Pub Date : 2024-11-17DOI: 10.1016/j.ijepes.2024.110372
Kaikai Zhang , Guibin Zou , Jinliang Zhang , Houlei Li , Yazhong Sun , Guoliang Li
Hybrid energy storage system (HESS) can stabilize renewable energy power generation, but unreasonable energy storage power distribution and photovoltaic-load forecast errors will affect the economic benefits of the whole system. Aiming at the microgrid (MG) composed of photovoltaic (PV) and HESS, an energy management strategy (EMS) of MG considering forecast errors is proposed. Firstly, an optimization model considering the depreciation cost of battery is established. Secondly, day-ahead EMS is implemented under multiple operating modes considering minimum fluctuation and optimal economy. Then, according to the real-time forecast results and the feedback of system operation status, the intraday rolling energy management strategy (REMS) is developed to alleviate the impact of forecast errors. Finally, the real-time state of charge (SOC) of the supercapacitor (SC) is introduced to adjust the filter coefficient, which avoids the SC working in the charge/discharge restricted area for a long time and improves the adjustment effect. The results of the case analysis show that the proposed intraday REMS can effectively reduce the influence of forecast errors on energy management.
{"title":"Microgrid energy management strategy considering source-load forecast error","authors":"Kaikai Zhang , Guibin Zou , Jinliang Zhang , Houlei Li , Yazhong Sun , Guoliang Li","doi":"10.1016/j.ijepes.2024.110372","DOIUrl":"10.1016/j.ijepes.2024.110372","url":null,"abstract":"<div><div>Hybrid energy storage system (HESS) can stabilize renewable energy power generation, but unreasonable energy storage power distribution and photovoltaic-load forecast errors will affect the economic benefits of the whole system. Aiming at the microgrid (MG) composed of photovoltaic (PV) and HESS, an energy management strategy (EMS) of MG considering forecast errors is proposed. Firstly, an optimization model considering the depreciation cost of battery is established. Secondly, day-ahead EMS is implemented under multiple operating modes considering minimum fluctuation and optimal economy. Then, according to the real-time forecast results and the feedback of system operation status, the intraday rolling energy management strategy (REMS) is developed to alleviate the impact of forecast errors. Finally, the real-time state of charge (SOC) of the supercapacitor (SC) is introduced to adjust the filter coefficient, which avoids the SC working in the charge/discharge restricted area for a long time and improves the adjustment effect. The results of the case analysis show that the proposed intraday REMS can effectively reduce the influence of forecast errors on energy management.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"164 ","pages":"Article 110372"},"PeriodicalIF":5.0,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651822","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 : 2024-11-17DOI: 10.1016/j.ijepes.2024.110377
Lixin Jia , Lihang Feng , Dong Wang , Jiapeng Jiang , Guannan Wang , Jiantao Shi
The continuous introduction of technologies such as distributed generation, wind power, and photovoltaic energy poses challenges to identifying abnormal waveforms in power disturbances. Due to the constant increase in abnormal features, existing waveform recognition schemes for power disturbance abnormalities cannot meet the requirements of high accuracy and reliability. In this paper, a Dimension-Enhanced Residual Multi-Scale Attention Framework for identifying power disturbance abnormal waveforms is proposed. This framework first employs the Phase Adaptive Adjustment (PAA) method to address the phase offset problem of original recording data, then uses the Gramian Angle Field method to perform dimensionality expansion on the data processed by PAA, and finally utilizes the Residual Pyramid Squeeze Attention Network (ResPSANet) for identifying power disturbance abnormal waveforms. Experiments demonstrate that the proposed approach improves the performance of power disturbance abnormal waveform recognition by 10% compared to existing schemes.
{"title":"A dimension-enhanced residual multi-scale attention framework for identifying anomalous waveforms of fault recorders","authors":"Lixin Jia , Lihang Feng , Dong Wang , Jiapeng Jiang , Guannan Wang , Jiantao Shi","doi":"10.1016/j.ijepes.2024.110377","DOIUrl":"10.1016/j.ijepes.2024.110377","url":null,"abstract":"<div><div>The continuous introduction of technologies such as distributed generation, wind power, and photovoltaic energy poses challenges to identifying abnormal waveforms in power disturbances. Due to the constant increase in abnormal features, existing waveform recognition schemes for power disturbance abnormalities cannot meet the requirements of high accuracy and reliability. In this paper, a Dimension-Enhanced Residual Multi-Scale Attention Framework for identifying power disturbance abnormal waveforms is proposed. This framework first employs the Phase Adaptive Adjustment (PAA) method to address the phase offset problem of original recording data, then uses the Gramian Angle Field method to perform dimensionality expansion on the data processed by PAA, and finally utilizes the Residual Pyramid Squeeze Attention Network (ResPSANet) for identifying power disturbance abnormal waveforms. Experiments demonstrate that the proposed approach improves the performance of power disturbance abnormal waveform recognition by 10% compared to existing schemes.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"164 ","pages":"Article 110377"},"PeriodicalIF":5.0,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651823","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 : 2024-11-15DOI: 10.1016/j.ijepes.2024.110367
Fei Tang , Mo Chen , Yuhan Guo , Jinzhou Sun , Xiaoqing Wei , Jiaquan Yang , Xuehao He
This paper addresses the issue of complex fault oscillation modes and weak voltage points in large power systems by proposing a network structure optimization method that balances system synchrony and node voltage stability. The method uses slow synchrony clustering theory to establish node classification criteria and a comprehensive synchrony indicator for quantitative description of network synchrony performance. Simultaneously, it employs the holomorphic embedding method to solve the voltage sigma indicator for quantitative assessment of voltage stability. A model that considers both system synchrony and node voltage stability is then developed and optimized using a discrete particle swarm algorithm in simulations with 13-node, 118-node, and 2383-wp systems, compared to other classical algorithms. Simulation results show that the proposed optimization method effectively improves the synchrony clustering performance and voltage stability of the test systems, offering faster optimization speed and better results compared to other classical algorithms.
{"title":"Grid structure optimization using slow coherency theory and holomorphic embedding method","authors":"Fei Tang , Mo Chen , Yuhan Guo , Jinzhou Sun , Xiaoqing Wei , Jiaquan Yang , Xuehao He","doi":"10.1016/j.ijepes.2024.110367","DOIUrl":"10.1016/j.ijepes.2024.110367","url":null,"abstract":"<div><div>This paper addresses the issue of complex fault oscillation modes and weak voltage points in large power systems by proposing a network structure optimization method that balances system synchrony and node voltage stability. The method uses slow synchrony clustering theory to establish node classification criteria and a comprehensive synchrony indicator for quantitative description of network synchrony performance. Simultaneously, it employs the holomorphic embedding method to solve the voltage sigma indicator for quantitative assessment of voltage stability. A model that considers both system synchrony and node voltage stability is then developed and optimized using a discrete particle swarm algorithm in simulations with 13-node, 118-node, and 2383-wp systems, compared to other classical algorithms. Simulation results show that the proposed optimization method effectively improves the synchrony clustering performance and voltage stability of the test systems, offering faster optimization speed and better results compared to other classical algorithms.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"164 ","pages":"Article 110367"},"PeriodicalIF":5.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651821","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 : 2024-11-14DOI: 10.1016/j.ijepes.2024.110328
Kunpeng Zhang , Tianhao Liu , Yutian Liu , Huan Ma , Linlin Ma
As the penetration of renewable energy continues to rise, the occurrence of ramp events in renewable generation poses significant challenges to power system security and efficient renewable energy utilization. To optimize the power allocation of hybrid energy storage systems (HESS) and enhance adjustable reserves to mitigate ramp events, a day-ahead and intraday two-stage multi-objective optimal dispatch strategy is proposed for hybrid power generation systems containing wind, photovoltaic, battery and hydrogen energy storage system (ESS). First, a novel optimization objective is presented to regulate the response priorities of different ESS by minimizing the energy loss, and balance the conservatism by a penalty factor. Then, a two-stage optimal dispatch model is proposed including two sub-models. The day-ahead multi-objective dispatch model considers generation plan, available storage capacity and energy loss, which identifies time slots when adjustable reserves is insufficient; the intraday dispatch model dynamically adjusts penalty factor for each time slot based on the day-ahead results to enhance adjustable reserves in advance. This combination of day-ahead and intraday dispatch models improves the farsightedness and computational efficiency. Finally, a non-isometric scaling method is presented to improve the distribution of Pareto optimal solutions for the non-dominated sorting genetic algorithm III (NSGA-III). Simulation results based on the actual data from Belgium and China demonstrate that the proposed method effectively mitigates the ramp stress and improves renewable energy utilization, with high computational efficiency and robustness to parameters.
{"title":"Two-stage multi-objective optimal dispatch of hybrid power generation system for ramp stress mitigation","authors":"Kunpeng Zhang , Tianhao Liu , Yutian Liu , Huan Ma , Linlin Ma","doi":"10.1016/j.ijepes.2024.110328","DOIUrl":"10.1016/j.ijepes.2024.110328","url":null,"abstract":"<div><div>As the penetration of renewable energy continues to rise, the occurrence of ramp events in renewable generation poses significant challenges to power system security and efficient renewable energy utilization. To optimize the power allocation of hybrid energy storage systems (HESS) and enhance adjustable reserves to mitigate ramp events, a day-ahead and intraday two-stage multi-objective optimal dispatch strategy is proposed for hybrid power generation systems containing wind, photovoltaic, battery and hydrogen energy storage system (ESS). First, a novel optimization objective is presented to regulate the response priorities of different ESS by minimizing the energy loss, and balance the conservatism by a penalty factor. Then, a two-stage optimal dispatch model is proposed including two sub-models. The day-ahead multi-objective dispatch model considers generation plan, available storage capacity and energy loss, which identifies time slots when adjustable reserves is insufficient; the intraday dispatch model dynamically adjusts penalty factor for each time slot based on the day-ahead results to enhance adjustable reserves in advance. This combination of day-ahead and intraday dispatch models improves the farsightedness and computational efficiency. Finally, a non-isometric scaling method is presented to improve the distribution of Pareto optimal solutions for the non-dominated sorting genetic algorithm III (NSGA-III). Simulation results based on the actual data from Belgium and China demonstrate that the proposed method effectively mitigates the ramp stress and improves renewable energy utilization, with high computational efficiency and robustness to parameters.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"163 ","pages":"Article 110328"},"PeriodicalIF":5.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661475","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 : 2024-11-13DOI: 10.1016/j.ijepes.2024.110331
Zhuoli Zhao, Jianzhao Lu, Jingmin Fan, Chang Liu, Changsong Peng, Loi Lei Lai
With the increasing penetration of wind and solar energy as primary energy sources, their impact on the power generation system cannot be ignored due to the high uncertainty of their changes. Traditional time-domain simulation methods are insufficient to capture the system’s dynamic behavior under nearly infinite scenarios. This paper proposes the reduced-order model-based reachability analysis to obtain the dynamic trajectories of critical state variables after introducing primary energy disturbances into the proposed hybrid wind-solar microgrids. The small-signal model of the hybrid wind-solar microgrid is established and its order is reduced based on the singular perturbation theory. Moreover, the zonotope-based primary energy disturbance model is developed, which expresses the primary energy disturbances in the form of a set and enables the primary energy disturbances to participate in reachability analysis, thereby reducing the need for multiple simulations and improving computational efficiency. By comparing the full-order and reduced-order models' dynamic responses, it is evident that the maximum error between them during the dynamic process is only 1.7%, validating the accuracy of the reduced-order model. From the simulation results, it can be observed that the proposed reduced-order model-based reachability analysis can effectively improve the calculation speed while achieving almost the same results as the full-order model. Furthermore, utilizing the proposed method for computing reachable sets with small time steps has reduced the computation time by up to almost 5 times, confirming the efficiency and feasibility of the proposed method.
Pub Date : 2024-11-13DOI: 10.1016/j.ijepes.2024.110352
Farzad Dehghan Marvasti , Ahmad Mirzaei , Reza Bakhshi-Jafarabadi , Marjan Popov
Wavelet transform has proven to be a capable tool for protection purposes in high voltage direct current (HVDC) transmission lines due to its desired speed and accuracy. However, the need to enhance the WT-based protection methods in terms of sensitivity and selectivity is of interest. This paper proposes a new non-unit WT-based protection method with adaptive threshold setting. According to the improved time-domain analytical approach, line-mode fault-generated voltage traveling wave is adopted to identify the internal faults. The simulation results for a multi-terminal modular multilevel converter-based HVDC grid in PSCAD/EMTDC corroborate accurate and fast internal faults detection of the proposed method, up to 850 Ω, i.e., almost three times larger than conventional schemes. In addition, the reliable performance of the presented method in a noisy environment, using relatively low sampling frequencies, and different sizes of current limiting inductors is demonstrated in the presented analysis. The generality of the presented analytical approach ensures that the proposed protection method can be extended to more complex HVDC grids.
{"title":"Non-unit wavelet transform-based protection principle for modular multi-level converter-based HVDC grids using adaptive threshold setting","authors":"Farzad Dehghan Marvasti , Ahmad Mirzaei , Reza Bakhshi-Jafarabadi , Marjan Popov","doi":"10.1016/j.ijepes.2024.110352","DOIUrl":"10.1016/j.ijepes.2024.110352","url":null,"abstract":"<div><div>Wavelet transform has proven to be a capable tool for protection purposes in high voltage direct current (HVDC) transmission lines due to its desired speed and accuracy. However, the need to enhance the WT-based protection methods in terms of sensitivity and selectivity is of interest. This paper proposes a new non-unit WT-based protection method with adaptive threshold setting. According to the improved time-domain analytical approach, line-mode fault-generated voltage traveling wave is adopted to identify the internal faults. The simulation results for a multi-terminal modular multilevel converter-based HVDC grid in PSCAD/EMTDC corroborate accurate and fast internal faults detection of the proposed method, up to 850 Ω, i.e., almost three times larger than conventional schemes. In addition, the reliable performance of the presented method in a noisy environment, using relatively low sampling frequencies, and different sizes of current limiting inductors is demonstrated in the presented analysis. The generality of the presented analytical approach ensures that the proposed protection method can be extended to more complex HVDC grids.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"163 ","pages":"Article 110352"},"PeriodicalIF":5.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661474","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 : 2024-11-13DOI: 10.1016/j.ijepes.2024.110369
Houman Moloudi Zargari, Vahid Talavat, Tohid Ghanizadeh Bolandi
Among the various challenges of transmission line distance protection, it has become increasingly difficult to deal with the symmetrical phenomenon of power swing (PS) and it can lead to maloperation of distance relays and power system blackout. Distance relays have challenges in distinguishing the symmetrical fault during power swing and would send a wrong trip command to the circuit breakers, leading to major blackouts. To solve this issue, the main objective of this study is to provide a fast, simple, and local protection scheme based on differential current component (DCC), which is extracted by the difference in predicted and actual samples of local current component. Autoregression technique is used to predict the future cycle current samples using available samples. Based on the DCC, a novel index named differential current component ratio (DCCR) is introduced to effectively detect the internal symmetrical faults from fast/slow power swing conditions. Several tests are run for different fault types, different power swing frequencies, different fault locations, different fault resistances and different fault inception instants. Finally, the results are compared with the available methods. Results demonstrate the high accuracy of the proposed method in fast and accurate detection of internal symmetrical faults during power swing in different frequency rates in less than one cycle.
{"title":"A fast, simple and local protection scheme for fault detection and classification during power swings based on differential current component","authors":"Houman Moloudi Zargari, Vahid Talavat, Tohid Ghanizadeh Bolandi","doi":"10.1016/j.ijepes.2024.110369","DOIUrl":"10.1016/j.ijepes.2024.110369","url":null,"abstract":"<div><div>Among the various challenges of transmission line distance protection, it has become increasingly difficult to deal with the symmetrical phenomenon of power swing (PS) and it can lead to maloperation of distance relays and power system blackout. Distance relays have challenges in distinguishing the symmetrical fault during power swing and would send a wrong trip command to the circuit breakers, leading to major blackouts. To solve this issue, the main objective of this study is to provide a fast, simple, and local protection scheme based on differential current component (DCC), which is extracted by the difference in predicted and actual samples of local current component. Autoregression technique is used to predict the future cycle current samples using available samples. Based on the DCC, a novel index named differential current component ratio (DCCR) is introduced to effectively detect the internal symmetrical faults from fast/slow power swing conditions. Several tests are run for different fault types, different power swing frequencies, different fault locations, different fault resistances and different fault inception instants. Finally, the results are compared with the available methods. Results demonstrate the high accuracy of the proposed method in fast and accurate detection of internal symmetrical faults during power swing in different frequency rates in less than one cycle.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"163 ","pages":"Article 110369"},"PeriodicalIF":5.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661472","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 : 2024-11-12DOI: 10.1016/j.ijepes.2024.110350
Qi Wang, Shutan Wu, Zhong Wu, Jianxiong Hu, Quanpeng He, Yujian Ye, Yi Tang
False data injection attacks (FDIAs), as strategically designed cyberattacks, can bypass bad data detection mechanisms and thus pose potential economic and stability risks to power systems. In addition to increasing the detection capability of the system, the proactive property transformation of the system can effectively utilize the information gap between the attacker and the system operator, increasing the detection rate against FDIAs. In this paper, a moving target defense (MTD) method based on topology switching (TS) actions is proposed to overcome FDIAs. Specifically, we investigated the feasibility of proactive defense via TS actions, which reconfigured the topology via busbar switching. To make sequential defense decisions on the basis of the perceived current state of the system, the game between the attacker and the defender was modeled as a Markov decision process (MDP). Finally, the deep reinforcement learning-based MTD optimal algorithm was designed to achieve a fast and efficient decision-making strategy. The simulation results demonstrated the effects of the proposed method against FDIAs.
{"title":"Topology switching-based moving target defense against false data injection attacks on a power system","authors":"Qi Wang, Shutan Wu, Zhong Wu, Jianxiong Hu, Quanpeng He, Yujian Ye, Yi Tang","doi":"10.1016/j.ijepes.2024.110350","DOIUrl":"10.1016/j.ijepes.2024.110350","url":null,"abstract":"<div><div>False data injection attacks (FDIAs), as strategically designed cyberattacks, can bypass bad data detection mechanisms and thus pose potential economic and stability risks to power systems. In addition to increasing the detection capability of the system, the proactive property transformation of the system can effectively utilize the information gap between the attacker and the system operator, increasing the detection rate against FDIAs. In this paper, a moving target defense (MTD) method based on topology switching (TS) actions is proposed to overcome FDIAs. Specifically, we investigated the feasibility of proactive defense via TS actions, which reconfigured the topology via busbar switching. To make sequential defense decisions on the basis of the perceived current state of the system, the game between the attacker and the defender was modeled as a Markov decision process (MDP). Finally, the deep reinforcement learning-based MTD optimal algorithm was designed to achieve a fast and efficient decision-making strategy. The simulation results demonstrated the effects of the proposed method against FDIAs.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"163 ","pages":"Article 110350"},"PeriodicalIF":5.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661471","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 : 2024-11-12DOI: 10.1016/j.ijepes.2024.110366
Wei Yan , Guanneng Xu , Xu Zhang , Ruifeng Zhao
The data integration of high voltage (HV) and medium voltage (MV) distribution power grids is a development trend in future energy management systems. This integration brings about challenges such as handling large-scale networks and bad data, which can impact the speed and accuracy of state estimation. To address these issues, a forward/backward sweep (FBS) robust state estimation algorithm (FB-SE) is proposed, particularly suitable for simple loop and radial distribution systems. The algorithm consists of two stages: 1) Bad Data Pre-processing (BDP); and 2) Branch Power Flow State Estimation; Both stages follow the FBS strategy. The preprocessing of bad data only involves one FBS calculation, aiming to identify and rectify obvious bad data; The branch power flow state estimation introduces the weights of state estimation and uses a classification normalized residual index function to achieve weight allocation. The state estimation algorithm proposed in this paper avoids solving Jacobian matrices and linear equations. It can also handle simple ring networks by opening the loop and performing power compensation. The simulation results based on IEEE examples show that this algorithm has advantages in computation speed, robustness, and convergence.
{"title":"A forward/backward robust state estimation algorithm for radial and simple loop distribution systems","authors":"Wei Yan , Guanneng Xu , Xu Zhang , Ruifeng Zhao","doi":"10.1016/j.ijepes.2024.110366","DOIUrl":"10.1016/j.ijepes.2024.110366","url":null,"abstract":"<div><div>The data integration of high voltage (HV) and medium voltage (MV) distribution power grids is a development trend in future energy management systems. This integration brings about challenges such as handling large-scale networks and bad data, which can impact the speed and accuracy of state estimation. To address these issues, a forward/backward sweep (FBS) robust state estimation algorithm (FB-SE) is proposed, particularly suitable for simple loop and radial distribution systems. The algorithm consists of two stages: 1) Bad Data Pre-processing (BDP); and 2) Branch Power Flow State Estimation; Both stages follow the FBS strategy. The preprocessing of bad data only involves one FBS calculation, aiming to identify and rectify obvious bad data; The branch power flow state estimation introduces the weights of state estimation and uses a classification normalized residual index function to achieve weight allocation. The state estimation algorithm proposed in this paper avoids solving Jacobian matrices and linear equations. It can also handle simple ring networks by opening the loop and performing power compensation. The simulation results based on IEEE examples show that this algorithm has advantages in computation speed, robustness, and convergence.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"163 ","pages":"Article 110366"},"PeriodicalIF":5.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661480","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 bulk amount of power generated from the present-day large-scale DFIG-installed wind farms are preferably transmitted to utility grid through series compensated transmission lines. Currently, TCSC compensation is more attractive compared to fixed series compensation due to its numerous technical advantages. However, the nonlinear relationship of the output power verses wind speed and the different operating modes of the DFIG and TCSC cause rapid variation in the line impedance during both normal as well as fault conditions. Consequently, the widely used fixed impedance-based distance relays when used for protection of such lines find limitation. In this paper, a fast discrete S-transform feature-assisted back propagation neural network technique is proposed using the relay end current measurements for effective detection and classification of faults in such crucial transmission lines. The efficacy of the scheme is evaluated on numerous fault and non-fault cases simulated through MATLAB/Simulink on different standard test systems under varying system operating conditions. The results clearly show the superiority of the proposed method in comparision to the existing approaches in terms of its low computational burden, fast fault detection time (< 10 ms) and accuracies (= 100 %) and fast fault classification time (< 10 ms) and accuracies (99.99 %).
{"title":"Intelligent protective relaying for the series compensated line with high penetration of wind energy sources","authors":"Subodh Kumar Mohanty , Paresh Kumar Nayak , Pierluigi Siano , Aleena Swetapadma","doi":"10.1016/j.ijepes.2024.110362","DOIUrl":"10.1016/j.ijepes.2024.110362","url":null,"abstract":"<div><div>The bulk amount of power generated from the present-day large-scale DFIG-installed wind farms are preferably transmitted to utility grid through series compensated transmission lines. Currently, TCSC compensation is more attractive compared to fixed series compensation due to its numerous technical advantages. However, the nonlinear relationship of the output power verses wind speed and the different operating modes of the DFIG and TCSC cause rapid variation in the line impedance during both normal as well as fault conditions. Consequently, the widely used fixed impedance-based distance relays when used for protection of such lines find limitation. In this paper, a fast discrete S-transform feature-assisted back propagation neural network technique is proposed using the relay end current measurements for effective detection and classification of faults in such crucial transmission lines. The efficacy of the scheme is evaluated on numerous fault and non-fault cases simulated through MATLAB/Simulink on different standard test systems under varying system operating conditions. The results clearly show the superiority of the proposed method in comparision to the existing approaches in terms of its low computational burden, fast fault detection time (< 10 ms) and accuracies (= 100 %) and fast fault classification time (< 10 ms) and accuracies (99.99 %).</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"163 ","pages":"Article 110362"},"PeriodicalIF":5.0,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662017","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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