observed patterns [...] Read more.

Accurate wind power forecasting is crucial for optimizing grid scheduling and improving wind power utilization. However, real-world wind power time series exhibit dynamic statistical properties, such as changing mean and variance over time, which make it difficult for models to apply observed patterns from the past to the future. Additionally, the execution speed and high computational resource demands of complex prediction models make them difficult to deploy on edge computing nodes such as wind farms. To address these issues, this paper explores the potential of linear models for wind power forecasting and constructs NFLM, a linear, lightweight, short-term wind power forecasting model that is more adapted to the characteristics of wind power data. The model captures both short-term and long-term sequence variations through continuous and interval sampling. To mitigate the interference of dynamic features, we propose a normalization feature learning block (NFLBlock) as the core component of NFLM for processing sequences. This module normalizes input data and uses a stacked multilayer perceptron to extract cross-temporal and cross-dimensional dependencies. Experiments with data from two real wind farms in Guangxi, China, showed that compared with other advanced wind power forecasting methods, the MSE of NFLM in the 24-step ahead forecasting of the two wind farms is respectively reduced by 23.88% and 21.03%, and the floating-point operations (FLOPs) and parameter count only require 36.366 M and 0.59 M, respectively. The results show that NFLM can achieve good prediction accuracy with fewer computing resources. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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17 pages, 36519 KiB  

Open AccessArticle

An Improved Mask2Former-HRNet Method for Insulator Defect Detection

by Yaoran Huo, Lan Xiao, Zhenyu Tang, Jian Zhou, Xu Dai, Yuhao Xiao and Xia Fang

Processes 2025, 13(2), 316; https://doi.org/10.3390/pr13020316 - 23 Jan 2025

Viewed by 815

Abstract

To solve the problem of scale variation in insulator images captured by drones, caused by the lack of control over angle and distance, which makes it hard to detect subtle defects, this paper proposes an instance segmentation method based on an improved Mask2Former-HRNet [...] Read more.

To solve the problem of scale variation in insulator images captured by drones, caused by the lack of control over angle and distance, which makes it hard to detect subtle defects, this paper proposes an instance segmentation method based on an improved Mask2Former-HRNet model for precise localization and defect detection of transmission line insulators. First, a mask-guided and matching component is added to Mask2Former to reduce the misjudgment rate of insulator defects by including noisy label masks. Second, the HRNet backbone network is used to better capture the spatial and shape information of insulators, as it has a stronger feature transfer ability. Deformable convolutions are introduced to handle deformation issues caused by varying angles in insulator images. Then, an attention mechanism is added to focus on key content, improving the network’s attention to crucial information. Finally, experimental results on defect detection of transmission line insulator images captured by drones show that the proposed method increases the detection accuracy by 8.41% and reduces the misjudgment rate by 4.11%. Comparative experiments indicate that the proposed method outperforms existing methods in several evaluation metrics. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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17 pages, 17333 KiB  

Open AccessArticle

A Three-Granularity Pose Estimation Framework for Multi-Type High-Voltage Transmission Towers Using Part Affinity Fields (PAFs)

by Yaoran Huo, Xu Dai, Zhenyu Tang, Yuhao Xiao, Yupeng Zhang and Xia Fang

Energies 2025, 18(3), 488; https://doi.org/10.3390/en18030488 - 22 Jan 2025

Viewed by 628

Abstract

At present, Unmanned Aerial Vehicles (UAVs) combined with deep learning have become an important means of transmission line inspection; however, the current approach has the problems of high demand for manual operation, low inspection efficiency, inspection results that do not reflect the distribution [...] Read more.

At present, Unmanned Aerial Vehicles (UAVs) combined with deep learning have become an important means of transmission line inspection; however, the current approach has the problems of high demand for manual operation, low inspection efficiency, inspection results that do not reflect the distribution of defects on transmission towers, and the need for a large number of manually annotated captured images. In order to achieve the UAV understanding the structure of transmission towers and identifying the defects in the parts of transmission towers, a three-granularity pose estimation framework for multi-type high-voltage transmission towers using Part Affinity Fields (PAFs) is presented here. The framework classifies the structural critical points of high-voltage transmission towers and uses PAFs to provide a basis for the connection between the critical points to achieve the pose estimation for multi-type towers. On the other hand, a three-fine-grained prediction incorporating an intermediate supervisory mechanism is designed so as to overcome the problem of dense and overlapping keypoints of transmission towers. The dataset used in this study consists of real image data of high-voltage transmission towers and complementary images of virtual scenes created through the fourth-generation Unreal Engine (UE4). In various types of electrical tower detection, the average keypoint identification AF of the proposed model exceeds 96% and the average skeleton connection AF exceeds 93% at all granularities, which demonstrates good results on the test set and shows some degree of generalization to electricity towers not included in the dataset. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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15 pages, 376 KiB  

Open AccessArticle

Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems

by Hao Sun, Shaosen Li, Jianxiang Huang, Hao Li, Guanxin Jing, Ye Tao and Xincui Tian

Energies 2025, 18(2), 313; https://doi.org/10.3390/en18020313 - 12 Jan 2025

Viewed by 1008

Abstract

Predicting the cooling capacity of converter valves is crucial for maintaining the stability and efficiency of high-voltage direct current (HVDC) systems. This task involves handling complex, multi-dimensional time-series data with strong inter-variable dependencies and temporal dynamics. Traditional machine learning methods, while effective in [...] Read more.

Predicting the cooling capacity of converter valves is crucial for maintaining the stability and efficiency of high-voltage direct current (HVDC) systems. This task involves handling complex, multi-dimensional time-series data with strong inter-variable dependencies and temporal dynamics. Traditional machine learning methods, while effective in static scenarios, struggle to capture these dependencies, and existing deep learning models often lack the ability to jointly model spatial and temporal relationships. To address these challenges, we propose a novel framework that integrates Graph Neural Networks (GNNs) with temporal dynamics. The GNN component captures spatial dependencies by representing the data as a graph, where nodes correspond to system variables, and edges encode their relationships. Temporal dependencies are modeled using temporal convolutional layers and recurrent neural networks (RNNs), enabling the framework to learn both short-term variations and long-term trends. Additionally, a graph attention mechanism dynamically adjusts the importance of variable relationships, improving prediction accuracy and interoperability. The proposed method demonstrates superior performance over traditional machine learning and deep learning baselines on real-world cooling system data. These results validate the effectiveness of the framework for industrial applications such as cooling system monitoring and predictive maintenance. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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16 pages, 4246 KiB  

Open AccessArticle

Numerical Simulation and Experimental Research on Cutting Force of Milling Deicing Robot Milling Cutter

by Junlong Zhou, Chao Tang, Maolin Zhu, Wenchao Chen, Hongchun Yang, Donghong Wei and Gaohui He

Processes 2025, 13(1), 140; https://doi.org/10.3390/pr13010140 - 7 Jan 2025

Viewed by 581

Abstract

During deicing operations on transmission lines, the cutting forces generated by the milling cutter of a deicing robot exert significant reaction forces on the robot body. Excessive cutting forces can compromise the robot’s locomotion stability and deicing performance. This study introduces an optimization [...] Read more.

During deicing operations on transmission lines, the cutting forces generated by the milling cutter of a deicing robot exert significant reaction forces on the robot body. Excessive cutting forces can compromise the robot’s locomotion stability and deicing performance. This study introduces an optimization of the traditional straight-plate milling cutter by designing two new types of deicing milling cutters: oblique-cut and straight-cut milling cutters. The effects of cutter geometry, milling speed, and feed rate on cutting forces were systematically investigated using finite element simulations. A deicing test platform was constructed to validate the simulation results. The findings indicate that the cutting force hierarchy among the three designs is as follows: straight-plate > oblique-cut > straight-cut. Notably, the straight-cut milling cutter reduces cutting forces by 16–33% compared with the traditional straight-plate cutter. Furthermore, higher milling speeds and faster feed rates along the transmission line increase cutting forces. These studies provide valuable guidance for optimizing milling cutter designs in deicing robots. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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22 pages, 4426 KiB  

Open AccessArticle

Collaborative Optimal Configuration of Active–Reactive Flexible Resources Based on Wasserstein Confidence Set

by Xiaoke Lin, Zhaobin Du, Lanfen Cheng, Peizheng Xuan and Ziqin Zhou

Electronics 2025, 14(1), 59; https://doi.org/10.3390/electronics14010059 - 26 Dec 2024

Viewed by 690

Abstract

Flexible resources (FRs) have significant potential in ensuring the dynamic balance between supply and demand as well as enhancing the security of active distribution networks (ADNs). However, determining the optimal FR capacity in an economically reasonable manner remains a challenging task. This paper [...] Read more.

Flexible resources (FRs) have significant potential in ensuring the dynamic balance between supply and demand as well as enhancing the security of active distribution networks (ADNs). However, determining the optimal FR capacity in an economically reasonable manner remains a challenging task. This paper addresses the lack of representativeness of wind turbine (WT) and photovoltaic (PV) power output scenarios in the planning stage by generating a basic set of joint WT-PV output scenarios using random sampling. Subsequently, a Wasserstein confidence set (WCS) is established based on data-driven technology to better represent the unknown distribution of the actual WT-PV joint fluctuations. This provides a more detailed description of the scenario set, enabling the precise quantification of the risk of resource allocation scenarios and enhancing the flexibility and rigor of the subsequent optimal configuration model (OCM). To improve the coordination of active–reactive FRs, a bi-level OCM with multi-timescale considerations is developed. Compared to traditional configuration methods, the proposed model not only improves economic efficiency but also ensures that system voltage remains within safe limits after configuration. The effectiveness and superiority of the proposed optimal configuration method are demonstrated through simulations on an improved 33-bus test system, where the model achieved a 9.208% reduction in annual cost compared to robust methods while maintaining voltage quality and avoiding overvoltage or equipment overloads. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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23 pages, 7117 KiB  

Open AccessArticle

A Grounding Current Prediction Method Based on Frequency-Enhanced Transformer

by Na Zhang, Gang Yang, Zilong Fu and Junsheng Hou

Energies 2025, 18(1), 32; https://doi.org/10.3390/en18010032 - 25 Dec 2024

Viewed by 663

Abstract

Concerning the problem that the coupling relationship in substation scenarios is complex and the Transformer model makes it difficult to capture the correlation between multiple variables of grounding current, resulting in low accuracy of grounding current prediction, a ground current prediction method based [...] Read more.

Concerning the problem that the coupling relationship in substation scenarios is complex and the Transformer model makes it difficult to capture the correlation between multiple variables of grounding current, resulting in low accuracy of grounding current prediction, a ground current prediction method based on frequency-enhanced Transformer is proposed. Firstly, in the data preprocessing stage, the best frequency domain decomposition algorithm is designed to obtain the high-frequency and low-frequency component data containing different component features so as to enhance the initial features that the model focuses on. Secondly, the data slicing and embedding module is designed to replace the original embedding module of the Transformer to realize the enhanced extraction of local features of the data. Finally, in the feature extraction stage, an enhanced attention mechanism is introduced to replace the standard attention mechanism to capture the intrinsic features of the sequence time dimension and the variable dimension in parallel so as to improve the extraction ability of Transformer multivariate features. Experimental results on the self-built grounding current dataset and the public dataset show that the proposed method outperforms existing advanced methods, verifying the effectiveness of the proposed method. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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17 pages, 2107 KiB  

Open AccessArticle

Local Iterative Calculation Method and Fault Analysis of Short-Circuit Current in High-Voltage Grid with Large-Scale New Energy Equipment Integration

by Zhongping Liu, Baisong Su, Qingjing Ji and Yan Hu

Sustainability 2024, 16(24), 11144; https://doi.org/10.3390/su162411144 - 19 Dec 2024

Viewed by 795

Abstract

This paper delves into the critical issues of relay protection setting calculation in high-voltage power grids with large-scale integration of renewable energy sources, such as wind and solar power. By analyzing the topological structure of renewable energy systems, models of permanent magnet direct-drive [...] Read more.

This paper delves into the critical issues of relay protection setting calculation in high-voltage power grids with large-scale integration of renewable energy sources, such as wind and solar power. By analyzing the topological structure of renewable energy systems, models of permanent magnet direct-drive wind turbines and photovoltaic power sources are established, with a particular focus on the short-circuit current characteristics of these renewable energy sources. Subsequently, a fault iterative method for short-circuit current calculation is proposed. This method effectively improves the accuracy of short-circuit current calculation by iteratively analyzing the fault region and considering the voltage-controlled current source characteristics of renewable energy sources. The paper also conducts in-depth research on various aspects of relay protection settings after the integration of renewable energy devices, including main transformer neutral grounding strategies, tie-line protection and reclosing principles, islanding prevention, and boundary backup protection management. By applying this method to a practical engineering case in G Province, China, the short-circuit current is calculated, and partial setting values are determined, demonstrating the ability of this method to enhance system safety and stability. This research provides valuable insights for operators of modern power systems. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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13 pages, 1316 KiB  

Open AccessArticle

Optimization Operation Strategy for Comprehensive Energy System Considering Multi-Mode Hydrogen Transportation

by Qingming Liu, Zhengkun Zhou, Jingyan Chen, Dan Zheng and Hongbo Zou

Processes 2024, 12(12), 2893; https://doi.org/10.3390/pr12122893 - 18 Dec 2024

Cited by 1 | Viewed by 935

Abstract

The transformation from a fossil fuel economy to a low-carbon economy has reshaped the way energy is transmitted. As most renewable energy is obtained in the form of electricity, using green electricity to produce hydrogen is considered a promising energy carrier. However, most [...] Read more.

The transformation from a fossil fuel economy to a low-carbon economy has reshaped the way energy is transmitted. As most renewable energy is obtained in the form of electricity, using green electricity to produce hydrogen is considered a promising energy carrier. However, most studies have not considered the transportation mode of hydrogen. In order to encourage the utilization of renewable energy and hydrogen, this paper proposes a comprehensive energy system optimization operation strategy considering multi-mode hydrogen transport. Firstly, to address the shortcomings in the optimization operation of existing systems regarding hydrogen transport, modeling is conducted for multi-mode hydrogen transportation through hydrogen tube trailers and pipelines. This model reflects the impact of multi-mode hydrogen delivery channels on hydrogen utilization, which helps promote the consumption of new energy in electrolysis cells to meet application demands. Based on this, the constraints of electrolyzers, combined heat and power units, hydrogen fuel cells, and energy storage systems in integrated energy systems (IESs) are further considered. With the objective of minimizing the daily operational cost of the comprehensive energy system, an optimization model for the operation considering multi-mode hydrogen transport is constructed. Lastly, based on simulation examples, the impact of multi-mode hydrogen transportation on the operational cost of the system is analyzed in detail. The results indicate that the proposed optimization strategy can reduce the operational cost of the comprehensive energy system. Hydrogen tube trailers and pipelines will have a significant impact on operational costs. Properly allocating the quantity of hydrogen tube trailers and pipelines is beneficial for reducing the operational costs of the system. Reasonable arrangement of hydrogen transportation channels is conducive to further promoting the green and economic operation of the system. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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20 pages, 1384 KiB  

Open AccessReview

Framework and Outlooks of Multi-Source–Grid–Load Coordinated Low-Carbon Operational Systems Considering Demand-Side Hierarchical Response

by Yong Cui, Jian Zheng, Wenying Wu, Kun Xu, Desen Ji and Tian Di

Energies 2024, 17(23), 6208; https://doi.org/10.3390/en17236208 - 9 Dec 2024

Viewed by 787

Abstract

In the context of advancing new power systems, a multi-source–grid–load interactive operation framework considering low-carbon demand hierarchical response is developed to further explore the support value of the multi-source–grid–load interaction mechanism for the low-carbon economic operation of the power system. The framework analyzes [...] Read more.

In the context of advancing new power systems, a multi-source–grid–load interactive operation framework considering low-carbon demand hierarchical response is developed to further explore the support value of the multi-source–grid–load interaction mechanism for the low-carbon economic operation of the power system. The framework analyzes the support mechanisms of carbon tracking and load-side demand response for the low-carbon economic dispatch of the system and derives the carbon flow calculation method based on the network node correlation matrix, laying the foundation for developing low-carbon demand response strategies. Meanwhile, considering the marginal contribution of each load-side node to the system carbon emissions, a combined Shapley–Topsis low-carbon demand hierarchical response mechanism is designed to guide load nodes in implementing accurate low-carbon hierarchical responses, thereby ensuring the optimal allocation and efficient utilization of system resources. Finally, based on the proposed framework, promising future research perspectives are proposed to provide critical insights for constructing a low-carbon and reliable new energy system. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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15 pages, 1847 KiB  

Open AccessArticle

Validation of Electromechanical Transient Model for Large-Scale Renewable Power Plants Based on a Fast-Responding Generator Method

by Dawei Zhao, Yujie Ning, Chuanzhi Zhang, Jin Ma, Minhui Qian and Yanzhang Liu

Energies 2024, 17(23), 5831; https://doi.org/10.3390/en17235831 - 21 Nov 2024

Viewed by 672

Abstract

The requirements for accurate models of renewable energy power plants are urgent for power system operation analysis. Most existing model research in this area is for wind turbine and photovoltaic (PV) power generation units; a rare renewable power plant model validation mainly adopts [...] Read more.

The requirements for accurate models of renewable energy power plants are urgent for power system operation analysis. Most existing model research in this area is for wind turbine and photovoltaic (PV) power generation units; a rare renewable power plant model validation mainly adopts the single-machine infinite-bus system. The single equivalent machine method is always used, and the interactions between the power plant and the grid are ignored. The voltage at the interface bus is treated as constant, although this is not consistent with its actual characteristics. The phase shifter method of hybrid dynamic simulation has been applied in the model validation of wind farms. However, this method is heavily dependent on phasor measurement units (PMU) data, resulting in a limited application scope, and it is difficult to realize the model error location step by step. In this paper, the fast-responding generator method is used for renewable power plant model validation. The complete scheme comprising model validation, error localization, parameter sensitivity analysis, and parameter correction is proposed. Model validation is conducted based on measured records from a large-scale PV power plant in northwest China. The comparison of simulated and measured data verifies the feasibility and accuracy of the proposed scheme. Compared to the conventional model validation method, the maximum deviation of the active power simulation values obtained by the method proposed in this paper is only 38.8% of that of the conventional method, and the overall simulation curve fits the actual measured values significantly better. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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35 pages, 8784 KiB  

Open AccessArticle

Parameter Identification of Solid Oxide Fuel Cell Using Elman Neural Network and Dynamic Fitness Distance Balance-Manta Ray Foraging Optimization Algorithm

by Hongbiao Li, Dengke Gao, Linlong Shi, Fei Zheng and Bo Yang

Processes 2024, 12(11), 2504; https://doi.org/10.3390/pr12112504 - 11 Nov 2024

Viewed by 875

Abstract

An accurate solid oxide fuel cell model is a prerequisite for optimizing the operation and state estimation of subsequent cell systems. Hence, this work aimed to utilize a vigoroso algorithmic tool, i.e., Elman neural network, for data prediction to enrich cell measurement data [...] Read more.

An accurate solid oxide fuel cell model is a prerequisite for optimizing the operation and state estimation of subsequent cell systems. Hence, this work aimed to utilize a vigoroso algorithmic tool, i.e., Elman neural network, for data prediction to enrich cell measurement data and employ the trained network model for noise reduction of voltage–current data. Furthermore, to obtain reliable cell parameters, a novel parameter identification model based on the dynamic fitness distance balance-manta ray foraging optimization (dFDB-MRFO) algorithm is proposed. Two datasets were applied to extract the electrochemical model and simple electrochemical model parameters of the solid oxide fuel cell model. To verify adequately the superiority of this method, which is compared with another seven conventional heuristic algorithms, four performance indicators were selected as evaluation criteria. Comprehensive case studies demonstrated that through data processing, the precision and robustness of identification could be effectively heightened. In general, the model fitting data obtained via parameter identification using dFDB-MRFO have excellent fitting precision contrast with the measured voltage–current data. Notably, the fitting degree obtained by dFDB-MRFO in the simple electrochemical model reached 99.95% and 99.91% under the two datasets, respectively. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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19 pages, 4670 KiB  

Open AccessArticle

Optimal Sliding Speed and Contact Pressure Design of On-Load Tap Changer Based on Multivariate Nonlinear Regression

by Zhiqi Xu, Sijiang Zhang, Jintao Zhang, Xiaobing Wang, Yanwen Xu, Zongying Li, Minghan Ma and Shuaibing Li

Electronics 2024, 13(22), 4349; https://doi.org/10.3390/electronics13224349 - 6 Nov 2024

Viewed by 840

Abstract

During the voltage regulation of on-load tap changers (OLTCs), the movement of the contacts can easily cause arcing, which may lead to erosion or malfunction. To reduce the energy and probability of arcing, we focus on designing an optimal range for the sliding [...] Read more.

During the voltage regulation of on-load tap changers (OLTCs), the movement of the contacts can easily cause arcing, which may lead to erosion or malfunction. To reduce the energy and probability of arcing, we focus on designing an optimal range for the sliding speed and contact pressure of the contacts to minimize arc energy. Initially, our research introduces a novel OLTC arc testing platform to simulate the motion of static and dynamic contacts, exploring the relationship between different sliding speeds, contact pressures, and factors like arc voltage waveform, arcing rate, arc resistance, and arc energy. Subsequently, by employing multiple nonlinear regression methods, we establish functional relationships between sliding speed and arc energy, as well as contact pressure and arc energy, evaluating the fit using correlation coefficients. Finally, through analyzing their nonlinear behaviors, we determine the ideal sliding speed and contact pressure. The results indicate that when the OLTC contacts slide at an optimal speed between 89 and 103 mm/s and optimal contact pressure between 1.5 and 1.7 N, the arc energy can be minimized, thereby enhancing the performance and lifespan of the on-load tap changer. This study offers feasible insights for the design and operation of OLTCs, aiding in the improvement of power system regulation. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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25 pages, 6153 KiB  

Open AccessArticle

State of Health Estimation of Lithium-Ion Battery Using Multi-Health Features Based on Savitzky–Golay Filter and Fitness-Distance Balance- and Lévy Roulette-Enhanced Coyote Optimization Algorithm-Optimized Long Short-Term Memory

by Hongbiao Li, Dengke Gao, Linlong Shi, Fei Zheng and Bo Yang

Processes 2024, 12(10), 2284; https://doi.org/10.3390/pr12102284 - 18 Oct 2024

Cited by 1 | Viewed by 1064

Abstract

Accurate and reliable state of health (SOH) estimation is extremely crucial for the safe and stable operation of lithium-ion batteries (LIBs). In this paper, a method based on Lévy roulette- and fitness-distance balance-enhanced coyote optimization algorithm-optimized long short-term memory (LRFDBCOA-LSTM) is employed for [...] Read more.

Accurate and reliable state of health (SOH) estimation is extremely crucial for the safe and stable operation of lithium-ion batteries (LIBs). In this paper, a method based on Lévy roulette- and fitness-distance balance-enhanced coyote optimization algorithm-optimized long short-term memory (LRFDBCOA-LSTM) is employed for SOH estimation of LIB. Firstly, six health features are extracted from battery charging and discharging data, and Savitzky–Golay is used to filter the feature data to improve correlation between feature and SOH. Then, Lévy roulette and fitness-distance balance (FDB) strategies are used to improve the coyote optimization algorithm (COA), i.e., LRFDBCOA. Meanwhile, the improved algorithm is used to optimize the internal parameters of long short-term memory (LSTM) neural network. Finally, the effectiveness of the proposed model is comprehensively validated using five evaluation indicators based on battery data obtained under three different testing conditions. The experimental results manifest that after algorithm improvement and network parameter optimization, the performance of the model is significantly improved. In addition, the method has high estimation accuracy, strong generalization, and strong robustness for SOH estimation with a maximum R² of 0.9896 and minimum R² of no less than 0.9711. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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21 pages, 4261 KiB  

Open AccessArticle

The Study of an Improved Particle Swarm Optimization Algorithm Applied to Economic Dispatch in Microgrids

by Ang Dong and Seon-Keun Lee

Electronics 2024, 13(20), 4086; https://doi.org/10.3390/electronics13204086 - 17 Oct 2024

Cited by 2 | Viewed by 1409

Abstract

With the widespread use of fossil fuels, the Earth’s environment is facing a severe threat of degradation. Traditional large-scale power grids have struggled to meet the ever-growing demands of modern society. The implementation and functioning of microgrids not only enhance the use of [...] Read more.

With the widespread use of fossil fuels, the Earth’s environment is facing a severe threat of degradation. Traditional large-scale power grids have struggled to meet the ever-growing demands of modern society. The implementation and functioning of microgrids not only enhance the use of renewable energy sources but also considerably diminish the environmental damage resulting from fossil fuel consumption. However, the inherent instability of renewable energy presents a major challenge to the reliability of microgrids. To address the uncertainties of wind and photovoltaic power generation, it is urgent to adopt effective operational control methods to adjust power distribution, thereby achieving an economically efficient system operation and ensuring a reliable power supply. This paper utilizes a microgrid system consisting of wind power, photovoltaic power generation, thermal power units, and energy storage devices as the research object, establishing an economic dispatch model aimed at minimizing the total operating cost of the system. To solve this problem, the paper introduces second-order oscillatory particles and improves the Particle Swarm Optimization algorithm, proposing a second-order oscillatory chaotic mapping particle swarm optimization (SCMPSO). The simulation results show that this method can effectively optimize system operating costs while ensuring the stable operation of the microgrid. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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