Reliability evaluation of distribution systems containing renewable distributed generations

Reliability evaluation of distribution networks, including islanded microgrid cases, is presented. The Monte Carlo simulation algorithm is applied to a test network. The network includes three types of distributed energy resources solar photovoltaic (PV), wind turbine (WT) and gas turbine (GT). These distributed generators contribute to supply part of the load during grid-connected mode, but supply the entire load during islanded microgrid operation. PV and WT stochastic models have been used to simulate the randomness of these resources. This study shows that the implementation of distributed generations can improve the reliability of the distribution networks

Coot bird algorithms based tuning PI controller for optimal microgrid autonomous operation

This paper develops a novel methodology for optimal control of islanded microgrids (MGs) based on the coot bird metaheuristic optimizer (CBMO). To this end, the optimum gains for the PI controller are found using the CBMO under a multi-objective optimization framework. The Response Surface Methodology (RSM) is incorporated into the developed procedure to achieve a compromise solution among the different objectives. To prove the effectiveness of the new proposal, a benchmark MG is tested under various scenarios, 1) isolate the system from the grid (autonomous mode), 2) islanded system exposure to load changes, and 3) islanded system exposure to a 3 phase fault. Extensive simulations are performed to validate the new method taking conventional data from PSCAD/EMTDC software. The validity of the suggested optimizer is proved by comparing its results with that achieved using the LMSRE-based adaptive control, sunflower optimization algorithm (SFO), Ziegler-Nichols method and the particle swarm optimization (PSO) techniques. The article shows the superiority of the suggested CBMO over the LMSRE-based adaptive control, SFO, Ziegler-Nichols and the PSO techniques in the transient responses of the system.The Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia.https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639Electrical, Electronic and Computer Engineerin

Enhancing the Heat Transfer Due to Hybrid Nanofluid Flow Induced by a Porous Rotary Disk with Hall and Heat Generation Effects

A study of hybrid-nanofluid flow induced by the uniform rotation of a circular porous disk is presented for the purpose of facilitating the heat transfer rate. The Hall and Ohmic heating effects resulting from an applied magnetic field and the source of heat generation/absorption are also considered to see their impact on flow behavior and enhancing the heat transfer rate. The physical problem under the given configuration is reduced to a set of nonlinear partial differential equations using the conservation laws. Similarity transformations are adopted to obtain a system of ordinary differential equations which are further solved using the Shooting Method. Results are presented via graphs and tables thereby analyzing the heat transfer mechanism against different variations of physical parameters. Outcomes indicate that the wall suction plays a vital role in determining the behavior of different parameters on the velocity components. It is notable that the wall suction results in a considerable reduction in all the velocity components. The enhanced Hartman number yields a growth in the radial velocity and a decay in the axial velocity. Moreover, consequences of all parametric effects on the temperature largely depend upon the heat generation/absorption

Enhancing the Heat Transfer Due to Hybrid Nanofluid Flow Induced by a Porous Rotary Disk with Hall and Heat Generation Effects

A study of hybrid-nanofluid flow induced by the uniform rotation of a circular porous disk is presented for the purpose of facilitating the heat transfer rate. The Hall and Ohmic heating effects resulting from an applied magnetic field and the source of heat generation/absorption are also considered to see their impact on flow behavior and enhancing the heat transfer rate. The physical problem under the given configuration is reduced to a set of nonlinear partial differential equations using the conservation laws. Similarity transformations are adopted to obtain a system of ordinary differential equations which are further solved using the Shooting Method. Results are presented via graphs and tables thereby analyzing the heat transfer mechanism against different variations of physical parameters. Outcomes indicate that the wall suction plays a vital role in determining the behavior of different parameters on the velocity components. It is notable that the wall suction results in a considerable reduction in all the velocity components. The enhanced Hartman number yields a growth in the radial velocity and a decay in the axial velocity. Moreover, consequences of all parametric effects on the temperature largely depend upon the heat generation/absorption

Power Ripple Control Method for Modular Multilevel Converter under Grid Imbalances

Modular multilevel converters (MMCs) are primarily adopted for high-voltage applications, and are highly desired to be operated even under fault conditions. Researchers focused on improving current controllers to reduce the adverse effects of faults. Vector control in the DQ reference domain is generally adopted to control the MMC applications. Under unstable grid conditions, it is challenging to control double-line frequency oscillations in the DQ reference frame. Therefore, active power fluctuations are observed in the active power due to the uncontrolled AC component’s double line frequency component. This paper proposes removing the active power’s double-line frequency under unbalanced grid conditions during DQ transformation. Feedforward and feedback control methods are proposed to eliminate ripple in active power under fault conditions. An extraction method for AC components is also proposed for the power ripple control to eliminate the phase error occurring with the conventional high-pass filters. The system’s stability with the proposed controller is tested and compared with a traditional MMC controller using the Nyquist stability criterion. A real-time digital simulator (RTDS) and Xilinx Virtex 7-based FPGA were used to verify the proposed control methods under single-line-to-ground (SLG) faults

Power Ripple Control Method for Modular Multilevel Converter under Grid Imbalances

Modular multilevel converters (MMCs) are primarily adopted for high-voltage applications, and are highly desired to be operated even under fault conditions. Researchers focused on improving current controllers to reduce the adverse effects of faults. Vector control in the DQ reference domain is generally adopted to control the MMC applications. Under unstable grid conditions, it is challenging to control double-line frequency oscillations in the DQ reference frame. Therefore, active power fluctuations are observed in the active power due to the uncontrolled AC component’s double line frequency component. This paper proposes removing the active power’s double-line frequency under unbalanced grid conditions during DQ transformation. Feedforward and feedback control methods are proposed to eliminate ripple in active power under fault conditions. An extraction method for AC components is also proposed for the power ripple control to eliminate the phase error occurring with the conventional high-pass filters. The system’s stability with the proposed controller is tested and compared with a traditional MMC controller using the Nyquist stability criterion. A real-time digital simulator (RTDS) and Xilinx Virtex 7-based FPGA were used to verify the proposed control methods under single-line-to-ground (SLG) faults

Robust Tracking Control of Piezo-Actuated Nanopositioning Stage Using Improved Inverse LSSVM Hysteresis Model and RST Controller

Nanopositioning technology is widely used in high-resolution applications. It often uses piezoelectric actuators due to their superior characteristics. However, piezoelectric actuators exhibit a hysteresis phenomenon that limits their positioning accuracy. To compensate for the hysteresis effect, developing an accurate hysteresis model of piezoelectric actuators is very important. This task is challenging, requiring some considerations of the multivalued mapping of hysteresis loops and the generalization capabilities of the model. This challenge can be dealt with by developing a machine learning-based model, whose inverse model can be used to efficiently design an accurate feedforward controller for hysteresis compensation. However, this approach depends on model accuracy and the type of data used to train the model. Thus, accurate prediction of the hysteresis behavior may not be guaranteed in the presence of disturbances. In this paper, a machine learning-based model is used to design a hysteresis compensator and then combined with a robust feedback controller to enhance the robustness of a nanopositioning control system. The proposed model is based on hysteresis operators, the least square support vector machine (LSSVM) method, and particle swarm optimization (PSO) algorithm. The inverse model is used to design the feedforward controller, and the RST controller is employed to develop feedback control. Our main contribution is the introduction of a hybrid controller capable of compensating for the hysteresis effect, and at the same time, eliminating remaining modeling errors and rejecting disturbances. The performance of the proposed approach is evaluated through MATLAB simulation, as well as through real-time experiments. The experimental results of our approach demonstrate superior tracking performance compared with the PID-LSSVM controller

Application of Least-Squares Support-Vector Machine Based on Hysteresis Operators and Particle Swarm Optimization for Modeling and Control of Hysteresis in Piezoelectric Actuators

Nanopositioning systems driven by piezoelectric actuators are widely used in different fields. However, the hysteresis phenomenon is a major factor in reducing the positioning accuracy of piezoelectric actuators. This effect makes the task of accurate modeling and position control of piezoelectric actuators challenging. In this paper, the learning and generalization capabilities of the model are efficiently enhanced to describe and compensate for the rate-independent and rate-dependent hysteresis using a kernel-based learning method. The proposed model is inspired by the classical Preisach hysteresis model, in which a set of hysteresis operators is used to address the problem of mapping, and then least-squares support-vector machines (LSSVM) combined with a particle swarm optimization (PSO) algorithm are used for identification. Two control schemes are proposed for hysteresis compensation, and their performance is evaluated through real-time experiments on a nanopositioning platform. First, an inverse model-based feedforward controller is designed based on the LSSVM model, and then a combined feedback/feedforward control scheme is designed using a classical control strategy (PID) to further enhance the tracking performance. For performance evaluation, different datasets with a variety of hysteresis loops are used during the simulation and experimental procedures. The results show that the proposed method is successful in enhancing the generalization capabilities of LSSVM training and achieving the best tracking performance based on the combination of feedforward control and PID feedback control. The proposed control scheme outperformed the inverse Preisach model-based control scheme in terms of both positioning accuracy and execution time. The control scheme that uses the LSSVM based on nonlinear autoregressive exogenous (NARX) models has significantly less computational complexity compared to our control scheme but at the expense of accuracy

Robust Tracking Control of Piezo-Actuated Nanopositioning Stage Using Improved Inverse LSSVM Hysteresis Model and RST Controller

Nanopositioning technology is widely used in high-resolution applications. It often uses piezoelectric actuators due to their superior characteristics. However, piezoelectric actuators exhibit a hysteresis phenomenon that limits their positioning accuracy. To compensate for the hysteresis effect, developing an accurate hysteresis model of piezoelectric actuators is very important. This task is challenging, requiring some considerations of the multivalued mapping of hysteresis loops and the generalization capabilities of the model. This challenge can be dealt with by developing a machine learning-based model, whose inverse model can be used to efficiently design an accurate feedforward controller for hysteresis compensation. However, this approach depends on model accuracy and the type of data used to train the model. Thus, accurate prediction of the hysteresis behavior may not be guaranteed in the presence of disturbances. In this paper, a machine learning-based model is used to design a hysteresis compensator and then combined with a robust feedback controller to enhance the robustness of a nanopositioning control system. The proposed model is based on hysteresis operators, the least square support vector machine (LSSVM) method, and particle swarm optimization (PSO) algorithm. The inverse model is used to design the feedforward controller, and the RST controller is employed to develop feedback control. Our main contribution is the introduction of a hybrid controller capable of compensating for the hysteresis effect, and at the same time, eliminating remaining modeling errors and rejecting disturbances. The performance of the proposed approach is evaluated through MATLAB simulation, as well as through real-time experiments. The experimental results of our approach demonstrate superior tracking performance compared with the PID-LSSVM controller

Hybrid cascaded MLI development for PV‐grid connection applications

Abstract For grid‐connected solar photovoltaic (PV) applications, a hybrid cascaded MLI (H‐CMLI) topology based on cascaded H‐bridge MLI is proposed in this study. The proposed configuration is a hybrid of two common MLIs: Cascaded H‐bridge and three‐phase cascaded voltage source inverter (VSI). For the same voltage level, the proposed topology enhances the number of the generated voltage levels while employing fewer components, relative to other traditional MLI topologies. Furthermore, the voltage stresses on switches in the proposed topology are decreased, compared to when each part operates independently. The proposed topology could be utilized for many applications, such as PV‐grid connection, active power filters, and STATCOM. To verify the effectiveness of the proposed topology, the model is developed in MATLAB/Simulink environment, in which a closed‐loop control scheme based on the traditional one is used to achieve the PV‐grid connection. Alongside the simulation results, the proposed topology and its control scheme are experimentally implemented in the laboratory. Two cases were experimentally applied to validate the successful performance of the proposed H‐CMLI: Open‐loop control and closed‐loop control for PV‐grid integration. The proposed control scheme effectively maintains the DC link voltage at its reference voltages. Also, the closed‐loop control scheme is capable of providing the power factor at unity and maintaining the total harmonic distortion (THD) of grid current within the acceptable limit. To demonstrate the proposed topology's functionality and viability, experimental results are provided