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.

Nowadays, light emitting diode (LED) lamps have been widely utilized for lighting system due to its low-energy consumption. The harmonic emission standard is ignored by most of the manufacturers, high harmonic current will increase harmonic injection and cause fire risk. Existing research focuses on investigating harmonic emissions from several specific LED drivers, but a systematic evaluation approach is not given. The contribution of this paper proposed a LED harmonic evaluation in the management view, which can evaluate the harmonics of the LED lamps, accelerate the elimination of inferior LED lamps, and improve the power quality of distribution network. The evaluation approach combines G1 method and entropy method, which can make the weighting more scientific and rational. An evaluation model is established by collecting data, then the G1-entropy method is used to calculate the weights of harmonic characteristics in this model. Finally, we analyze and discuss the results, a specific evaluation approach is proposed, which can thoroughly and accurately represent the harmonic characteristics of LED lamps.

Rockburst may cause damage to engineering equipment, disrupt construction progress, and endanger human life. To this day, the occurrence of rockburst remains complex and difficult to predict. This study proposes the D-P-Transformer algorithm to address this issue by improving the embedding structure of the Transformer for specific applications to rockburst data. To reduce the computational requirement, sparse self-attention is adopted to replace self-attention. A distilling operation and multiple layer replicas are simultaneously used to enhance the robustness and speed up the algorithm’s process. Taking all relevant rockburst factors into consideration, multiple experiments are conducted on seven large-scale rockburst datasets with different training ratios to verify the reliability of the proposed D-P-Transformer rockburst prediction algorithm. As compared to the original algorithm, the proposed algorithm shows average reductions of 24.45%, 46.56%, 17.32%, and 48.11% in the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE), respectively. The results indicate that the novel D-P-Transformer rockburst prediction algorithm is superior to the Transformer prediction algorithm, and could be used for coal mine rockburst prediction analysis.