Pub Date : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281936
A. Delavari, J. Prévost, M. Gaha, Amira Dems
Hydro-Québec TransÉnergie (HQT) has been employing predictive modelling methods to manage its assets for over a decade. To meet new needs and to respond to changes in the energy market, HQT undertakes an important research project in order to improve existing tools for asset management and modelling system. In this paper, we present a quick review of the global asset management model at HQT. Furthermore, we introduce a Contingency Analysis (CA) approach which will be integrated in the reliability simulator module of the HQT global asset management model. To this end, we augment the traditional bus-branch data model to provide a detailed node-breaker representation that contains detailed nodes, breakers and other switching devices. Then, we analyze the impact of the equipment unavailability on the network behaviour using a detailed CA approach. This contingency analysis approach not only informs the asset management engineers in case of violations, but also suggests several remedial actions to eliminate the violation.
{"title":"Hydro-Québec’s new approach for asset management","authors":"A. Delavari, J. Prévost, M. Gaha, Amira Dems","doi":"10.1109/PESGM41954.2020.9281936","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281936","url":null,"abstract":"Hydro-Québec TransÉnergie (HQT) has been employing predictive modelling methods to manage its assets for over a decade. To meet new needs and to respond to changes in the energy market, HQT undertakes an important research project in order to improve existing tools for asset management and modelling system. In this paper, we present a quick review of the global asset management model at HQT. Furthermore, we introduce a Contingency Analysis (CA) approach which will be integrated in the reliability simulator module of the HQT global asset management model. To this end, we augment the traditional bus-branch data model to provide a detailed node-breaker representation that contains detailed nodes, breakers and other switching devices. Then, we analyze the impact of the equipment unavailability on the network behaviour using a detailed CA approach. This contingency analysis approach not only informs the asset management engineers in case of violations, but also suggests several remedial actions to eliminate the violation.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128200965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-02DOI: 10.1109/PESGM41954.2020.9282076
Jianghua Wu, P. Luh, Yonghong Chen, B. Yan, Mikhail A. Bragin
Sub-hourly Unit Commitment (UC) problems have been suggested as a way to improve power system efficiency. Such problems, however, are much more difficult than hourly UC problems. This is not just because of the increased number of period to consider, but also because of much reduced unit ramping capabilities leading to more complicated convex hulls. As a result, state-of-the-art and practice methods such as branch-and-cut suffer from poor performance. In this paper, our recent Surrogate Absolute-Value Lagrangian Relaxation (SAVLR) method, which overcame major difficulties of standard Lagrangian Relaxation, is enhanced by synergistically incorporating the concept of Ordinal Optimization (OO). By using OO, solving subproblems becomes much faster. Testing of Midcontinent ISO (MISO)’s problem with 15 minutes as the time interval over 36 hours involving about 1,100 units and 15000 virtuals demonstrates that the new method obtains near-optimal solutions efficiently and significantly outperforms branch-and-cut.
亚小时机组承诺问题是提高电力系统效率的重要途径。然而，这些问题比每小时的UC问题要困难得多。这不仅是因为需要考虑的周期数量增加了，而且还因为大大降低了单位爬坡能力，导致了更复杂的凸包。因此，最先进的和实践的方法，如分支和切割，表现不佳。在本文中，我们的替代绝对值拉格朗日松弛(SAVLR)方法克服了标准拉格朗日松弛的主要困难，并通过协同结合有序优化(OO)的概念进行了改进。通过使用OO，解决子问题变得快得多。对Midcontinent ISO (MISO)的问题进行了15分钟的测试，测试时间间隔超过36小时，涉及约1100个单元和15000个虚拟机，结果表明，新方法有效地获得了接近最优的解决方案，显著优于分支切断。
{"title":"A Decomposition and Coordination Approach for Large Sub-hourly Unit Commitment","authors":"Jianghua Wu, P. Luh, Yonghong Chen, B. Yan, Mikhail A. Bragin","doi":"10.1109/PESGM41954.2020.9282076","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9282076","url":null,"abstract":"Sub-hourly Unit Commitment (UC) problems have been suggested as a way to improve power system efficiency. Such problems, however, are much more difficult than hourly UC problems. This is not just because of the increased number of period to consider, but also because of much reduced unit ramping capabilities leading to more complicated convex hulls. As a result, state-of-the-art and practice methods such as branch-and-cut suffer from poor performance. In this paper, our recent Surrogate Absolute-Value Lagrangian Relaxation (SAVLR) method, which overcame major difficulties of standard Lagrangian Relaxation, is enhanced by synergistically incorporating the concept of Ordinal Optimization (OO). By using OO, solving subproblems becomes much faster. Testing of Midcontinent ISO (MISO)’s problem with 15 minutes as the time interval over 36 hours involving about 1,100 units and 15000 virtuals demonstrates that the new method obtains near-optimal solutions efficiently and significantly outperforms branch-and-cut.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115857860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281473
Dan A. Rosa de Jesús, P. Mandal, Yuan-Kang Wu, T. Senjyu
This paper presents a new prediction approach based on deep learning ensemble for very short-term (10-minuteahead) wind power forecasting for a look-ahead period of 1h, 3h, and 6h. The proposed deep learning ensemble approach combines several individual and hybrid deep learning models, such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Hybrid Deep Neural Network (HDNN), with the formation of four different ensembles, in particular HDNN+CNN,HDNN+LSTM, CNN+LSTM, and HDNN+CNN+LSTM. The proposed approach considers the historical data of wind speed as major input through ensemble averaging in order to produce the final wind power prediction. The major advantage of the proposed ensemble learning is that they make the best use of predictions from multiple deep learning models and their capability to effectively “cancel out” the individual errors, which in turn help enhance the final prediction accuracy. The simulation on actual data, acquired from the real wind farm in Texas, demonstrates the effectiveness of the presented approach to produce a higher degree of very short-term wind power forecast accuracy for multiple seasons of the year in comparison to other soft computing as well as to individual deep learning models.
{"title":"Deep Learning Ensemble Based New Approach for Very Short-Term Wind Power Forecasting","authors":"Dan A. Rosa de Jesús, P. Mandal, Yuan-Kang Wu, T. Senjyu","doi":"10.1109/PESGM41954.2020.9281473","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281473","url":null,"abstract":"This paper presents a new prediction approach based on deep learning ensemble for very short-term (10-minuteahead) wind power forecasting for a look-ahead period of 1h, 3h, and 6h. The proposed deep learning ensemble approach combines several individual and hybrid deep learning models, such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Hybrid Deep Neural Network (HDNN), with the formation of four different ensembles, in particular HDNN+CNN,HDNN+LSTM, CNN+LSTM, and HDNN+CNN+LSTM. The proposed approach considers the historical data of wind speed as major input through ensemble averaging in order to produce the final wind power prediction. The major advantage of the proposed ensemble learning is that they make the best use of predictions from multiple deep learning models and their capability to effectively “cancel out” the individual errors, which in turn help enhance the final prediction accuracy. The simulation on actual data, acquired from the real wind farm in Texas, demonstrates the effectiveness of the presented approach to produce a higher degree of very short-term wind power forecast accuracy for multiple seasons of the year in comparison to other soft computing as well as to individual deep learning models.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132083002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281555
Muyang Liu, M. Murad, Junru Chen, F. Milano
This paper proposes a model for protective relays in dynamic simulations. The model consists of three layers: measurement, decision-making and actuator. This eyes-brain-muscle structure models the construction of a real-world relay and therefore allows easy involvement of accurate dynamics, such as measurement noise, communication latency between the layers and arc extinction in actuator. The paper provides a case study based on the WSCC 9-bus system with overcurrent and under/over-voltage protections on the 230 kV sub-system. The case study illustrates the dynamic behavior of the protective relays modeled by the proposed method and provides the examples for relay reliability tests.
{"title":"Modeling of Protective Relays for Transient Stability Analysis","authors":"Muyang Liu, M. Murad, Junru Chen, F. Milano","doi":"10.1109/PESGM41954.2020.9281555","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281555","url":null,"abstract":"This paper proposes a model for protective relays in dynamic simulations. The model consists of three layers: measurement, decision-making and actuator. This eyes-brain-muscle structure models the construction of a real-world relay and therefore allows easy involvement of accurate dynamics, such as measurement noise, communication latency between the layers and arc extinction in actuator. The paper provides a case study based on the WSCC 9-bus system with overcurrent and under/over-voltage protections on the 230 kV sub-system. The case study illustrates the dynamic behavior of the protective relays modeled by the proposed method and provides the examples for relay reliability tests.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132225670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281640
O. Ceylan, G. Taşkın, S. Paudyal
Due to increasing volume of measurements in smart grids, surrogate based learning approaches for modeling the power grids are becoming popular. This paper uses regression based models to find the unknown state variables on power systems. Generally, to determine these states, nonlinear systems of power flow equations are solved iteratively. This study considers that the power flow problem can be modeled as an data driven type of a model. Then, the state variables, i.e., voltage magnitudes and phase angles are obtained using machine learning based approaches, namely, Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), and Support Vector Regression (SVR). Several simulations are performed on the IEEE 14 and 30-Bus test systems to validate surrogate based learning based models. Moreover, input data was modified with noise to simulate measurement errors. Numerical results showed that all three models can find state variables reasonably well even with measurement noise.
{"title":"A Comparative Study of Surrogate Based Learning Methods in Solving Power Flow Problem","authors":"O. Ceylan, G. Taşkın, S. Paudyal","doi":"10.1109/PESGM41954.2020.9281640","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281640","url":null,"abstract":"Due to increasing volume of measurements in smart grids, surrogate based learning approaches for modeling the power grids are becoming popular. This paper uses regression based models to find the unknown state variables on power systems. Generally, to determine these states, nonlinear systems of power flow equations are solved iteratively. This study considers that the power flow problem can be modeled as an data driven type of a model. Then, the state variables, i.e., voltage magnitudes and phase angles are obtained using machine learning based approaches, namely, Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), and Support Vector Regression (SVR). Several simulations are performed on the IEEE 14 and 30-Bus test systems to validate surrogate based learning based models. Moreover, input data was modified with noise to simulate measurement errors. Numerical results showed that all three models can find state variables reasonably well even with measurement noise.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132421899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increasing penetration of distributed energy resources (DERs) at the demand side, power quality in the distribution network has become a critical problem. Currently, the operator/utility would pay for the cost increment caused by out-of-order DERs. In this paper, a P2P energy sharing (ES) model and a cost sharing mechanism are proposed to reduce the total cost and encourage DERs to maintain power quality. In the proposed model, end-users with DERs can flexibly share surplus energies with others instructed by the aggregator. A linear model is applied to analyze the active and reactive power flow in the distribution network and ensure the sharing outcomes meet the operational requirements. To encourage participation, the total cost saving is shared based on the identified contribution. Case studies on the coalition composed of 10 end-users based on the real-world data validate the effectiveness of the proposed model and mechanism.
{"title":"Cost Sharing Mechanism for Reactive Power Management Amidst P2P Energy Sharing","authors":"Yuxuan Gu, Jianxiao Wang, Kedi Zheng, Qixin Chen, Wenyuan Tang, Jianing Liu, Kaiwen Zeng","doi":"10.1109/PESGM41954.2020.9281554","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281554","url":null,"abstract":"With the increasing penetration of distributed energy resources (DERs) at the demand side, power quality in the distribution network has become a critical problem. Currently, the operator/utility would pay for the cost increment caused by out-of-order DERs. In this paper, a P2P energy sharing (ES) model and a cost sharing mechanism are proposed to reduce the total cost and encourage DERs to maintain power quality. In the proposed model, end-users with DERs can flexibly share surplus energies with others instructed by the aggregator. A linear model is applied to analyze the active and reactive power flow in the distribution network and ensure the sharing outcomes meet the operational requirements. To encourage participation, the total cost saving is shared based on the identified contribution. Case studies on the coalition composed of 10 end-users based on the real-world data validate the effectiveness of the proposed model and mechanism.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129983181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281884
Zefan Tang, Yanyuan Qin, Zimin Jiang, Walter O. Krawec, Peng Zhang
The classical key distribution systems used for data transmission in networked microgrids (NMGs) rely on mathematical assumptions, which however can be broken by attacks from quantum computers. This paper addresses this quantum-era challenge by using quantum key distribution (QKD). Specifically, the novelty of this paper includes 1) a QKD-enabled communication architecture it devises for NMGs, 2) a real-time QKD- enabled NMGs testbed it builds in an RTDS environment, and 3) a novel two-level key pool sharing (TLKPS) strategy it designs to improve the system resilience against cyberattacks. Test results validate the effectiveness of the presented strategy, and provide insightful resources for building quantum-secure NMGs.
{"title":"Quantum-Secure Networked Microgrids","authors":"Zefan Tang, Yanyuan Qin, Zimin Jiang, Walter O. Krawec, Peng Zhang","doi":"10.1109/PESGM41954.2020.9281884","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281884","url":null,"abstract":"The classical key distribution systems used for data transmission in networked microgrids (NMGs) rely on mathematical assumptions, which however can be broken by attacks from quantum computers. This paper addresses this quantum-era challenge by using quantum key distribution (QKD). Specifically, the novelty of this paper includes 1) a QKD-enabled communication architecture it devises for NMGs, 2) a real-time QKD- enabled NMGs testbed it builds in an RTDS environment, and 3) a novel two-level key pool sharing (TLKPS) strategy it designs to improve the system resilience against cyberattacks. Test results validate the effectiveness of the presented strategy, and provide insightful resources for building quantum-secure NMGs.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130120213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281558
N. Bhusal, Mukesh Gautam, M. Benidris
The frequency of extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks (cyber and physical attacks) has increased dramatically in recent years. These events have severely impacted power systems ranging from long outage times to major equipment (e.g., substations, transmission lines, power plants, and distribution system) destruction. Distribution system failures and outages are major contributors to power supply interruptions. Network reconfiguration and movable energy resources (MERs) can play a vital role in supplying loads during and after contingencies. This paper proposes a two-stage strategy to determine the minimum sizes of MERs with network reconfiguration for distribution service restoration and supplying local and isolated loads. Sequential Monte Carlo simulations are used to model the outages of distribution system components. After a contingency, the first stage determines the network reconfiguration based on the spanning tree search algorithm. In the second stage, if some system loads cannot be fed by network reconfiguration, MERs are deployed and the optimal routes to reach isolated areas are determined based on the Dijkstra’s shortest path algorithm (DSPA). The traveling time obtained from the DSPA is incorporated with the proposed sequential Monte Carlo simulation-based approach to determine the sizes of MERs. The proposed method is applied on several distribution systems including the IEEE-13 and IEEE-123 node test feeders. The results show that network reconfiguration can reduce the required sizes of MERs to supply the isolated areas.
{"title":"Sizing of Movable Energy Resources for Service Restoration and Reliability Enhancement","authors":"N. Bhusal, Mukesh Gautam, M. Benidris","doi":"10.1109/PESGM41954.2020.9281558","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281558","url":null,"abstract":"The frequency of extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks (cyber and physical attacks) has increased dramatically in recent years. These events have severely impacted power systems ranging from long outage times to major equipment (e.g., substations, transmission lines, power plants, and distribution system) destruction. Distribution system failures and outages are major contributors to power supply interruptions. Network reconfiguration and movable energy resources (MERs) can play a vital role in supplying loads during and after contingencies. This paper proposes a two-stage strategy to determine the minimum sizes of MERs with network reconfiguration for distribution service restoration and supplying local and isolated loads. Sequential Monte Carlo simulations are used to model the outages of distribution system components. After a contingency, the first stage determines the network reconfiguration based on the spanning tree search algorithm. In the second stage, if some system loads cannot be fed by network reconfiguration, MERs are deployed and the optimal routes to reach isolated areas are determined based on the Dijkstra’s shortest path algorithm (DSPA). The traveling time obtained from the DSPA is incorporated with the proposed sequential Monte Carlo simulation-based approach to determine the sizes of MERs. The proposed method is applied on several distribution systems including the IEEE-13 and IEEE-123 node test feeders. The results show that network reconfiguration can reduce the required sizes of MERs to supply the isolated areas.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130172785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-02DOI: 10.1109/PESGM41954.2020.9282122
Xiuqiang He, H. Geng, Ruiqi Li, B. Pal
Grid-connected renewable energy conversion systems (RECSs) are usually required by grid codes to possess the low voltage ride through (LVRT) and reactive power support capabilities so as to cope with grid voltage sags. During LVRT, RECS’s terminal voltage becomes sensitive and changeable with its output current, which brings a great challenge for the RECS to resynchronize with the grid by means of phase-locked loops (PLLs). This paper indicates that loss of synchronism (LOS) of PLLs is responsible for the transient instability of grid-connected RECSs during LVRT, and the LOS is essentially due to the transient interaction between the PLL and the weak terminal voltage. For achieving a quantitative analysis, an equivalent swing equation model is developed to describe the transient interaction. Based on the model, the transient instability mechanism of RECSs during LVRT is clarified. Furthermore, a transient stability enhancement method is proposed to avoid the possibility of transient instability. Simulations performed on the New England 39-bus test system verify the effectiveness of the method.
并网可再生能源转换系统(recs)通常被电网规范要求具有低电压穿越(LVRT)和无功支持能力，以应对电网电压跌落。在LVRT过程中，RECS的终端电压随输出电流的变化而变得敏感，这给RECS通过锁相环与电网重新同步带来了很大的挑战。本文认为锁相环的失同步(loss of synchronism, LOS)是LVRT期间并网recs暂态失稳的原因，其本质是锁相环与弱端电压之间的暂态相互作用。为了实现定量分析，建立了一个等效摆动方程模型来描述瞬态相互作用。在此基础上，阐明了LVRT过程中recs的暂态失稳机理。此外，还提出了一种暂态稳定增强方法，以避免暂态不稳定的可能性。在新英格兰39总线测试系统上进行的仿真验证了该方法的有效性。
{"title":"Transient Stability Analysis and Enhancement of Renewable Energy Conversion System During LVRT","authors":"Xiuqiang He, H. Geng, Ruiqi Li, B. Pal","doi":"10.1109/PESGM41954.2020.9282122","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9282122","url":null,"abstract":"Grid-connected renewable energy conversion systems (RECSs) are usually required by grid codes to possess the low voltage ride through (LVRT) and reactive power support capabilities so as to cope with grid voltage sags. During LVRT, RECS’s terminal voltage becomes sensitive and changeable with its output current, which brings a great challenge for the RECS to resynchronize with the grid by means of phase-locked loops (PLLs). This paper indicates that loss of synchronism (LOS) of PLLs is responsible for the transient instability of grid-connected RECSs during LVRT, and the LOS is essentially due to the transient interaction between the PLL and the weak terminal voltage. For achieving a quantitative analysis, an equivalent swing equation model is developed to describe the transient interaction. Based on the model, the transient instability mechanism of RECSs during LVRT is clarified. Furthermore, a transient stability enhancement method is proposed to avoid the possibility of transient instability. Simulations performed on the New England 39-bus test system verify the effectiveness of the method.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134370261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281411
S. Purushothaman
Temperature monitoring of electrical equipment is useful to detect deficient conditions like loose connections that cause overheating and could lead to failures. The monitoring systems typically implement fixed threshold-based logic and provide warnings or alarms. However, the industry is starting to leverage additional variables like load (current through equipment), ambient conditions, etc., and implementing pattern recognition or artificial intelligence-based techniques to identify deficiencies at an early stage. The implementation of these advanced techniques generally requires dedicated computational resources and software. This paper presents a simple analytical physics-based model that can be used to provide an early anomaly detection capability for current-carrying conductors in electrical equipment. The analytical model is developed as a second order equation that can be easily included in a monitoring system platform without the need for additional computational resources and external software. This paper includes the theory and simulation results from a finite element model to validate the analytical model. The finite element model was set up in COMSOL to simulate the heat transfer in a current-carrying conductor and validate the proposed analytical physics-based model.
{"title":"Physics-based Early Detection Algorithm for Temperature Monitoring Systems in Electrical Equipment","authors":"S. Purushothaman","doi":"10.1109/PESGM41954.2020.9281411","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281411","url":null,"abstract":"Temperature monitoring of electrical equipment is useful to detect deficient conditions like loose connections that cause overheating and could lead to failures. The monitoring systems typically implement fixed threshold-based logic and provide warnings or alarms. However, the industry is starting to leverage additional variables like load (current through equipment), ambient conditions, etc., and implementing pattern recognition or artificial intelligence-based techniques to identify deficiencies at an early stage. The implementation of these advanced techniques generally requires dedicated computational resources and software. This paper presents a simple analytical physics-based model that can be used to provide an early anomaly detection capability for current-carrying conductors in electrical equipment. The analytical model is developed as a second order equation that can be easily included in a monitoring system platform without the need for additional computational resources and external software. This paper includes the theory and simulation results from a finite element model to validate the analytical model. The finite element model was set up in COMSOL to simulate the heat transfer in a current-carrying conductor and validate the proposed analytical physics-based model.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131513485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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