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.

Lately, adequate protection strategies need to be developed when Microgrids (MGs) are connected to smart grids to prevent undesirable tripping. Conventional relay settings need to be adapted to changes in Distributed Generator (DG) penetrations or grid reconfigurations, which is a complicated task that can be solved efficiently using Artificial Intelligence (AI)-based protection. This paper compares and validates the difference between conventional protection (overcurrent and differential) strategies and a new strategy based on Artificial Neural Networks (ANNs), which have been shown as adequate protection, especially with reconfigurable smart grids. In addition, the limitations of the conventional protections are discussed. The AI protection is employed through the communication between all Protective Devices (PDs) in the grid, and a backup strategy that employs the communication among the PDs in the same line. This paper goes a step further to validate the protection strategies based on simulations using the MATLAB^TM platform and experimental results using a scaled grid. The AI-based protection method gave the best solution as it can be adapted for different grids with high accuracy and faster response than conventional protection, and without the need to change the protection settings. The scaled grid was designed for the smart grid to advocate the behavior of the protection strategies experimentally for both conventional and AI-based protections.

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.

Model Predictive Control (MPC) is one of the most suitable controllers for industrial applications, especially for constrained systems. However, it requires high computational burden, which is considered as the main drawback. Proportional Integral Derivative (PID) controller is the most widely used controller in particular for Single Input Single Output (SISO) system and for cascaded control loops, but it is difficult to be tuned especially for a constrained system. Therefore, a combination of PID and MPC is addressed. The basic concept of the proposed technique concentrates on the tuning of PID controller gains based on the MPC performance for the closed loop system considering constraints, which will be applied in a control system consisting of two hierarchical levels structure. The algorithm is applied to control the speed of Permanent Magnet Synchronous Motor (PMSM), which is considered as Multi Input Multi Output (MIMO) system.

This paper deeply investigates the torque ripple reduction of synchronous reluctance motors (SynRel), considering the impact of the current harmonic injection (CHI) on the total losses by employing d -axis, q -axis, and dq -axes injections. An in-depth analysis of the torque behavior has been carried out by means of finite element (FE) simulations, highlighting the dependence of the torque ripple on the magnetic saturation. The current reference trajectories are derived based on the torque ripple model taking into account the magnetic saturation, while the total loss is minimized by considering the copper and iron losses by the FE-based or experimental evaluation to select the optimal solution. The effectiveness of the torque ripple reduction technique using different CHIs is demonstrated by the FE simulations and experiments for SynRel 15kW prototype.