Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

Abstract

In this paper, we present a new approach to use machine learning (ML) for the calibration of a physical model allowing the reproduction of the vibratory behavior of an overhead line conductor. This physical model known as Strip Theory (ST) has the advantage of being very precise but very complicated and cumbersome in its software operations and manipulations. A second model known as the Wake Oscillator (WO) has been implemented in order to meet the limitations of the ST model. In order to be able to use the WO model instead of the ST model, very heavy manual adjustments are required, which makes its use complicated.Precisely, the WO must be able to generate a time series similar to a time series generated by the ST model. In order to respond to this limitation, a machine learning model known as ENS has been proposed. The machine learning model will therefore take as input the data from the WO model and output the data from the ST model. A series of Machine learning models have been implemented and tested. The ENS algorithm was retained with a best Pearson's linear coefficient of determination (R2 Score) of almost 0.7 and a Root mean square deviation (RMSE) of 7.57e-09. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

“…We retrieve the data from the WO to train the model and the model data from ST as test data. We apply this procedure by first concatenating the time series and then separating the simulation and reference data with 80% of the data for training and 20% for validation [102][103][104][105][106][107][108][109][110][111][112][113]. In some cases, we get non-exploitable values in the time series of ST such as missing values (NA).…”

Calibration of a Stereoscopic Vision System in the Presence of Errors in Pitch Angle

“…These four models are: fitting a log-distance path loss model, Gaussian Process Regression, Artificial Neural Network, and Random Forest Regression. In [ 12 ], machine learning is used to calibrate a physical model that tries to reproduce the vibrational behavior of an overhead line conductor, which is a novel approach to the problem in question. To achieve their goal, the authors train and test different models.…”