Uncertainty analysis of dynamic thermal rating based on environmental parameter estimation

Wang, Yanling; Tao, Weihua; Yan, Zhijie; Wei, Ran

doi:10.1186/s13638-018-1181-7

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…At a first glance, the error distributions for all weather parameters do not seem to belong to a normal Gaussian distribution, as usually assumed in the literature [18,19,23,24]. We have tested this hypothesis using the Kolmogorov-Smirnov normality test.…”

Section: Weather Datamentioning

confidence: 92%

“…Once the PDFs for all input parameters are known, the MC procedure samples them by the inverse transformation methods and transforms the input PDFs to the output PDF, namely the distribution of ampacity. If we suppose that the confidence interval corresponding to the output confidence of 100p% is required, where p is the value of confidence level, then the number of MC repeated calculations is M times, where M satisfies M ≥ 1 10p 10 4 [24]. The solution procedure is shown schematically in Fig.…”

Section: Assessmentmentioning

confidence: 99%

“…the weather forecast uncertainty, is transformed into uncertainty of the predicted ampacity. This approach was proposed for the first time in [22] and then further developed by other researchers [18,19,23,24]. Nevertheless, in these papers, the meteorological data was assumed to follow a priori Gaussian distribution, which is a strong assumption not supported by real data observations.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty Assessment of Dynamic Thermal Line Rating for Operational Use at Transmission System Operators

Rashkovska,

Jančič,

Depolli

et al. 2022

Preprint

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Transmission system operators (TSOs) in recent years have faced challenges in order to ensure maximum transmission capacity of the system to satisfy market needs, while maintaining operational safety and permissible impact on the environment. A great help in the decision-making process was introduced with the Dynamic Thermal Rating (DTR)an instrument to monitor and predict the maximal allowed ampacity of the power grid based on weather measurements and forecast. However, the introduction of DTR raises a number of questions related to the accuracy and uncertainty of the results of thermal assessment and the level of acceptable risk and its management. In this paper, we present a solution for estimating DTR uncertainty, appropriate for operational use at TSOs. With the help of conductor surface temperature measurements, weather measurements and predicted weather data, we also estimate the error of the weather forecast and the DTR itself. Following the results of the data analyses, we build an operative solution for estimating the ampacity uncertainty based on Monte Carlo random simulations and integrate it into the operational environment of ELES -the operator of the Slovenian electric power transmission network.

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Section: Weather Datamentioning

confidence: 92%

Section: Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainty Assessment of Dynamic Thermal Line Rating for Operational Use at Transmission System Operators

Rashkovska,

Jančič,

Depolli

et al. 2022

Preprint

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“…A bridge is viewed as a structural system with the environmental temperature load as its input and the strain data at different measured points as its output. Usually, the environmental temperature load ξ ( t ) follows different probability distribution depending on different cases such as the normal distribution, 30–35 the Gumbel distribution, 36 the combination of several random distribution 37 or the non-Gaussian distribution 32,38 . For the medium- and small-span bridges, the structural form is simple and the degree of statically indeterminate structure is relatively low.…”

Section: Damage Detection Of Bridges Monitored Within One Cluster Basmentioning

confidence: 99%

Damage detection of bridges monitored within one cluster based on the residual between the cumulative distribution functions of strain monitoring data

Zhang

Liu

2020

Structural Health Monitoring

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With the structural health monitoring technique, several medium- and small-span bridges possessing the same or similar structural characteristics located in a local road network are simultaneously monitored within one cluster. Nevertheless, insufficient research has been performed on detecting the damage of all the bridges monitored within one cluster under time-varying environmental temperatures. To address this issue, a method is proposed to assess the damage in all the bridges within one cluster utilizing the residual between the cumulative distribution functions of strain monitoring data. First, an algorithm for reconstructing the strain monitoring dataset is introduced, effectively improving the computational efficiency at removing as much measured noise from a large amount of monitoring data as possible. Second, a cluster analysis algorithm considering the similarity among the strain monitoring data at different measured points is proposed to classify the strain monitoring data for all the bridges monitored within one cluster. Different classes of strain monitoring data are established by identifying similar probability distribution patterns. Third, for each class, a damage detection index is proposed based on the residual between the cumulative distribution functions of strain monitoring data. All the bridges monitored within one cluster are acted upon by similar environmental temperatures during the same monitoring period; hence, the effects of the environmental temperature on the proposed index are mitigated indirectly during the same monitoring period. Thus, the proposed index is implemented to effectively detect the damage in all the bridges belonging to each class. Finally, the effectiveness of the proposed method is demonstrated through a numerical simulation and with strain monitoring data obtained from actual bridges.

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“…Most research related to uncertainties in RTTR estimation deals only with the size of RTTR forecast errors [13]- [17] while less attention has been given to the RTTR uncertainty caused by measurement errors (MEs). In order to estimate RTTR errors from inaccurate weather measurements, an error propagation method and a Monte Carlo (MC) method were applied in [18] and [19] respectively. In addition to MEs of weather variables, the uncertainties of conductor properties including emissivity and absorptivity were taken into account by [20] where affine arithmetic was used to translate the ranges of all the considered sources of uncertainty into the lower and upper RTTR boundaries.…”

mentioning

confidence: 99%

Impacts of Measurement Errors on Real-Time Thermal Rating Estimation for Overhead Lines

Fan

Stephen

Bell

et al. 2023

IEEE Trans. Power Delivery

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To exploit additional capacity on legacy transmission assets, network owners have developed a variety of techniques to support real-time thermal rating (RTTR) of overhead lines so as to get the maximum possible capacity within their operating range. Most RTTR techniques are inferred from effective weather conditions (EWCs) that are used to assess the thermal equilibrium of conductors indicated by the measured temperature 𝑻 𝒄 , thus making RTTR estimates sensitive to measurement errors (MEs). To highlight the impacts that MEs have on RTTRs, this paper describes two EWC-based approaches to RTTR estimation and assesses the accuracy of their resulting estimates under steady state conditions given different levels of 𝑻 𝒄 and weather variables. Furthermore, the paper develops a Monte Carlo-based approach to model the propagation of measurement uncertainties through to RTTR estimates. Numerous rating scenarios for a particular instance are generated through combining a Monte Carlo method, where possible MEs are sampled within the sensor specification based limits, with an enhanced EWC-based approach that models transient changes of 𝑻 𝒄 in each scenario. The lower RTTR percentiles extracted from the rating samples can not only mitigate the RTTR overestimation due to MEs, but also inform system operators of the risk associated with RTTR decisions.

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Uncertainty analysis of dynamic thermal rating based on environmental parameter estimation

Cited by 8 publications

References 11 publications

Uncertainty Assessment of Dynamic Thermal Line Rating for Operational Use at Transmission System Operators

Uncertainty Assessment of Dynamic Thermal Line Rating for Operational Use at Transmission System Operators

Damage detection of bridges monitored within one cluster based on the residual between the cumulative distribution functions of strain monitoring data

Impacts of Measurement Errors on Real-Time Thermal Rating Estimation for Overhead Lines

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