Existing load forecasting methods typically assume that recent load data are available for prediction. This is not in conformity with reality since there is a time gap between the flow date (when power is consumed) and when measurement values are obtained. To this end, this letter proposes an online learning-based probabilistic load forecasting method considering the impact of the data gap. Specifically, an adaptive ensemble backpropagation-enabled online quantile regression algorithm is developed to optimize the parameters of the attention network recursively using the newly obtained load observations. To further improve the reliability and sharpness of prediction intervals under significant data gaps, we introduce an online interval calibration technique. The proposed online learning method allows us to adaptively capture the dynamic changes in load patterns and alleviate the information lags caused by data gaps. Comparative tests utilizing real-world datasets reveal the superiority of the proposed method.
{"title":"Online Probabilistic Load Forecasts Considering Data Gaps","authors":"Pengfei Zhao;Weihao Hu;Di Cao;Longcheng Dai;Qi Huang;Zhe Chen","doi":"10.17775/CSEEJPES.2024.06300","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.06300","url":null,"abstract":"Existing load forecasting methods typically assume that recent load data are available for prediction. This is not in conformity with reality since there is a time gap between the flow date (when power is consumed) and when measurement values are obtained. To this end, this letter proposes an online learning-based probabilistic load forecasting method considering the impact of the data gap. Specifically, an adaptive ensemble backpropagation-enabled online quantile regression algorithm is developed to optimize the parameters of the attention network recursively using the newly obtained load observations. To further improve the reliability and sharpness of prediction intervals under significant data gaps, we introduce an online interval calibration technique. The proposed online learning method allows us to adaptively capture the dynamic changes in load patterns and alleviate the information lags caused by data gaps. Comparative tests utilizing real-world datasets reveal the superiority of the proposed method.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"12 1","pages":"557-562"},"PeriodicalIF":5.9,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11069412","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-03-16DOI: 10.17775/CSEEJPES.2024.02520
Xuelei Feng;Tianhao Wen;Xiaohan Liu;Yang Liu;Q. H. Wu
This paper proposes a novel cascaded high-gain state and perturbation observer (CHGSPO)-based feedback linearization control (FLC) strategy for mitigating the sub-synchronous resonance (SSR) caused by the interactions between the series capacitor and doubly-fed induction generator-based wind farms (DFIGWFs). The CHGSPO is designed to estimate both the state and nonlinear perturbations of the series-compensated DFIGWF system. The nonlinear perturbation contains the disturbance originated from SSR, nonlinearities and uncertainties of the system model. The estimated state and perturbations are used by the FLC to eliminate the nonlinearities of the system and realize complete decoupling control of the DFIGWF. Additionally, the FLC effectively suppresses oscillatory signals detected by the CHGSPO. The proposed CHGSPO-based FLC exhibits remarkable robustness against uncertainties and external disturbances. The results of modal analysis and time domain simulations demonstrate the effectiveness of the proposed control strategy in SSR mitigation of the series-compensated DFIGWF system.
{"title":"Mitigating SSR of Series-Compensated DFIG Wind Farms Based on Cascaded High-Gain State and Perturbation Observers","authors":"Xuelei Feng;Tianhao Wen;Xiaohan Liu;Yang Liu;Q. H. Wu","doi":"10.17775/CSEEJPES.2024.02520","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.02520","url":null,"abstract":"This paper proposes a novel cascaded high-gain state and perturbation observer (CHGSPO)-based feedback linearization control (FLC) strategy for mitigating the sub-synchronous resonance (SSR) caused by the interactions between the series capacitor and doubly-fed induction generator-based wind farms (DFIGWFs). The CHGSPO is designed to estimate both the state and nonlinear perturbations of the series-compensated DFIGWF system. The nonlinear perturbation contains the disturbance originated from SSR, nonlinearities and uncertainties of the system model. The estimated state and perturbations are used by the FLC to eliminate the nonlinearities of the system and realize complete decoupling control of the DFIGWF. Additionally, the FLC effectively suppresses oscillatory signals detected by the CHGSPO. The proposed CHGSPO-based FLC exhibits remarkable robustness against uncertainties and external disturbances. The results of modal analysis and time domain simulations demonstrate the effectiveness of the proposed control strategy in SSR mitigation of the series-compensated DFIGWF system.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 4","pages":"1582-1595"},"PeriodicalIF":5.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006461","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-03-16DOI: 10.17775/CSEEJPES.2024.05770
Ruizhi Yu;Wei Gu;Yijun Xu;Shuai Lu;Suhan Zhang
As an effective emulator of ill-conditioned power flow, continuous Newton methods (CNMs) have been extensively investigated using explicit and implicit numerical integration algorithms. However, explicit CNMs often suffer from non-convergence due to their limited stability region, while implicit CNMs require additional iterative loops to solve nonlinear equations. To address this, we propose a semi-implicit version of CNM. We formulate the power flow equations as a set of differential algebraic equations (DAEs), and solve the DAEs with the stiffly accurate Rosenbrock type method (SARM). The proposed method succeeds the numerical robustness from the implicit CNM framework while prevents the iterative solution of nonlinear systems, hence revealing higher convergence speed and computation efficiency. We develop a novel 4-stage, 3rd-order hyper-stable SARM with an embedded 2nd-order formula for adaptive step size control. This design enhances convergence through damping adjustment. Case studies on ill-conditioned systems verify the alleged performance. An algorithm extension for MATPOWER is made available on Github for benchmarking.
{"title":"Semi-Implicit Continuous Newton Method for Power Flow Analysis","authors":"Ruizhi Yu;Wei Gu;Yijun Xu;Shuai Lu;Suhan Zhang","doi":"10.17775/CSEEJPES.2024.05770","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.05770","url":null,"abstract":"As an effective emulator of ill-conditioned power flow, continuous Newton methods (CNMs) have been extensively investigated using explicit and implicit numerical integration algorithms. However, explicit CNMs often suffer from non-convergence due to their limited stability region, while implicit CNMs require additional iterative loops to solve nonlinear equations. To address this, we propose a semi-implicit version of CNM. We formulate the power flow equations as a set of differential algebraic equations (DAEs), and solve the DAEs with the stiffly accurate Rosenbrock type method (SARM). The proposed method succeeds the numerical robustness from the implicit CNM framework while prevents the iterative solution of nonlinear systems, hence revealing higher convergence speed and computation efficiency. We develop a novel 4-stage, 3rd-order hyper-stable SARM with an embedded 2nd-order formula for adaptive step size control. This design enhances convergence through damping adjustment. Case studies on ill-conditioned systems verify the alleged performance. An algorithm extension for MATPOWER is made available on Github for benchmarking.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 4","pages":"1957-1961"},"PeriodicalIF":5.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006441","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-03-16DOI: 10.17775/CSEEJPES.2023.09570
Shenghu Li;Yikai Li
For the ultra HVDC (UHVDC) with the hierarchical connection mode at the inverter side, considering the change of the Thevenin equivalent parameters (TEP) of post-fault AC grid, a coordinated control strategy to the subsequent commutation failure (SCF) at both layers is newly proposed. The originality of this work is manifested in three aspects. 1) The mechanism of the SCF at the fault layer is newly found by deriving the analytical expression of the extinction angle with the TEP, and that at the non-fault layer is newly found by the voltage-time area theory with the DC current coupling. 2) An estimation model for the TEPs of two AC grids at the inverter side is proposed with the post-fault quantities. To address the random noise and inaccurate measurement data, an adaptive robust least squares method based on the median principle is proposed to solve the TEP model. 3) A coordinated control strategy with the estimated TEP is proposed to compensate for the extinction angle at the fault layer and limit the DC current at the non-fault layer, thus suppressing the SCF. The simulation results verify the suppression effect of the proposed control on the SCF under different fault conditions.
{"title":"Suppression to Subsequent Commutation Failure of UHVDC in Hierarchical Connection Mode with Estimated Equivalence of Post-fault of AC Grids","authors":"Shenghu Li;Yikai Li","doi":"10.17775/CSEEJPES.2023.09570","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.09570","url":null,"abstract":"For the ultra HVDC (UHVDC) with the hierarchical connection mode at the inverter side, considering the change of the Thevenin equivalent parameters (TEP) of post-fault AC grid, a coordinated control strategy to the subsequent commutation failure (SCF) at both layers is newly proposed. The originality of this work is manifested in three aspects. 1) The mechanism of the SCF at the fault layer is newly found by deriving the analytical expression of the extinction angle with the TEP, and that at the non-fault layer is newly found by the voltage-time area theory with the DC current coupling. 2) An estimation model for the TEPs of two AC grids at the inverter side is proposed with the post-fault quantities. To address the random noise and inaccurate measurement data, an adaptive robust least squares method based on the median principle is proposed to solve the TEP model. 3) A coordinated control strategy with the estimated TEP is proposed to compensate for the extinction angle at the fault layer and limit the DC current at the non-fault layer, thus suppressing the SCF. The simulation results verify the suppression effect of the proposed control on the SCF under different fault conditions.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"12 1","pages":"339-351"},"PeriodicalIF":5.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of photovoltaic and energy storage in industrial parks enhances economic benefits. However, uncertainties in photovoltaic output and future electricity prices pose challenges to optimal configuration. To address these issues, this paper develops a credibility copula-based robust multistage plan. Firstly, it addresses endogenous uncertainties in electricity pricing and exogenous uncertainties in photovoltaic output. Meanwhile, the copula function is used to couple endogenous uncertainties of the time-of-use, on-grid and demand power prices. Secondly, based on credibility theory, a fuzzy chance constraint model of endogenous uncertainties of future electricity prices and exogenous uncertainties of the PV output is derived. Finally, the method transforms fuzzy chance constraints into deterministic robust optimization through clear equivalence classes. Simulation analyses using data from an industrial park validate the applicability and effectiveness of the proposed approach.
{"title":"Credibility Copula-Based Robust Multistage Plan for Industrial Parks Under Exogenous and Endogenous Uncertainties","authors":"Zehao Shi;Jiajia Chen;Yanxin Wang;Yanlei Zhao;Bingyin Xu","doi":"10.17775/CSEEJPES.2024.09120","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.09120","url":null,"abstract":"The integration of photovoltaic and energy storage in industrial parks enhances economic benefits. However, uncertainties in photovoltaic output and future electricity prices pose challenges to optimal configuration. To address these issues, this paper develops a credibility copula-based robust multistage plan. Firstly, it addresses endogenous uncertainties in electricity pricing and exogenous uncertainties in photovoltaic output. Meanwhile, the copula function is used to couple endogenous uncertainties of the time-of-use, on-grid and demand power prices. Secondly, based on credibility theory, a fuzzy chance constraint model of endogenous uncertainties of future electricity prices and exogenous uncertainties of the PV output is derived. Finally, the method transforms fuzzy chance constraints into deterministic robust optimization through clear equivalence classes. Simulation analyses using data from an industrial park validate the applicability and effectiveness of the proposed approach.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 3","pages":"987-998"},"PeriodicalIF":6.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006442","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-03-16DOI: 10.17775/CSEEJPES.2024.06280
Yan Xie;Shiying Ma;Jiakai Shen;Xiaojun Tang;Jianmiao Ren;Liwen Zheng
The construction of UHV AC/DC hybrid power grids and the integration of large-scale renewable energy have led to significant frequency stability issues. To enhance the frequency regulation capacity within areas and fully utilize the mutual supportive capacity among areas, active frequency control (AFC) theory has been proposed and developed based on the concept of active feed-forward control. However, existing AFC methods have limitations in computational solutions and control effects in large-scale, wide-area interconnected power systems. To address these shortcomings, this paper proposes an improved AFC method based on multi-step-size model predictive control (MSS-MPC). Through multi-step-size discretization, an improved model predictive control algorithm for AFC is derived based on the iterative solution of the multi-step-size linear quadratic regulator model, which ensures control accuracy through precise prediction and enhances the control performance through additional prediction. Based on the collaboration of two-level dispatching agencies and the consideration of differences in control objectives among areas at different stages, an improved three-stage AFC strategy is proposed, which aims to suppress additional frequency disturbances after control model switching by proposing the active-passive switching strategy. Case studies demonstrate that, compared with the existing AFC method, the proposed AFC method with the MSS-MPC algorithm has a better control effect and better computational performance.
{"title":"Wide-Area Active Frequency Control with Multi-Step-Size MPC","authors":"Yan Xie;Shiying Ma;Jiakai Shen;Xiaojun Tang;Jianmiao Ren;Liwen Zheng","doi":"10.17775/CSEEJPES.2024.06280","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.06280","url":null,"abstract":"The construction of UHV AC/DC hybrid power grids and the integration of large-scale renewable energy have led to significant frequency stability issues. To enhance the frequency regulation capacity within areas and fully utilize the mutual supportive capacity among areas, active frequency control (AFC) theory has been proposed and developed based on the concept of active feed-forward control. However, existing AFC methods have limitations in computational solutions and control effects in large-scale, wide-area interconnected power systems. To address these shortcomings, this paper proposes an improved AFC method based on multi-step-size model predictive control (MSS-MPC). Through multi-step-size discretization, an improved model predictive control algorithm for AFC is derived based on the iterative solution of the multi-step-size linear quadratic regulator model, which ensures control accuracy through precise prediction and enhances the control performance through additional prediction. Based on the collaboration of two-level dispatching agencies and the consideration of differences in control objectives among areas at different stages, an improved three-stage AFC strategy is proposed, which aims to suppress additional frequency disturbances after control model switching by proposing the active-passive switching strategy. Case studies demonstrate that, compared with the existing AFC method, the proposed AFC method with the MSS-MPC algorithm has a better control effect and better computational performance.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 3","pages":"972-986"},"PeriodicalIF":6.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006428","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-03-16DOI: 10.17775/CSEEJPES.2024.07190
Yuegong Li;Guorong Zhu;Jianghua Lu;Hua Geng
Generally, voltage support at the point of common coupling (PCC) of a wind farm is achieved through centralized static var generators (SVGs). Since the reactive power requirements occupy their capacity in a steady state, the reactive power support capacity of the SVG is limited during high voltage ride through (HVRT) or low voltage ride through (LVRT). While wind turbines can provide voltage support in accordance with the grid code, their responses are usually delayed due to communication and transmission lags. To enhance the dynamic performance of wind farms during fault ride-through, a reactive power substitution (RPS) control strategy is proposed in this paper. In a steady state, this RPS control method preferentially utilizes the remaining capacity of wind turbines to substitute for the output of the SVG. Considering differences in terminal voltage characteristics and operating conditions, this RPS control method employs a particle swarm optimization (PSO) algorithm to ensure that wind turbines can provide their optimal reactive power support capacity. When the grid voltage swells or drops, the SVG has a sufficient reactive power reserve to support the grid quickly. This paper utilizes a regional power grid incorporating two wind farms connected to different buses as a case study to validate this RPS control strategy.
{"title":"Voltage Support Capacity Improvement for Wind Farms with Reactive Power Substitution Control","authors":"Yuegong Li;Guorong Zhu;Jianghua Lu;Hua Geng","doi":"10.17775/CSEEJPES.2024.07190","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.07190","url":null,"abstract":"Generally, voltage support at the point of common coupling (PCC) of a wind farm is achieved through centralized static var generators (SVGs). Since the reactive power requirements occupy their capacity in a steady state, the reactive power support capacity of the SVG is limited during high voltage ride through (HVRT) or low voltage ride through (LVRT). While wind turbines can provide voltage support in accordance with the grid code, their responses are usually delayed due to communication and transmission lags. To enhance the dynamic performance of wind farms during fault ride-through, a reactive power substitution (RPS) control strategy is proposed in this paper. In a steady state, this RPS control method preferentially utilizes the remaining capacity of wind turbines to substitute for the output of the SVG. Considering differences in terminal voltage characteristics and operating conditions, this RPS control method employs a particle swarm optimization (PSO) algorithm to ensure that wind turbines can provide their optimal reactive power support capacity. When the grid voltage swells or drops, the SVG has a sufficient reactive power reserve to support the grid quickly. This paper utilizes a regional power grid incorporating two wind farms connected to different buses as a case study to validate this RPS control strategy.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 3","pages":"999-1017"},"PeriodicalIF":6.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006434","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to cope with the global environmental crisis caused by energy generation and achieve carbon neutrality, it is imperative to promote a new power system dominated by renewable energy sources (RESs). This paper focuses on the uncertainty of RESs and the distribution characteristics of carbon emission flows (CEFs), and studies the low-carbon operation and power system planning problem. Firstly, this paper extends the uncertainty of RES to the meteorological field and establishes meteorological robust constraints of photovoltaic (PV) generation. Based on the CEF theory, the carbon transmission trajectory is accurately delineated to improve the operation of power system. Considering further constraints from the power flow, CEF, and component operation characteristics of the active distribution network (ADN), this paper formulates a low-carbon joint planning model of ADN with PV, battery energy storage system (BESS), and distributed gas generator (DGG), taking into account economy and carbon reduction. In the case study, the low-carbon planning and operation scheme are analyzed in detail across multiple dimensions including time and space. The solution results show that the planning model can effectively leverage the low-carbon performance of PV and BESS, and improve the distribution of CEF. Through case comparison, the model can also efficiently reduce the total cost of the system and enhance carbon emission reduction benefits by 35.10 to 41.04%.
{"title":"Low-Carbon Joint Planning for Distribution Network Considering Carbon Emission Flow and Uncertainties from Photovoltaic Power Generation","authors":"Yanlin Li;Zhigang Lu;Jiangfeng Zhang;Xiaoqiang Guo;Xueping Li;Xiangxing Kong;Jiangyong Zhang","doi":"10.17775/CSEEJPES.2023.08850","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.08850","url":null,"abstract":"In order to cope with the global environmental crisis caused by energy generation and achieve carbon neutrality, it is imperative to promote a new power system dominated by renewable energy sources (RESs). This paper focuses on the uncertainty of RESs and the distribution characteristics of carbon emission flows (CEFs), and studies the low-carbon operation and power system planning problem. Firstly, this paper extends the uncertainty of RES to the meteorological field and establishes meteorological robust constraints of photovoltaic (PV) generation. Based on the CEF theory, the carbon transmission trajectory is accurately delineated to improve the operation of power system. Considering further constraints from the power flow, CEF, and component operation characteristics of the active distribution network (ADN), this paper formulates a low-carbon joint planning model of ADN with PV, battery energy storage system (BESS), and distributed gas generator (DGG), taking into account economy and carbon reduction. In the case study, the low-carbon planning and operation scheme are analyzed in detail across multiple dimensions including time and space. The solution results show that the planning model can effectively leverage the low-carbon performance of PV and BESS, and improve the distribution of CEF. Through case comparison, the model can also efficiently reduce the total cost of the system and enhance carbon emission reduction benefits by 35.10 to 41.04%.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"12 1","pages":"411-423"},"PeriodicalIF":5.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006429","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-03-16DOI: 10.17775/CSEEJPES.2024.03030
Qi Chen;Gang Mu;Hongbo Liu;Changgang Wang
The data acquisition technologies used in power systems have been continuously improving, thus laying the solid foundation for data-driven operation analysis of power systems. However, existing methods for analyzing the relationship between operational variables mainly depend on the mathematical model and element parameters of the power system. Therefore, a thorough data-based analysis method is required to investigate the spatiotemporal characteristics of power system operation, especially for new types of power systems. The causal inference method, which has been successfully applied in many fields, is a powerful tool for investigating the interaction of data variables. In this study, a causal inference method is proposed based on supervisory control and data acquisition (SCADA) data for investigating the spatiotemporal causal relationships in power systems. Initially, a multiple data-sequence regression model is proposed to analyze the relationship of operation data variables. Next, the linear non-Gaussian acyclic model (LiNGAM) is used to calculate the causal index of the operational variables, and its limitations are analyzed. Furthermore, a new causal index of “full variable amplitude LiNGAM (FVA-LiNGAM)” is proposed by incorporating prior causal direct knowledge and considering the effect of real variable amplitude. Using the FVA-LiNGAM causal index, the causal relationship of operation variables can be investigated with higher spatiotemporal accuracy than that of the original LiNGAM index. Taking a real SCADA data subset of a provincial power system as an example, the validity of the FVA-LiNGAM causal index is verified. The variation patterns in spatiotemporal causality are explored using actual SCADA data sequences. The result shows that there indeed exists some spatiotemporal causality variation patterns between the operating variables of the power system.
{"title":"Data-Sequence Modeling Based Causal Evaluation Method for Power Systems and Spatiotemporal Causality Variation Patterns","authors":"Qi Chen;Gang Mu;Hongbo Liu;Changgang Wang","doi":"10.17775/CSEEJPES.2024.03030","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.03030","url":null,"abstract":"The data acquisition technologies used in power systems have been continuously improving, thus laying the solid foundation for data-driven operation analysis of power systems. However, existing methods for analyzing the relationship between operational variables mainly depend on the mathematical model and element parameters of the power system. Therefore, a thorough data-based analysis method is required to investigate the spatiotemporal characteristics of power system operation, especially for new types of power systems. The causal inference method, which has been successfully applied in many fields, is a powerful tool for investigating the interaction of data variables. In this study, a causal inference method is proposed based on supervisory control and data acquisition (SCADA) data for investigating the spatiotemporal causal relationships in power systems. Initially, a multiple data-sequence regression model is proposed to analyze the relationship of operation data variables. Next, the linear non-Gaussian acyclic model (LiNGAM) is used to calculate the causal index of the operational variables, and its limitations are analyzed. Furthermore, a new causal index of “full variable amplitude LiNGAM (FVA-LiNGAM)” is proposed by incorporating prior causal direct knowledge and considering the effect of real variable amplitude. Using the FVA-LiNGAM causal index, the causal relationship of operation variables can be investigated with higher spatiotemporal accuracy than that of the original LiNGAM index. Taking a real SCADA data subset of a provincial power system as an example, the validity of the FVA-LiNGAM causal index is verified. The variation patterns in spatiotemporal causality are explored using actual SCADA data sequences. The result shows that there indeed exists some spatiotemporal causality variation patterns between the operating variables of the power system.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 4","pages":"1429-1441"},"PeriodicalIF":5.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006425","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-03-16DOI: 10.17775/CSEEJPES.2024.08620
Zhenglong Sun;Zhifeng He;Naiyuan Liu;Rui Zhang;Bo Wang;Guowei Cai
With the sustained increase in the penetration rate of grid-forming (GFM) converters in power systems, problems such as overcurrent and transient instability during system faults have drawn significant attention. This paper proposes an enhanced dynamic dq-axis current-limiting strategy (ED-CLiS) for GFM converters, which improves transient stability across all stages before and after current limiting. First, a dynamic dq-axis current-limiting strategy (D-CLiS) is proposed. This strategy regulates the power angle to prevent it from going out of control during current limiting, which is achieved by controlling the $d$- and $q$-axis current reference values to reshape the ${P}-delta^{prime}$ curve. Additionally, a large reverse acceleration area can induce excessive angle $delta^{prime}$ swings, causing the D-CLiS to be triggered repeatedly and resulting in converter instability after the control exit, even without faults. A reference power control structure (RPCS) is proposed, which suppresses the phenomenon by adjusting the power reference value to control the converter's reverse acceleration area. Finally, the control strategy is verified through simulations.
{"title":"Enhanced Transient Stability Strategy for Grid-Forming Converter Based on Current Limiting","authors":"Zhenglong Sun;Zhifeng He;Naiyuan Liu;Rui Zhang;Bo Wang;Guowei Cai","doi":"10.17775/CSEEJPES.2024.08620","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.08620","url":null,"abstract":"With the sustained increase in the penetration rate of grid-forming (GFM) converters in power systems, problems such as overcurrent and transient instability during system faults have drawn significant attention. This paper proposes an enhanced dynamic dq-axis current-limiting strategy (ED-CLiS) for GFM converters, which improves transient stability across all stages before and after current limiting. First, a dynamic dq-axis current-limiting strategy (D-CLiS) is proposed. This strategy regulates the power angle to prevent it from going out of control during current limiting, which is achieved by controlling the <tex>$d$</tex>- and <tex>$q$</tex>-axis current reference values to reshape the <tex>${P}-delta^{prime}$</tex> curve. Additionally, a large reverse acceleration area can induce excessive angle <tex>$delta^{prime}$</tex> swings, causing the D-CLiS to be triggered repeatedly and resulting in converter instability after the control exit, even without faults. A reference power control structure (RPCS) is proposed, which suppresses the phenomenon by adjusting the power reference value to control the converter's reverse acceleration area. Finally, the control strategy is verified through simulations.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 3","pages":"960-971"},"PeriodicalIF":6.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006437","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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