Pub Date : 2025-02-21DOI: 10.35833/MPCE.2024.000404
Zhiyuan Meng;Xiangyang Xing;Xiangjun Li;Jiadong Sun
The virtual synchronous generator (VSG), utilized as a control strategy for grid-forming inverters, is an effective method of providing inertia and voltage support to the grid. However, the VSG exhibits limited capabilities in low-voltage ride-through (LVRT) mode. Specifically, the slow response of the power loop poses challenges for VSG in grid voltage support and increases the risk of overcurrent, potentially violating present grid codes. This paper reveals the mechanism behind the delayed response speed of VSG control during the grid faults. On this basis, a compound compensation control strategy is proposed for improving the LVRT capability of the VSG, which incorporates adaptive frequency feedforward compensation (AFFC), direct power angle compensation (DPAC), internal potential compensation (IPC), and transient virtual impedance (TVI), effectively expediting the response speed and reducing transient current. Furthermore, the proposed control strategy ensures that the VSG operates smoothly back to its normal control state following the restoration from the grid faults. Subsequently, a large-signal model is developed to facilitate parameter design and stability analysis, which incorporates grid codes and TVI. Finally, the small-signal stability analysis and simulation and experimental results prove the correctness of the theoretical analysis and the effectiveness of the proposed control strategy.
{"title":"Compound Compensation Control for Improving Low-Voltage Ride-Through Capability of Virtual Synchronous Generators","authors":"Zhiyuan Meng;Xiangyang Xing;Xiangjun Li;Jiadong Sun","doi":"10.35833/MPCE.2024.000404","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000404","url":null,"abstract":"The virtual synchronous generator (VSG), utilized as a control strategy for grid-forming inverters, is an effective method of providing inertia and voltage support to the grid. However, the VSG exhibits limited capabilities in low-voltage ride-through (LVRT) mode. Specifically, the slow response of the power loop poses challenges for VSG in grid voltage support and increases the risk of overcurrent, potentially violating present grid codes. This paper reveals the mechanism behind the delayed response speed of VSG control during the grid faults. On this basis, a compound compensation control strategy is proposed for improving the LVRT capability of the VSG, which incorporates adaptive frequency feedforward compensation (AFFC), direct power angle compensation (DPAC), internal potential compensation (IPC), and transient virtual impedance (TVI), effectively expediting the response speed and reducing transient current. Furthermore, the proposed control strategy ensures that the VSG operates smoothly back to its normal control state following the restoration from the grid faults. Subsequently, a large-signal model is developed to facilitate parameter design and stability analysis, which incorporates grid codes and TVI. Finally, the small-signal stability analysis and simulation and experimental results prove the correctness of the theoretical analysis and the effectiveness of the proposed control strategy.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 3","pages":"1064-1077"},"PeriodicalIF":5.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185300","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 : 2025-02-21DOI: 10.35833/MPCE.2024.000495
Masoud Zare Shahabadi;Hajar Atrianfar;Hossein A. Abyaneh
This study introduces a distributed specified-time control mechanism (DSTCM) for secondary control in islanded microgrids (MGs) operating under directed switching communication topologies. The proposed mechanism ensures convergence properties that are independent of initial conditions, enabling the design of an exact offline settling time to reduce power losses and limit the upper bound of convergence time. By employing a piecewise function-based communication approach and directed switching graphs, the proposed mechanism effectively reduces computational and communication demands on the system. Moreover, the proposed mechanism significantly enhances power system performance while minimizing adjustment costs, delivering precise control actions under various operating conditions. The accuracy and effectiveness of the proposed mechanism are validated through extensive MATLAB simulations, demonstrating its ability to regulate MG voltages and frequencies, achieve accurate proportional active power sharing, and maintain state-of-charge (SoC) balancing. Its superiority over previously established mechanisms is also confirmed by a comparative analysis.
{"title":"Distributed Specified-Time Control Mechanism for Secondary Control in Islanded Microgrids Under Directed Switching Communication Topologies","authors":"Masoud Zare Shahabadi;Hajar Atrianfar;Hossein A. Abyaneh","doi":"10.35833/MPCE.2024.000495","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000495","url":null,"abstract":"This study introduces a distributed specified-time control mechanism (DSTCM) for secondary control in islanded microgrids (MGs) operating under directed switching communication topologies. The proposed mechanism ensures convergence properties that are independent of initial conditions, enabling the design of an exact offline settling time to reduce power losses and limit the upper bound of convergence time. By employing a piecewise function-based communication approach and directed switching graphs, the proposed mechanism effectively reduces computational and communication demands on the system. Moreover, the proposed mechanism significantly enhances power system performance while minimizing adjustment costs, delivering precise control actions under various operating conditions. The accuracy and effectiveness of the proposed mechanism are validated through extensive MATLAB simulations, demonstrating its ability to regulate MG voltages and frequencies, achieve accurate proportional active power sharing, and maintain state-of-charge (SoC) balancing. Its superiority over previously established mechanisms is also confirmed by a comparative analysis.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 6","pages":"2144-2156"},"PeriodicalIF":6.1,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899807","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610636","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 : 2025-02-21DOI: 10.35833/MPCE.2024.000887
Shu Zheng;Zhi Wu;Xiao Zhang;Wei Gu;Jingtao Zhao;Zhihua Xu
With the increasing integration of uncertain distributed renewable energies (DREs) into distribution networks (DNs), communication bottlenecks and the limited deployment of measurement devices pose significant challenges for advanced data-driven voltage control strategies such as deep reinforcement learning (DRL). To address these issues, this paper proposes an offline-training online-execution framework for volt-var control in DNs. In the offline-training phase, a graph convolutional network (GCN) -based denoising autoencoder (DAE), referred to as the deep learning (DL) agent, is designed and trained to capture spatial correlations among limited physical quantities. This agent predicts voltage values for nodes with missing measurements using historical load data, DRE outputs, and global voltages from simulations. Furthermore, the dual-timescale voltage control problem is formulated as a multi-agent Markov decision process. A DRL agent employing the multi-agent soft actor-critic (MASAC) algorithm is trained to regulate the tap position of on-load tap changer (OLTC) and reactive power output of photovoltaic (PV) inverters. In the online-execution phase, the DL agent supplements the limited measurement data, providing enhanced global observations for the DRL agent. This enables precise equipment control based on improved system state estimation. The proposed framework is validated on two modified IEEE test systems. Numerical results demonstrate its ability to effectively reconstruct missing measurements and achieve rapid, and accurate voltage control even under severe measurement deficiencies.
{"title":"Offline-Training Online-Execution Framework for Volt-Var Control in Distribution Networks","authors":"Shu Zheng;Zhi Wu;Xiao Zhang;Wei Gu;Jingtao Zhao;Zhihua Xu","doi":"10.35833/MPCE.2024.000887","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000887","url":null,"abstract":"With the increasing integration of uncertain distributed renewable energies (DREs) into distribution networks (DNs), communication bottlenecks and the limited deployment of measurement devices pose significant challenges for advanced data-driven voltage control strategies such as deep reinforcement learning (DRL). To address these issues, this paper proposes an offline-training online-execution framework for volt-var control in DNs. In the offline-training phase, a graph convolutional network (GCN) -based denoising autoencoder (DAE), referred to as the deep learning (DL) agent, is designed and trained to capture spatial correlations among limited physical quantities. This agent predicts voltage values for nodes with missing measurements using historical load data, DRE outputs, and global voltages from simulations. Furthermore, the dual-timescale voltage control problem is formulated as a multi-agent Markov decision process. A DRL agent employing the multi-agent soft actor-critic (MASAC) algorithm is trained to regulate the tap position of on-load tap changer (OLTC) and reactive power output of photovoltaic (PV) inverters. In the online-execution phase, the DL agent supplements the limited measurement data, providing enhanced global observations for the DRL agent. This enables precise equipment control based on improved system state estimation. The proposed framework is validated on two modified IEEE test systems. Numerical results demonstrate its ability to effectively reconstruct missing measurements and achieve rapid, and accurate voltage control even under severe measurement deficiencies.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1726-1737"},"PeriodicalIF":6.1,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090133","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 : 2025-02-21DOI: 10.35833/MPCE.2024.001046
Xuan Liu;Antonio J. Conejo
We propose a quasi-deterministic proxy for the network-constrained stochastic unit commitment (SUC) problem. The proposed proxy can identify very similar commitment decisions as those obtained by solving the SUC problem with a large scenario set. Its computational performance, though, is close to that of a deterministic unit commitment problem. The proposed proxy has the same formulation as the SUC problem but only includes one or two envelope scenarios, generated based on the original scenario set. The two envelope scenarios capture the maximum and minimum net-load conditions in the original scenario set. We use a systematic method to assess the quality of commitment decisions obtained by the proposed proxy. The considered case study is based on the Illinois 200-bus system.
{"title":"Quasi-Deterministic Proxy for Network-Constrained Stochastic Unit Commitment","authors":"Xuan Liu;Antonio J. Conejo","doi":"10.35833/MPCE.2024.001046","DOIUrl":"https://doi.org/10.35833/MPCE.2024.001046","url":null,"abstract":"We propose a quasi-deterministic proxy for the network-constrained stochastic unit commitment (SUC) problem. The proposed proxy can identify very similar commitment decisions as those obtained by solving the SUC problem with a large scenario set. Its computational performance, though, is close to that of a deterministic unit commitment problem. The proposed proxy has the same formulation as the SUC problem but only includes one or two envelope scenarios, generated based on the original scenario set. The two envelope scenarios capture the maximum and minimum net-load conditions in the original scenario set. We use a systematic method to assess the quality of commitment decisions obtained by the proposed proxy. The considered case study is based on the Illinois 200-bus system.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 4","pages":"1167-1175"},"PeriodicalIF":5.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716291","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 : 2025-02-21DOI: 10.35833/MPCE.2024.000903
Jinhao Wang;Chao Wu;Qianchen Sun;Yakun Liu;Yong Wang
This paper demonstrates a new type of sub-synchronous oscillation of the grid-forming (GFM) converter, which occurs at low rather than high power levels. To reveal the intrinsic mechanism, a simplified analytical small-signal impedance model of the GFM converter is derived. It is found that the reactive power control loop (RPCL) can have a significant impact on the system stability. In particular, the voltage matrix introduced by the RPCL is the key factor causing instability in the GFM grid-connected system at low operating points. Therefore, this paper proposes a control strategy that reshapes the RPCL to counteract the negative effect of the voltage matrix by introducing a q-axis current feedforward, ensuring stable operation at any operating point. Finally, experimental results validate the correctness and effectiveness of the proposed control strategy.
{"title":"Reshaping Reactive Power Control Loop to Suppress Sub-synchronous Oscillation of Grid-forming Converters at Low Power Levels","authors":"Jinhao Wang;Chao Wu;Qianchen Sun;Yakun Liu;Yong Wang","doi":"10.35833/MPCE.2024.000903","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000903","url":null,"abstract":"This paper demonstrates a new type of sub-synchronous oscillation of the grid-forming (GFM) converter, which occurs at low rather than high power levels. To reveal the intrinsic mechanism, a simplified analytical small-signal impedance model of the GFM converter is derived. It is found that the reactive power control loop (RPCL) can have a significant impact on the system stability. In particular, the voltage matrix introduced by the RPCL is the key factor causing instability in the GFM grid-connected system at low operating points. Therefore, this paper proposes a control strategy that reshapes the RPCL to counteract the negative effect of the voltage matrix by introducing a q-axis current feedforward, ensuring stable operation at any operating point. Finally, experimental results validate the correctness and effectiveness of the proposed control strategy.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1653-1663"},"PeriodicalIF":6.1,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089996","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 : 2025-01-30DOI: 10.35833/MPCE.2024.000478
Francisco S. Fernandes;Ricardo J. Bessa;João Peças Lopes
In a high-risk sector, such as power system, transparency and interpretability are key principles for effectively deploying artificial intelligence (AI) in control rooms. Therefore, this paper proposes a novel methodology, the evolving symbolic model (ESM), which is dedicated to generating highly interpretable data-driven models for dynamic security assessment (DSA), namely in system security classification (SC) and the definition of preventive control actions. The ESM uses simulated annealing for a data-driven evolution of a symbolic model template, enabling different cooperative learning schemes between humans and AI. The Madeira Island power system is used to validate the application of the ESM for DSA. The results show that the ESM has a classification accuracy comparable to pruned decision trees (DTs) while boasting higher global inter-pretability. Moreover, the ESM outperforms an operator-defined expert system and an artificial neural network in defining preventive control actions.
{"title":"Evolving Symbolic Model for Dynamic Security Assessment in Power Systems","authors":"Francisco S. Fernandes;Ricardo J. Bessa;João Peças Lopes","doi":"10.35833/MPCE.2024.000478","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000478","url":null,"abstract":"In a high-risk sector, such as power system, transparency and interpretability are key principles for effectively deploying artificial intelligence (AI) in control rooms. Therefore, this paper proposes a novel methodology, the evolving symbolic model (ESM), which is dedicated to generating highly interpretable data-driven models for dynamic security assessment (DSA), namely in system security classification (SC) and the definition of preventive control actions. The ESM uses simulated annealing for a data-driven evolution of a symbolic model template, enabling different cooperative learning schemes between humans and AI. The Madeira Island power system is used to validate the application of the ESM for DSA. The results show that the ESM has a classification accuracy comparable to pruned decision trees (DTs) while boasting higher global inter-pretability. Moreover, the ESM outperforms an operator-defined expert system and an artificial neural network in defining preventive control actions.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 4","pages":"1113-1126"},"PeriodicalIF":5.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858603","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716333","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 : 2025-01-30DOI: 10.35833/MPCE.2024.000618
Jie Wang;Hongjie Jia;Xiaolong Jin;Xiaodan Yu;Yunfei Mu;Kai Hou;Wei Wei;Jiarui Zhang;He Meng
The increasing focus on carbon neutrality has led to heightened interest in multiple microgrids (MGs) due to their potential to significantly reduce emissions by the integrated electricity-heat-carbon sharing among them. In this paper, a decentralized peer-to-peer (P2P) framework for integrated electricity-heat-carbon sharing is proposed to optimize the trading process of multi-energy and carbon among multiple MGs. The proposed framework considers certified emission reductions (CERs) of photovoltaic (PV) systems in each MG, and carbon allocation and trading among multiple MGs. The P2P trading behaviors among multiple MGs are modelled as a non-cooperative game. A decentralized optimization method is then developed using a price-based incentive scheme to solve the non-cooperative game and optimize the transactions of the electricity-heat-carbon jointly. The optimization problem is solved using sub-gradient in a decentralized manner. And the Nash equilibrium of the non-cooperative game is proven to exist uniquely, ensuring the convergence of the model. Furthermore, the proposed decentralized optimization method safeguards the private information of the MGs. Numerical results show that the total operational cost of the MGs and the carbon emissions can be reduced significantly.
{"title":"A Decentralized Peer-to-Peer Framework for Integrated Electricity-Heat-Carbon Sharing Among Multiple Microgrids","authors":"Jie Wang;Hongjie Jia;Xiaolong Jin;Xiaodan Yu;Yunfei Mu;Kai Hou;Wei Wei;Jiarui Zhang;He Meng","doi":"10.35833/MPCE.2024.000618","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000618","url":null,"abstract":"The increasing focus on carbon neutrality has led to heightened interest in multiple microgrids (MGs) due to their potential to significantly reduce emissions by the integrated electricity-heat-carbon sharing among them. In this paper, a decentralized peer-to-peer (P2P) framework for integrated electricity-heat-carbon sharing is proposed to optimize the trading process of multi-energy and carbon among multiple MGs. The proposed framework considers certified emission reductions (CERs) of photovoltaic (PV) systems in each MG, and carbon allocation and trading among multiple MGs. The P2P trading behaviors among multiple MGs are modelled as a non-cooperative game. A decentralized optimization method is then developed using a price-based incentive scheme to solve the non-cooperative game and optimize the transactions of the electricity-heat-carbon jointly. The optimization problem is solved using sub-gradient in a decentralized manner. And the Nash equilibrium of the non-cooperative game is proven to exist uniquely, ensuring the convergence of the model. Furthermore, the proposed decentralized optimization method safeguards the private information of the MGs. Numerical results show that the total operational cost of the MGs and the carbon emissions can be reduced significantly.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1787-1799"},"PeriodicalIF":6.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858604","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100448","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 : 2025-01-30DOI: 10.35833/MPCE.2024.000507
Xin Chen;Long Huo;Chengqian Sun
Short-term voltage stability (STVS) assessment is a critical monitoring technology in modern power systems. During daily operations, transmission lines may switch on or off due to scheduled maintenance or unexpected faults, which poses challenges to the STVS assessment under varying topology change conditions. To adapt the STVS assessment to the system topology changes, we propose a deep-learning-based STVS assessment model with the topology-adaptive voltage dynamic feature and the fine-tuning domain transfer for power systems with changing system topologies. The topology-adaptive voltage dynamic feature, extracted from streaming time-series data of phasor measurement units (PMUs), is used to characterize transient voltage stability. The voltage dynamic features depend on the balance of reactive power flow and system topology, effectively revealing both spatiotemporal patterns of post-disturbance system dynamics. The simulation results based on large disturbances in the New England 39-bus power system demonstrate that the proposed model achieves superior STVS assessment performance, with an accuracy of 99.65% in predicting voltage stability compared with the existing deep learning methods. The proposed model also performs well when applied to the larger IEEE 145-bus power system. The fine-tuning domain transfer of the proposed model adapts very well to system topology changes in power systems. It achieves an accuracy of 99.50% in predicting the STVS for the New England 39-bus power system with the transmission line alternation. Further-more, the proposed model demonstrates strong robustness to noisy and missing data.
{"title":"Deep-Learning-Based Short-Term Voltage Stability Assessment with Topology-Adaptive Voltage Dynamic Feature and Domain Transfer","authors":"Xin Chen;Long Huo;Chengqian Sun","doi":"10.35833/MPCE.2024.000507","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000507","url":null,"abstract":"Short-term voltage stability (STVS) assessment is a critical monitoring technology in modern power systems. During daily operations, transmission lines may switch on or off due to scheduled maintenance or unexpected faults, which poses challenges to the STVS assessment under varying topology change conditions. To adapt the STVS assessment to the system topology changes, we propose a deep-learning-based STVS assessment model with the topology-adaptive voltage dynamic feature and the fine-tuning domain transfer for power systems with changing system topologies. The topology-adaptive voltage dynamic feature, extracted from streaming time-series data of phasor measurement units (PMUs), is used to characterize transient voltage stability. The voltage dynamic features depend on the balance of reactive power flow and system topology, effectively revealing both spatiotemporal patterns of post-disturbance system dynamics. The simulation results based on large disturbances in the New England 39-bus power system demonstrate that the proposed model achieves superior STVS assessment performance, with an accuracy of 99.65% in predicting voltage stability compared with the existing deep learning methods. The proposed model also performs well when applied to the larger IEEE 145-bus power system. The fine-tuning domain transfer of the proposed model adapts very well to system topology changes in power systems. It achieves an accuracy of 99.50% in predicting the STVS for the New England 39-bus power system with the transmission line alternation. Further-more, the proposed model demonstrates strong robustness to noisy and missing data.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1545-1555"},"PeriodicalIF":6.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858605","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089982","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 : 2025-01-30DOI: 10.35833/MPCE.2024.000687
Youze Fu;Yandong Chen;Zili Wang;Zhiwei Xie;Xuyang Li
The self-synchronizing voltage source inverter (SSVSI) is widely studied because of its grid-forming capability. However, the slow response of the active power control loop (APCL) under the weak grid makes it difficult for the SSVSI to quickly support the frequency of a low-inertia grid. In this paper, a grid framework is established to analyze the frequency support service process of the SSVSI, and the shortcomings of the regulation of the damping coefficient and virtual inertia co-efficient for frequency support are analyzed. Then, an adaptive additional damping control method is proposed to optimize the ability of SSVSI to support the grid frequency. The proposed control method adjusts the damping of the APCL without affecting the system steady-state characteristics, which improves the active power response speed of the SSVSI. Besides, the proposed control method adaptively adjusts the additional damping coefficient based on the active power response without measuring the grid parameters. Compared with other forms of control, the proposed control method excels in minimizing the rate of change of frequency (RoCoF) and the frequency deviation (FD) within the grid, without succumbing to the constraints posed by unknown grid parameters. Furthermore, the analysis of the system stability is also presented. Finally, the experimental hardware results obtained from a miniaturized grid proto-type are presented, corroborating the effectiveness of the proposed control method.
{"title":"Fast Frequency Support of Self-Synchronizing Voltage Source Inverter Under Weak Grid Based on Adaptive Additional Damping Control","authors":"Youze Fu;Yandong Chen;Zili Wang;Zhiwei Xie;Xuyang Li","doi":"10.35833/MPCE.2024.000687","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000687","url":null,"abstract":"The self-synchronizing voltage source inverter (SSVSI) is widely studied because of its grid-forming capability. However, the slow response of the active power control loop (APCL) under the weak grid makes it difficult for the SSVSI to quickly support the frequency of a low-inertia grid. In this paper, a grid framework is established to analyze the frequency support service process of the SSVSI, and the shortcomings of the regulation of the damping coefficient and virtual inertia co-efficient for frequency support are analyzed. Then, an adaptive additional damping control method is proposed to optimize the ability of SSVSI to support the grid frequency. The proposed control method adjusts the damping of the APCL without affecting the system steady-state characteristics, which improves the active power response speed of the SSVSI. Besides, the proposed control method adaptively adjusts the additional damping coefficient based on the active power response without measuring the grid parameters. Compared with other forms of control, the proposed control method excels in minimizing the rate of change of frequency (RoCoF) and the frequency deviation (FD) within the grid, without succumbing to the constraints posed by unknown grid parameters. Furthermore, the analysis of the system stability is also presented. Finally, the experimental hardware results obtained from a miniaturized grid proto-type are presented, corroborating the effectiveness of the proposed control method.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 4","pages":"1458-1467"},"PeriodicalIF":5.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858608","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716122","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 : 2025-01-30DOI: 10.35833/MPCE.2024.000518
Peng Wang;Haoran Zhao;Jia Luo;Vladimir Terzija
Oscillation accidents emerge in power systems integrated with increasing penetration of renewable energy sources. The impedance of electromagnetic dynamics is investigated in recent years, where the mechanical dynamics are neglected. So far, the low-frequency oscillations are not well addressed with the impedance analysis method. A novel analytical impedance is formulated and implemented for wind energy conversion system consisting of wind turbine generators (WTGs) and wind farm, which fills the gap in the mechanical dynamics of the impedance. Instead of assuming constant values, the electromechanical dynamics of the rotor speed and the pitch angle are involved in the WTG impedance. Besides, the impedance framework is generally and modularly designed and is adaptive to different operating regions. With the developed analytical impedance, the stability assessment can cover the low-frequency oscillations, providing an in-depth insight into the mechanical parameters influencing the small-signal stability performance. As an application, the impedance characteristic and stability performance of systems with active power reserve for grid supporting are analyzed and optimized. Furthermore, the shafting torsional vibrations of WTGs in wind farms are analyzed with modal decomposition and the low-frequency impedance model. The improved accuracy of the developed analytical impedance is illustrated by comparison with commonly used impedance, which ignores the coupling between the electrical and mechanical dynamics. It is proven that the mechanical dynamics have a significant influence on the impedance, particularly in the low-frequency range. Experimental validation is carried out to validate the low-frequency impedance model and the stability performance.
{"title":"Low-Frequency Impedance Modeling of Wind Energy Conversion System Considering Mechanical Dynamics and Operating Regions","authors":"Peng Wang;Haoran Zhao;Jia Luo;Vladimir Terzija","doi":"10.35833/MPCE.2024.000518","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000518","url":null,"abstract":"Oscillation accidents emerge in power systems integrated with increasing penetration of renewable energy sources. The impedance of electromagnetic dynamics is investigated in recent years, where the mechanical dynamics are neglected. So far, the low-frequency oscillations are not well addressed with the impedance analysis method. A novel analytical impedance is formulated and implemented for wind energy conversion system consisting of wind turbine generators (WTGs) and wind farm, which fills the gap in the mechanical dynamics of the impedance. Instead of assuming constant values, the electromechanical dynamics of the rotor speed and the pitch angle are involved in the WTG impedance. Besides, the impedance framework is generally and modularly designed and is adaptive to different operating regions. With the developed analytical impedance, the stability assessment can cover the low-frequency oscillations, providing an in-depth insight into the mechanical parameters influencing the small-signal stability performance. As an application, the impedance characteristic and stability performance of systems with active power reserve for grid supporting are analyzed and optimized. Furthermore, the shafting torsional vibrations of WTGs in wind farms are analyzed with modal decomposition and the low-frequency impedance model. The improved accuracy of the developed analytical impedance is illustrated by comparison with commonly used impedance, which ignores the coupling between the electrical and mechanical dynamics. It is proven that the mechanical dynamics have a significant influence on the impedance, particularly in the low-frequency range. Experimental validation is carried out to validate the low-frequency impedance model and the stability performance.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 4","pages":"1224-1237"},"PeriodicalIF":5.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858606","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716306","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}
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