Accurate Identification of Harmonic Distortion for Micro-Grids Using Artificial Intelligence-Based Predictive Models

Abstract

This paper proposes an accurate harmonic identification strategy for microgrids and distributed power systems. The harmonic identification strategy is one of the complex tasks in microgrids due to the need of high computational burden in terms of memory and computational time. The complexity of the considered problem is resulted from solving the transcendental nonlinear equations that characterize harmonics especially in real time is considered as a highly challenged problem. The proposed identification strategy aims at detecting individual and total harmonic distortion levels that is generated from several harmonic sources. In the current paper, the Machine Learning Regression Analysis (MLRA) including location-specific data and the Artificial Neural Networks (ANNs) are proposed to identify the harmonic distortion. To enhance the identification and prediction performance, the standard IEEE 34-bus test feeder with verified harmonic sources power system is emulated for various scenarios using Electric Transient Analysis Program (ETAP). An extracting procedure for individual and total harmonic components are employed. The higher reduction level of error with approximately maximum median of 3.7127e $-^{15}$ % assures the accurate prediction of harmonic components. In addition, this work investigates the impact of several practical cases when the voltages of the renewable clean energy source arrays vary (e.g., solar cell, wind turbine, and EV), and a double-stage topology is needed to have the same amount of voltage at the input of the inverter before inversion.

Keywords

• ANNs
• ETAP
• harmonic distortion
• machine learning
• micro-grid