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.
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.
Many issues can degrade the electrical drive performance such as cross-coupling, time delay, external disturbances, and parameter variation. The Synchronous Reference Frame (SRF) PI Current Controller (CC) is the most popular control scheme for the motor drive current control due to its simplicity. However, the PI controller does not have an optimal dynamic response due to the reasonably low transient response of the integral parts. Furthermore, the tuning of the PI controller depends heavily on the machine's parameters. Recently, alternative control schemes such as Model Predictive Control (MPC) and Active Disturbance Rejection Control (ADRC) are studied due to their dynamic performance and disturbance rejection capability, respectively. This paper presents a comparative study between the conventional PI, ADRC, and MPC control schemes applied for Permanent Magnet Synchronous Motor (PMSM) taking into consideration the operational issues of electrical drives.
This paper investigates the detection of inter-turn phase faults in Permanent Magnet Synchronous Generators (PMSGs) using the Unscented Kalman Filter (UKF) compared to the Extended Kalman Filter (EKF). PMSGs are subject to several faults such as bearing, eccentricity, demagnetization, short circuit, and inter-turn faults. Accurate and early detection of the fault type is crucial for robust operation. Several techniques can detect these faults. UKF and EKF are presented here as one of the model-based fault diagnosis techniques. In the presented simulation, a comparison between the UKF and EKF estimation response of the fault has been shown. Both techniques have provided the ability to detect the inter-turn fault with the proposed PMSG fault model. However, the difference between the estimation response accuracy and speed plays an important role to decide the most effective technique.
Permanent Magnet Synchronous Motors (PMSMs) are now extensively used in many critical applications. There is an increasing need for the motor and control system to have fault tolerant capabilities. This paper presents a fault tolerant control strategy to operate the PMSM during inter-turn fault conditions. The proposed technique combines the Model Predictive Control (MPC) for the speed and current control loops, and an almost error-free Unscented Kalman Filter (UKF) to estimate the PMSM inter-turn fault ratio. The PMSM statespace model for healthy and faulty conditions will be presented. Also, the equations and the remedial action of the MPC and UKF are provided in detail. The proposed algorithm is applied to PMSM model as a case study with a range of simulation analysis and discussion of results.
Fault detection is critical for industrial applications to maintain a stable operation and to reduce maintenance costs. Many fault detection techniques have been introduced recently to cope with the increasing demand for more safe operations. One of the most promising fault detection algorithms is the Unscented Kalman Filter (UKF). UKF is a model-based algorithm that could be used to detect different fault types for a given system. On the other hand, the three-tank system is a well-known benchmark that simulates many industrial applications. The fault detection of the three-tank system is quite challenging as it is a Multi-Input Multi-Output (MIMO) nonlinear system. Therefore, UKF will be employed as a fault detection strategy for this system to detect sensor and actuator faults. The performance of the UKF will be investigated under different operating and fault conditions to show its merits for the given case study.
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 MATLABTM 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.
scite is a Brooklyn-based startup that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
334 Leonard St
Brooklyn, NY 11211
Copyright © 2023 scite Inc. All rights reserved.
Made with 💙 for researchers