Khalid Alqunun’s research while affiliated with University of Ha'il and other places

This study introduces an innovative method for addressing the bearing fault detection problem in rotating machinery. The proposed approach integrates multi-feature extraction, advanced feature selection, and state-of-the-art classification techniques using convolutional neural network (CNN) models. Leveraging the comprehensive Fault Bearing Dataset from Case Western Reserve University (CWRU), continuous wavelet transforms (CWT) and CNNs are utilized for feature extraction. The methodology also incorporates machine learning model tuning through Tree-Structured Parzen Estimators (TPE) for optimal hyperparameter adjustment, ensuring high-performance classification. Experimental results, based on the ResNet-50-SVM hybrid model, showed the effectiveness of the proposed approach in achieving an impressive accuracy of 95.51%. This confirms that the proposed methodology represents a significant advancement in bearing fault detection, providing an effective solution for predictive and preventive maintenance in industrial applications.

Energy reserve becomes major concern in developing high security and economic power distribution networks that have rapid expansion in electricity demand. Providing energy reserve from conventional generators is very challenging due to their physical constraints and high variation in loads. Therefore, this paper provides an optimization technique to procure spinning reserve through demand response aggregators DRA. The proposed model allows the DRA to interact with independent system operator ISO and end-user customers to trade energy in the electricity market. The proposed DRA robust model is designed based on multiple energy bidding prices and variable spinning reserve quantities to allow the enrollment of various participants. The mixed-integer linear programming method is used for the DRA model to merge the energy consumption cost and the spinning reserve cost in one linear optimization problem. The linear optimization problem allows the DRA model to contain high number of parameters, variables, and indices to reach the optimal economic decisions within a short time. The mathematical DRA model creates opportunities to maximize the possible energy reserve with the minimum accumulated operation cost. The operation of the existed generators and the availability of the spinning reserve are examined during high congestion of the transmission lines to ensure the effectiveness of the proposed model. Case studies of a power network with 10 generating units are used to inspect the availability of the spinning reserve provided by DRA.

The use of standalone photovoltaic power systems (PVPS) as a viable solution for rural electrification has gained significant momentum. Electricity for essential home requirements is now commonly available thanks to these systems. Typically, PVPS incorporate devices for storing energy, predominantly Lead-acid (LA) batteries, to address the supply-demand disparity inherent in solar energy. However, the limited lifespan of LA batteries results in increased costs for PVPS. To address this issue, researchers have introduced a solution called the supercapacitor-battery hybrid energy storage scheme (HESS). The objective of this approach is to foster the longevity of batteries by minimizing the stress incurred by fluctuations in charging and discharging. Currently, there are a variety of HESS and associated energy management tactics to choose from, each tailored to specific uses. These systems differ in topology, intricacy, and control algorithms. This research article offers a thorough assessment of the latest advancements in the field of HESS and examines various topologies that could potentially increase the longevity of Lead-acid batteries in PVPS. The study includes theoretical analyses and numerical simulations using MATLAB Simulink to evaluate different HESS topologies in rural residential energy systems. The investigation and comparison of the efficacy of these topologies in lowering the impact on the battery is conducted. Additionally, the proposed strategy’s effectiveness is confirmed by assessing the maximum estimated battery lifespan in all potential operation scenarios. Extensive simulation studies were conducted using MATLAB/Simulink to analyze the microgrid’s operation in various scenarios throughout the year. Ultimately, a HESS has been experimentally proven and its effectiveness in a PVPS standalone setting has been simulated in order to confirm the accuracy of the initial analysis.

The doubly-fed induction generator (DFIG)-based wind turbine system (DFIG-WTS) is one of the most renewable energy sources used for producing electricity. Generally, a back-to-back converter connects the DFIG-WTS to the power grid. However, the DFIG-WTS operates under a wide range of intermittent wind speeds, which may result in highly variable and nonlinear system dynamics. Hence, implementing a robust adaptive control strategy becomes essential to guarantee optimal performance and stability of the system. In this paper, a detailed design and implementation of a model reference adaptive control (MRAC) scheme, with control laws derived using Lyapunov’s stability method, is presented to control the rotor side converter (RSC). In the proposed control scheme, the MRAC is applied to regulate the machine rotor voltages. Due to the complexity and nonlinearity of the DFIG-WTS along with the uncertainty of wind conditions, a singular perturbation technique based on the Chang transformation method is used to reduce the studied model by decoupling it into slow and fast modes. The proposed adaptive control scheme is tested and validated on a DFIG-WTS where both the full and reduced models are studied. Simulation results demonstrate that the proposed control scheme significantly enhances system stability and dynamic performance, particularly in regulating rotor winding voltages, electromagnetic torque, rotor angular speed and flux dynamics, across a wide range of wind conditions.

Rotor fault diagnosis is of paramount importance in ensuring the safety and reliability of rotating machinery. In recent years, there has been a growing interest among researchers in leveraging advanced signal processing and machine learning techniques to enhance the accuracy and performance of fault detection. This paper focuses on applying Discrete Wavelet Transform (DWT) for feature extraction from current signals. Additionally, it explores the effectiveness of hyperparameter optimization techniques, specifically Bayesian Optimization (BO), in conjunction with Machine Learning (ML) algorithms for precisely classifying rotor health conditions. The specific objective of this study is to differentiate between rotors with four fractured bars and those in a healthy state. Accuracy values serve as the primary metric for evaluating algorithm performance. A suite of classification methods, including Support Vector Machine (SVM), k-nearest Neighbor (KNN), Random Forest (RF), Extra Trees (ET), and Decision Trees (DT), are employed, and their accuracies are thoroughly assessed. The experimental results reveal that the combination of the Bayesian method and RF yields the highest performance, achieving an im-pressive accuracy rate of 96.92 %. Furthermore, ET, SVM, KNN, and DT methods exhibit noteworthy performance and demonstrate their potential in effectively detecting and classifying broken rotor bar (BRB) faults based on their severity.

Induction motors, known for their reliability and efficiency, are widely used in various industrial applications. However, their susceptibility to failure, particularly in harsh environments, poses significant operational challenges. Early detection of faults is essential to avoid unplanned downtime and costly repairs. In this study, we propose a novel diagnostic approach derived from the analysis of four distinct types of harmonics: (i) Rotor Slot Harmonics (RSH), (ii) time harmonics (TH), (iii) eccentricity fault harmonics (EFH), and (iv) rotor bar fault harmonics (RBFH). These harmonics are examined through fast Fourier transform (FFT) analysis, enabling the identification and characterization of various fault types. Experimental results indicate that TH appears to be the most sensitive harmonic for detecting stator faults, with a residual fault detection value of 4.5 A at full load and 5.05 A at half load. Real-time FFT processing of stator current signals, compared to a healthy reference signal, gives rise to the advanced residual optimal mapping (AROMA) approach. This approach allows for accurate detection and severity assessment of stator faults, where fault severity values are measured at 33.65 A for full load and 17.24 A for half load. Our innovative strategy seeks to utilize the remaining differences among the harmonics displayed by the established healthy state stored in the database and those detected in the real operational healthy state. This enhanced sensitivity and precision in fault detection and severity assessment aim to significantly reduce unplanned downtime and associated costs.

Due to the unpredictable nature of the wind, uncertainty in the characteristics of wind electrical conversion systems (WECSs), and inefficient management tactics, wind turbines have historically had operational inefficiencies. In order to overcome these drawbacks, the hybrid excitation synchronous generator (HESG), an alternative to traditional generators, is presented in this study along with the suggestion to use robust regulators to regulate HESGs. This research begins with a thorough review of the literature on generators often seen in modern wind systems. Next, a simulation platform that merges a WECS with a HESG tied to an isolated load is built using the MATLAB Simulink environment. Pitch angle control investigation shows a new experimental approach to determine the link between turbine output and the reference pitch angle. Furthermore, an evaluation of the mechanical stability of the WECS is conducted by a comparison of the performance of a H∞ controller and a CRONE controller. The simulation results demonstrate the efficiency of the CRONE controller in reducing mechanical vibrations in the WECS. By reducing vibrations, the proposed control technique enhances the overall performance and efficiency of the wind turbine system. The field is extended by the demonstration of how HESGs and reliable control systems can enhance wind turbine performance while eliminating inherent limitations.

Transmission expansion planning (TEP) is a vital process of ensuring power systems' reliable and efficient operation. The optimization of TEP is a complex challenge, necessitating the application of mathematical programming techniques and meta-heuristics. However, selecting the right optimization algorithm is crucial, as each algorithm has its strengths and limitations. Therefore, testing new optimization algorithms is essential to enhance the toolbox of methods. This paper presents a comprehensive study on the application of ten recent meta-heuristic algorithms for solving the TEP problem across three distinct power networks varying in scale. The ten meta-heuristic algorithms considered in this study include Sinh Cosh Optimizer, Walrus Optimizer, Snow Geese Algorithm, Triangulation Topology Aggregation Optimizer, Electric Eel Foraging Optimization, Kepler Optimization Algorithm (KOA), Dung Beetle Optimizer, Sea-Horse Optimizer, Special Relativity Search, and White Shark Optimizer (WSO). Three TEP models incorporating fault current limiters and thyristor-controlled series compensation devices are utilized to evaluate the performance of the meta-heuristic algorithms, each representing a different scale and complexity level. Factors such as convergence speed, solution quality, and scalability are considered in evaluating the algorithms’ performance. The results demonstrated that KOA achieved the best performance across all tested systems in terms of solution quality. KOA’s average value was 6.8% lower than the second-best algorithm in some case studies. Additionally, the results indicated that WSO required approximately 2–3 times less time than the other algorithms. However, despite WSO’s rapid convergence, its average solution value was comparatively higher than that of some other algorithms. In TEP, prioritizing solution quality is paramount over algorithm speed.

In this paper, an optimal design approach for a permanent magnet synchronous machine (PMSM), as an electric vehicle (EV) traction machine is proposed in such a way that a high power density machine can be achieved. A surface mounted PMSM is selected for its high efficiency and torque density. The machine parameters and the design constraints are established based on the EV characteristics. Guided by the analytical results of the design approach, a finite element study is performed on the machine to verify the relevance of the proposed approach. Then, a new optimization method, combining particle swarm optimization (PSO) and chaos theory, is applied for the machine design parameters in order to maximize the power density. This algorithm, referred to as CPSO, utilizes the chaos system properties such as its sensitivity to the initial conditions and its dynamical behaviour to enhance the global search capabilities of the PSO algorithm. The simulation results show the effectiveness of this algorithm compared to other meta-heuristic algorithms in increasing the machine power density. Furthermore, it is found that the optimized PMSM offers a 41.60% improvement in power while respecting the EV characteristics.