The efficient operation of power-electronic-based systems heavily relies on the reliability and longevity of battery-powered systems. An accurate prediction of the remaining useful life (RUL) of batteries is essential for their effective maintenance, reliability, and safety. However, traditional RUL prediction methods and deep learning-based approaches face challenges in managing battery degradation processes, such as achieving robust prediction performance, to ensure scalability and computational efficiency. There is a need to develop adaptable models that can generalize across different battery types that operate in diverse operational environments. To solve these issues, this research work proposes a TransRUL model to enhance battery RUL prediction. The proposed model incorporates advanced approaches of a time series transformer using a dual encoder with integration positional encoding and multi-head attention. This research utilized data collected by the Centre for Advanced Life Cycle Engineering (CALCE) on CS_2-type lithium-ion batteries that spanned four groups that used a sliding window technique to generate features and labels. The experimental results demonstrate that TransRUL obtained superior performance as compared with other methods in terms of the following evaluation metrics: mean absolute error (MAE), root-mean-squared error (RMSE), and $R^2$ values. The efficient computational power of the TransRUL model will facilitate the real-time prediction of the RUL, which is vital for power-electronic-based appliances. This research highlights the potential of the TransRUL model, which significantly enhances the accuracy of battery RUL prediction and additionally improves the management and control of battery-based systems.

Now a days HVDC Transmission System are being used due to its advantage over HVAC Transmission systems based on several features like minor Transmission losses, efficient control of Electric power flow, stability and very economical for long Transmission lines. Transmission Line having length of several kilometers and located in open environment for this fault occurring chances are very high as compared other part of power system. These faults have several effects, its necessary to detect and classified these faults within very short time for safe operation of power system. This paper presents the solution of fault detection and classification of overhead HVDC Transmission lines by applying Discrete Wavelet Transform (DWT). Faults in HVDC Transmission line of several types have been created using MATLAB/Simulink. The current signals of HVDC Transmission Lines are attained from Current Transformer and Scope block in work space, and then decompose by means of DWT to obtain the details coefficients up to three levels. Furthermore, Norm of detail coefficient at 3^rd level is calculated. To detect and Classification the faults on HVDC Transmission lines Value of Norm are compared with threshold values of the system. The proposed approach has been successfully tested for various types of faults at different line location.

The demand of electrical power energy has grown exponentially in recent times and to meet this demand electrical power system network needs more sophistication and consequently more complexity. Transmission lines, expanded over several kilometers, are the backbone of the electrical power system which acts as interconnection between power houses and electricity consumers. Transmission lines are mostly located in the open and therefore, environmental effects can result in fault occurrences. The ability to detect and diagnose the faults can help greatly in the protection of transmission line. This paper presents modern solution of fault detection and diagnosis of overhead transmission lines by implementing Discrete Wavelet Transform (DWT). Faults in transmission line of various categories have been created using MATLAB/Simulink. The current signals of each phase are obtained from sending end, and then decompose using DWT to obtain the details coefficients up to five levels. Furthermore, normalized values are calculated from the norm of detail coefficients. In order to detect and diagnose the faults on transmission lines normalized values of each phase are compared with threshold values of the system. The proposed approach has been successfully tested for various categories of faults at different operating conditions.

In the last decade, there has been a significant increase in the need for sustainable power resources, which requires intelligent power sources to fulfill the power requirements of various devices. A non-isolated and non-inverting four-switch buck-boost power converter links several terminal devices with varying input voltage sources. Still, it faces mode shifting issues requiring trustworthy mode sensing while intended to work in varying modes. This paper suggests a novel control mechanism of current-mode control based on proportional-integral (PI) controllers for these converters with smooth mode shifting ability. The selection of absolute values of current and output voltage ensures the robust control of the system. Furthermore, the PI controllers are fed with fixed reference current and voltage values depending on the terminal device’s ratings and converter’s operational phase. The PI control structures deliver smooth transitions with proper tuning of parameters to bypass disturbances at the converter’s output. In this paper, the current-mode control strategy offers excellent performance in rejecting input voltage fluctuations and reacting to transients at the load side. The proposed power converter achieves a high-grade substitution among different operating modes and other macro-level interests. MATLAB Simulation results provide steady-state analysis in the continuous conduction mode for devices of ratings from 5 volts to 75 volts and 0.9 amperes to 6 amperes.