An Ice Accretion Forecasting System (IAFS) for Power Transmission Lines Using Numerical Weather Prediction

Musilek, Petr; Arnold, Dan; Lozowski, Edward P.

doi:10.2151/sola.2009-007

Cited by 17 publications

(22 citation statements)

References 7 publications

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“…To distinguish between rain and freezing rain, air temperature and fraction of frozen precipitation can be used as described in Ramer (1993). However, as shown by Musilek et al (2009), diagnosis of precipitation type with fixed thresholds for these variables may not lead to satisfactory results. Consequently, these authors developed an IAFS (Ice Accretion Forecasting System) that uses a fuzzy algorithm to control engagement of the ice accretion model, only at times when freezing rain is identified by the algorithm.…”

Section: Icing Modelmentioning

confidence: 99%

“…The IAFS algorithm (Musilek et al, 2009) was employed to discount precipitation that fell in frozen form. The best performing setup, IAFS5GA (Pytlak et al, 2010), was used for the simulations.…”

Section: Forecast Of Freezing Rainmentioning

confidence: 99%

“…It would be highly desirable to model and forecast such icing events, so that the consequent damages can be prevented or mitigated. The goal of this paper is to evaluate the forecasting capability of our Ice Accretion Forecasting System (IAFS), which is based on a Numerical Weather Prediction (NWP) model and an ice accretion model augmented with an intelligent freezing rain detection algorithm (Musilek et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting severe ice storms using numerical weather prediction: the March 2010 Newfoundland event

Hosek

Musilek

Lozowski

et al. 2011

Nat. Hazards Earth Syst. Sci.

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Abstract.The northeast coast of North America is frequently hit by severe ice storms.These freezing rain events can produce large ice accretions that damage structures, frequently power transmission and distribution infrastructure. For this reason, it is highly desirable to model and forecast such icing events, so that the consequent damages can be prevented or mitigated. The case study presented in this paper focuses on the March 2010 ice storm event that took place in eastern Newfoundland. We apply a combination of a numerical weather prediction model and an ice accretion algorithm to simulate a forecast of this event.The main goals of this study are to compare the simulated meteorological variables to observations, and to assess the ability of the model to accurately predict the ice accretion load for different forecast horizons. The duration and timing of the freezing rain event that occurred between the night of 4 March and the morning of 6 March was simulated well in all model runs. The total precipitation amounts in the model, however, differed by up to a factor of two from the observations. The accuracy of the model air temperature strongly depended on the forecast horizon, but it was acceptable for all simulation runs. The simulated accretion loads were also compared to the design values for power delivery structures in the region. The results indicated that the simulated values exceeded design criteria in the areas of reported damage and power outages.

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Section: Icing Modelmentioning

confidence: 99%

Section: Forecast Of Freezing Rainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecasting severe ice storms using numerical weather prediction: the March 2010 Newfoundland event

Hosek

Musilek

Lozowski

et al. 2011

Nat. Hazards Earth Syst. Sci.

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show abstract

“…Therefore, some researchers have started to consider more impact factors and apply other methods to study the icing forecasting problem. Paper [14] proposes an ice accretion forecasting system (IAFS) which is based on a state-of-the-art, mesoscale, numerical weather prediction model, a precipitation type classifier, and an ice accretion model. Paper [15] studies the medium and long term icing thickness forecasting on transmission lines and presents a forecasting model based on the fuzzy Markov chain prediction; Paper [16] studies the icing thickness forecasting model by using fuzzy logic theory.…”

Section: Introductionmentioning

confidence: 99%

Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

Niu

2016

Applied Sciences

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Accurate forecasting of icing thickness has great significance for ensuring the security and stability of the power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on the fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of the fireworks algorithm is employed to select the proper input features with the purpose of eliminating redundant influence. In addition, the aim of the W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through simulation experiments using real-world icing data from the monitoring center of the key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

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“…There have been only a few systems proposed that can provide automated forecasting of ice accretion for electric power utilities (DeGaetano et al 2008;Musilek et al 2009). In this paper, we describe an extension to the ice accretion forecasting system (IAFS) previously described by Musilek et al (2009).…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Optimization of an Ice Accretion Forecasting System

Pytlak

Musilek

Lozowski

et al. 2010

Monthly Weather Review

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The ability to model and forecast accretion of ice on structures is very important for many industrial sectors. For example, studies conducted by the power transmission industry indicate that the majority of failures are caused by icing on overhead conductors and other components of power networks. This paper presents an extension to the ice accretion forecasting system (IAFS) that is comprised of a numerical weather prediction model, a precipitation-type algorithm, and an ice accretion model. To optimize the performance of IAFS, the parameters of the precipitation-type algorithm are estimated using a genetic algorithm. The system is developed by hindcasting a well-documented freezing-rain event and calibrated using four additional ice storms. Subsequently, the system is tested using three independent storms. The results show a significant improvement in consistency, accuracy, and skill of IAFS. The methodology described in this contribution is not limited to ice accretion modeling-it provides a general approach for setting operational parameters of data-processing algorithms to achieve interoperability of numerical weather prediction models with add-on applications based on empirical observations.

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An Ice Accretion Forecasting System (IAFS) for Power Transmission Lines Using Numerical Weather Prediction

Cited by 17 publications

References 7 publications

Forecasting severe ice storms using numerical weather prediction: the March 2010 Newfoundland event

Forecasting severe ice storms using numerical weather prediction: the March 2010 Newfoundland event

Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

Evolutionary Optimization of an Ice Accretion Forecasting System

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