Edge Distributed Dynamic Line Rating Through On Line Learning of Radial Basis Function Neural Networks for Temperature Prediction

In the context of energy transformation and a growing supply of renewable energy, additional flexibility of transmission lines is required. Dynamic Line Rating provides a solution to this question by allocating additional capacities in the power lines depending on the actual weather conditions. Modern implementations of Dynamic Line Rating are typically based on costly on-line sensors or unreliable weather stations mounted on pylons. This paper proposes a novel solution to the Dynamic Line Rating, by implementing a tiny machine learning approach suitable for integration into the sensor and a real-time decision algorithm for the detection of potential new allocation spots. The proposed forecasting methodology is based on a self-learning radial basis function model, that forecasts the temperature of the conductor 5-, 10-, and 15 minutes into the future. The model can be accommodated on a cheap microcontroller device and is suitable for on-device self-calibration at the desired retraining frequency (RF). The forecasting results are promising, achieving mean absolute errors below 0.3 degrees C in the best test scenarios, a clear improvement over a naive persistence benchmark, and competitive performance when compared with the enhanced proposed Echo State Network (ESN). Subsequently, an algorithmic non-parametric Change Detection Test is executed on top of the forecaster to determine the possible points of change for dynamic allocation of the Over Head Line capacity, qualitatively and quantitatively demonstrating the potential capacity expansion compared to traditional Static Line Rating.