Pub Date : 2025-03-31DOI: 10.1109/OAJPE.2025.3556004
Koji Yamashita;Nanpeng Yu;Evangelos Farantatos;Lin Zhu
As the power transmission system’s energy sources become increasingly diversified, the grid stability is experiencing increased fluctuations, thereby necessitating more frequent and near real-time monitoring by grid operators. The power system security has been monitored through real-time contingency analysis and dynamic security assessment framework, both of which are typically based on time-domain simulations or power flow calculations. Achieving higher accuracy in grid health level prediction often requires time-consuming simulation and analysis. To improve computational efficiency, this paper develops machine learning models with phasor measurement unit (PMU) data to monitor the power system health index, focusing on rotor angle stability and frequency stability. The proposed machine learning models accurately predict frequency and angle stability indicators, essential for evaluating grid health considering various contingencies, even when dealing with limited PMU deployment in transmission grids. The proposed framework leverages a physics-informed graph convolution network and graph attention network with ordinal encoders, which are benchmarked with multi-layer perceptron models. These models are trained on dataset derived from an augmented IEEE 118-bus system with different demand levels and fuel mix, including tailored dynamic generator models, generator controller models, and grid protection models. The numerical studies explored the performance of the proposed and baseline machine learning models under both full PMU coverage and various partial PMU coverage conditions, where different data imputation methods are used for substations without PMUs. The findings from this study offer valuable insights, such as machine learning model selection and critical PMU locations regarding power equipment, into the design of data-driven grid health index prediction models for power systems.
{"title":"Graph Learning-Based Power System Health Assessment Model","authors":"Koji Yamashita;Nanpeng Yu;Evangelos Farantatos;Lin Zhu","doi":"10.1109/OAJPE.2025.3556004","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3556004","url":null,"abstract":"As the power transmission system’s energy sources become increasingly diversified, the grid stability is experiencing increased fluctuations, thereby necessitating more frequent and near real-time monitoring by grid operators. The power system security has been monitored through real-time contingency analysis and dynamic security assessment framework, both of which are typically based on time-domain simulations or power flow calculations. Achieving higher accuracy in grid health level prediction often requires time-consuming simulation and analysis. To improve computational efficiency, this paper develops machine learning models with phasor measurement unit (PMU) data to monitor the power system health index, focusing on rotor angle stability and frequency stability. The proposed machine learning models accurately predict frequency and angle stability indicators, essential for evaluating grid health considering various contingencies, even when dealing with limited PMU deployment in transmission grids. The proposed framework leverages a physics-informed graph convolution network and graph attention network with ordinal encoders, which are benchmarked with multi-layer perceptron models. These models are trained on dataset derived from an augmented IEEE 118-bus system with different demand levels and fuel mix, including tailored dynamic generator models, generator controller models, and grid protection models. The numerical studies explored the performance of the proposed and baseline machine learning models under both full PMU coverage and various partial PMU coverage conditions, where different data imputation methods are used for substations without PMUs. The findings from this study offer valuable insights, such as machine learning model selection and critical PMU locations regarding power equipment, into the design of data-driven grid health index prediction models for power systems.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"181-193"},"PeriodicalIF":3.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10945887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-20DOI: 10.1109/OAJPE.2025.3553408
Elmer O. Hancco Catata;Marcelo Vinícius De Paula;Ernesto Ruppert Filho;Tárcio André Dos Santos Barros
An efficient switching method is proposed for Direct Instantaneous Torque Control (DITC) in Switched Reluctance Generators (SRG) operating at low speeds, aiming to enhance system efficiency and reduce torque ripple. In the traditional DITC strategy, the magnetization state in the outgoing phase is enabled at low operating speeds, leading to decreased efficiency and unnecessary torque ripple. The proposed DITC strategy improves efficiency at low speeds while maintaining low torque ripple levels. It prioritizes the freewheeling and demagnetization states during the outgoing period. When the back electromotive force (back EMF) is small, the magnetization state is disabled, using the freewheeling state to smoothly increase torque and the demagnetization state to decrease torque. The magnetization state is reintroduced as the back EMF increases. To implement the modified DITC, an artificial neural network is used to estimate electromagnetic torque. Experimental tests were conducted for both fixed and variable SRG speeds. The proposed method is compared with other methods in the literature. Experimental tests carried out at fixed and variable SRG speeds show that the proposed method significantly enhances efficiency by up to 20% and reduces torque ripple by up to 21% compared to existing methods.
{"title":"Energy-Efficient Direct Instantaneous Torque Control of Switched Reluctance Generator at Low Speeds","authors":"Elmer O. Hancco Catata;Marcelo Vinícius De Paula;Ernesto Ruppert Filho;Tárcio André Dos Santos Barros","doi":"10.1109/OAJPE.2025.3553408","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3553408","url":null,"abstract":"An efficient switching method is proposed for Direct Instantaneous Torque Control (DITC) in Switched Reluctance Generators (SRG) operating at low speeds, aiming to enhance system efficiency and reduce torque ripple. In the traditional DITC strategy, the magnetization state in the outgoing phase is enabled at low operating speeds, leading to decreased efficiency and unnecessary torque ripple. The proposed DITC strategy improves efficiency at low speeds while maintaining low torque ripple levels. It prioritizes the freewheeling and demagnetization states during the outgoing period. When the back electromotive force (back EMF) is small, the magnetization state is disabled, using the freewheeling state to smoothly increase torque and the demagnetization state to decrease torque. The magnetization state is reintroduced as the back EMF increases. To implement the modified DITC, an artificial neural network is used to estimate electromagnetic torque. Experimental tests were conducted for both fixed and variable SRG speeds. The proposed method is compared with other methods in the literature. Experimental tests carried out at fixed and variable SRG speeds show that the proposed method significantly enhances efficiency by up to 20% and reduces torque ripple by up to 21% compared to existing methods.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"171-180"},"PeriodicalIF":3.3,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10935298","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-07DOI: 10.1109/OAJPE.2025.3549113
Xinyi Yang;Tao Chen;Yuanshi Zhang;Ciwei Gao;Xingyu Yan;Hongxun Hui;Xiaomeng Ai
The widespread integration of renewable energy into the grid emphasizes the issues of power system uncertainty and insufficient flexibility. Heterogeneous flexible distributed resources can address the above challenges by interacting with distribution networks. This paper proposes a multi-timescale optimal operation strategy for an energy community that aggregates multiple distributed resources. Based on flexibility indicators including the degree of load variation and task laxity, a tri-level structure involving distribution system operators (DSOs), aggregators, and the home energy management system (HEMS) is developed. The aggregator serves as mediator between customers and DSOs, gathering the end user’s flexibility through the rescheduling of household appliances to leverage both upward and downward energy adjustments. According to different scenarios and application requirements, a multi-time-scale rolling optimal dispatch model is proposed. The day-ahead dispatch is combined with the Model Predictive Control (MPC) method to achieve fine-grained rolling adjustment of the power dispatch instructions of distributed resources with different time scales. Finally, a simulation experiment example is constructed to verify the effectiveness of the proposed method. The simulation results demonstrate that the economic benefits of end users and aggregators are improved with more grid-friendly load curves.
{"title":"The Optimal Operation Strategy of an Energy Community Aggregator for Heterogeneous Distributed Flexible Resources","authors":"Xinyi Yang;Tao Chen;Yuanshi Zhang;Ciwei Gao;Xingyu Yan;Hongxun Hui;Xiaomeng Ai","doi":"10.1109/OAJPE.2025.3549113","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3549113","url":null,"abstract":"The widespread integration of renewable energy into the grid emphasizes the issues of power system uncertainty and insufficient flexibility. Heterogeneous flexible distributed resources can address the above challenges by interacting with distribution networks. This paper proposes a multi-timescale optimal operation strategy for an energy community that aggregates multiple distributed resources. Based on flexibility indicators including the degree of load variation and task laxity, a tri-level structure involving distribution system operators (DSOs), aggregators, and the home energy management system (HEMS) is developed. The aggregator serves as mediator between customers and DSOs, gathering the end user’s flexibility through the rescheduling of household appliances to leverage both upward and downward energy adjustments. According to different scenarios and application requirements, a multi-time-scale rolling optimal dispatch model is proposed. The day-ahead dispatch is combined with the Model Predictive Control (MPC) method to achieve fine-grained rolling adjustment of the power dispatch instructions of distributed resources with different time scales. Finally, a simulation experiment example is constructed to verify the effectiveness of the proposed method. The simulation results demonstrate that the economic benefits of end users and aggregators are improved with more grid-friendly load curves.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"157-170"},"PeriodicalIF":3.3,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916768","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-06DOI: 10.1109/OAJPE.2025.3548911
Qian Zhang;P. R. Kumar;Le Xie
This paper introduces a framework and computational algorithm that utilizes energy storage systems in pairs to improve transmission capacity in electric power systems. Recognizing prolonged development timelines and urgent needs for inter-regional transmission corridors, this paper proposes a near-term supplementary solution that schedules pairs of energy storage systems to increase the throughput of congested transmission lines effectively. We establish a theoretical lower bound on the minimum capacity required for electric power delivery, defined as a function of cumulative power over time. In sharp contrast with conventional transmission planning based on peak power delivery, this new framework allows transmission capacity to be designed around average power delivery needs. This shift would significantly enhance asset utilization in a future grid with large renewable power fluctuations. Numerical experiments demonstrate the proposed method across various grids. In the RTS-GMLC system, the minimum line capacity required was reduced by 36.8% compared to peak-based planning and further decreased by 43.5% when contingency scenarios were considered. In the Texas synthetic grid, the approach achieved a 46.2% reduction in line capacity while maintaining system reliability. These results highlight storage’s potential as a transmission asset, providing practical guidance for planning and policy while enabling insights into future market designs.
{"title":"An Average Power-Based Planning Framework of Transmission Expansion: A New Role for Energy Storage","authors":"Qian Zhang;P. R. Kumar;Le Xie","doi":"10.1109/OAJPE.2025.3548911","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3548911","url":null,"abstract":"This paper introduces a framework and computational algorithm that utilizes energy storage systems in pairs to improve transmission capacity in electric power systems. Recognizing prolonged development timelines and urgent needs for inter-regional transmission corridors, this paper proposes a near-term supplementary solution that schedules pairs of energy storage systems to increase the throughput of congested transmission lines effectively. We establish a theoretical lower bound on the minimum capacity required for electric power delivery, defined as a function of cumulative power over time. In sharp contrast with conventional transmission planning based on peak power delivery, this new framework allows transmission capacity to be designed around average power delivery needs. This shift would significantly enhance asset utilization in a future grid with large renewable power fluctuations. Numerical experiments demonstrate the proposed method across various grids. In the RTS-GMLC system, the minimum line capacity required was reduced by 36.8% compared to peak-based planning and further decreased by 43.5% when contingency scenarios were considered. In the Texas synthetic grid, the approach achieved a 46.2% reduction in line capacity while maintaining system reliability. These results highlight storage’s potential as a transmission asset, providing practical guidance for planning and policy while enabling insights into future market designs.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"122-134"},"PeriodicalIF":3.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10915681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-04DOI: 10.1109/OAJPE.2025.3547731
Kapu V. Sri Ram Prasad;K. Dhananjay Rao;Guruvulu Naidu Ponnada;Umit Cali;Taha Selim Ustun
This paper presents a hybrid prediction technique for fault detection of induction machines. The established hybrid forecast scheme signifies the combined execution of Bald-Eagle- Search-Optimization (BESO) and Random-Decision-Forest-Algorithm (RDFA), called as BESO-RDFA prediction scheme. This proposed technique is used to predict the fault within a short period in the rotating machines. By considering the machine defects the RDFA is trained by using the BESO-based exact prediction with data in online mode. The MATLAB/Simulink work platform is employed to execute the model, which is then assessed using multiple techniques to forecast attributes and models of impending stator failure. A new robust diagnostic design is established to analyze the incipient stator winding failures. Simulation analysis shows the detection and isolation method with great sensitivity indicating the incipient winding failures.
{"title":"A Novel Fault Diagnosis of Induction Motor by Using Various Soft Computation Techniques: BESO-RDFA","authors":"Kapu V. Sri Ram Prasad;K. Dhananjay Rao;Guruvulu Naidu Ponnada;Umit Cali;Taha Selim Ustun","doi":"10.1109/OAJPE.2025.3547731","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3547731","url":null,"abstract":"This paper presents a hybrid prediction technique for fault detection of induction machines. The established hybrid forecast scheme signifies the combined execution of Bald-Eagle- Search-Optimization (BESO) and Random-Decision-Forest-Algorithm (RDFA), called as BESO-RDFA prediction scheme. This proposed technique is used to predict the fault within a short period in the rotating machines. By considering the machine defects the RDFA is trained by using the BESO-based exact prediction with data in online mode. The MATLAB/Simulink work platform is employed to execute the model, which is then assessed using multiple techniques to forecast attributes and models of impending stator failure. A new robust diagnostic design is established to analyze the incipient stator winding failures. Simulation analysis shows the detection and isolation method with great sensitivity indicating the incipient winding failures.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"146-156"},"PeriodicalIF":3.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10909623","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-03DOI: 10.1109/OAJPE.2025.3545858
Xuemei Dai;Chunyu Chen;Bixing Ren;Shengfei Yin
An alkaline water electrolyzer (AWE) that converts surplus electricity from fluctuating power of a wind farm (WF) is a promising technology for large-scale and cost-effective hydrogen production. By considering the complementarity of the AWEs and the WF in offering market services, this paper treats the AWE and the WF as a coalition and proposes a joint bidding strategy in the energy and regulation markets to maximize the coalition’s revenue. To overcome the influence of wind and hydrogen uncertainties, we first establish a data-driven distributionally robust chance-constrained bidding model, which reduces market risks by observing uncertainty-related chance constraints for any distribution in the ambiguity set. Then, we use the Shapley value method to evaluate the marginal contribution of the AWE and the WF. Further we propose a game-theory-based bidding revenue allocation scheme. Eventually, case studies based on real-world market data demonstrate that the total profit of the proposed joint bidding strategy increases 27.4% if compared with individual bidding strategy. The average marginal cost of hydrogen production can be reduced by $5.1~ {$}/$ kg if compared with only participating in the energy market.
{"title":"Data-Driven Chance-Constrained Capacity Offering for Wind-Electrolysis Joint Systems","authors":"Xuemei Dai;Chunyu Chen;Bixing Ren;Shengfei Yin","doi":"10.1109/OAJPE.2025.3545858","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3545858","url":null,"abstract":"An alkaline water electrolyzer (AWE) that converts surplus electricity from fluctuating power of a wind farm (WF) is a promising technology for large-scale and cost-effective hydrogen production. By considering the complementarity of the AWEs and the WF in offering market services, this paper treats the AWE and the WF as a coalition and proposes a joint bidding strategy in the energy and regulation markets to maximize the coalition’s revenue. To overcome the influence of wind and hydrogen uncertainties, we first establish a data-driven distributionally robust chance-constrained bidding model, which reduces market risks by observing uncertainty-related chance constraints for any distribution in the ambiguity set. Then, we use the Shapley value method to evaluate the marginal contribution of the AWE and the WF. Further we propose a game-theory-based bidding revenue allocation scheme. Eventually, case studies based on real-world market data demonstrate that the total profit of the proposed joint bidding strategy increases 27.4% if compared with individual bidding strategy. The average marginal cost of hydrogen production can be reduced by <inline-formula> <tex-math>$5.1~ {$}/$ </tex-math></inline-formula>kg if compared with only participating in the energy market.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"111-121"},"PeriodicalIF":3.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-04DOI: 10.1109/OAJPE.2025.3538619
Mario D. Baquedano-Aguilar;Sean Meyn;Arturo Bretas
This paper presents a methodology for reducing the complexity of large-scale power network models using spectral clustering, aggregation of electrical components, and cost function approximation. Two approaches are explored using unconstrained and constrained spectral clustering to determine areas for effective system reduction. Once the system areas are determined, both loads and generators by type are aggregated, and their new cost function is approximated through polynomial curve-fitting or statistical methods. The performance of reduced networks is evaluated in terms of their ability to follow the true daily cost of the original system over a 24-hour period considering a set of several days. Two test systems are taken as test beds. Application of the methodology to a modified version of the IEEE 39-bus system reduces it from 17 generators to a 4-bus system and 9 generators with about 93% of accuracy. Similarly, the IEEE 118-bus system is reduced from 19 generators to a 3-bus system with three aggregated units achieving over 99% of accuracy. These findings address scalability challenges and enhance accuracy for high and mid-loading level conditions, and by aggregating thermal units with similar cost functions.
{"title":"Coherency-Constrained Spectral Clustering for Power Network Reduction","authors":"Mario D. Baquedano-Aguilar;Sean Meyn;Arturo Bretas","doi":"10.1109/OAJPE.2025.3538619","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3538619","url":null,"abstract":"This paper presents a methodology for reducing the complexity of large-scale power network models using spectral clustering, aggregation of electrical components, and cost function approximation. Two approaches are explored using unconstrained and constrained spectral clustering to determine areas for effective system reduction. Once the system areas are determined, both loads and generators by type are aggregated, and their new cost function is approximated through polynomial curve-fitting or statistical methods. The performance of reduced networks is evaluated in terms of their ability to follow the true daily cost of the original system over a 24-hour period considering a set of several days. Two test systems are taken as test beds. Application of the methodology to a modified version of the IEEE 39-bus system reduces it from 17 generators to a 4-bus system and 9 generators with about 93% of accuracy. Similarly, the IEEE 118-bus system is reduced from 19 generators to a 3-bus system with three aggregated units achieving over 99% of accuracy. These findings address scalability challenges and enhance accuracy for high and mid-loading level conditions, and by aggregating thermal units with similar cost functions.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"88-99"},"PeriodicalIF":3.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-28DOI: 10.1109/OAJPE.2025.3535709
Mohamed Massaoudi;Maymouna Ez Eddin;Ali Ghrayeb;Haitham Abu-Rub;Shady S. Refaat
With the escalating intricacy and expansion of the interconnected electrical grid, the likelihood of power system (PS) collapse has escalated dramatically. There is an increased emphasis on immunizing renewable-dominated power systems from large-scale cascading failures and cyberattacks through optimal power grid partitioning (PGP). By altering the network’s topology, partitioning aims to create areas within the PS that are not only robust but also have increased flexibility in generation and improved controllability over variable demand. This article provides an updated review of the cutting-edge machine learning and data-driven techniques used for PGP in networked PSs. To this end, an in-depth exploration of the basic principles of PGP and performance quantification is provided. The coherency adequacy and controlled islanding within the power network are comprehensively discussed. Subsequently, state-of-the-art research that envisions the use of clustering-based machine learning and deep learning-based solutions for PGP is presented. Finally, key research gaps and future directions for effective PGP are outlined. This paper provides PS researchers with a bird’s eye view of the current state of mainstream PGP implementations. Additionally, it assists stakeholders in selecting the most appropriate clustering algorithms for PGP applications.
{"title":"Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning","authors":"Mohamed Massaoudi;Maymouna Ez Eddin;Ali Ghrayeb;Haitham Abu-Rub;Shady S. Refaat","doi":"10.1109/OAJPE.2025.3535709","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3535709","url":null,"abstract":"With the escalating intricacy and expansion of the interconnected electrical grid, the likelihood of power system (PS) collapse has escalated dramatically. There is an increased emphasis on immunizing renewable-dominated power systems from large-scale cascading failures and cyberattacks through optimal power grid partitioning (PGP). By altering the network’s topology, partitioning aims to create areas within the PS that are not only robust but also have increased flexibility in generation and improved controllability over variable demand. This article provides an updated review of the cutting-edge machine learning and data-driven techniques used for PGP in networked PSs. To this end, an in-depth exploration of the basic principles of PGP and performance quantification is provided. The coherency adequacy and controlled islanding within the power network are comprehensively discussed. Subsequently, state-of-the-art research that envisions the use of clustering-based machine learning and deep learning-based solutions for PGP is presented. Finally, key research gaps and future directions for effective PGP are outlined. This paper provides PS researchers with a bird’s eye view of the current state of mainstream PGP implementations. Additionally, it assists stakeholders in selecting the most appropriate clustering algorithms for PGP applications.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"59-75"},"PeriodicalIF":3.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855832","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Classical fixed-parameter power system stabilizers (PSS) are typically designed to work well for a limited and specific set of operating conditions. However, the integration of low-inertia, inverter-based renewable energy resources (RES) has led to rapid fluctuations in power dispatch, rendering non-adaptive PSSs obsolete. This paper presents a novel hybrid gray-box modeling approach for real-time adaptation of PSS parameters during operation, thereby enabling the PSS to effectively handle a broader range of operating conditions. In our proposed method, we employ a two-stage process. First, we utilize a modified Heffron-Phillips model and meta-heuristics to synthesize the PSS’s compensating transfer function across a broad spectrum of operating conditions independently of external system parameters. Second, we leverage machine learning techniques to extrapolate the tuning results, thus ensuring adaptability across the full range of operating conditions. The effectiveness of this design methodology is rigorously evaluated in multi-machine power systems. Simulation results demonstrate that the proposed SMART-PSS exhibits robust performance compared to conventional fixed-parameter controllers, reducing the maximum phase deviation by 70% to 96%. This makes it highly suitable for modern power systems, which face diverse and dynamic operational challenges.
{"title":"Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (SMART-PSS)","authors":"Khaled Aleikish;Jonas Kristiansen Nøland;Thomas Øyvang","doi":"10.1109/OAJPE.2025.3532768","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3532768","url":null,"abstract":"Classical fixed-parameter power system stabilizers (PSS) are typically designed to work well for a limited and specific set of operating conditions. However, the integration of low-inertia, inverter-based renewable energy resources (RES) has led to rapid fluctuations in power dispatch, rendering non-adaptive PSSs obsolete. This paper presents a novel hybrid gray-box modeling approach for real-time adaptation of PSS parameters during operation, thereby enabling the PSS to effectively handle a broader range of operating conditions. In our proposed method, we employ a two-stage process. First, we utilize a modified Heffron-Phillips model and meta-heuristics to synthesize the PSS’s compensating transfer function across a broad spectrum of operating conditions independently of external system parameters. Second, we leverage machine learning techniques to extrapolate the tuning results, thus ensuring adaptability across the full range of operating conditions. The effectiveness of this design methodology is rigorously evaluated in multi-machine power systems. Simulation results demonstrate that the proposed SMART-PSS exhibits robust performance compared to conventional fixed-parameter controllers, reducing the maximum phase deviation by 70% to 96%. This makes it highly suitable for modern power systems, which face diverse and dynamic operational challenges.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"36-45"},"PeriodicalIF":3.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10850756","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-23DOI: 10.1109/OAJPE.2025.3525881
{"title":"IEEE Open Access Journal of Power and Energy Publication Information","authors":"","doi":"10.1109/OAJPE.2025.3525881","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3525881","url":null,"abstract":"","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"C2-C2"},"PeriodicalIF":3.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10851795","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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