In recent years, power transformer fault diagnosis based on deep learning has gradually become mainstream. However, inadequate use of the data collected by sensors and difficulties in extracting the key fault features lead to poor accuracy of the model. To address this problem, a fusion residual attention diagnosis (FRAD) model is proposed to evaluate power transformer faults using vibration signals. Specifically, a gradient guided filtering module (GGFM) is designed to generate images from the original vibration signals and to fuse different position images. An up-dimensioning convolutional block attention module (UCBAM) is proposed to improve atrous deep residual network (IADRN), based on which the fused images are used to complete the fault diagnosis of the transformer. Datasets collected from a 10kV real power transformer validate the proposed model. The experimental results show that the proposed model outperforms comparison methods in terms of Precision, Recall, F1 score, and Accuracy. Especially, the fault diagnosis accuracy of the proposed model is 99.38%.

Time-series prediction technology plays a significant role in evaluating the health status of power transformers and forecasting inchoate operation failure. This study presents a variational mode decomposition (VMD) model with the application of autoregressive integrated moving average (ARIMA) to develop a prediction technology for the DGA time series. Additionally, the two-step stationary test method regulates the nonstationary of the time series. The VMD decomposes the dissolved gas analysis (DGA) time series into sub-components to reduce the nonstationary, where the boost marine predators algorithm (BMPA) optimizes the parameters K and α of the VMD to retain the useful information of the original signal. The ARIMA predicts the future result for each sub-components, and the ARIMA parameter is optimized by the BMPA for optimal solutions of the Bayesian information criterion (BIC). The performance of the proposed approach is measured by predicting the health status of the diurnal new values of the DGA of a power transformer. The experiment result shows that the proposed model exhibits high efficacy in predicting the DGA time series and obtains the health status of the power transformer.

Accurate vibrational tendency forecasting of hydropower generator unit (HGU) is of great significance to guarantee the safe and economic operation of hydropower station. For this purpose, a novel hybrid approach combined with multiscale dominant ingredient chaotic analysis, kernel extreme learning machine (KELM), and adaptive mutation grey wolf optimizer (AMGWO) is proposed. Among the methods, variational mode decomposition (VMD), phase space reconstruction (PSR), and singular spectrum analysis (SSA) are suitably integrated into the proposed analysis strategy. First of all, VMD is employed to decompose the monitored vibrational signal into several subseries with various frequency scales. Then, SSA is applied to divide each decomposed subseries into dominant and residuary ingredients, after which an additional forecasting component is calculated by integrating the residual of VMD with all the residuary ingredients orderly. Subsequently, the proposed AMGWO is implemented to simultaneously adapt the intrinsic parameters in PSR and KELM for all the forecasting components. Ultimately, the prediction results of the raw vibration signal are obtained by assembling the results of all the predicted prediction components. Furthermore, six relevant contrastive models are adopted to verify the feasibility and availability of the modified strategies employed in the proposed model. The experimental results illustrate that (1) VMD plays a positive role for the prediction accuracy promotion; (2) the proposed dominant ingredient chaotic analysis based on the realization of time-frequency decomposition can further enhance the capability of the forecasting model; and (3) the appropriate parameters for each forecasting component can be optimized by the proposed AMGWO effectively, which can contribute to elevating the forecasting performance distinctly.