Tarek M. Mostafa

Internal metal-loss defects are a leading cause of unplanned pipeline outages, highlighting the critical need for accurate and high-resolution in-line inspection systems. In this paper, an inductive sensing-based inspection system is presented for detecting and characterizing internal metal-loss defects in metallic pipelines. The proposed system utilizes inductance-to-digital converters coupled with planar sensor coils, eliminating the need for separate dedicated transmitter coil/modules or magnetizer assemblies. This integrated transceiver architecture provides a compact, cost-effective, and low-power solution suitable for prolonged pipeline inspections. To automate defect detection and reconstruction, a hybrid neural network (HNN) is introduced as an inversion scheme. The HNN combines convolutional and recurrent subnetworks to effectively extract spatial and sequential features from inductance measurements. Experimental and simulation validations demonstrate the system’s capability to reliably detect and accurately characterize internal defects as small as 4 mm in diameter and 1 mm in depth.

This article describes an advanced tool that uses electromagnetic resonance coupling and machine learning techniques to detect and characterize metal loss on the inner surface of a metallic pipe. The proposed tool uses a transmitter coil placed along the axis of the pipe and four sensor coils installed around the transmitter coil. Any defect on the pipe surface leads to changes in the impedance of the transmitter and sensor coils as well as in the mutual coupling between them, thus creating a detectable variation in the outputs of one or multiple sensor coils. An artificial neural network is developed to reconstruct two-dimensional pipe cross-sections and to completely characterize the defects using these variations. The proposed tool is tested and validated via simulations, and data are collected using an experimental prototype. Results show that the tool can fully characterize the size, location (azimuthal angle), and level (thickness) of metal loss.

In order to formulate the long-term and short-term development plans to meet the energy needs, there is a great demand for accurate energy forecasting. Energy autonomy helps to decompose a large-scale grid control into a small sized decisions to attain robustness and scalability through energy independence level of a country. Most of the existing energy demand forecasting models predict the amount of energy at a regional or national scale and failed to forecast the demand for power generation for small-scale decentralized energy systems, like micro grids, buildings, and energy communities. A novel model called Sailfish Whale Optimization-based Deep Long Short- Term memory (SWO-based Deep LSTM) to forecast electricity demand in the distribution systems is proposed. The proposed SWO is designed by integrating the Sailfish Optimizer (SO) with the Whale Optimization Algorithm (WOA). The Hilbert-Schmidt Independence Criterion (HSIC) is applied on the dataset, which is collected from the Central electricity authority, Government of India, for selecting the optimal features using the technical indicators. The proposed algorithm is implemented in MATLAB software package and the study was done using real-time data. The optimal features are trained using Deep LSTM model. The results of the proposed model in terms of install capacity prediction, village electrified prediction, length of R & D lines prediction, hydro, coal, diesel, nuclear prediction, etc. are compared with the existing models. The proposed model achieves percentage improvements of 10%, 9.5%,6%, 4% and 3% in terms of Mean Squared Error (MSE) and 26%, 21%, 16%, 12% and 6% in terms of Root Mean Square Error (RMSE) for Bootstrap-based Extreme Learning Machine approach (BELM), Direct Quantile Regression (DQR), Temporally Local Gaussian Process (TLGP), Deep Echo State Network (Deep ESN) and Deep LSTM respectively. The hybrid approach using the optimization algorithm with the deep learning model leads to faster convergence rate during the training process and enables the small-scale decentralized systems to address the challenges of distributed energy resources. The time series datasets of different utilities are trained using the hybrid model and the temporal dependencies in the sequence of data are predicted with point of interval as 5 years-head. Energy autonomy of the country till the year 2048 is assessed and compared.

In this study, we propose a four-plate structure with two shielding plates to produce shielded capacitive power transfer (S-CPT) at an operating frequency of 6.78 MHz for a 10 W system. By eliminating the inductors at the secondary side to form an asymmetrical topology, an S-CPT system was developed with a class-E power amplifier. Using MATLAB software, analysis was performed to obtain the parameters in the S-CPT system regarding resonance and impedance matching, and the proposed coupler structure was investigated through electric field simulation. The shield plate voltage stability was also investigated by analysing both the simulation and hardware experiment results. A prototype of S-CPT was established to validate the analysis results and to demonstrate the voltage at the shield plate of the proposed coupler structure. The experimental results are in good agreement with the simulation results. The proposed S-CPT exhibits an AC–AC efficiency of 84%, with a 56% voltage ground stability reduction because of implementing a balun.

Owing to the flexibility of coupling structure, low standing losses and low Electromagnetic Interference (EMI), capacitive power transfer (CPT) has drawn much attention as one of the new wireless power transfer (WPT) technologies. However, to date, the load effect on the system performance has not been analyzed comprehensively, and the CPT system is usually fully tuned (tank network resonant frequency fully tuned to the fundamental components of the nominal switching frequency), which is challenging to maintain practically. Based on these considerations, this paper first provides a detailed analysis of how the load can affect the performance of a CPT system. An optimal load resistance is shown to exist for a CPT system with a non-fully/partially tuned LCC tank (tank network resonant frequency is designed to be lower than the fundamental components of the switching frequency). By dynamically transforming the load to its optimal value, the system can transfer maximum power to the load while maintaining zero voltage switching (ZVS) operation. Then, a controlled buck-boost converter based on perturbation and observation algorithm is introduced to the system to obtain the optimal equivalent resistance against the actual load variations via tracking the maximum power transfer point. The final prototype has demonstrated that a maximum power of 10 W is obtained, with an end-to-end power efficiency of about 70% over a wide range of load variations from 5 Ω to 1 kΩ .

This paper proposes a capacitive power transfer (CPT) system with a step‐down transformer on the secondary side to reduce the circuit quality factor ( Q ), and thereby reduce the sensitivity to parameters variations, as well as the voltage stress across the coupling interface. The system operating principle is analyzed mathematically, and the focus is given to understand the effect of the leakage inductance ( L _lk ) of the non‐ideal transformer on the system performance. The analytical and simulation results show that at a given constant output power, the voltage across the plates is significantly reduced, and the system becomes less sensitive to the coupling variations by increasing the turns ratio of the step‐down transformer. It is found that L _lk can be advantageously utilized as a tuning inductor ( L ) or part of it. The proposed method is verified by building a prototype CPT system that delivered 25 W at an operation frequency of 1 MHz, and an efficiency of more than 70%. Simultaneously, the voltage stress across the single pair of the coupling plates is reduced from 252 V of a conventional CPT system without a step‐down transformer, to 50.4 V using a high‐frequency transformer with a turns ratio of 5.

This paper introduces a DC–DC buck converter on the secondary side of the capacitive power transfer system to reduce the voltage and electric field across the interface, and to reduce the circuit Q, and thus the system sensitivity. The system is mathematically analyzed to study the improvement in sensitivity and voltage stress. The leakage electric field emissions around the plates are investigated by simulation. The analytical and simulation results show that by reducing the duty cycle of the buck converter at a constant output power, the voltage across the plates can be significantly reduced and the circuit becomes less sensitive to the variations in parameters. Experimental results demonstrated that Q and the voltage stress over the capacitive interface are reduced by changing the duty cycle of the buck converter. For delivering 10 W of power, the maximum voltage stress across one pair of the coupling plates is reduced from 211 V in the conventional system without using a DC–DC converter, to 65 V and 44 V at duty cycles of 30% and 20%, respectively. The system achieves an end-to-end power efficiency of 80% at an output power of 10 W and a duty cycle of 30%.