Harmonic Characteristics Data-Driven THD Prediction Method for LEDs Using MEA-GRNN and Improved-AdaBoost Algorithm

Abstract

Light-emitting Diode (LED) lamps have been widely used due to versatility and energy efficiency. However, LEDs are nonlinear loads, the massive usage will inject harmonics into the lighting system, which has influenced the power quality. Total Harmonic Distortion (THD) is an important parameter to evaluate the power quality, but the prediction of THD for LEDs is a challenging task. This paper addresses this issue by designing harmonic characteristics detection experiment and using artificial intelligence algorithm. Firstly, LED lamps with different driving circuits were tested, the relevant data of each harmonic were sampled and analyzed. Then, a THD prediction method based on an improved AdaBoost algorithm is proposed. In this method, a Generalized Regression Neural Network (GRNN) model is established, and its parameters are optimized by Mind Evolution Algorithm (MEA) to improve the search ability of GRNN. On this basis, the AdaBoost algorithm is utilized to integrate multiple MEA-GRNN individuals to form a strong predictor, which improves the generalization ability of the model. To avoid the integration failure caused by improper selection of threshold value, a sigmoid adaptive factor is added to improve the accuracy of AdaBoost algorithm. Finally, the Ada-MEA-GRNN model is trained and simulated with the LED harmonic data collected by the experiment. The simulation results show that the prediction accuracy of the proposed method is better than BP and GRNN, which can reach 95.48%. Meanwhile, even if the input dimension is reduced, the error is still small.

Keywords

• LED lamps
• THD prediction
• ensemble learning
• mind evolution algorithm (MEA)
• generalized regression neural network (GRNN)