Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.2
Journals
Energies
Special Issues
Intelligent Analysis and Control of Modern Power Systems
energies-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Intelligent Analysis and Control of Modern Power Systems

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

Deadline for manuscript submissions: 9 October 2024 | Viewed by 7098

Special Issue Editors

Dr. Lipeng Zhu

E-Mail Website
Guest Editor
College of Electrical and Information Engineering, Hunan University, Changsha 410012, China
Interests: power system stability and control; synchrophasor measurement technologies; data management and analytics in smart grids
Dr. Yue Song

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
Interests: stability; control; optimization; microgrids; power systems
Dr. Xinran Zhang

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
Interests: power system stability; load modeling

Special Issue Information

Dear Colleagues,

With the increasing deployment of various advanced sensing and measurement devices, e.g., synchronous phasor measurement units (PMUs) and smart meters, modern electric power systems are experiencing a much-needed upgrade, becoming smart grids with better observability and visibility under various complicated operating conditions. In this circumstance, huge volumes of operational data are available, which means that we are able to carry out data-driven system analysis and facilitate the control of systems for diverse forms of power grids, including bulk power systems, distribution networks, microgrids, etc. In fact, with the help of various advanced machine learning and computational intelligence techniques, a substantial number of promising data-driven applications can be developed to intelligently monitor and control practical power grids, e.g., wide-area state estimation, system dynamics identification, fault diagnosis and localization, dynamic stability/security assessment, and online stability control. Compared with conventional model-based approaches, data-driven intelligent solutions have no limitation on system scale or complexity, and they are, as such, useful for analysing practical large-scale grids with extremely high complexity, especially in the context of increasing uncertainty introduced by today’s high levels of renewable energy.

Despite the significant progress in recent years, data-driven solutions still face many challenges in practice. Typically, for data-driven analysis and the control of practical large-scale power systems, the challenging issues include: insufficient/improper fusion of model-/data-based schemes; a lack of interpretability of data-driven solutions; the scarcity of risky or insecure scenarios for learning data preparation; the vulnerability to practical defective data acquisition conditions; insufficient adaptability to online time-varying conditions; data privacy and security issues during data analytics. If these challenges are well tackled and the potential of data-driven solutions is fully explored, modern power systems may be anatomized and controlled more intelligently and reliably, which could result in much stronger real-time situational awareness and better decision making.

This Special Issue aims to solicit innovative research efforts that address recent advances and new trends in the data-driven intelligent analysis and control of modern power systems. Topics of interest include, but are not limited to:

  • Data management and analytics in modern power grids;
  • Data-driven wide-area state estimation;
  • Data-driven energy management and operational optimization;
  • Data-driven renewable energy/load forecasting;
  • Data-driven power system dynamics identification and modelling;
  • Data-driven power system dynamic stability/security assessment;
  • Data-driven power system wide-area control and protection;
  • Power system fault detection and localization based on data analytics;
  • Power system cybersecurity enhancement based on advanced data analytics;
  • Data-driven situational awareness and the visualization of power systems;
  • Machine learning and reinforcement learning for power system online decision making;
  • Data-enabled intelligent applications in transmission/distribution networks and microgrids;
  • Implementation and pilot projects of data analytics in practical power grids.

Dr. Lipeng Zhu
Dr. Yue Song
Dr. Xinran Zhang
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

  • data analytics
  • computational intelligence
  • machine learning
  • power system dynamics
  • power system analysis and control
  • smart grid
  • situational awareness
  • decision making

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 3891 KiB  
Article
Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map
by Heungseok Lee, Jongju Kim, June Ho Park and Sang-Hwa Chung
Energies 2023, 16(23), 7743; https://0-doi-org.brum.beds.ac.uk/10.3390/en16237743 - 24 Nov 2023
Viewed by 750
Abstract
This study proposes a model for transient stability assessment, which is a convolutional neural network model combined with a saliency map (S–CNN model). The convolutional neural network model is trained on dynamic data acquired through the data measurement devices of a power system. [...] Read more.
This study proposes a model for transient stability assessment, which is a convolutional neural network model combined with a saliency map (S–CNN model). The convolutional neural network model is trained on dynamic data acquired through the data measurement devices of a power system. Applying the saliency map to the acquired dynamic data visually highlights the critical aspects of transient stability assessment. This reduces data training time by eliminating unnecessary aspects during the convolutional neural network model training, thus improving training efficiency. As a result, the proposed model can achieve high performance in transient stability assessment. The dynamic data are acquired by configuring benchmark models, IEEE 39 and 118 bus systems, through MATLAB/Simulink and performing time-domain simulations. Based on the acquired dynamic data, the performance of the proposed model is verified through a confusion matrix. Furthermore, an analysis of the effects of noise interference on the performance is conducted. Full article
(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)
►▼ Show Figures

Figure 1

18 pages, 4167 KiB  
Article
Optimal Scheduling of Power Systems with High Proportions of Renewable Energy Accounting for Operational Flexibility
by Yi Lin, Wei Lin, Wei Wu and Zhenshan Zhu
Energies 2023, 16(14), 5537; https://0-doi-org.brum.beds.ac.uk/10.3390/en16145537 - 21 Jul 2023
Cited by 3 | Viewed by 830
Abstract
The volatility and uncertainty of high-penetration renewable energy pose significant challenges to the stability of the power system. Current research often fails to consider the insufficient system flexibility during real-time scheduling. To address this issue, this paper proposes a flexibility scheduling method for [...] Read more.
The volatility and uncertainty of high-penetration renewable energy pose significant challenges to the stability of the power system. Current research often fails to consider the insufficient system flexibility during real-time scheduling. To address this issue, this paper proposes a flexibility scheduling method for high-penetration renewable energy power systems that considers flexibility index constraints. Firstly, a quantification method for flexibility resources and demands is introduced. Then, considering the constraint of the flexibility margin index, optimization scheduling strategies for different time scales, including day-ahead scheduling and intra-day scheduling, are developed with the objective of minimizing total operational costs. The intra-day optimization is divided into 15 min and 1 min time scales, to meet the flexibility requirements of different time scales in the power system. Finally, through simulation studies, the proposed strategy is validated to enhance the system’s flexibility and economic performance. The daily operating costs are reduced by 3.1%, and the wind curtailment rate is reduced by 4.7%. The proposed strategy not only considers the economic efficiency of day-ahead scheduling but also ensures a sufficient margin to cope with the uncertainty of intra-day renewable energy fluctuations. Full article
(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)
►▼ Show Figures

Figure 1

15 pages, 1330 KiB  
Article
Data-Driven Dynamic Stability Assessment in Large-Scale Power Grid Based on Deep Transfer Learning
by Weijia Wen, Xiao Ling, Jianxin Sui and Junjie Lin
Energies 2023, 16(3), 1142; https://0-doi-org.brum.beds.ac.uk/10.3390/en16031142 - 20 Jan 2023
Cited by 3 | Viewed by 1448
Abstract
For data-driven dynamic stability assessment (DSA) in modern power grids, DSA models generally have to be learned from scratch when faced with new grids, resulting in high offline computational costs. To tackle this undesirable yet often overlooked problem, this work develops a light-weight [...] Read more.
For data-driven dynamic stability assessment (DSA) in modern power grids, DSA models generally have to be learned from scratch when faced with new grids, resulting in high offline computational costs. To tackle this undesirable yet often overlooked problem, this work develops a light-weight framework for DSA-oriented stability knowledge transfer from off-the-shelf test systems to practical power grids. A scale-free system feature learner is proposed to characterize system-wide features of various systems in a unified manner. Given a real-world power grid for DSA, selective stability knowledge transfer is intelligently carried out by comparing system similarities between it and the available test systems. Afterward, DSA model fine-tuning is performed to make the transferred knowledge adapt well to practical DSA contexts. Numerical test results on a realistic system, i.e., the provincial GD Power Grid in China, verify the effectiveness of the proposed framework. Full article
(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)
►▼ Show Figures

Figure 1

17 pages, 1638 KiB  
Article
Comprehensive Analysis of Transient Overvoltage Phenomena for Metal-Oxide Varistor Surge Arrester in LCC-HVDC Transmission System with Special Protection Scheme
by Jaesik Kang
Energies 2022, 15(19), 7034; https://0-doi-org.brum.beds.ac.uk/10.3390/en15197034 - 25 Sep 2022
Cited by 1 | Viewed by 1549
Abstract
This paper proposes a systematic and deterministic method for metal-oxide varistor (MOV) surge arrester selection based on the comprehensive analysis in line-commutated converter (LCC)-based high-voltage direct current (HVDC) transmission systems. For the MOV surge arrester, this paper investigates several significant impacts on the [...] Read more.
This paper proposes a systematic and deterministic method for metal-oxide varistor (MOV) surge arrester selection based on the comprehensive analysis in line-commutated converter (LCC)-based high-voltage direct current (HVDC) transmission systems. For the MOV surge arrester, this paper investigates several significant impacts on the transient overvoltage (TOV) phenomena, which is affected by practical factors such as an operating point of the LCC-HVDC system, synchronous machine operating status of the power system, AC passive filter trip, and communication delay in a special protection system (SPS). In order to determine an appropriate rating of surge arrester, especially for TOV, this paper considers a pattern, magnitude, and duration of TOV based on various fault scenarios in an electrical power system with an LCC-HVDC system. A screening study method with 60 Hz and RMS-based balance system is conducted for examining a wide range of fault scenarios, and then for the specific test cases that need a detailed analysis, electro-magnetic transient (EMT)-based analysis models are developed with an approvable boundary setting method through the equivalent network translation tool. A detailed EMT study is subsequent based on the distinguished cases; as a result, the exact number of metal-oxide resistor stacks could be obtained through the detailed TOV study according to this procedure. The efficacy of the selection method from the proposed procedure based on the comprehensive analysis are verified on a specific power system with a 1.5 GW DC ± 500 kV symmetric monopole LCC-HVDC transmission system. Full article
(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)
►▼ Show Figures

Figure 1

15 pages, 3921 KiB  
Article
A Multistage Current Charging Method for Energy Storage Device of Microgrid Considering Energy Consumption and Capacity of Lithium Battery
by Chuanping Wu, Yu Liu, Tiannian Zhou and Shiran Cao
Energies 2022, 15(13), 4526; https://0-doi-org.brum.beds.ac.uk/10.3390/en15134526 - 21 Jun 2022
Cited by 2 | Viewed by 1144
Abstract
Modular multilevel converter battery energy storage systems (MMC-BESSs) have become an important device for the energy storage of grid-connected microgrids. The efficiency of the power transmission of MMC-BESSs has become a new research hotspot. This paper outlines a multi-stage charging method to minimize [...] Read more.
Modular multilevel converter battery energy storage systems (MMC-BESSs) have become an important device for the energy storage of grid-connected microgrids. The efficiency of the power transmission of MMC-BESSs has become a new research hotspot. This paper outlines a multi-stage charging method to minimize energy consumption and maximize the capacity of MMC-BESSs. Firstly, based on condition monitoring and data collection, the functional relationship between the internal resistance/capacity and other states of lithium batteries is established. Since the energy consumption of the battery is related to internal resistance, current, and time, the energy consumption calculation expression of the battery pack is established, and the objective function is designed to optimize energy consumption and capacity in order to determine the charging current curve of each stage. Compared with the constant current charging method, the proposed multistage current charging method for an MMC-BESS decreases energy consumption by 4.3% and increases the capacity of 5 SOC intervals by 1.56%. Full article
(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop