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Advanced Artificial Intelligence Application for Power Systems
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Advanced Artificial Intelligence Application for Power Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (2 December 2023) | Viewed by 7076

Special Issue Editors

Dr. Teke Gush

E-Mail Website
Guest Editor
College of Information and Communication Engineering, Sungkyunkwan University(SKKU), Suwon 440746, Republic of Korea
Interests: renewable energy grid integration; hosting capacity maximization; smart-inverter application; artificial intelligence application for power systems
Dr. Raza Haider

E-Mail Website
Guest Editor
Department of Electrical Engineering, Balochistan University of Engineering and Technology, Khuzdar,98100, Pakistan
Interests: power systems; distributed generation; microgrid protection; vehicle-to-grid

Special Issue Information

Dear Colleagues,

Power systems have undergone significant changes in recent years, primarily due to the integration of distributed energy resources (DERs), such as photovoltaic (PV), wind power, etc., into the distribution grid. However, as DERs proliferate in the distribution network, technical and economic issues arise, such as reverse power flow, under/overvoltage, feeder and transformer overloading, and protection issues. Most of the conventional methods for addressing these issues are ineffective, complex, and non-adaptable. 

Therefore, the main aim of this Special Issue is to collect articles on the application of advanced artificial intelligence (AI) to address power system issues. The topics of interest for publication include, but are not limited to:

  • Intelligent detection, classification, and location of faults in power systems;
  • Intelligent islanding detection;
  • Renewable energy and load forecasting;
  • Data-driven optimal power flow; and 
  • Data-driven power systems operation and planning.

Dr. Teke Gush
Dr. Raza Haider
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

  • power system
  • artificial intelligence
  • fault detection
  • fault classification
  • fault location
  • islanding detection
  • load forecasting
  • renewable energy forecasting
  • optimal power flow
  • power systems operation and planning

Published Papers (6 papers)

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Research

17 pages, 813 KiB  
Article
N − k Static Security Assessment for Power Transmission System Planning Using Machine Learning
by David L. Alvarez, Mohamed Gaha, Jacques Prévost, Alain Côté, Georges Abdul-Nour and Toualith Jean-Marc Meango
Energies 2024, 17(2), 292; https://doi.org/10.3390/en17020292 - 06 Jan 2024
Viewed by 631
Abstract
This paper presents a methodology for static security assessment of transmission network planning using machine learning (ML). The objective is to accelerate the probabilistic risk assessment of the Hydro-Quebec (HQ) TransÉnergie transmission grid. The model takes the expected power supply and the status [...] Read more.
This paper presents a methodology for static security assessment of transmission network planning using machine learning (ML). The objective is to accelerate the probabilistic risk assessment of the Hydro-Quebec (HQ) TransÉnergie transmission grid. The model takes the expected power supply and the status of the elements in a N−k contingency scenario as inputs. The output is the reliability metric Expecting Load Shedding Cost (ELSC). To train and test the regression model, stochastic data are performed, resulting in a set of N−k and k=1,2,3 contingency scenarios used as inputs. Subsequently, the output is computed for each scenario by performing load shedding using an optimal power flow algorithm, with the objective function of minimizing ELSC. Experimental results on the well-known IEEE-39 bus test system and PEGASE-1354 system demonstrate the potential of the proposed methodology in generalizing ELSC during an N−k contingency. For up to k=3 the coefficient of determination R2 obtained was close to 98% for both case studies, achieving a speed-up of over four orders of magnitude with the use of a Multilayer Perceptron (MLP). This approach and its results have not been addressed in the literature, making this methodology a contribution to the state of the art. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence Application for Power Systems)
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21 pages, 4070 KiB  
Article
A Novel Attention Temporal Convolutional Network for Transmission Line Fault Diagnosis via Comprehensive Feature Extraction
by Guangxun E, He Gao, Youfu Lu, Xuehan Zheng, Xiaoying Ding and Yuanhao Yang
Energies 2023, 16(20), 7105; https://doi.org/10.3390/en16207105 - 16 Oct 2023
Viewed by 584
Abstract
Traditional transmission line fault diagnosis approaches ignore local structure feature information during feature extraction and cannot concentrate more attention on fault samples, which are difficult to diagnose. To figure out these issues, an enhanced feature extraction-based attention temporal convolutional network (EATCN) is developed [...] Read more.
Traditional transmission line fault diagnosis approaches ignore local structure feature information during feature extraction and cannot concentrate more attention on fault samples, which are difficult to diagnose. To figure out these issues, an enhanced feature extraction-based attention temporal convolutional network (EATCN) is developed to diagnose transmission line faults. The proposed EATCN suggests a new comprehensive feature-preserving (CFP) technique to maintain the global and local structure features of original process data during dimension reduction, where the local structure-preserving technique is incorporated into the principal component analysis model. Furthermore, to diagnose transmission line faults more effectively, a CFP-based attention TCN scheme is constructed to classify the global and local structure features of a fault snapshot dataset. To be specific, to cope with the gradient disappearance problem and improve learning capability, a skip connection attention (SCA) network is developed by incorporating a skip connection structure and two fully connected layers into the existing attention mechanism. By combining the developed SCA network with the conventional TCN’s residual blocks, an EATCN-based diagnosis model is then constructed to dynamically pay attention to various imported global and local structure features. Detailed experiments on the datasets of the simulated power system are performed to test the effectiveness of the developed EATCN-based fault diagnosis scheme. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence Application for Power Systems)
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20 pages, 8993 KiB  
Article
Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand Using Deep Reinforcement Learning
by Cephas Samende, Zhong Fan, Jun Cao, Renzo Fabián, Gregory N. Baltas and Pedro Rodriguez
Energies 2023, 16(19), 6770; https://doi.org/10.3390/en16196770 - 22 Sep 2023
Cited by 3 | Viewed by 1396
Abstract
Smart energy networks provide an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for the deep decarbonisation of energy production. However, given the variability of the renewables as well as the energy demand, [...] Read more.
Smart energy networks provide an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for the deep decarbonisation of energy production. However, given the variability of the renewables as well as the energy demand, it is imperative to develop effective control and energy storage schemes to manage the variable energy generation and achieve desired system economics and environmental goals. In this paper, we introduce a hybrid energy storage system composed of battery and hydrogen energy storage to handle the uncertainties related to electricity prices, renewable energy production, and consumption. We aim to improve renewable energy utilisation and minimise energy costs and carbon emissions while ensuring energy reliability and stability within the network. To achieve this, we propose a multi-agent deep deterministic policy gradient approach, which is a deep reinforcement learning-based control strategy to optimise the scheduling of the hybrid energy storage system and energy demand in real time. The proposed approach is model-free and does not require explicit knowledge and rigorous mathematical models of the smart energy network environment. Simulation results based on real-world data show that (i) integration and optimised operation of the hybrid energy storage system and energy demand reduce carbon emissions by 78.69%, improve cost savings by 23.5%, and improve renewable energy utilisation by over 13.2% compared to other baseline models; and (ii) the proposed algorithm outperforms the state-of-the-art self-learning algorithms like the deep-Q network. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence Application for Power Systems)
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21 pages, 1787 KiB  
Article
Learning Data-Driven Stable Corrections of Dynamical Systems—Application to the Simulation of the Top-Oil Temperature Evolution of a Power Transformer
by Chady Ghnatios, Xavier Kestelyn, Guillaume Denis, Victor Champaney and Francisco Chinesta
Energies 2023, 16(15), 5790; https://doi.org/10.3390/en16155790 - 04 Aug 2023
Cited by 2 | Viewed by 746
Abstract
Many engineering systems can be described by using differential models whose solutions, generally obtained after discretization, can exhibit a noticeable deviation with respect to the response of the physical systems that those models are expected to represent. In those circumstances, one possibility consists [...] Read more.
Many engineering systems can be described by using differential models whose solutions, generally obtained after discretization, can exhibit a noticeable deviation with respect to the response of the physical systems that those models are expected to represent. In those circumstances, one possibility consists of enriching the model in order to reproduce the physical system behavior. The present paper considers a dynamical system and proposes enriching the model solution by learning the dynamical model of the gap between the system response and the model-based prediction while ensuring that the time integration of the learned model remains stable. The proposed methodology was applied in the simulation of the top-oil temperature evolution of a power transformer, for which experimental data provided by the RTE, the French electricity transmission system operator, were used to construct the model enrichment with the hybrid rationale, ensuring more accurate predictions. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence Application for Power Systems)
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14 pages, 8801 KiB  
Article
A Hybrid Deep Neural Network Architecture for Day-Ahead Electricity Forecasting: Post-COVID Paradigm
by Neilson Luniere Vilaça, Marly Guimarães Fernandes Costa and Cicero Ferreira Fernandes Costa Filho
Energies 2023, 16(8), 3546; https://doi.org/10.3390/en16083546 - 19 Apr 2023
Viewed by 928
Abstract
Predicting energy demand in adverse scenarios, such as the COVID-19 pandemic, is critical to ensure the supply of electricity and the operation of essential services in metropolitan regions. In this paper, we propose a deep learning model to predict the demand for the [...] Read more.
Predicting energy demand in adverse scenarios, such as the COVID-19 pandemic, is critical to ensure the supply of electricity and the operation of essential services in metropolitan regions. In this paper, we propose a deep learning model to predict the demand for the next day using the “IEEE DataPort Competition Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm” database. The best model uses hybrid deep neural network architecture (convolutional network–recurrent network) to extract spatial-temporal features from the input data. A preliminary analysis of the input data was performed, excluding anomalous variables. A sliding window was applied for importing the data into the network input. The input data was normalized, using a higher weight for the demand variable. The proposed model’s performance was better than the models that stood out in the competition, with a mean absolute error of 2361.84 kW. The high similarity between the actual demand curve and the predicted demand curve evidences the efficiency of the application of deep networks compared with the classical methods applied by other authors. In the pandemic scenario, the applied technique proved to be the best strategy to predict demand for the next day. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence Application for Power Systems)
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15 pages, 10490 KiB  
Article
Prediction Error-Based Power Forecasting of Wind Energy System Using Hybrid WT–ROPSO–NARMAX Model
by Aamer A. Shah, Almani A. Aftab, Xueshan Han, Mazhar Hussain Baloch, Mohamed Shaik Honnurvali and Sohaib Tahir Chauhdary
Energies 2023, 16(7), 3295; https://doi.org/10.3390/en16073295 - 06 Apr 2023
Viewed by 1125
Abstract
The volatility and intermittency of wind energy result in highly unpredictable wind power output, which poses challenges to the stability of the intact power system when integrating large-scale wind power. The accuracy of wind power prediction is critical for maximizing the utilization of [...] Read more.
The volatility and intermittency of wind energy result in highly unpredictable wind power output, which poses challenges to the stability of the intact power system when integrating large-scale wind power. The accuracy of wind power prediction is critical for maximizing the utilization of wind energy, improving the quality of power supply, and maintaining the stable operation of the power grid. To address this challenge, this paper proposes a novel hybrid forecasting model, referred to as Hybrid WT–PSO–NARMAX, which combines wavelet transform, randomness operator-based particle swarm optimization (ROPSO), and non-linear autoregressive moving average with external inputs (NARMAX). The model is specifically designed for power generation forecasting in wind energy systems, and it incorporates the interactions between the wind system’s supervisory control and data acquisition’s (SCADA) actual power record and numerical weather prediction (NWP) meteorological data for one year. In the proposed model, wavelet transform is utilized to significantly improve the quality of the chaotic meteorological and SCADA data. The NARMAX techniques are used to map the non-linear relationship between the NWP meteorological variables and SCADA wind power. ROPSO is then employed to optimize the parameters of NARMAX to achieve higher forecasting accuracy. The performance of the proposed model is compared with other forecasting strategies, and it outperforms in terms of forecasting accuracy improvement. Additionally, the proposed Prediction Error-Based Power Forecasting (PEBF) approach is introduced, which retrains the model to update the results whenever the difference between forecasted and actual wind powers exceeds a certain limit. The efficiency of the developed scheme is evaluated through a real case study involving a 180 MW grid-connected wind energy system located in Shenyang, China. The proposed model’s forecasting accuracy is evaluated using various assessment metrics, including mean absolute error (MAE) and root mean square error (RMSE), with the average values of MAE and RMSE being 0.27% and 0.30%, respectively. The simulation and numerical results demonstrated that the proposed model accurately predicts wind output power. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence Application for Power Systems)
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