Target Recognition of Coal and Gangue Based on Improved YOLOv5s and Spectral Technology.

Aiming at the problems of long detection time and low detection accuracy in the existing coal gangue recognition, this paper proposes a method to collect the multispectral images of coal gangue using spectral technology and match with the improved YOLOv5s (You Only Look Once Version-5s) neural network model to apply it to coal gangue target recognition and detection, which can effectively reduce the detection time and improve the detection accuracy and recognition effect of coal gangue. In order to take the coverage area, center point distance and aspect ratio into account at the same time, the improved YOLOv5s neural network replaces the original GIou Loss loss function with CIou Loss loss function. At the same time, DIou NMS replaces the original NMS, which can effectively detect overlapping targets and small targets. In the experiment, 490 sets of multispectral data were obtained through the multispectral data acquisition system. Using the random forest algorithm and the correlation analysis of bands, the spectral images of the sixth, twelfth and eighteenth bands from twenty-five bands were selected to form a pseudo RGB image. A total of 974 original sample images of coal and gangue were obtained. Through two image noise reduction methods, namely, Gaussian filtering algorithm and non-local average noise reduction, 1948 images of coal gangue were obtained after preprocessing the dataset. This was divided into a training set and test set according to an 8:2 ratio and trained in the original YOLOv5s neural network, improved YOLOv5s neural network and SSD neural network. By identifying and detecting the three neural network models obtained after training, the results can be obtained, the loss value of the improved YOLOv5s neural network model is smaller than the original YOLOv5s neural network and SSD neural network, the recall rate is closer to 1 than the original YOLOv5s neural network and SSD neural network, the detection time is the shortest, the recall rate is 100% and the average detection accuracy of coal and gangue is the highest. The average precision of the training set is increased to 0.995, which shows that the improved YOLOv5s neural network has a better effect on the detection and recognition of coal gangue. The detection accuracy of the improved YOLOv5s neural network model test set is increased from 0.73 to 0.98, and all overlapping targets can also be accurately detected without false detection or missed detection. At the same time, the size of the improved YOLOv5s neural network model after training is reduced by 0.8 MB, which is conducive to hardware transplantation.

Research on transformer fault diagnosis based on an IWHO optimized MS1DCNN algorithm and LIF spectrum.

Accurate diagnosis of transformer faults can effectively improve the enduring reliability of power grid operation. Aiming at overcoming the problems of long time consumption and low diagnostic rate in the past diagnosis methods, this article designs a laser-induced fluorescence (LIF) detection system, which can be combined with a multi-scale one-dimensional convolution neural network (MS1DCNN) to diagnose transformer fault categories. The structural parameters of MS1DCNN are optimized using the improved wild horse optimizer (IWHO). Electrical fault oil, thermal fault oil, normal oil and locally damped oil are used as raw materials for the experiment. First, the LIF spectral data of the four kinds of oil samples are obtained, and the spectral data obtained are pretreated by standard normal variate (SNV) and multiple scattering correction (MSC), and the dimensions are reduced by linear discriminant analysis (LDA) and kernel principal component analysis (KPCA). Then the dimensionality reduced data are imported into the MS1DCNN algorithm for learning, and the parameters of MS1DCNN are optimized using the IWHO algorithm. Finally, the experiment shows that the efficiency and precision of LIF technology for raw data extraction are higher than for traditional methods; in comparison with the same type of algorithm, MSC has a better preprocessing effect, KPCA has a better dimensionality reduction effect, MS1DCNN has a better prediction effect, and IWHO has a better optimization effect. Compared to them, the MSC-KPCA-IWHO-MS1DCNN model has the best diagnostic ability, with a mean square error (MSE) of 4.9037 × 10-4, mean absolute error (MAE) of 0.0179, and goodness of fit (R2) of 0.9996. Transformer fault intelligent diagnosis is necessary for the sustained and stable operation of power networks.

Transformer fault diagnosis research based on LIF technology and IAO optimization of LightGBM.

Transformer fault diagnosis is a necessary operation to ensure the stable operation of a power system. In view of the problems of the low diagnostic rate and long time needed in traditional methods, such as the dissolved gas in oil method, a laser-induced fluorescence (LIF) spectral technology is proposed in this paper, which incorporated an improved aquila optimizer (IAO) and light gradient boosting machine (LightGBM), to predict the types of transformer faults. The original AO was improved using the Nelder Mead (NM) simple search method and opposition-based learning (OBL) mechanism, which could improve the parameter optimization ability of the model. Normal oil, thermal fault oil, local moisture oil, and electrical fault oil were selected as experimental samples. First, the spectral images of the four oil samples were obtained by LIF technology, and the fluorescence spectral curves obtained were preprocessed by multivariate scattering correction (MSC) and normalization (normalize), while kernel-based principle component analysis (KPCA) was used for dimensional reduction. The dimensionality-reduced data were then imported into the LightGBM model for training, and the IAO algorithm was used to optimize the parameters of the LightGBM. Finally, the experiment showed that the LIF technology demonstrated good recognition of the fault types for transformer fault diagnosis; the data purity after MSC preprocessing was higher than that of other processing methods; the prediction effect of the LightGBM model was superior to other prediction models; the LightGBM model optimized by IAO had better convergence, parameter optimization ability, and prediction accuracy than the LightGBM model optimized by the original AO and particle swarm optimization (PSO). Among the models, the MSC-IAO-LightGBM model had the best effect on fault prediction, with the mean square error (MSE) reaching 9.0643 × 10-7, mean absolute error (MAE) reaching 8.7439 × 10-4, and goodness of fit (R2) approaching 1. It can be implemented as a new diagnostic method in transformer fault detection, which is of great significance to ensure the stable and safe operation of power systems.