Evolutionary Optimization of an Ice Accretion Forecasting System

Abstract

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.

Conductor icing: Comparison of a glaze icing model with experiments under severe laboratory conditions with moderate wind speed

“…In the presence of wind, they also affect the aerodynamics of the conductors. In 2009, Musilek et al made some preliminary advances in this endeavour (Pytlak et al, 2010).…”

Wind power forecasting by an empirical model using NWP outputs

“…The NWP model can be also used for other purposes, e.g. to forecast occurrence of damaging icing on wind turbines and power transmission lines [10]. Another issue to be addressed is the uncertainty of the forecast.…”

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

“…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. This approach was refined by Pytlak et al (2010), using ASOS observations and a genetic algorithm to identify the optimal setup for the engagement function, based on model surface temperature, and the fraction of frozen precipitation. This advanced IAFS was incorporated into the present study.…”

“…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. This algorithm uses fraction of frozen precipitation and surface wet bulb temperature to determine an engagement function for the Simple Model.…”