Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183576
Č. Zeljković, Predrag Mršić, Bojan Erceg, Đorđe Lekić, Nemanja Kitić, P. Matić, T. Șoimoșan
This paper discusses the problem of powering a remote rural mobile base station using a standalone hybrid renewable energy system. A wind turbine and photovoltaic system are employed as the complementary power generation technologies, while the diesel generator serves as a backup power supply. A battery is required to reduce the impact of intermittency of renewable sources. On the consumption side, along with telecommunication electronic equipment, the consumption of cooling devices as a result of the ambient temperature, is also taken into account. The behavior of the base station in electrical and thermal terms is tested using the sequential Monte Carlo simulation. Adequate models have been used to generate wind, irradiance, and temperature input series, using the monthly averages for calibration, as the statistic information that is widely available in meteorological atlases, even for remote rural locations. The developed software provides all the variables of interest either in the form of chronological diagrams or probability histograms. The simulation platform can also be incorporated as a module of an algorithm for selection of optimal capacity of the generating system elements and for the optimal control of the cooling devices.
{"title":"A Monte Carlo Simulation Platform for Studying the Behavior of Wind-PV-Diesel-Battery Powered Mobile Telephony Base Stations","authors":"Č. Zeljković, Predrag Mršić, Bojan Erceg, Đorđe Lekić, Nemanja Kitić, P. Matić, T. Șoimoșan","doi":"10.1109/PMAPS47429.2020.9183576","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183576","url":null,"abstract":"This paper discusses the problem of powering a remote rural mobile base station using a standalone hybrid renewable energy system. A wind turbine and photovoltaic system are employed as the complementary power generation technologies, while the diesel generator serves as a backup power supply. A battery is required to reduce the impact of intermittency of renewable sources. On the consumption side, along with telecommunication electronic equipment, the consumption of cooling devices as a result of the ambient temperature, is also taken into account. The behavior of the base station in electrical and thermal terms is tested using the sequential Monte Carlo simulation. Adequate models have been used to generate wind, irradiance, and temperature input series, using the monthly averages for calibration, as the statistic information that is widely available in meteorological atlases, even for remote rural locations. The developed software provides all the variables of interest either in the form of chronological diagrams or probability histograms. The simulation platform can also be incorporated as a module of an algorithm for selection of optimal capacity of the generating system elements and for the optimal control of the cooling devices.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123166571","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183641
Theodoros Konstantinou, N. Savvopoulos, N. Hatziargyriou
Weather variables are commonly used in many applications in power systems. One of the most common weather variables is the wind speed. Wind speed is used mainly in renewable energy forecasting, thermal rating of transmission lines and extreme events estimation. Unfortunately, wind is a very volatile physical phenomenon. The prediction of wind speed is a very difficult procedure with low accuracy, while all the errors are incorporated in the final functions that use this variable. A way to tackle this issue is to post-process the wind predictions with data driven methods to estimate the probabilistic density function of the wind speed. In this paper we propose a probabilistic wind speed forecasting method based on the use of artificial neural networks.
{"title":"Post-processing Numerical Weather Prediction for Probabilistic Wind Forecasting","authors":"Theodoros Konstantinou, N. Savvopoulos, N. Hatziargyriou","doi":"10.1109/PMAPS47429.2020.9183641","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183641","url":null,"abstract":"Weather variables are commonly used in many applications in power systems. One of the most common weather variables is the wind speed. Wind speed is used mainly in renewable energy forecasting, thermal rating of transmission lines and extreme events estimation. Unfortunately, wind is a very volatile physical phenomenon. The prediction of wind speed is a very difficult procedure with low accuracy, while all the errors are incorporated in the final functions that use this variable. A way to tackle this issue is to post-process the wind predictions with data driven methods to estimate the probabilistic density function of the wind speed. In this paper we propose a probabilistic wind speed forecasting method based on the use of artificial neural networks.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126197535","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183564
G. Celli, Luigi Sechi, G. G. Soma
The efficient development of modern distribution system requires the deployment of flexibility services provided by Distributed Energy Resources, like distributed generation, electric energy storage and demand response. This kind of planning tools have to be risk-based, in order to deal with the high level of uncertainties introduced by these new technologies. Suitable models and methodologies for the consideration of the value at risk associated to each choice are essential to compare innovative and conventional planning solutions. In the paper, Demand Response has been modelled with its possible payback effect and the optimal exploitation of this flexibility service with a predefined confidence (residual risk) has been estimated by means of a Robust Linear Programming optimization. The effectiveness of the proposed methodology is demonstrated on a simple distribution network.
{"title":"A Robust Approach to manage Demand Response for power distribution system planning","authors":"G. Celli, Luigi Sechi, G. G. Soma","doi":"10.1109/PMAPS47429.2020.9183564","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183564","url":null,"abstract":"The efficient development of modern distribution system requires the deployment of flexibility services provided by Distributed Energy Resources, like distributed generation, electric energy storage and demand response. This kind of planning tools have to be risk-based, in order to deal with the high level of uncertainties introduced by these new technologies. Suitable models and methodologies for the consideration of the value at risk associated to each choice are essential to compare innovative and conventional planning solutions. In the paper, Demand Response has been modelled with its possible payback effect and the optimal exploitation of this flexibility service with a predefined confidence (residual risk) has been estimated by means of a Robust Linear Programming optimization. The effectiveness of the proposed methodology is demonstrated on a simple distribution network.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130262750","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183563
A. M. Gómez, G. Guerreiro, Hannes Wiklund, Johanna Lindstén, S. Ramakrishna, Kateryna Morozovska, P. Hilber
Dynamic rating is a technology which allows loading power lines above their rated limits. More often, dynamic rating is used to transport new power and connect additional generators to the grid using existing infrastructure. However, this study explores the possibility to use dynamic rating for improving the security of supply and assisting fast reconnection of disconnected customers during emergency and fault situations occurring at other lines. DLR allows improving power system reliability during emergency conditions using Optimal Power Flow (OPF), which additionally helps to minimize costs of system operation. Large costs involving investment for new infrastructure and penalties for interruptions in the power supply can be considerably reduced by implementing DLR. Also, DLR can improve the reliability of the system by providing real-time information on the status of power lines. Using Optimal Power Flow ensures that the lines loading, bus voltage magnitudes and angles as well as generation injections are within the acceptable limits as per the utility regulations. Faults are modelled as cases when one of the lines becomes disconnected. The bottlenecks in the system during post-fault situations are identified to determine optimal lines in the system on which DLR could be implemented.
{"title":"Emergency power flow re-routing in a distribution system by using dynamic line rating","authors":"A. M. Gómez, G. Guerreiro, Hannes Wiklund, Johanna Lindstén, S. Ramakrishna, Kateryna Morozovska, P. Hilber","doi":"10.1109/PMAPS47429.2020.9183563","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183563","url":null,"abstract":"Dynamic rating is a technology which allows loading power lines above their rated limits. More often, dynamic rating is used to transport new power and connect additional generators to the grid using existing infrastructure. However, this study explores the possibility to use dynamic rating for improving the security of supply and assisting fast reconnection of disconnected customers during emergency and fault situations occurring at other lines. DLR allows improving power system reliability during emergency conditions using Optimal Power Flow (OPF), which additionally helps to minimize costs of system operation. Large costs involving investment for new infrastructure and penalties for interruptions in the power supply can be considerably reduced by implementing DLR. Also, DLR can improve the reliability of the system by providing real-time information on the status of power lines. Using Optimal Power Flow ensures that the lines loading, bus voltage magnitudes and angles as well as generation injections are within the acceptable limits as per the utility regulations. Faults are modelled as cases when one of the lines becomes disconnected. The bottlenecks in the system during post-fault situations are identified to determine optimal lines in the system on which DLR could be implemented.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127818251","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183448
S. Massucco, G. Mosaico, M. Saviozzi, F. Silvestro, A. Fidigatti, E. Ragaini
With the large-scale adoption of Advanced Metering Infrastructure (AMI), power systems are now characterized by a wealth of information that can be exploited for better monitoring, management, and control. On the other hand, specific techniques have to be employed to face the challenges brought by this large amount of data (Big Data). Traditional load modeling methodologies do not use the streams of data generated by AMI, providing static load profiles. In this work, an adaptive streaming algorithm is described to model any load through a Markov Chain. The proposed algorithm is able to cluster the load curves with a minimal computational effort, allowing realtime load modeling. The presented procedure’s performance is evaluated by experimental validation and compared with two reference methodologies (Dynamical Clustering and k-Means) in terms of accuracy and computational time.
{"title":"An Instantaneous Growing Stream Clustering Algorithm for Probabilistic Load Modeling/Profiling","authors":"S. Massucco, G. Mosaico, M. Saviozzi, F. Silvestro, A. Fidigatti, E. Ragaini","doi":"10.1109/PMAPS47429.2020.9183448","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183448","url":null,"abstract":"With the large-scale adoption of Advanced Metering Infrastructure (AMI), power systems are now characterized by a wealth of information that can be exploited for better monitoring, management, and control. On the other hand, specific techniques have to be employed to face the challenges brought by this large amount of data (Big Data). Traditional load modeling methodologies do not use the streams of data generated by AMI, providing static load profiles. In this work, an adaptive streaming algorithm is described to model any load through a Markov Chain. The proposed algorithm is able to cluster the load curves with a minimal computational effort, allowing realtime load modeling. The presented procedure’s performance is evaluated by experimental validation and compared with two reference methodologies (Dynamical Clustering and k-Means) in terms of accuracy and computational time.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122435991","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183607
E. Ciapessoni, D. Cirio, A. Pitto, G. Pirovano, M. Sforna
The contact of overhead lines with vegetation represents a significant cause of failures, also as secondary effects of weather events such as strong wind, ice/snow accumulation. Thus, the management of the right of ways (ROW) of overhead lines is a key aspect to improve grid resilience. This paper proposes a probabilistic vulnerability model of overhead line failure due to inadvertent contact with vegetation, supporting the assessment of loss of load risk. The model can help TSOs to plan vegetation trimming campaigns and to alert operators in case of extreme weather events. Simulations on a realistic electric system demonstrate how the vulnerability model identifies the areas subject to tree fall. Moreover, its application within a resilience assessment methodology allows to relate the load interruption risk with both weather conditions (wind and snow loads) and deviations from ROW management standards.
{"title":"Modelling the vulnerability of overhead lines against tree contacts for resilience assessment","authors":"E. Ciapessoni, D. Cirio, A. Pitto, G. Pirovano, M. Sforna","doi":"10.1109/PMAPS47429.2020.9183607","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183607","url":null,"abstract":"The contact of overhead lines with vegetation represents a significant cause of failures, also as secondary effects of weather events such as strong wind, ice/snow accumulation. Thus, the management of the right of ways (ROW) of overhead lines is a key aspect to improve grid resilience. This paper proposes a probabilistic vulnerability model of overhead line failure due to inadvertent contact with vegetation, supporting the assessment of loss of load risk. The model can help TSOs to plan vegetation trimming campaigns and to alert operators in case of extreme weather events. Simulations on a realistic electric system demonstrate how the vulnerability model identifies the areas subject to tree fall. Moreover, its application within a resilience assessment methodology allows to relate the load interruption risk with both weather conditions (wind and snow loads) and deviations from ROW management standards.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131386593","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183393
Thanet Chitsuphaphan, Xinan Yang, Hongsheng Dai
Battery has been used as a tool to smooth the latency of electricity supply and demand since its created. Electric vehicle (EV), which becomes more and more popular in recent years, can be seen as a consumer with its own electricity storage/battery. This article explores the potential of connecting EV battery to standard home battery and their optimal usage for energy storage in a small household system with photovoltaic (PV) power supplies. A two-stage stochastic programming model is developed with the day-ahead PV generation forecasting via spatial exponential smoothing, to optimise the storage level in home and EV batteries throughout the day so as to match self-supply and consumption to the maximum extend and save cost. Real data are used in the tests according to the UK household electricity survey and EV database, so as to inform the optimal battery size for household usage under different size of the PV supplies.
{"title":"Stochastic Programming for Residential Energy Management with Electric Vehicle under Photovoltaic Power Generation Uncertainty","authors":"Thanet Chitsuphaphan, Xinan Yang, Hongsheng Dai","doi":"10.1109/PMAPS47429.2020.9183393","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183393","url":null,"abstract":"Battery has been used as a tool to smooth the latency of electricity supply and demand since its created. Electric vehicle (EV), which becomes more and more popular in recent years, can be seen as a consumer with its own electricity storage/battery. This article explores the potential of connecting EV battery to standard home battery and their optimal usage for energy storage in a small household system with photovoltaic (PV) power supplies. A two-stage stochastic programming model is developed with the day-ahead PV generation forecasting via spatial exponential smoothing, to optimise the storage level in home and EV batteries throughout the day so as to match self-supply and consumption to the maximum extend and save cost. Real data are used in the tests according to the UK household electricity survey and EV database, so as to inform the optimal battery size for household usage under different size of the PV supplies.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134512554","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183490
Yuxiang Wan, Lin Cheng, Manjun Liu
Power electronic transformer (PET) is a new type of Distribution FACTS. The equipment reliability is directly determined by its historical operating conditions and the component health status, especially the IGBT modules. This paper proposes an operational reliability model for the power electronic transformers based on MMC, which considers the effects of the fatigue accumulation and short-term operating conditions of IGBT modules. Firstly, the loss of IGBT modules under different loads is analyzed to obtain their long-term and short-term junction temperature. Then, the contributions of different time-scale junction temperature variations on the IGBT module failure rate are analyzed based on IGBT failure mechanisms and reliability guidelines. Finally, the Markov state space for the PET reliability is established, and the validity of the operational reliability model is verified by a case study. The results show that the proposed method could effectively predict the time-varying equipment reliability performance in the short-term operation.
{"title":"Operational Reliability Assessment of Power Electronic Transformer Considering Operating Conditions and Fatigue Accumulation","authors":"Yuxiang Wan, Lin Cheng, Manjun Liu","doi":"10.1109/PMAPS47429.2020.9183490","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183490","url":null,"abstract":"Power electronic transformer (PET) is a new type of Distribution FACTS. The equipment reliability is directly determined by its historical operating conditions and the component health status, especially the IGBT modules. This paper proposes an operational reliability model for the power electronic transformers based on MMC, which considers the effects of the fatigue accumulation and short-term operating conditions of IGBT modules. Firstly, the loss of IGBT modules under different loads is analyzed to obtain their long-term and short-term junction temperature. Then, the contributions of different time-scale junction temperature variations on the IGBT module failure rate are analyzed based on IGBT failure mechanisms and reliability guidelines. Finally, the Markov state space for the PET reliability is established, and the validity of the operational reliability model is verified by a case study. The results show that the proposed method could effectively predict the time-varying equipment reliability performance in the short-term operation.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116455624","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183657
M. J. Chihota, B. Bekker
The penetration of distributed energy resources (DERs) into distribution networks has greatly increased. Although there are widespread benefits, unregulated penetration can lead to several technical problems. Research activity on understanding the technical impacts has increased, motivated by two needs: the evaluation of the adequacy of existing networks to host DERs, which contributes to regulations for penetration, and development of new principles for designing and planning new electrification systems. Appropriate tools for this analysis must be based on probabilistic techniques to address input uncertainties. The application of probabilistic load flow approaches to account for uncertainties related to consumer loads and power generation from renewable energy sources is widely reported. However, uncertainty regarding future DER penetration scenarios, particularly placement, is not adequately addressed. This paper explores the impact on accuracy of different strategies to modelling and simulating uncertain DER placement through the application of three different placement strategies on a practical feeder located in South Africa.
{"title":"Modelling and Simulation of Uncertainty in the Placement of Distributed Energy Resources for Planning Applications","authors":"M. J. Chihota, B. Bekker","doi":"10.1109/PMAPS47429.2020.9183657","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183657","url":null,"abstract":"The penetration of distributed energy resources (DERs) into distribution networks has greatly increased. Although there are widespread benefits, unregulated penetration can lead to several technical problems. Research activity on understanding the technical impacts has increased, motivated by two needs: the evaluation of the adequacy of existing networks to host DERs, which contributes to regulations for penetration, and development of new principles for designing and planning new electrification systems. Appropriate tools for this analysis must be based on probabilistic techniques to address input uncertainties. The application of probabilistic load flow approaches to account for uncertainties related to consumer loads and power generation from renewable energy sources is widely reported. However, uncertainty regarding future DER penetration scenarios, particularly placement, is not adequately addressed. This paper explores the impact on accuracy of different strategies to modelling and simulating uncertain DER placement through the application of three different placement strategies on a practical feeder located in South Africa.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114986043","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183569
Yerzhigit Bapin, S. Ekisheva, M. Papic, Vasilios Zarikas
This paper presents a first comprehensive statistical study of transmission inventory and outage data of overhead AC circuits in the Kazakhstan Electricity Grid Operating Company (KEGOC). The analysis is based on the data collected and reported to the KEGOC centralized outage data collection system during the years 2013 to 2018. The outage-data statistics of KEGOC have been analyzed to demonstrate the leading cause-code and seasonal contributions to the automatic outages of overhead transmission lines of 200 kV and above voltage classes. The KEGOC’s 6-year collected outage data are used to estimate basic reliability indices (frequency of automatic and sustained automatic outages per circuit and per hundred kilometers, outage duration, and an element unavailability) that are needed to perform any type of probabilistic reliability studies. Also, these 6-year collected outage data are used to assess and benchmark the reliability performance of a system’s zones. The importance of these kinds of data and analysis for reliability applications is stressed.
{"title":"Outage Data Analysis of the Overhead Transmission Lines in Kazakhstan Power System","authors":"Yerzhigit Bapin, S. Ekisheva, M. Papic, Vasilios Zarikas","doi":"10.1109/PMAPS47429.2020.9183569","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183569","url":null,"abstract":"This paper presents a first comprehensive statistical study of transmission inventory and outage data of overhead AC circuits in the Kazakhstan Electricity Grid Operating Company (KEGOC). The analysis is based on the data collected and reported to the KEGOC centralized outage data collection system during the years 2013 to 2018. The outage-data statistics of KEGOC have been analyzed to demonstrate the leading cause-code and seasonal contributions to the automatic outages of overhead transmission lines of 200 kV and above voltage classes. The KEGOC’s 6-year collected outage data are used to estimate basic reliability indices (frequency of automatic and sustained automatic outages per circuit and per hundred kilometers, outage duration, and an element unavailability) that are needed to perform any type of probabilistic reliability studies. Also, these 6-year collected outage data are used to assess and benchmark the reliability performance of a system’s zones. The importance of these kinds of data and analysis for reliability applications is stressed.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116899016","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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