High voltage (HV) infrastructures market is growing due to the corresponding growth in industries and population. To ensure continuous and reliable electrical power supply, existing substation and transmission lines are being upgraded to accommodate the additional load requirements. These upgrades involve up-rating the existing transmission lines or the installation of new lines. To save on easement cost and reduce the environmental impacts, transmission lines are occupied the same easement or path. This parallel option introduces the risk of induced voltage which could reach an unsafe condition and jeopardize the safety of works and people. This paper analysis and highlight the hidden risk associated with two parallel transmission lines that connected the same high voltage substation. The theoretical study which is supported by the case study shows the high risk potential tempering with the OHEW on the isolated circuit while the other one is still energized

A new approach of power factor correction technique is reclaimed in this paper. In power systems, the power factor correction usually consists of branching capacitive loads to the power line in order to reduce the reactive power generated from inductive loads. The determination of the capacitance value depends on different parameters such as the voltage, current, frequency and the phase angle between the voltage and current. Thus, lots of measurements and calculations are required to branch the best capacitor value and reduce the power loss. The majority of industrial correctors use trial and error techniques or are limited to few steps of power correction. The system proposed in this paper uses a simple algorithm with fewer mathematical operations to determine the best capacitance value which can be selected among 256 different choices. The system engages the required capacitance in one shot in order to expand the lifetime of the switching relays and capacitors.

This paper introduces a novel control technique for embedded systems based on error-dependent sampling, referred to as Error-Dependent Sampling Control (EDSC). Unlike traditional control methods, which rely on fixed sampling intervals, EDSC dynamically adjusts the sampling rate based on the magnitude of the error signal. By incrementing or decrementing the control signal by a fixed step size of 1 when an error is present, and maintaining the output when the error is zero, this approach minimizes computational complexity. The sampling frequency increases as the error grows, resulting in faster response times during larger errors and fewer updates during smaller errors, thus optimizing system performance and reducing energy consumption. The proposed method utilizes timer interrupts, making it well-suited for embedded systems with limited processing power. The advantages of EDSC are highlighted through its efficiency in both energy consumption and computational load, particularly for low-power microcontrollers. This technique is compared with traditional control methods such as PID and other variable sampling approaches, demonstrating its potential for real-time applications in embedded control systems.

Recently, fault detection has played a crucial role in ensuring the safety and reliability of inverter operation. Switch failures are primarily classified into Open-Circuit (OC) and short-circuit faults. While OC failures have limited negative impacts, prolonged system operation under such conditions may lead to further malfunctions. This paper demonstrates the effectiveness of employing Artificial Intelligence (AI) approaches for detecting single OC faults in a Packed E-Cell (PEC) inverter. Two promising strategies are considered: Random Forest Decision Tree (RFDT) and Feed-Forward Neural Network (FFNN). A comprehensive literature review of various fault detection approaches is first conducted. The PEC inverter’s modulation scheme and the significance of OC fault detection are highlighted. Next, the proposed methodology is introduced, followed by an evaluation based on five performance metrics, including an in-depth comparative analysis. This paper focuses on improving the robustness of fault detection strategies in PEC inverters using MATLAB/Simulink software. Simulation results show that the RFDT classifier achieved the highest accuracy of 93%, the lowest log loss value of 0.56, the highest number of correctly predicted estimations among the total samples, and nearly perfect ROC and PR curves, demonstrating exceptionally high discriminative ability across all fault categories.

Recently, fault detection has proven its crucial role in guaranteeing the safety and dependability of inverter functioning. This paper demonstrates the effectiveness of employing Artificial Intelligence (AI) approaches for fault detection of single Open Circuit (OC) switch fault in a Packed E-Cell (PEC) inverter. Two optimum strategies have been considered Random Forest Decision Tree (RFDT) and Feedforward Neural Network (FFNN). Initially, Discrete Wavelet Analysis (DWA) is employed for the investigation of the output voltage, using the first level of detailed coefficients. After that, three statistical features are extracted from this analysis to serve as input to the classifier during learning. The output of the classifier, which is referred to the fault class, is then trained according to each strategy process. Finally, a comparison with deep analysis based on selected criteria is illustrated and the results obtained using MATLAB/Simulink confirm the comprehensive review of the evaluated strategies.