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Application of Intelligent Techniques in Power System Stability, Control and Protection

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

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 25239

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Special Issue Editors

Dr. Veerapandiyan Veerasamy

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Guest Editor

School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
Interests: power system control and analysis; deep learning classifiers; power engineering computing

Dr. Shailendra Singh

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Guest Editor

School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
Interests: grid operation and control; smart energy distribution systems; microgrid control

Dr. Sunil Kumar Singh

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Guest Editor

School of Electrical Engineering, Shri Ramswaroop Memorial University, Barabanki, Uttar-Pradesh, India
Interests: power system protection; FACTS devices; artificial intelligence; application of machine learning in power system; hybrid power systems

Special Issue Information

Dear Colleagues,

In the last few years, the depletion of fossil fuels and concerns related to global environmental climatic conditions have led to a significant transition from conventional power generation towards sustainable green energy sources. Presently, in order to meet the emission limitation protocols, developing countries are forced to place greater emphasis on renewable energy resources. However, the increasing penetration of renewable energies (REs) such as solar and wind power generation into the grid to meet the load demand introduces numerous challenges in order to main the stability and reliability of the grid. REs are stochastic in nature due to their strong dependencies on weather conditions, leading to stability issues and sometimes cascading failure in the grid system. As a result, there is an increasing need for the development of efficient control and protection techniques to handle system uncertainties due to RE sources.

For this reason, an amelioration in the power system that facilitates large amounts of RE integration and operates in a more effective, efficient, economical, and sustainable manner is required. Recently, artificial intelligence techniques have gained momentum thanks to their ability to improve the performance of modern power networks by designing robust control and protection mechanisms for the accurate and reliable operation of the system.

The context of energy transition presents several challenges associated to grid stability, reliability, protection, and security, as well as energy production/consumption optimization given the inclusion of RE resources. Addressing these challenges requires the development of scalable advanced optimization techniques for energy consumption, in order to facilitate the penetration (integration) of distributed/centralized renewable energy systems into electric grids, to reduce the peak load, to maintain frequency and voltage stability, and to reinforce grid protection. The advent of prosumers as participants in the grid restricts the application of conventional relaying methodologies in modern power systems.

For over a decade, significant research has been carried out on the applications of AI techniques such as machine learning, deep learning and reinforcement learning in power system stability analysis, control and protection. The major applications include security assessment, stability assessment, fault diagnosis, network control, etc.

This Special Issue aims to collate experimental/numerical/simulation-based investigations with novel solutions as well as review papers with state-of-the-art findings that can deliver significant contributions to the power system research community. The emphasis is on advanced design and modelling studies for handling the challenges of current power networks. Although this Special Issue is open to all contributions related to the application of AI in power systems, potential focus areas are summarized as follows:

Smart energy system;
Power system stability;
Microgrids;
Distribution automation and control;
HVDC and FACTS control;
Static security assessment;
Application of intelligent techniques in protection;
Computational intelligence techniques;
Grid integration of electric vehicles and control;
Grid operation and management with RE;
Peer-to-peer energy management;
Advancement in machine learning.

Dr. Veerapandiyan Veerasamy
Dr. Shailendra Singh
Dr. Sunil Kumar Singh
Guest Editors

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Keywords

optimal power flow
voltage and frequency stability
robust controllers
microgrids
fault detection and classification
artificial intelligence
distributed renewable energy sources
islanding detection

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Published Papers (11 papers)

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Research

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19 pages, 5239 KiB

Open AccessArticle

Studying the Optimal Frequency Control Condition for Electric Vehicle Fast Charging Stations as a Dynamic Load Using Reinforcement Learning Algorithms in Different Photovoltaic Penetration Levels

by Ibrahim Altarjami and Yassir Alhazmi

Energies 2024, 17(11), 2593; https://doi.org/10.3390/en17112593 - 28 May 2024

Viewed by 1016

Abstract

This study investigates the impact of renewable energy penetration on system stability and validates the performance of the (Proportional-Integral-Derivative) PID-(reinforcement learning) RL control technique. Three scenarios were examined: no photovoltaic (PV), 25% PV, and 50% PV, to evaluate the impact of PV penetration on system stability. The results demonstrate that while the absence of renewable energy yields a more stable frequency response, a higher PV penetration (50%) enhances stability in tie-line active power flow between interconnected systems. This shows that an increased PV penetration improves frequency balance and active power flow stability. Additionally, the study evaluates three control scenarios: no control input, PID-(Particle Swarm Optimization) PSO, and PID-RL, to validate the performance of the PID-RL control technique. The findings show that the EV system with PID-RL outperforms the other scenarios in terms of frequency response, tie-line active power response, and frequency difference response. The PID-RL controller significantly enhances the damping of the dominant oscillation mode and restores the stability within the first 4 s—after the disturbance in first second. This leads to an improved stability compared to the EV system with PID-PSO (within 21 s) and without any control input (oscillating more than 30 s). Overall, this research provides the improvement in terms of frequency response, tie-line active power response, and frequency difference response with high renewable energy penetration levels and the research validates the effectiveness of the PID-RL control technique in stabilizing the EV system. These findings can contribute to the development of strategies for integrating renewable energy sources and optimizing control systems, ensuring a more stable and sustainable power grid. Full article

(This article belongs to the Special Issue Application of Intelligent Techniques in Power System Stability, Control and Protection)

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19 pages, 1829 KiB

Open AccessArticle

Bidding Strategy for Wind and Thermal Power Joint Participation in the Electricity Spot Market Considering Uncertainty

by Zhiwei Liao, Wenjuan Tao, Bowen Wang and Ye Liu

Energies 2024, 17(7), 1714; https://doi.org/10.3390/en17071714 - 3 Apr 2024

Cited by 1 | Viewed by 1023

Abstract

As the proportion of new energy sources, such as wind power, in the electricity system rapidly increases, their participation in spot market competition has become an inevitable trend. However, the uncertainty of clearing price and wind power output will lead to bidding deviation and bring revenue risks. In response to this, a bidding strategy is proposed for wind farms to participate in the spot market jointly with carbon capture power plants (CCPP) that have flexible regulation capabilities. First, a two-stage decision model is constructed in the day-ahead market and real-time balancing market. Under the joint bidding mode, CCPP can help alleviate wind power output deviations, thereby reducing real-time imbalanced power settlement. On this basis, a tiered carbon trading mechanism is introduced to optimize day-ahead bidding, aiming at maximizing revenue in both the electricity spot market and carbon trading market. Secondly, conditional value at risk (CVaR) is introduced to quantitatively assess the risks posed by uncertainties in the two-stage decision model, and the risk aversion coefficient is used to represent the decision-maker’s risk preference, providing corresponding strategies. The model is transformed into a mixed-integer linear programming model using piecewise linearization and McCormick enveloping. Finally, the effectiveness of the proposed model and methods is verified through numerical examples. Full article

(This article belongs to the Special Issue Application of Intelligent Techniques in Power System Stability, Control and Protection)

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21 pages, 6640 KiB

Open AccessArticle

Research on Decision Optimization and the Risk Measurement of the Power Generation Side Based on Quantile Data-Driven IGDT

by Zhiwei Liao, Bowen Wang, Wenjuan Tao, Ye Liu and Qiyun Hu

Energies 2024, 17(7), 1585; https://doi.org/10.3390/en17071585 - 26 Mar 2024

Viewed by 809

Abstract

In an environment marked by dual carbon goals and substantial fluctuations in coal market prices, coal power generation enterprises face an urgent imperative to make scientifically informed decisions regarding production management amidst significant market uncertainties. To tackle this challenge, this paper proposes a methodology for optimizing electricity generation side market decisions and assessing risks using quantile data-driven information-gap decision theory (QDD-IGDT). Initially, a dual-layer decision optimization model for electricity production is formulated, taking into account coal procurement and blending processes. This model optimizes the selection of spot coal and long-term contract coal prices and simplifies the dual-layer structure into an equivalent single-layer model using the McCormick envelope and Karush–Kuhn–Tucker (KKT) conditions. Subsequently, a quantile dataset is generated utilizing a short-term coal price interval prediction model based on the quantile regression neural network (QRNN). Interval constraints on expected costs are introduced to develop an uncertainty decision risk measurement model grounded in QDD-IGDT, quantifying decision risks arising from coal market uncertainties to bolster decision robustness. Lastly, case simulations are executed by using real production data from a power generation enterprise, and the dual-layer decision optimization model is solved by employing the McCormick–KKT–Gurobi approach. Additionally, decision risks associated with coal market uncertainties are assessed through a one-dimensional search under interval constraints on expected cost volatility. The findings demonstrate the effectiveness of the proposed research methodology in cost optimization within the context of coal market uncertainties, underscoring its validity and economic efficiency. Full article

(This article belongs to the Special Issue Application of Intelligent Techniques in Power System Stability, Control and Protection)

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28 pages, 5540 KiB

Open AccessArticle

An Autonomous Distributed Coordination Strategy for Sustainable Consumption in a Microgrid Based on a Bio-Inspired Approach

by Marcel García, Jose Aguilar and María D. R-Moreno

Energies 2024, 17(3), 757; https://doi.org/10.3390/en17030757 - 5 Feb 2024

Viewed by 1115

Abstract

Distributed energy resources have demonstrated their potential to mitigate the limitations of large, centralized generation systems. This is achieved through the geographical distribution of generation sources that capitalize on the potential of their respective environments to satisfy local demand. In a microgrid, the control problem is inherently distributed, rendering traditional control techniques inefficient due to the impracticality of central governance. Instead, coordination among its components is essential. The challenge involves enabling these components to operate under optimal conditions, such as charging batteries with surplus solar energy or deactivating controllable loads when market prices rise. Consequently, there is a pressing need for innovative distributed strategies like emergent control. Inspired by phenomena such as the environmentally responsive behavior of ants, emergent control involves decentralized coordination schemes. This paper introduces an emergent control strategy for microgrids, grounded in the response threshold model, to establish an autonomous distributed control approach. The results, utilizing our methodology, demonstrate seamless coordination among the diverse components of a microgrid. For instance, system resilience is evident in scenarios where, upon the failure of certain components, others commence operation. Moreover, in dynamic conditions, such as varying weather and economic factors, the microgrid adeptly adapts to meet demand fluctuations. Our emergent control scheme enhances response times, performance, and on/off delay times. In various test scenarios, Integrated Absolute Error (IAE) metrics of approximately 0.01% were achieved, indicating a negligible difference between supplied and demanded energy. Furthermore, our approach prioritizes the utilization of renewable sources, increasing their usage from 59.7% to 86.1%. This shift not only reduces reliance on the public grid but also leads to significant energy cost savings. Full article

(This article belongs to the Special Issue Application of Intelligent Techniques in Power System Stability, Control and Protection)

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22 pages, 6327 KiB

Open AccessArticle

Adaptive Machine-Learning-Based Transmission Line Fault Detection and Classification Connected to Inverter-Based Generators

by Khalfan Al Kharusi, Abdelsalam El Haffar and Mostefa Mesbah

Energies 2023, 16(15), 5775; https://doi.org/10.3390/en16155775 - 2 Aug 2023

Cited by 3 | Viewed by 2035

Abstract

Adaptive protection schemes have been developed to address the problem of behavior-changing power systems integrated with inverter-based generation (IBG). This paper proposes a machine-learning-based fault detection and classification technique using a setting-group-based adaptation approach. Multigroup settings were designed depending on the types of power generation (synchronous generator, PV plant, and type-3 wind farm) connected to a transmission line in the 39-Bus New England System. For each system topology, an optimized pretrained ensemble tree classifier was used. The adaptation process has two phases: an offline learning phase to tune the classifiers and select the optimum subset of features, and an online phase where the circuit breaker (CB) status and the active output power of the generators are continuously monitored to identify the current system topology and to select the appropriate setting group. The proposed system achieved an average accuracy of 99.4%, a 99.5% average precision, a 99.9% average specificity, and a 99.4% average sensitivity of classification. The robustness analysis was conducted by applying several fault scenarios not considered during training, which include different transmission network configurations and different penetration levels of IBGs. The case of incorrect selection of the appropriate setting group resulting from selecting the wrong topology is also considered. It was noticed that the performance of developed classifiers deteriorates when the transmission network is reconfigured and the incorrect setting group is selected. Full article

(This article belongs to the Special Issue Application of Intelligent Techniques in Power System Stability, Control and Protection)

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19 pages, 5107 KiB

Open AccessArticle

Categorizing 15 kV High-Voltage HDPE Insulator’s Leakage Current Surges Based on Convolution Neural Network Gated Recurrent Unit

by Wen-Bin Liu, Phuong Nguyen Thanh, Ming-Yuan Cho and Thao Nguyen Da

Energies 2023, 16(5), 2500; https://doi.org/10.3390/en16052500 - 6 Mar 2023

Cited by 2 | Viewed by 1776

Abstract

The leakage currents are appropriate for determining the contamination level of insulators in the power distribution system, which are efficiently cleaned or replaced during the maintenance schedule. In this research, the hybrid convolution neural network and gated recurrent unit model (CNN-GRU) are developed to categorize the leakage current pulse of the 15 kV HDPE insulator in the transmission towers in Taiwan. Many weather parameters are accumulated in the online monitoring system, which is installed in different transmission towers in coastal areas that suffer from heavy pollution. The Pearson correlation matrix is computed for selecting the high correlative features with the leakage current. Hyperparameter optimization is employed to decide the enhancing framework of the CNN-GRU methodology. The performance of the CNN-GRU is completely analyzed with other deep learning algorithms, which comprise the GRU, bidirectional GRU, LSTM, and bidirectional LSTM. The developed CNN-GRU acquired the most remarkable improvements of 79.48% CRE, 83.54% validating CRE, 14.14% CP, 20.89% validating CP, 66.24% MAE, 63.59% validating MAE, 73.24% MSE, and 71.59% validating MSE benchmarks compared with other methodologies. Therefore, the hybrid CNN-GRU methodology provides comprehensive information about the contamination degrees of insulator surfaces derived from the property of leakage currents. Full article

(This article belongs to the Special Issue Application of Intelligent Techniques in Power System Stability, Control and Protection)

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15 pages, 2579 KiB

Open AccessArticle

Regional Load Frequency Control of BP-PI Wind Power Generation Based on Particle Swarm Optimization

by Jikai Sun, Mingrui Chen, Linghe Kong, Zhijian Hu and Veerapandiyan Veerasamy

Energies 2023, 16(4), 2015; https://doi.org/10.3390/en16042015 - 17 Feb 2023

Cited by 13 | Viewed by 1698

Abstract

The large-scale integration of wind turbines (WTs) in renewable power generation induces power oscillations, leading to frequency aberration due to power unbalance. Hence, in this paper, a secondary frequency control strategy called load frequency control (LFC) for power systems with wind turbine participation is proposed. Specifically, a backpropagation (BP)-trained neural network-based PI control approach is adopted to optimize the conventional PI controller to achieve better adaptiveness. The proposed controller was developed to realize the timely adjustment of PI parameters during unforeseen changes in system operation, to ensure the mutual coordination among wind turbine control circuits. In the meantime, the improved particle swarm optimization (IPSO) algorithm is utilized to adjust the initial neuron weights of the neural network, which can effectively improve the convergence of optimization. The simulation results demonstrate that the proposed IPSO-BP-PI controller performed evidently better than the conventional PI controller in the case of random load disturbance, with a significant reduction to near 10 s in regulation time and a final stable error of less than

10^{- 3}

for load frequency. Additionally, compared with the conventional PI controller counterpart, the frequency adjustment rate of the IPSO-BP-PI controller is significantly improved. Furthermore, it achieves higher control accuracy and robustness, demonstrating better integration of wind energy into traditional power systems. Full article

(This article belongs to the Special Issue Application of Intelligent Techniques in Power System Stability, Control and Protection)

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28 pages, 8295 KiB

Open AccessArticle

A Hybrid Grey Wolf Assisted-Sparrow Search Algorithm for Frequency Control of RE Integrated System

by Bashar Abbas Fadheel, Noor Izzri Abdul Wahab, Ali Jafer Mahdi, Manoharan Premkumar, Mohd Amran Bin Mohd Radzi, Azura Binti Che Soh, Veerapandiyan Veerasamy and Andrew Xavier Raj Irudayaraj

Energies 2023, 16(3), 1177; https://doi.org/10.3390/en16031177 - 20 Jan 2023

Cited by 15 | Viewed by 2251

Abstract

Nowadays, renewable energy (RE) sources are heavily integrated into the power system due to the deregulation of the energy market along with environmental and economic benefits. The intermittent nature of RE and the stochastic behavior of loads create frequency aberrations in interconnected hybrid power systems (HPS). This paper attempts to develop an optimization technique to tune the controller optimally to regulate frequency. A hybrid Sparrow Search Algorithm-Grey Wolf Optimizer (SSAGWO) is proposed to optimize the gain values of the proportional integral derivative controller. The proposed algorithm helps to improve the original algorithms’ exploration and exploitation. The optimization technique is coded in MATLAB and applied for frequency regulation of a two-area HPS developed in Simulink. The efficacy of the proffered hybrid SSAGWO is first assessed on standard benchmark functions and then applied to the frequency control of the HPS model. The results obtained from the multi-area multi-source HPS demonstrate that the proposed hybrid SSAGWO optimized PID controller performs significantly by 53%, 60%, 20%, and 70% in terms of settling time, peak undershoot, control effort, and steady-state error values, respectively, than other state-of-the-art algorithms presented in the literature. The robustness of the proffered method is also evaluated under the random varying load, variation of HPS system parameters, and weather intermittency of RE resources in real-time conditions. Furthermore, the controller’s efficacy was also demonstrated by performing a sensitivity analysis of the proposed system with variations of 75% and 125% in the inertia constant and system loading, respectively, from the nominal values. The results show that the proposed technique damped out the transient oscillations with minimum settling time. Moreover, the stability of the system is analyzed in the frequency domain using Bode analysis. Full article

(This article belongs to the Special Issue Application of Intelligent Techniques in Power System Stability, Control and Protection)

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18 pages, 7115 KiB

Open AccessArticle

Neural Network Controlled Solar PV Battery Powered Unified Power Quality Conditioner for Grid Connected Operation

by Okech Emmanuel Okwako, Zhang-Hui Lin, Mali Xin, Kamaraj Premkumar and Alukaka James Rodgers

Energies 2022, 15(18), 6825; https://doi.org/10.3390/en15186825 - 18 Sep 2022

Cited by 22 | Viewed by 4203

Abstract

The Unified Power Quality Conditioner (UPQC) is a technology that has successfully addressed power quality issues. In this paper, a photovoltaic system with battery storage powered Unified Power Quality Conditioner is presented. Total harmonic distortion of the grid current during extreme voltage sag and swell conditions is more than 5% when UPQC is controlled with synchronous reference frame theory (SRF) and instantaneous reactive power theory (PQ) control. The shunt active filter of the UPQC is controlled by the artificial neural network to overcome the above problem. The proposed artificial neural network controller helps to simplify the control complexity and mitigate power quality issues effectively. This study aims to use a neural network to control a shunt active filter of the UPQC to maximise the supply of active power loads and grid and also used to mitigate the harmonic problem due to non-linear loads in the grid. The performance of the model is tested under various case scenarios, including non-linear load conditions, unbalanced load conditions, and voltage sag and voltage swell conditions. The simulations were performed in MATLAB/Simulink software. The results showed excellent performance of the proposed approach and were compared with PQ and SRF control. The percent total harmonic distortion (%THD) of the grid current was measured and discussed for all cases. The results show that the %THD is within the acceptable limits of IEEE-519 (less than 5%) in all test case scenarios by the proposed controller. Full article

(This article belongs to the Special Issue Application of Intelligent Techniques in Power System Stability, Control and Protection)

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Review

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22 pages, 11018 KiB

Open AccessReview

High Impedance Fault Models for Overhead Distribution Networks: A Review and Comparison with MV Lab Experiments

by Juan Carlos Huaquisaca Paye, João Paulo A. Vieira, Jonathan Muñoz Tabora, André P. Leão, Murillo Augusto M. Cordeiro, Ghendy C. Junior, Adriano P. de Morais and Patrick E. Farias

Energies 2024, 17(5), 1125; https://doi.org/10.3390/en17051125 - 27 Feb 2024

Cited by 2 | Viewed by 1274

Abstract

Detecting and locating high impedance faults (HIF) in overhead distribution networks (ODN) remains one of the biggest challenges for manufacturers and researchers due to the complexity of this phenomenon, where the electrical current magnitude is similar to that of the loads. To simulate HIF, the selection of the HIF model is important, because it has to correctly reproduce the characteristics of this phenomenon, so that it does not negatively influence the simulations results. Therefore, HIF models play a fundamental role in proposing solutions and validating the effectiveness of the proposed methods to detect and localize HIF in ODN. This paper presents a systematic review of HIF models. It is intended to facilitate the selection of the HIF model to be considered. The models are validated based on experimental data from medium voltage (MV) laboratories, specifically, recorded waveforms from two HIF tests conducted in an MV lab were analyzed and compared with three established HIF models. The efficacy of these models was assessed against MV lab test data to ensure a precise representation of both transient and steady-state conditions for fault conductance and current waveforms. The findings show that the two nonlinear resistor models better approximate the waveforms obtained in the experimental tests performed in this study. Full article

(This article belongs to the Special Issue Application of Intelligent Techniques in Power System Stability, Control and Protection)

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26 pages, 498 KiB

Open AccessReview

Fault Detection, Isolation and Service Restoration in Modern Power Distribution Systems: A Review

by Ishan Srivastava, Sunil Bhat, B. V. Surya Vardhan and Neeraj Dhanraj Bokde

Energies 2022, 15(19), 7264; https://doi.org/10.3390/en15197264 - 3 Oct 2022

Cited by 17 | Viewed by 5671

Abstract

This study examines the conceptual features of Fault Detection, Isolation, and Restoration (FDIR) following an outage in an electric distribution system.This paper starts with a discussion of the premise for distribution automation, including its features and the different challenges associated with its implementation in a smart grid paradigm. Then, this article explores various concepts, control schemes, and approaches related to FDIR. Service restoration is one of the main strategies for such distribution automation, through which the healthy section of the power distribution network is re-energized by changing the topology of the network. In a smart grid paradigm, the presence of intelligent electronic devices can facilitate the automatic implementation of the service restoration scheme. The concepts of service restoration and various approaches are thoroughly presented in this article. A comparison is made among various significant approaches reported for distribution automation. The outcome of our literature survey and scope for future research concludes this review. Full article

(This article belongs to the Special Issue Application of Intelligent Techniques in Power System Stability, Control and Protection)

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