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Big Data Analysis and Application in Power System
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Big Data Analysis and Application in Power System

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: 5 May 2025 | Viewed by 6054

Special Issue Editors

Prof. Dr. Bo Wang

E-Mail Website
Guest Editor
School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Interests: power safety image interpretation; big data and AI applications in energy systems; multimodal data fusion in power systems
Dr. Hengrui Ma

E-Mail Website
Guest Editor
New Energy Photovoltaic Industry Research Center, Qinghai University, Xining 810016, China
Interests: AI applications in energy systems; multi-energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Fuqi Ma

E-Mail Website
Guest Editor
School of Electrical Engineering, Xi'an University of Technology, Xi’an 710054, China
Interests: power safety image interpretation; power vision understanding; edge intelligence
Dr. Hongxia Wang

E-Mail Website
Guest Editor
Ritchie School of Engineering and Computer Science, University of Denver, Denver, CO, USA
Interests: big data and multimodal data fusion in power systems

Special Issue Information

Dear Colleagues,

In recent years, with the rapid development of power industry and the continuous construction of intelligent power systems, a large amount of information data is generated in the process of power production, marketing and service, and each of them will accumulate a large amount of historical data. The application of data in power system has developed from structured data to the unstructured data and even multi-physical field data.

This special issue aims to present and disseminate the latest development of big data in energy production, multi-energy system operation, and security risk analysis.

Topics of interest for this publication include, but are not limited to:

  • Microgrid architecture, monitoring and analysis
  • Multi-physical field fusion computational imaging technology
  • Production safety risk identification technology
  • Personal safety risk analysis technology driven by multi-source data
  • Energy forecasting, i.e. wind, solar, load, price
  • Optimization and control of low-carbon energy system
  • Demand response and resources analytics
  • Technologies, problems and applications of multimodal data in future power systems
  • Multimodal data based analysis of power equipment and energy systems interactions

Prof. Dr. Bo Wang
Dr. Hengrui Ma
Dr. Fuqi Ma
Dr. Hongxia Wang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • big data
  • multimodal data fusion
  • energy systems
  • power safety

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Published Papers (6 papers)

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Research

21 pages, 9837 KiB  
Article
Novel Distributed Power Flow Controller Topology and Its Coordinated Output Optimization in Distribution Networks
by Yangqing Dan, Ke Sun, Jun Wang, Yanan Fei, Le Yu and Licheng Sun
Energies 2025, 18(9), 2148; https://doi.org/10.3390/en18092148 - 22 Apr 2025
Viewed by 136
Abstract
Conventional Distributed Power Flow Controllers (DPFCs) rely on third-harmonic currents to facilitate active power exchange between the series side and the system, requiring specific Δ/YN and YN/Δ transformer configurations at branch terminals. This limitation restricts their application in distribution networks. To overcome these [...] Read more.
Conventional Distributed Power Flow Controllers (DPFCs) rely on third-harmonic currents to facilitate active power exchange between the series side and the system, requiring specific Δ/YN and YN/Δ transformer configurations at branch terminals. This limitation restricts their application in distribution networks. To overcome these constraints, this paper proposes a Novel Distributed Power Flow Controller (NDPFC) topology specifically designed for distribution networks. This design eliminates the need for third-harmonic currents and specific transformer configurations, enhancing deployment flexibility. The paper first explains the NDPFC operating principles and verifies its power flow regulation capabilities through a typical distribution network system. Furthermore, we develop electromagnetic transient mathematical models for both series and shunt components of the NDPFC, proposing a triple-loop control strategy for Series-I and Series-II control methods to enhance system robustness and control precision. A systematic stability analysis confirms the proposed controller’s robustness under various operating conditions. Simulation results demonstrate that in various distribution network scenarios, the NDPFC effectively achieves comprehensive power flow regulation, compensates three-phase imbalances, and facilitates renewable energy integration, significantly improving distribution network power quality. A comparative analysis shows that the NDPFC achieves 15% faster response times and 12% lower losses compared to conventional power flow controllers. Full article
(This article belongs to the Special Issue Big Data Analysis and Application in Power System)
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23 pages, 16776 KiB  
Article
A Joint Estimation Method of Distribution Network Topology and Line Parameters Based on Power Flow Graph Convolutional Networks
by Yu Wang, Xiaodong Shen, Xisheng Tang and Junyong Liu
Energies 2024, 17(21), 5272; https://doi.org/10.3390/en17215272 - 23 Oct 2024
Viewed by 971
Abstract
Accurate identification of network topology and line parameters is essential for effective management of distribution systems. An innovative joint estimation method for distribution network topology and line parameters is presented, utilizing a power flow graph convolutional network (PFGCN). This approach addresses the limitations [...] Read more.
Accurate identification of network topology and line parameters is essential for effective management of distribution systems. An innovative joint estimation method for distribution network topology and line parameters is presented, utilizing a power flow graph convolutional network (PFGCN). This approach addresses the limitations of traditional methods that rely on costly voltage phase angle measurements. The node correlation principle is applied to construct a node correlation matrix, and a minimum distance iteration algorithm is proposed to generate candidate topologies, which serve as graph inputs for the parameter estimation model. Based on the topological dependencies and convolutional properties of AC power flow equations, a PFGCN model is designed for line parameter estimation. Parameter refinement is achieved through an alternating iterative process of pseudo-trend calculation and neural network training. Training convergence and loss function values are used as feedback to filter and validate candidate topologies, enabling precise joint estimation of both topologies and parameters. The proposed method’s accuracy, transferability, and robustness are demonstrated through experiments on the IEEE-33 and modified IEEE-69 distribution systems. Multiple metrics, including MAPE, IAE, MAE, and R2, highlight the proposed method’s advantages over Adaptive Ridge Regression (ARR). In the C33 scenario, the proposed method achieves MAPEs of 4.6% for g and 5.7% for b, outperforming the ARR method with MAPEs of 7.1% and 7.9%, respectively. Similarly, in the IC69 scenario, the proposed method records MAPEs of 3.0% for g and 5.9% for b, surpassing the ARR method’s 5.1% and 8.3%. Full article
(This article belongs to the Special Issue Big Data Analysis and Application in Power System)
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20 pages, 27208 KiB  
Article
Optimization of Organic Rankine Cycle for Hot Dry Rock Power System: A Stackelberg Game Approach
by Zhehao Hu, Wenbin Wu and Yang Si
Energies 2024, 17(20), 5151; https://doi.org/10.3390/en17205151 - 16 Oct 2024
Cited by 2 | Viewed by 979
Abstract
Due to its simple structure and stable operation, the Organic Rankine Cycle (ORC) has gained significant attention as a primary solution for low-grade thermal power generation. However, the economic challenges associated with development difficulties in hot dry rock (HDR) geothermal power systems have [...] Read more.
Due to its simple structure and stable operation, the Organic Rankine Cycle (ORC) has gained significant attention as a primary solution for low-grade thermal power generation. However, the economic challenges associated with development difficulties in hot dry rock (HDR) geothermal power systems have necessitated a better balance between performance and cost effectiveness within ORC systems. This paper establishes a game pattern of the Organic Rankine Cycle with performance as the master layer and economy as the slave layer, based on the Stackelberg game theory. The optimal working fluid for the ORC is identified as R600. At the R600 mass flow rate of 50 kg/s, the net system cycle work is 4186 kW, the generation efficiency is 14.52%, and the levelized cost of energy is 0.0176 USD/kWh. The research establishes an optimization method for the Organic Rankine Cycle based on the Stackelberg game framework, where the network of the system is the primary optimization objective, and the heat transfer areas of the evaporator and condenser serve as the secondary optimization objective. An iterative solving method is utilized to achieve equilibrium between the performance and economy of the ORC system. The proposed method is validated through a case study utilizing hot dry rock data from Qinghai Gonghe, allowing for a thorough analysis of the working fluid and system parameters. The findings indicate that the proposed approach effectively balances ORC performance with economic considerations, thereby enhancing the overall revenue of the HDR power system. Full article
(This article belongs to the Special Issue Big Data Analysis and Application in Power System)
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14 pages, 364 KiB  
Article
Two-Stage Robust Resilience Enhancement of Distribution System against Line Failures via Hydrogen Tube Trailers
by Libin Yang, Zhengxi Li, Tingxiang Liu, Na An, Wanpeng Zhou and Yang Si
Energies 2024, 17(20), 5028; https://doi.org/10.3390/en17205028 - 10 Oct 2024
Viewed by 844
Abstract
Due to the properties of zero emission and high energy density, hydrogen plays a significant role in future power system, especially in extreme scenarios. This paper focuses on scheduling hydrogen tube trailers (HTTs) before contingencies so that they can enhance resilience of distribution [...] Read more.
Due to the properties of zero emission and high energy density, hydrogen plays a significant role in future power system, especially in extreme scenarios. This paper focuses on scheduling hydrogen tube trailers (HTTs) before contingencies so that they can enhance resilience of distribution systems after contingencies by emergency power supply. The whole process is modeled as a two-stage robust optimization problem. At stage 1, the locations of hydrogen tube trailers and their capacities of hydrogen are scheduled before the contingencies of distribution line failures are realized. After the line failures are observed, hydrogen is utilized to generate power by hydrogen fuel cells at stage 2. To solve the two-stage robust optimization problem, we apply a column and constraint generation (C&CG) algorithm, which divided the problem into a stage-1 scheduling master problem and a stage-2 operation subproblem. Finally, experimental results show the effectiveness of enhancing resilience of hydrogen and the efficiency of the C&CG algorithm in scheduling hydrogen tube trailers. Full article
(This article belongs to the Special Issue Big Data Analysis and Application in Power System)
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20 pages, 8764 KiB  
Article
Detection Method of External Damage Hazards in Transmission Line Corridors Based on YOLO-LSDW
by Hongbo Zou, Jinlong Yang, Jialun Sun, Changhua Yang, Yuhong Luo and Jiehao Chen
Energies 2024, 17(17), 4483; https://doi.org/10.3390/en17174483 - 6 Sep 2024
Cited by 3 | Viewed by 1053
Abstract
To address the frequent external damage incidents to transmission line corridors caused by construction machinery such as excavators and cranes, this paper constructs a dataset of external damage hazards in transmission line corridors and proposes a detection method based on YOLO-LSDW for these [...] Read more.
To address the frequent external damage incidents to transmission line corridors caused by construction machinery such as excavators and cranes, this paper constructs a dataset of external damage hazards in transmission line corridors and proposes a detection method based on YOLO-LSDW for these hazards. Firstly, by incorporating the concept of large separable kernel attention (LSKA), the spatial pyramid pooling layer is improved to enhance the information exchange between different feature levels, effectively reducing background interference on external damage hazard targets. Secondly, in the neck network, the traditional convolution is replaced with a ghost-shuffle convolution (GSConv) method, introducing a lightweight slim-neck feature fusion structure. This improves the extraction capability for small object features by fusing deep semantic information with shallow detail features, while also reducing the model’s computational load and parameter count. Then, the original YOLOv8 head is replaced with a dynamic head, which combines scale, spatial, and task attention mechanisms to enhance the model’s detection performance. Finally, the wise intersection over union (WIoU) loss function is adopted to optimize the model’s convergence speed and detection performance. Evaluated on the self-constructed dataset of external damage hazards in transmission line corridors, the improved algorithm shows significant improvements in key metrics, with mAP@0.5 and mAP@0.5:0.95 increasing by 3.4% and 4.6%, respectively, compared to YOLOv8s. Additionally, the model’s computational load and parameter count are reduced, and it maintains a high detection speed of 96.2 frames per second, meeting real-time detection requirements. Full article
(This article belongs to the Special Issue Big Data Analysis and Application in Power System)
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15 pages, 489 KiB  
Article
Optimal Siting and Sizing of Hydrogen Production Modules in Distribution Networks with Photovoltaic Uncertainties
by Zhiyong Li, Wenbin Wu, Yang Si and Xiaotao Chen
Energies 2023, 16(22), 7636; https://doi.org/10.3390/en16227636 - 17 Nov 2023
Cited by 1 | Viewed by 1116
Abstract
Hydrogen production modules (HPMs) play a crucial role in harnessing abundant photovoltaic power by producing and supplying hydrogen to factories, resulting in significant operational cost reductions and efficient utilization of the photovoltaic panel output. However, the output of photovoltaic power is stochastic, which [...] Read more.
Hydrogen production modules (HPMs) play a crucial role in harnessing abundant photovoltaic power by producing and supplying hydrogen to factories, resulting in significant operational cost reductions and efficient utilization of the photovoltaic panel output. However, the output of photovoltaic power is stochastic, which will affect the revenue of investing in an HPM. This paper presents a comprehensive analysis of HPMs, starting with the modeling of their operational process and investigating their influence on distribution system operations. Building upon these discussions, a deterministic optimization model is established to address the corresponding challenges. Furthermore, a two-stage stochastic planning model is proposed to determine optimal locations and sizes of HPMs in distribution systems, accounting for uncertainties. The objective of the two-stage stochastic planning model is to minimize the distribution system’s operational costs plus the investment costs of the HPM subject to power flow constraints. To tackle the stochastic nature of photovoltaic power, a data-driven algorithm is introduced to cluster historical data into representative scenarios, effectively reducing the planning model’s scale. To ensure an efficient solution, a Benders’ decomposition-based algorithm is proposed, which is an iterative method with a fast convergence speed. The proposed model and algorithms are validated using a widely utilized IEEE 33-bus system through numerical experiments, demonstrating the optimality of the HPM plan generated by the algorithm. The proposed model and algorithms offer an effective approach for decision-makers in managing uncertainties and optimizing HPM deployment, paving the way for sustainable and efficient energy solutions in distribution systems. Sensitivity analysis verifies the optimality of the HPM’s siting and sizing obtained by the proposed algorithm, which also reveals immense economic and environmental benefits. Full article
(This article belongs to the Special Issue Big Data Analysis and Application in Power System)
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