Open-Circuit Fault Detection in a Multilevel Inverter Using Sub-Band Wavelet Energy

Abstract

Recent research has focused on sustainable development and renewable energy resources, thus motivating nonconventional cutting-edge technology development. Multilevel inverters are cost-efficient devices with IGBT switches that can be used in ac power applications with reduced harmonics. They are widely used in the power electronics industry. However, under extreme stress, the IGBT switches can experience a fault, which can lead to undesirable operation. There is a need for a reliable system for detecting switch faults. This paper proposes a signal processing method to detect open-circuit problems in IGBT switches. Relative wavelet energy has been used as a feature for a machine learning algorithm to diagnose and classify the faulted switches. The switching sequence can be altered to restore a healthy output voltage. Inverter faults have been diagnosed by using support vector machine (SVM) and decision tree (DT), and an ensemble model based on decision tree (DT) and XG boost algorithm was developed, which yielded 92%, 88%, and 94.12% accuracy, respectively.

Enhancing Short-Term Electrical Load Forecasting for Sustainable Energy Management in Low-Carbon Buildings

“…Specifically, the MAE, MSE, RMSE, MAPE, and NRMSE error rates were 0.030, 0.015, 0.120, 0.3421, 0.0027, respectively. These results indicate that the proposed model provides highly accurate predictions compared to the baseline models, including FCRBM [73], CNN-LSTM [71], LSTM [71], Linear regression [71], CNN [42], and Multilayered LSTM [20]. These results demonstrate the superiority of the proposed model in terms of daily ELF data prediction, making it a valuable tool for forecasting electricity consumption.…”

“…Table 2 provides a comparison of daily ELF results. For hourly load forecasting, the proposed model is compared with Linear regression [71], CNNLSTM [71], SE-AE [72], CNN Stacked LSTM [10], FCRBM [73], CNN-GRU [24], Residual GRU [74], Multilayered LSTM [20], CNNLSTM-autoencoder [23], CNN-BDLSTM [75], ANN [76], GRU [77], CNN-BiGRU [78], ESN [15], STLF-Net [79], and CNN [42]. By comparison, the worst performance was achieved using Linear regression [71], and a better performance was achieved using Multilayered LSTM [20].…”

“…For hourly load forecasting, the proposed model is compared with Linear regression [71], CNNLSTM [71], SE-AE [72], CNN Stacked LSTM [10], FCRBM [73], CNN-GRU [24], Residual GRU [74], Multilayered LSTM [20], CNNLSTM-autoencoder [23], CNN-BDLSTM [75], ANN [76], GRU [77], CNN-BiGRU [78], ESN [15], STLF-Net [79], and CNN [42]. By comparison, the worst performance was achieved using Linear regression [71], and a better performance was achieved using Multilayered LSTM [20]. However, the proposed model further reduced the error scores and achieved the best results with the lowest error rates: 0.0088 for MAE, 0.0007 for MSE, 0.0257 for RMSE, 0.2945 for MAPE, and 0.0024 for NRMSE.…”

Wavelet Analysis to Detect Ground Faults in Electrical Power Systems with Full Penetration of Converter Interface Generation

“…In Figure 4, the decomposition developed by the DWT can be seen. A and D represent the approximation and detail coefficients, respectively [35].…”

Solar Power Prediction Using Dual Stream CNN-LSTM Architecture

“…Solar power is considered the alternative when compared to fossil fuels due to various characteristics, such as being clean, green, and naturally replenished. Solar power generation, either as an islanded or grid-connected mode of operation, brings unstable uncertainty, which causes problems for the stability of the power systems, particularly for the integration of solar power in a large microgrid system [ 8 , 9 ]. To overcome these challenges a reliable solar power prediction is an effective way to decrease the uncertainty, which is important for the planning, management, and operation of energy systems [ 10 ].…”