Pub Date : 2025-06-20DOI: 10.1109/TSTE.2025.3576557
{"title":"IEEE Transactions on Sustainable Energy Information for Authors","authors":"","doi":"10.1109/TSTE.2025.3576557","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3576557","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"C4-C4"},"PeriodicalIF":8.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045648","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-20DOI: 10.1109/TSTE.2025.3576555
{"title":"IEEE Industry Applications Society Information","authors":"","doi":"10.1109/TSTE.2025.3576555","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3576555","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"C3-C3"},"PeriodicalIF":8.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045650","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-20DOI: 10.1109/TSTE.2025.3576561
{"title":"Share Your Preprint Research with the World!","authors":"","doi":"10.1109/TSTE.2025.3576561","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3576561","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2267-2267"},"PeriodicalIF":8.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045651","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-19DOI: 10.1109/TSTE.2025.3581286
Huayi Wu;Zhao Xu
Online voltage regulation in active distribution systems faces challenges stemming from privacy protection concerns and uncertainties introduced by renewable energy sources. To address these issues, a novel spatial-temporal transformer-based prototype federated reinforcement learning (STT-PFRL) model is proposed to mitigate voltage deviations while ensuring data privacy. Specifically, STT-PFRL operating within a decentralized framework trains the model by transmitting local prototype information between a central data server and local agents, avoiding raw data privacy leakage. Besides, a novel physics-aware spatial-temporal transformer network is proposed to improve the voltage regulation policy learning stability against uncertainties by embedding the spatial-temporal graphical physics information into the data aggregation process. Furthermore, the prototype learning-based federated soft actor-critic (ProtoFedSAC) algorithm incorporates a prototype layer to utilize diverse feature representations, thereby enhancing the model’s ability to handle heterogeneity in environmental data. Simulation results on 33- and 118-node distribution systems demonstrate the superior effectiveness and efficiency of STT-PFRL in voltage regulation.
{"title":"Prototype Federated Reinforcement Learning for Voltage Regulation in Distribution Systems With Physics-Aware Spatial-Temporal Graph Perception","authors":"Huayi Wu;Zhao Xu","doi":"10.1109/TSTE.2025.3581286","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3581286","url":null,"abstract":"Online voltage regulation in active distribution systems faces challenges stemming from privacy protection concerns and uncertainties introduced by renewable energy sources. To address these issues, a novel spatial-temporal transformer-based prototype federated reinforcement learning (STT-PFRL) model is proposed to mitigate voltage deviations while ensuring data privacy. Specifically, STT-PFRL operating within a decentralized framework trains the model by transmitting local prototype information between a central data server and local agents, avoiding raw data privacy leakage. Besides, a novel physics-aware spatial-temporal transformer network is proposed to improve the voltage regulation policy learning stability against uncertainties by embedding the spatial-temporal graphical physics information into the data aggregation process. Furthermore, the prototype learning-based federated soft actor-critic (ProtoFedSAC) algorithm incorporates a prototype layer to utilize diverse feature representations, thereby enhancing the model’s ability to handle heterogeneity in environmental data. Simulation results on 33- and 118-node distribution systems demonstrate the superior effectiveness and efficiency of STT-PFRL in voltage regulation.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 1","pages":"697-708"},"PeriodicalIF":10.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-12DOI: 10.1109/TSTE.2025.3579018
Yang Liu;Huanjin Yao;Pengyu Di;Yingjie Qin;Yiming Ma;Mohammed Alkahtani;Yihua Hu
The lack of suitable modeling methods for power systems with multiple doubly-fed induction generator-based wind turbines (DFIGWTs) integrated has left the analytical description of the boundary of the region of attraction (ROA) of such systems largely unexplored. To address this gap, this paper derives an ordinary differential equation (ODE) model for a power system with multiple DFIGWTs integrated. The proposed electromechanical model is validated in a single-machine-infinite-bus (SMIB) power system and a modified 3 machine 9 bus power system with root mean squared errors (RMSEs) of less than 9.5% for trajectory comparisons with the full model, demonstrating that it accurately captures the low-frequency dynamics of the full DFIGWT model. Subsequently, the ODE model is transformed into a polynomial differential-algebraic equation (DAE) model using a nonlinear coordinate transformation. To estimate the ROA, an enhanced expanding interior algorithm (EIA) based on sum of squares (SOS) programming is applied. The feasibility of the proposed model, along with the appropriate conservativeness of the improved EIA, is validated using two test systems that include multiple DFIGWTs and synchronous generators (SGs). By comparison, it is found that the time cost of the improved EIA is reduced by around 17% while maintaining the accuracy. These results demonstrate that the proposed approach has significant practical implications for the integration of wind farms into power systems, and offers an efficient tool for transient stability analysis.
{"title":"Region of Attraction Estimation for Power Systems With Multiple Integrated DFIG-Based Wind Turbines","authors":"Yang Liu;Huanjin Yao;Pengyu Di;Yingjie Qin;Yiming Ma;Mohammed Alkahtani;Yihua Hu","doi":"10.1109/TSTE.2025.3579018","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3579018","url":null,"abstract":"The lack of suitable modeling methods for power systems with multiple doubly-fed induction generator-based wind turbines (DFIGWTs) integrated has left the analytical description of the boundary of the region of attraction (ROA) of such systems largely unexplored. To address this gap, this paper derives an ordinary differential equation (ODE) model for a power system with multiple DFIGWTs integrated. The proposed electromechanical model is validated in a single-machine-infinite-bus (SMIB) power system and a modified 3 machine 9 bus power system with root mean squared errors (RMSEs) of less than 9.5% for trajectory comparisons with the full model, demonstrating that it accurately captures the low-frequency dynamics of the full DFIGWT model. Subsequently, the ODE model is transformed into a polynomial differential-algebraic equation (DAE) model using a nonlinear coordinate transformation. To estimate the ROA, an enhanced expanding interior algorithm (EIA) based on sum of squares (SOS) programming is applied. The feasibility of the proposed model, along with the appropriate conservativeness of the improved EIA, is validated using two test systems that include multiple DFIGWTs and synchronous generators (SGs). By comparison, it is found that the time cost of the improved EIA is reduced by around 17% while maintaining the accuracy. These results demonstrate that the proposed approach has significant practical implications for the integration of wind farms into power systems, and offers an efficient tool for transient stability analysis.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3095-3109"},"PeriodicalIF":10.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The fluctuations and uncertainty of solar power constantly threaten the secure operation and economic dispatch of power systems. Existing end-to-end point or probabilistic solar power prediction methods mostly lack effective integration of the two approaches, and the latent error caused by machine learning (ML) techniques is rarely taken into consideration. Hence in this paper, a combined extreme probabilistic solar power prediction (EPSPP) scheme is proposed, by integrating point forecasting with extreme error estimation. Firstly, the localized sample structure recognition (LSSR) is conducted to determine the neighborhood of meteorological conditions, where feature weights of Euclidean distance measurement are allocated with respect to the valid mutual information (MI) derived by two-dimensional diffusion kernel density estimation (2D-DKDE). Secondly, with the neighborhood generated by LSSR, an improved localized generalization error estimation (ILGEE) algorithm is put forward to infer the real-time maximal second-order origin moment of solar power point forecasting error corresponding to designated confidence levels. Finally, the solar power at each temporal moment is deduced as distinct Gaussian distributions, by modifying the mean value and variance according to statistical principles. For the sake of the so-called “extreme”, the proposed scheme could maintain reliability even under circumstances of the worst ML model precision. Cases from a real-world solar power station in Oregon, USA, are used to validate its effectiveness.
{"title":"Extreme Probabilistic Solar Power Prediction via Localized Sample Structure Recognition and Generalized Error Estimation","authors":"Jiacheng Liu;Jun Liu;Xinglei Liu;Tao Ding;Guangyao Wang;Xiaoming Liu;Yu Zhao","doi":"10.1109/TSTE.2025.3579335","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3579335","url":null,"abstract":"The fluctuations and uncertainty of solar power constantly threaten the secure operation and economic dispatch of power systems. Existing end-to-end point or probabilistic solar power prediction methods mostly lack effective integration of the two approaches, and the latent error caused by machine learning (ML) techniques is rarely taken into consideration. Hence in this paper, a combined extreme probabilistic solar power prediction (EPSPP) scheme is proposed, by integrating point forecasting with extreme error estimation. Firstly, the localized sample structure recognition (LSSR) is conducted to determine the neighborhood of meteorological conditions, where feature weights of Euclidean distance measurement are allocated with respect to the valid mutual information (MI) derived by two-dimensional diffusion kernel density estimation (2D-DKDE). Secondly, with the neighborhood generated by LSSR, an improved localized generalization error estimation (ILGEE) algorithm is put forward to infer the real-time maximal second-order origin moment of solar power point forecasting error corresponding to designated confidence levels. Finally, the solar power at each temporal moment is deduced as distinct Gaussian distributions, by modifying the mean value and variance according to statistical principles. For the sake of the so-called “extreme”, the proposed scheme could maintain reliability even under circumstances of the worst ML model precision. Cases from a real-world solar power station in Oregon, USA, are used to validate its effectiveness.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3110-3123"},"PeriodicalIF":10.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-11DOI: 10.1109/TSTE.2025.3578889
Quan Sui;Huashen He;Jing Liang;Zhongwen Li;Chengguo Su
Transporting hydrogen by vessels may be more cost-effective than hydrogen trailers and hydrogen tankers, but it is also more sensitive to environmental factors (e.g., river levels). In order to capitalize on the advantages of based-vessel waterway hydrogen chains, a new short-term scheduling strategy of integrated electric-hydrogen-Thermal systems considering the hydroelectric power plant peaking for hydrogen vessel (HV) navigation is proposed in this paper. First, a temporal-spatial operational model of waterway hydrogen chains is developed. In this model, the relationship between the electrolysis temperature, hydrogen production efficiency, and maximum available operational power of the reversible solid oxide fuel cell (RSOC) is modelled. The impact of the hydroelectric power plant underflow on HV transfer is also evaluated. On this basis, a flexible multi-day collaborative scheduling strategy of the electric-hydrogen integrated system is designed, where the main power source, i.e., thermoelectric plant (TEP), is allowed to operate in pure power generation mode or cogeneration mode to release the operation flexibility. This scheduling model is first linearized as a mixed-integer second-order conic programming (MISOCP) problem and then solved efficiently through a two-layer method. Finally, case studies on a modified IEEE 118-node power system verify the effectiveness of the proposed strategy.
{"title":"Short-Term Scheduling of Integrated Electric-Hydrogen-Thermal Systems Considering Hydroelectric Power Plant Peaking for Hydrogen Vessel Navigation","authors":"Quan Sui;Huashen He;Jing Liang;Zhongwen Li;Chengguo Su","doi":"10.1109/TSTE.2025.3578889","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3578889","url":null,"abstract":"Transporting hydrogen by vessels may be more cost-effective than hydrogen trailers and hydrogen tankers, but it is also more sensitive to environmental factors (e.g., river levels). In order to capitalize on the advantages of based-vessel waterway hydrogen chains, a new short-term scheduling strategy of integrated electric-hydrogen-Thermal systems considering the hydroelectric power plant peaking for hydrogen vessel (HV) navigation is proposed in this paper. First, a temporal-spatial operational model of waterway hydrogen chains is developed. In this model, the relationship between the electrolysis temperature, hydrogen production efficiency, and maximum available operational power of the reversible solid oxide fuel cell (RSOC) is modelled. The impact of the hydroelectric power plant underflow on HV transfer is also evaluated. On this basis, a flexible multi-day collaborative scheduling strategy of the electric-hydrogen integrated system is designed, where the main power source, i.e., thermoelectric plant (TEP), is allowed to operate in pure power generation mode or cogeneration mode to release the operation flexibility. This scheduling model is first linearized as a mixed-integer second-order conic programming (MISOCP) problem and then solved efficiently through a two-layer method. Finally, case studies on a modified IEEE 118-node power system verify the effectiveness of the proposed strategy.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3082-3094"},"PeriodicalIF":10.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To address the challenges of low efficiency, poor economic performance, and limited adaptability in renewable energy–coupled alkaline water electrolysis (AWE) systems, this study proposes a power–state rolling optimization strategy (PSROS) based on a two-stage optimization framework. First, the large-scale AWE system is divided into multiple modules to reduce the variable dimension of the optimization problem. Then, a simplified module-level optimal efficiency model is developed based on the efficiency characteristics of AWE units. Subsequently, multi-objective optimization models are constructed at the module and unit levels, comprehensively considering hydrogen production volume, lifespan degradation, and utilization balancing. Finally, a finite-horizon rolling optimization mechanism is introduced to solve the two-stage optimization problem, improving the continuity and rationality of scheduling decisions at the end of each optimization horizon. Annual case study results demonstrate that, under the non-battery scenario, PSROS improves system efficiency by 9.92%, 11.12%, and 3.81%, and reduces the levelized cost of hydrogen (LCOH) by 4.14, 5.43, and 2.35 CNY/kg compared with the simple start-stop strategy (SSSS), array rotation strategy (ARS), and rolling optimization strategy (ROS), respectively. With battery integration, the system efficiency is further improved by 0.77%, and the LCOH is further reduced by 0.49 CNY/kg.
{"title":"Collaborative Operation of Renewable Energy Hydrogen Production Systems Considering Balanced Utilization and Extended Lifespan of Multi-Electrolyzers","authors":"Shibo Wang;Lingguo Kong;Chao Liu;Chuang Liu;Guowei Cai;Shaobang Zhang;Shi You;Hanwen Zhang;Zhe Chen","doi":"10.1109/TSTE.2025.3578190","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3578190","url":null,"abstract":"To address the challenges of low efficiency, poor economic performance, and limited adaptability in renewable energy–coupled alkaline water electrolysis (AWE) systems, this study proposes a power–state rolling optimization strategy (PSROS) based on a two-stage optimization framework. First, the large-scale AWE system is divided into multiple modules to reduce the variable dimension of the optimization problem. Then, a simplified module-level optimal efficiency model is developed based on the efficiency characteristics of AWE units. Subsequently, multi-objective optimization models are constructed at the module and unit levels, comprehensively considering hydrogen production volume, lifespan degradation, and utilization balancing. Finally, a finite-horizon rolling optimization mechanism is introduced to solve the two-stage optimization problem, improving the continuity and rationality of scheduling decisions at the end of each optimization horizon. Annual case study results demonstrate that, under the non-battery scenario, PSROS improves system efficiency by 9.92%, 11.12%, and 3.81%, and reduces the levelized cost of hydrogen (LCOH) by 4.14, 5.43, and 2.35 CNY/kg compared with the simple start-stop strategy (SSSS), array rotation strategy (ARS), and rolling optimization strategy (ROS), respectively. With battery integration, the system efficiency is further improved by 0.77%, and the LCOH is further reduced by 0.49 CNY/kg.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3064-3081"},"PeriodicalIF":10.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-09DOI: 10.1109/TSTE.2025.3578278
Yonghao Gui;Hong Wang;Xiaoran Zha;Yaosuo Xue
The stochastic nature of renewable energy sources (RESs) necessitates treating power system frequency response as a random process with a nonstationary probability density function (PDF). Based upon the stochastic distribution control theory originated by the second author, this paper proposes a novel stochastic controller to improve the frequency PDF in power grids when integrating a large amount of RESs, thereby minimizing the effects of uncertainties and enhancing overall system stability. The key idea is to manipulate the controllable power generation resources so that the frequency PDF is make to follow a target PDF by using the stochastic distribution control theory originated by the second author. The proposed method can easily be plugged into existing automatic generation controls for multi-area transmission grids. The proposed method is validated via a modified Kundar’s two area system and 240-bus Western Electricity Coordinating Council systems. The simulation results show that the proposed control shapes the frequency PDF narrower and sharper, leading to a notable improvement toward minimizing the effects of randomness and uncertainty during grid operation.
{"title":"Probability Density Function Control of Frequency Fluctuations in Renewable-Rich Power Systems","authors":"Yonghao Gui;Hong Wang;Xiaoran Zha;Yaosuo Xue","doi":"10.1109/TSTE.2025.3578278","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3578278","url":null,"abstract":"The stochastic nature of renewable energy sources (RESs) necessitates treating power system frequency response as a random process with a nonstationary probability density function (PDF). Based upon the stochastic distribution control theory originated by the second author, this paper proposes a novel stochastic controller to improve the frequency PDF in power grids when integrating a large amount of RESs, thereby minimizing the effects of uncertainties and enhancing overall system stability. The key idea is to manipulate the controllable power generation resources so that the frequency PDF is make to follow a target PDF by using the stochastic distribution control theory originated by the second author. The proposed method can easily be plugged into existing automatic generation controls for multi-area transmission grids. The proposed method is validated via a modified Kundar’s two area system and 240-bus Western Electricity Coordinating Council systems. The simulation results show that the proposed control shapes the frequency PDF narrower and sharper, leading to a notable improvement toward minimizing the effects of randomness and uncertainty during grid operation.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3048-3063"},"PeriodicalIF":10.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-06DOI: 10.1109/TSTE.2025.3577286
Ziyang Chen;Tingna Shi;Yanfei Cao;Peng Song
This study addresses the critical challenge of constant power control for large-scale wind energy conversion system under the combined effects of pitch actuator degradation and multiple disturbances. In the paper, a novel fault-tolerant control strategy based on error-based active rejection control (E-ADRC) is proposed. The approach incorporates a composite control architecture, comprising a disturbance rejection tracking loop and a fault-tolerant compensation loop. Within the tracking loop, an enhanced E-ADRC algorithm is suggested which not only retains the robustness and ease of implementation of traditional E-ADRC but also significantly improves the attenuation of low-frequency wind disturbances—the turbine’s primary disruption. The fault-tolerant compensation loop applies independent control signals, derived from pitch angle residuals, to each faulty actuator, mitigating the extra fault disturbances in rotor speed tracking dynamics. This dual-loop structure enables the turbine to restore high-stability power output after a fault. Furthermore, the fault-tolerant compensation mechanism ensures that, even in cases of part of the three actuators failure, the previously misaligned pitch angles are synchronized, effectively suppressing the detrimental aerodynamic imbalance and reducing adverse loads. The superiority of this approach in enhancing power output stability and reducing structure fatigue damage have been validated through a refined hardware-in-the-loop test.
{"title":"Error-Based Active Disturbance Rejection Power Control for Large-Scale Wind Turbines Under Pitch Actuator Performance Degradation Failure","authors":"Ziyang Chen;Tingna Shi;Yanfei Cao;Peng Song","doi":"10.1109/TSTE.2025.3577286","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3577286","url":null,"abstract":"This study addresses the critical challenge of constant power control for large-scale wind energy conversion system under the combined effects of pitch actuator degradation and multiple disturbances. In the paper, a novel fault-tolerant control strategy based on error-based active rejection control (E-ADRC) is proposed. The approach incorporates a composite control architecture, comprising a disturbance rejection tracking loop and a fault-tolerant compensation loop. Within the tracking loop, an enhanced E-ADRC algorithm is suggested which not only retains the robustness and ease of implementation of traditional E-ADRC but also significantly improves the attenuation of low-frequency wind disturbances—the turbine’s primary disruption. The fault-tolerant compensation loop applies independent control signals, derived from pitch angle residuals, to each faulty actuator, mitigating the extra fault disturbances in rotor speed tracking dynamics. This dual-loop structure enables the turbine to restore high-stability power output after a fault. Furthermore, the fault-tolerant compensation mechanism ensures that, even in cases of part of the three actuators failure, the previously misaligned pitch angles are synchronized, effectively suppressing the detrimental aerodynamic imbalance and reducing adverse loads. The superiority of this approach in enhancing power output stability and reducing structure fatigue damage have been validated through a refined hardware-in-the-loop test.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3015-3030"},"PeriodicalIF":10.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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