Pub Date : 2016-10-01DOI: 10.1109/PMAPS.2016.7764223
Per Westerlund, P. Hilber, T. Lindquist
It is important to predict the current in a line in the electrical grid for example when planning thermography or handling dynamic rating. This paper takes data from a Swedish substation from 10 years and applies analysis of variance (ANOVA) to construct a linear model. The factors are the time of the day, the day of the week and the week number. About two thirds of the variance in the data can be explained by the model, but the means are too low to attain a current of at least one third of the current for which the equipment is rated. Thus the model is not good enough to plan thermography for the studied bay in the substation. However the model is able to predict the current and can also be used to predict power flows in the electric network.
{"title":"Prediction of current in a substation in order to schedule thermography","authors":"Per Westerlund, P. Hilber, T. Lindquist","doi":"10.1109/PMAPS.2016.7764223","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764223","url":null,"abstract":"It is important to predict the current in a line in the electrical grid for example when planning thermography or handling dynamic rating. This paper takes data from a Swedish substation from 10 years and applies analysis of variance (ANOVA) to construct a linear model. The factors are the time of the day, the day of the week and the week number. About two thirds of the variance in the data can be explained by the model, but the means are too low to attain a current of at least one third of the current for which the equipment is rated. Thus the model is not good enough to plan thermography for the studied bay in the substation. However the model is able to predict the current and can also be used to predict power flows in the electric network.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131808488","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764165
F. Adinolfi, S. Massucco, M. Saviozzi, F. Silvestro, E. Ciapessoni, D. Cirio, A. Pitto
Nowadays the management of interconnected transmission systems requires security assessment methods able to consider uncertainties due to the increasing presence of renewable generation. Furthermore, also the electrical demand is characterized by a certain level of variability which affects the accuracy of the expected consumption profiles. Thus, probabilistic approaches are an interesting research field to improve reliability of operational planning on future power systems. This work proposes a probabilistic methodology for the evaluation of the Net Transfer Capacity (NTC) between interconnected power grids. The method considers the forecast uncertainties on renewable generation and load consumption, by exploiting the Point Estimate Method (PEM) coupled with Third-order Polynomial Normal Transformation (TPNT). The proposed procedure is applied on a benchmark IEEE test system and validated through comparison with a conventional technique.
{"title":"Net transfer capacity assessment using point estimate method for probabilistic power flow","authors":"F. Adinolfi, S. Massucco, M. Saviozzi, F. Silvestro, E. Ciapessoni, D. Cirio, A. Pitto","doi":"10.1109/PMAPS.2016.7764165","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764165","url":null,"abstract":"Nowadays the management of interconnected transmission systems requires security assessment methods able to consider uncertainties due to the increasing presence of renewable generation. Furthermore, also the electrical demand is characterized by a certain level of variability which affects the accuracy of the expected consumption profiles. Thus, probabilistic approaches are an interesting research field to improve reliability of operational planning on future power systems. This work proposes a probabilistic methodology for the evaluation of the Net Transfer Capacity (NTC) between interconnected power grids. The method considers the forecast uncertainties on renewable generation and load consumption, by exploiting the Point Estimate Method (PEM) coupled with Third-order Polynomial Normal Transformation (TPNT). The proposed procedure is applied on a benchmark IEEE test system and validated through comparison with a conventional technique.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128278838","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764224
P. Pinson
Renewable energy forecasting is now of core interest to both academics, who continuously propose new forecast methodologies, and forecast users for optimal operations and participation in electricity markets. In view of the increasing amount of data being collected at power generation sites, thanks to substantial deployment of generating capacities and increased temporal resolution, it may now be possible to build large models accounting for all space-time dependencies. This will eventually allow to significantly improve the quality of short-term renewable power forecasts. However, in practice, it is often the case that operators of these generation sites do not want to share their data due to competitive interests. Consequently, approaches to privacy-preserving distributed learning are proposed and investigated here. These permit to take advantage of all potential data collected by others, without having to ever share any data, by decomposing the original large learning problem into a number of small learning problems that can be solved in a decentralized manner. As an example, emphasis is placed on Lasso-type estimation of autoregressive models with offsite observations. Different applications on medium to large datasets in Australia (22 wind farms) and France (85 wind farms) are used to illustrate the interest and performance of our proposal.
{"title":"Introducing distributed learning approaches in wind power forecasting","authors":"P. Pinson","doi":"10.1109/PMAPS.2016.7764224","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764224","url":null,"abstract":"Renewable energy forecasting is now of core interest to both academics, who continuously propose new forecast methodologies, and forecast users for optimal operations and participation in electricity markets. In view of the increasing amount of data being collected at power generation sites, thanks to substantial deployment of generating capacities and increased temporal resolution, it may now be possible to build large models accounting for all space-time dependencies. This will eventually allow to significantly improve the quality of short-term renewable power forecasts. However, in practice, it is often the case that operators of these generation sites do not want to share their data due to competitive interests. Consequently, approaches to privacy-preserving distributed learning are proposed and investigated here. These permit to take advantage of all potential data collected by others, without having to ever share any data, by decomposing the original large learning problem into a number of small learning problems that can be solved in a decentralized manner. As an example, emphasis is placed on Lasso-type estimation of autoregressive models with offsite observations. Different applications on medium to large datasets in Australia (22 wind farms) and France (85 wind farms) are used to illustrate the interest and performance of our proposal.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128279527","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764054
E. Rychagova, V. Levin
The problem of distributive electric network operation upgrading efficiency in terms of selecting the optimum strategy of maintenance of its components is considered. Maintenance and repair of the distributive network components are controllable random processes. For its description the homogeneous semi-Markov model with discrete conditions and continuous time is developed. Based on the complex criterion “expenses - reliability” the optimization of the transmission and distribution equipment maintenance is carried out.
{"title":"Improving the efficiency of maintenance and repair of electrical network equipment","authors":"E. Rychagova, V. Levin","doi":"10.1109/PMAPS.2016.7764054","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764054","url":null,"abstract":"The problem of distributive electric network operation upgrading efficiency in terms of selecting the optimum strategy of maintenance of its components is considered. Maintenance and repair of the distributive network components are controllable random processes. For its description the homogeneous semi-Markov model with discrete conditions and continuous time is developed. Based on the complex criterion “expenses - reliability” the optimization of the transmission and distribution equipment maintenance is carried out.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128657066","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764059
M. Papic, M. Clemons, S. Ekisheva, J. Langthorn, T. Ly, M. Pakeltis, R. Quest, J. Schaller, D. Till, K. Weisman
This paper describes the inception and basic structure of the North American Electric Reliability Corporation (NERC) Transmission Availability Data System (TADS) and ongoing activities carried out by the NERC TADS Working Group (WG). TADS data was first collected in 2008 after the NERC Board of Trustees approved the collection of TADS data. This paper presents an overview of basic concepts incorporated into the TADS collection system to uniformly and consistently quantify the reliability performance of the North American bulk transmission system. This paper discusses the categorization of transmission outage events including basic definitions of reliability indicators. Additionally, analysis results obtained from outage data collected in TADS during the period 2010-2014 are presented.
{"title":"Transmission Availability Data System (TADS) reporting and data analysis","authors":"M. Papic, M. Clemons, S. Ekisheva, J. Langthorn, T. Ly, M. Pakeltis, R. Quest, J. Schaller, D. Till, K. Weisman","doi":"10.1109/PMAPS.2016.7764059","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764059","url":null,"abstract":"This paper describes the inception and basic structure of the North American Electric Reliability Corporation (NERC) Transmission Availability Data System (TADS) and ongoing activities carried out by the NERC TADS Working Group (WG). TADS data was first collected in 2008 after the NERC Board of Trustees approved the collection of TADS data. This paper presents an overview of basic concepts incorporated into the TADS collection system to uniformly and consistently quantify the reliability performance of the North American bulk transmission system. This paper discusses the categorization of transmission outage events including basic definitions of reliability indicators. Additionally, analysis results obtained from outage data collected in TADS during the period 2010-2014 are presented.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133840298","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764124
O. Gomez, G. Anders, C. J. Zapata
This paper proposes a new probabilistic identification method of coherent generators using Monte Carlo simulation and graph modeling. The simulation generates operating states defined by component availability, demand and generation. For each state, the electrical condition is assessed using AC power flow and community detection is applied to a graph representation of the system to detect the coherent generators groups. Finally, the probability of occurrence of each coherent generators group is computed. This methodology was tested on the IEEE 118-bus test system. Results shows that the approach is computationally simple and fast, which makes it very appealing for large power systems.
{"title":"Probabilistic-based identification of coherent generators","authors":"O. Gomez, G. Anders, C. J. Zapata","doi":"10.1109/PMAPS.2016.7764124","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764124","url":null,"abstract":"This paper proposes a new probabilistic identification method of coherent generators using Monte Carlo simulation and graph modeling. The simulation generates operating states defined by component availability, demand and generation. For each state, the electrical condition is assessed using AC power flow and community detection is applied to a graph representation of the system to detect the coherent generators groups. Finally, the probability of occurrence of each coherent generators group is computed. This methodology was tested on the IEEE 118-bus test system. Results shows that the approach is computationally simple and fast, which makes it very appealing for large power systems.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133508510","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764174
Saeed Alyami, Yang Wang, Caisheng Wang
Solar power has become one of the mainstream distributed renewable energy sources due to its clean and renewable feature and the global push for renewable energy. In a distribution network with high penetration of photovoltaics (PVs), overvoltage is a common and major issue that needs to be addressed to assure system reliability and security. Increasing interests have been given to real time operation of PVs to fully utilize PV generation capacity while the voltage is regulated within a proper range. However, little research has been done on exploring the overvoltage risk at the planning phase. This paper proposes a probabilistic method to evaluate the overvoltage risk in a distribution network with different PV capacity sizes under different load levels. Kolmogorov-Smirnov test (K-S test) is used to identify the most proper probability distributions for solar irradiance in different months. To increase accuracy, an iterative process is used to obtain the maximum allowable injection of active power from PVs. The effectiveness of proposed method is verified on a 33-bus system.
{"title":"Overvoltage risk analysis in distribution networks with high penetration of PVs","authors":"Saeed Alyami, Yang Wang, Caisheng Wang","doi":"10.1109/PMAPS.2016.7764174","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764174","url":null,"abstract":"Solar power has become one of the mainstream distributed renewable energy sources due to its clean and renewable feature and the global push for renewable energy. In a distribution network with high penetration of photovoltaics (PVs), overvoltage is a common and major issue that needs to be addressed to assure system reliability and security. Increasing interests have been given to real time operation of PVs to fully utilize PV generation capacity while the voltage is regulated within a proper range. However, little research has been done on exploring the overvoltage risk at the planning phase. This paper proposes a probabilistic method to evaluate the overvoltage risk in a distribution network with different PV capacity sizes under different load levels. Kolmogorov-Smirnov test (K-S test) is used to identify the most proper probability distributions for solar irradiance in different months. To increase accuracy, an iterative process is used to obtain the maximum allowable injection of active power from PVs. The effectiveness of proposed method is verified on a 33-bus system.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125723315","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764063
A. M. Leite da Silva, Jose F. Costa Castro, R. A. Gonzalez-Fernandez
This paper presents a new methodology for assessing spinning reserve in generating systems with high penetration of renewable energy. A state-space model is proposed to represent the generation capacity failures and the intermittency of renewable sources based on historical scenarios. The uncertainty in the system supply is captured through risk indices that represent the probability of not meeting the short-term estimated demand. A security strategy associated with the probability distribution of reserve levels is also proposed to avoid the over-sizing of reserve capacity levels to handle unlikely extreme operating points. Risk indices are estimated via quasi-sequential MCS-CE (Monte Carlo Simulation via Cross-Entropy) method, where the corresponding parameters are optimally distorted based on CE concepts. The proposed method is applied to a modified version of the IEEE RTS-79 system to cope with renewable sources.
本文提出了一种新的可再生能源发电系统旋转储备评估方法。提出了一种基于历史情景的可再生能源发电容量失效和间歇性的状态空间模型。系统供给的不确定性是通过风险指数来体现的，这些风险指数表示不能满足短期估计需求的概率。提出了一种与储备水平概率分布相关联的安全策略，以避免储备能力水平过大以应对不可能出现的极端运行点。通过准序贯MCS-CE (Monte Carlo Simulation via Cross-Entropy)方法估计风险指标，其中相应的参数基于CE概念进行最优扭曲。将该方法应用于IEEE RTS-79系统的修改版本，以处理可再生能源。
{"title":"Spinning reserve assessment via quasi-sequential Monte Carlo simulation with renewable sources","authors":"A. M. Leite da Silva, Jose F. Costa Castro, R. A. Gonzalez-Fernandez","doi":"10.1109/PMAPS.2016.7764063","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764063","url":null,"abstract":"This paper presents a new methodology for assessing spinning reserve in generating systems with high penetration of renewable energy. A state-space model is proposed to represent the generation capacity failures and the intermittency of renewable sources based on historical scenarios. The uncertainty in the system supply is captured through risk indices that represent the probability of not meeting the short-term estimated demand. A security strategy associated with the probability distribution of reserve levels is also proposed to avoid the over-sizing of reserve capacity levels to handle unlikely extreme operating points. Risk indices are estimated via quasi-sequential MCS-CE (Monte Carlo Simulation via Cross-Entropy) method, where the corresponding parameters are optimally distorted based on CE concepts. The proposed method is applied to a modified version of the IEEE RTS-79 system to cope with renewable sources.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124713273","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764217
Tamara Becejac, P. Dehghanian, M. Kezunovic
The standard C37.118.1a-2014 has specified the permissible limits for PMU measurement errors under various static and dynamic test conditions. This paper proposes a statistical measure to evaluate the probability of PMU performance degradation with regards to certain standard requirements. The proposed approach is implemented using a field calibrator system for phasor measurement units (PMUs). Assessment of the test results provides an additional insight about: (a) whether the expected functionality and integrity of the PMUs is maintained over time; (b) which synchrophasor standard requirements are most vulnerable for a given device over time; (c) when the maintenance schedule needs to be expedited on certain PMUs based on observed performance degradation probabilities; and (d) the risks of loss of trustworthiness of various end-use synchrophasor-based applications. The applicability of the suggested technique is verified through implementation on several PMUs in a calibration and testing set-up.
{"title":"Probabilistic assessment of PMU integrity for planning of periodic maintenance and testing","authors":"Tamara Becejac, P. Dehghanian, M. Kezunovic","doi":"10.1109/PMAPS.2016.7764217","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764217","url":null,"abstract":"The standard C37.118.1a-2014 has specified the permissible limits for PMU measurement errors under various static and dynamic test conditions. This paper proposes a statistical measure to evaluate the probability of PMU performance degradation with regards to certain standard requirements. The proposed approach is implemented using a field calibrator system for phasor measurement units (PMUs). Assessment of the test results provides an additional insight about: (a) whether the expected functionality and integrity of the PMUs is maintained over time; (b) which synchrophasor standard requirements are most vulnerable for a given device over time; (c) when the maintenance schedule needs to be expedited on certain PMUs based on observed performance degradation probabilities; and (d) the risks of loss of trustworthiness of various end-use synchrophasor-based applications. The applicability of the suggested technique is verified through implementation on several PMUs in a calibration and testing set-up.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127554482","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764134
M. Amiri, B. Bagen, A. Gole
Power system probabilistic-based studies have been performed for many years and are widely accepted by researchers and utilities. Such studies are capable of considering uncertainties of power system such as random failures of equipment and uncertainties in load forecast. With the increase of the penetration of renewable energy sources to power systems, new and additional uncertainties are needed to be considered in power system analysis. This paper presents a study methodology for evaluating the effect of wind speed variations on the power quality of power systems. A Monte Carlo Simulation method is used in the studies described in this paper assuming wind speed follows a Weibull distribution. Power flow is performed using a commercial program.
{"title":"Probabilistic analysis of the effect of wind speed variations on power quality of power systems","authors":"M. Amiri, B. Bagen, A. Gole","doi":"10.1109/PMAPS.2016.7764134","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764134","url":null,"abstract":"Power system probabilistic-based studies have been performed for many years and are widely accepted by researchers and utilities. Such studies are capable of considering uncertainties of power system such as random failures of equipment and uncertainties in load forecast. With the increase of the penetration of renewable energy sources to power systems, new and additional uncertainties are needed to be considered in power system analysis. This paper presents a study methodology for evaluating the effect of wind speed variations on the power quality of power systems. A Monte Carlo Simulation method is used in the studies described in this paper assuming wind speed follows a Weibull distribution. Power flow is performed using a commercial program.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121175863","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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