Probabilistic Harmonic Estimation in Uncertain Transmission Networks Using Sequential ANNs

In the face of transition towards the decarbonisation, increasing penetration level of power electronic interfaced renewable connections such as wind farms and PV plants are constantly influencing the uncertainties of transmission network and leading to additional uncontrolled harmonic power flows. These potential harmonic distortion issues could result in significant financial losses. To address this problem, the estimation of harmonic propagation through transmission network with increasing penetration of nonlinear loads, power electronics based renewable generation and control devices is becoming increasingly important. This paper proposes a comprehensive framework of applying sequential artificial neural network (ANN) techniques to estimate individual order harmonic distortions and total harmonic distortions (THD) at unmonitored buses in large uncertain transmission networks based on offline measurements and simulations. This study will contribute to facilitate the standard compliance, reduce the extent of the monitor installation, accelerate the assessment of harmonic performance and mitigation studies, as well as contribute to the forecast of potential harmonic issues in large transmission system.