Mostafa Abdel Geliel

Fast and reliable fault detection is critical in photovoltaic (PV) systems to improve reliability and energy yield and reduce maintenance costs, ensuring safe and efficient operation under varying operating conditions. Although recent data-driven PV fault detection techniques (FDTs) in literature have demonstrated high diagnostic accuracies, they often suffer from practical limitations, offline operation, lack of fault localization and/or inability to concurrently identify faults. To address these challenges, a unified framework is proposed that simultaneously integrates real-time operation, fault classification and localization, and concurrent-fault identification in a single compact diagnostic approach. This is realized by developing a parallel feedforward neural network (FFNN) architecture whose performance is enhanced by a residual model-based structure, resulting in a more interpretable, scalable, reliable and accurate scheme. In addition, Grey Wolf Optimizer–Support Vector Machine (GWO–SVM) feature selection is incorporated to select the most influential diagnostic features, thus reducing data redundancy, enhancing diagnostic accuracy, and shortening training time. The proposed approach was tested to diagnose five types of PV faults (open circuit, short circuit, partial shading, degradation, and simultaneous faults), as well as classify their intensity and location. Simulation results show that the proposed FFNNs consistently outperform conventional Support Vector Machines (SVMs) in classification accuracy, with accuracies exceeding 98% and 99% for fault classification and localization, respectively, and above 95% for noisy data. Moreover, GWO-SVM proved to offer more stable feature subsets compared to Particle Swarm Optimization–SVM (PSO–SVM) in the considered feature selection structure. Simulation results validated the effectiveness of the proposed unified multiclassification fault diagnosis approach, even under system uncertainties, making it suited for real-world PV systems.

The present research focuses on two optimization approaches as an attempt to solve the inverse kinematics (IK) of the Viper650 robotic arm used in surgical applications. The discrete method uses Branch and Bound (B&B) used to find the best solution IK and it has not yet been applied to 6DOF IK. On the other hand, the continuous method uses Sequential Quadratic Programming (SQP), which deals with forward kinematics (FW) and IK as a numerical optimization problem and it is applied for the first time to this robot using MATLAB Simulink and Automation Control Environment (ACE) software. Through different trajectories, both optimization methods were tested on their execution time and path accuracy. The results demonstrated that the continuous method was quicker and more precise, whereas the discrete method gave more reliable solutions, causing the increase in the time to attain the necessary accuracy. The study demonstrates that the trade-off between accuracy and robustness, as well as the consequences of the findings, is a significant consideration in the selection of IK techniques in surgical robots.

Because of the unpredictable activity of solar energy sources, photovoltaic (PV) maximum power point tracking (MPPT) is essential to guarantee the continuous operation of electrical energy generation at optimal power levels. Several works have extensively examined the generation of the maximum power from the PV systems under normal and shading conditions. The fuzzy logic control (FLC) method is one of the effective MPPT techniques, but it needs to be adapted to work in partial shading conditions. The current paper presents the FLC-based on dynamic safety margin (DSM) as an MPPT technique for a PV system to overcome the limitations of FLC in shading conditions. The DSM is a performance index that measures the system state deviation from the normal situation. As a performance index, DSM is used to adapt the FLC controller output to rapidly reach the global maxima of the PV system. The ability of the proposed algorithm and its performance are evaluated using simulation and practical implementation results for single phase grid-connected PV system under normal and partial shading operating conditions.

Advanced control approaches are essential for industrial processes to enhance system performance and increase the production rate. Model Predictive Control (MPC) is considered as one of the promising advanced control algorithms. It is suitable for several industrial applications for its ability to handle system constraints. However, it is not widely implemented in the industrial field as most field engineers are not familiar with the advanced techniques conceptual structure, the relation between the parameter settings and control system actions. Conversely, the Proportional Integral Derivative (PID) controller is a common industrial controller known for its simplicity and robustness. Adapting the parameters of the PID considering system constraints is a challenging task. Both controllers, MPC and PID, merged in a hierarchical structure in this work to improve the industrial processes performance considering the operational constraints. The proposed control system is simulated and implemented on a three-tank benchmark system as a Multi-Input Multi-Output (MIMO) system. Since the main industrial goal of the proposed configuration is to be easily implemented using the available automation technology, PID controller is implemented in a PLC (Programable Logic Controller) controller as a lower controller level, while MPC controller and the adaptation mechanism are implemented within a SCADA (Supervisory Control And Data Acquisition) system as a higher controller level.

Polymer extrusion is a widely used industrial process that exists in almost all petrochemical industries as well as various other industrial applications. In order to achieve a high quality of end-product, it is crucial to precisely control the process conditions of the polymer throughout the extrusion process. In this paper, a brief description of the extrusion process and its main control loop are described. System identification of the gear melt pump is also illustrated which is considered as the most important equipment to be controlled within the extruder unit. In order to overcome the parameters and operating condition variations of the process a robust and adapted controller is required. Since the operators of the polymer extrusion process is familiar with classical PID controller. Therefore, the application of a Supervisory Model Predictive Controller (SMPC) is suggested and implemented to overcome the limitation of PID controller alone. SMPC is implemented under two cases; first when the traditional PID controller optimum gains are used to control the inner loop of the melt pump, and second when random PID controller gains are representing the change of process conditions. Finally, the results of both cases were illustrated, compared and discussed proving that the implementation of an SMPC indeed enhances the reliability of the process control algorithm.

Since the permeant magnet synchronous generator (PMSG) has many applications in particular safety-critical applications, enhancing PMSG availability has become essential. An effective tool for enhancing PMSG availability and reliability is continuous monitoring and diagnosis of the machine. Therefore, designing a robust fault diagnosis (FD) and fault tolerant system (FTS) of PMSG is essential for such applications. This paper describes an FD method that monitors online stator winding partial inter-turn faults in PMSGs. The fault appears in the direct and quadrature (dq)-frame equations of the machine. The extended Kalman filter (EKF) and unscented Kalman filter (UKF) were used to detect the percentage and the place of the fault. The proposed techniques have been simulated for different fault scenarios using Matlab^®/Simulink^®. The results of the EKF estimation responses simulation were validated with the practical implementation results of tests that were performed with a prototype PMSG used in the Arab Academy For Science and Technology (AAST) machine lab. The results showed impressive responses with different operating conditions when exposed to different fault states to prevent the development of complete failure.

Indoor air pollution has become a major concern of the general publics in recent years. Indoor exposure may likely cause more harmful health effects, as the indoor concentrations of many pollutants are often higher than those typically encountered outside. The factors affecting the indoor environment mainly include temperature, relative humidity, ventilation, particle pollutants, biological pollutants, and gaseous pollutants. Ventilation and source control are the two basic engineering tools for improving indoor air quality. Source control is the most effective strategy for maintaining clean indoor air, however, it is not always possible or practical. Ventilation, either natural or mechanical, is the second most effective approach to provide acceptable indoor air quality.Exhaust emissions in the closed vehicle garage onboard ships not sufficiently ventilated can cause damage on the human health and lead even to death and pollution. In this paper, an intelligent artificial neural network model is designed to simulate the carbon monoxide emissions to the permissible limit. The proposed model is based on data collected from real measurements at different load conditions and positions on a Roll-on/roll-off (RO-RO) vehicle garage onboard a ship vessel. The neural network model is designed, identified and verified with different ways at different loads and positions.