Stability Analysis and Dynamic Behavior Improvement of a Stand-alone AC Microgrid (original) (raw)
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Stability analysis of an autonomous microgrid operation based on Particle Swarm Optimization
2012 IEEE International Conference on Power System Technology (POWERCON), 2012
This paper presents the stability analysis for an in verter based Distributed Generation (DG) unit in an autonomous microgrid operation. The small-signal model of the controlled Voltage Source Inverter (VSI) system is developed in order to investigate the dynamic stability for the given operating point and under the proposed power controller. This model includes all the details of the proposed controller, while no switching actions are considered. System oscillatory modes and the sensitivity to the control parameters are the main performance indices which are considered, particularly when the micro grid is islanded or under the load change condition. In this work, the proposed power controller is composed of an inner current control loop and an outer power control loop, both based on a synchronous reference frame and conventional PI regulators. These controllers also utilize the Particle Swarm Optimization (PSO) for real-time self-tuning in order to improve the quality of the power supply. The complete small-signal model is linearized and used to define the system state matrix which is employed for eigenvalue analysis. The results prove that the stability analysis is fairly accurate and the controller offers reliable system's operation.
Power quality improvement in autonomous microgrid operation using particle swarm optimization
2011 IEEE PES Innovative Smart Grid Technologies, 2011
This paper presents an optimal power control strategy for an autonomous microgrid operation based on a realtime self-tuning method. The purpose of this work is to improve the quality of power supply where Distributed Generation (DG) units are connected to the grid. Dynamic response and harmonics distortion are the two main performance parameters which are considered in this work, particularly when the microgrid is islanded. The controller scheme is composed of an inner current control loop and an outer power control loop based on a synchronous reference frame and the conventional PI regulators. Particle Swarm Optimization (PSO) is an intelligent searching algorithm that is applied for real-time self-tuning of the system. The results show that the proposed controller provides an excellent dynamic response with acceptable harmonics level.
International Journal of Engineering and Scientific Research, 2016
Among many problems in micro-grids is the voltage and frequency control in isolated mode especially during islanding process. Neural networks have been successfully used for character recognition, image compression, and stock market prediction, but there is no direct application related to controlling distributed generations of Micro-grid. For this reason, this work was decided on, with the aim of controlling diesel generator outputs. Firstly primary controlling quickly provides the required power of micro-grid in isolated mode. Then in next step controllable distributed generations play the role of secondary controllers. This work uses Artificial Neural Network (ANN) for considering of active and reactive power inter-effect onvoltage and frequency. It examines the neural network algorithm that can be utilized for alleviating voltage and frequency issues of Micro-grid. MATLAB and Particle Swarm Optimization (PSO) are used for training neural network and simulating the Micro-grid model respectively. The Feed forward Back-propagation algorithm is used in this study and the Microgrid consists of wind, solar, and diesel power generations, and battery energy storage system(BESS). Neural network indicates how much real and reactive power needed from each generator so as to improves the stability in the system..
International Journal of Electrical Power & Energy Systems, 2013
This paper presents an optimal power control strategy, for an inverter based Distributed Generation (DG) unit, in an autonomous microgrid operation based on real-time self-tuning method. This research seeks to improve the quality of power supplied by DG units connected to the grid. Voltage and frequency regulation, dynamic response, steady-state response, and harmonic distortion are the main performance parameters considered, particularly when the microgrid is islanded or under the load change condition. The controller scheme comprises an inner current control loop and an outer power control loop based on a synchronous reference frame and conventional PI regulators. The power controller is designed for voltage-frequency (Vf) power control mode. Particle Swarm Optimization (PSO) is an intelligent searching algorithm that is applied for real-time self-tuning of the power control parameters. In this paper, the proposed strategy is that when the microgrid is islanded or under load change condition, the DG unit adopts the Vf control mode in order to regulate the system voltage and frequency. The simulation results show that the proposed controller provided an excellent response to satisfy the power quality requirements and proved the validity of the proposed strategy.
European Transactions on Electrical Power, 2011
Microgrids are state-of-the-art power distribution networks consisting of multiple distributed generators (DGs) and sensitive power loads. The goal of microgrid operation is to provide reliable and high-quality electric power to loads regardless of abnormal cases such as faults or loss-of-mains (islanding). This paper presents power control methods to coordinate multiple microgrid generators for both grid-connected and autonomous modes. To maintain required control performance and power quality during operating condition changes, hard toil of fine-tuning control parameters is required. This paper proposes an effective control parameter-tuning method using the particle swarm optimization (PSO) algorithm and gain-scheduling method. System requirements such as power quality regulation and load following performance are reflected in the cost function. The optimization algorithm implementation with time-domain simulation model is also explained.
Mgbachi C.A.C. (Ph.D)
Among many problems in micro-grids is the voltage and frequency control in isolated mode especially during islanding process. Neural networks have been successfully used for character recognition, image compression, and stock market prediction, but there is no direct application related to controlling distributed generations of Micro-grid. For this reason, this work was decided on, with the aim of controlling diesel generator outputs. Firstly primary controlling quickly provides the required power of micro-grid in isolated mode. Then in next step controllable distributed generations play the role of secondary controllers. This work uses Artificial Neural Network (ANN) for considering of active and reactive power inter-effect on voltage and frequency. It examines the neural network algorithm that can be utilized for alleviating voltage and frequency issues of Micro-grid. MATLAB and Particle Swarm Optimization (PSO) are used for training neural network and simulating the Micro-grid model respectively. The Feed forward Back-propagation algorithm is used in this study and the Micro-grid consists of wind, solar, and diesel power generations, and battery energy storage system (BESS). Neural network indicates how much real and reactive power needed from each generator so as to improves the stability in the system..
IEEE Access, 2020
The increasing penetration of Microgrids (MGs) into existing power systems and ''plug and play'' capability of Distributed Generators (DGs) causes large overshoots and settling times along with various power quality issues such as voltage and frequency flickers, current harmonics and short current transients. In this context, over the past few years, considerable research has been undertaken to investigate and address the mentioned issues using different control schemes in conjunction with soft computational techniques. The recent trends and advancements in the field of Artificial Intelligence (AI) have led the development of Swarm Intelligence (SI) based optimized controllers for smooth Renewable Energy Sources (RES) penetration and optimal voltage, frequency, and power-sharing regulation. Moreover, the recent studies have proved that the SI-based controllers provide enhanced dynamic response, optimized power quality and improved the dynamic stability of the MG systems as compared to the conventional control methods. Their importance in modern AC MG architectures can be judged from the growing number of publications in the recent past. However, literature, pertaining to SI applications to AC MG, is scattered with no comprehensive review on this significant development. As such, this study provides an overview of 15 different SI optimization techniques as applied to AC MG controls from 43 research publications including a detailed review of one of the elementary and most widely used SI based metaheuristic optimization algorithms called Particle Swarm Optimization (PSO) algorithm. This comprehensive review provides a valuable one-stop source of knowledge for the researchers and experts working on SI controller's applications for AC MG dynamic response and power quality improvements.
A PSO Solution for Improved Voltage Stability of a Hybrid AC-DC Microgrid
ISGT2011-India, 2011
The stability of dc and ac bus voltage is of the most important issues in all microgrids including ac, dc or ac/dc hybrid microgrids. In this paper, a hybrid ac/dc microgrid is proposed to reduce processes of multiple reverse conversions in an ac or dc microgrid and to facilitate the connection of various renewable ac and dc sources and loads to power system. Also, all control schemes used among all converters will be developed in order to improve the voltage stability of hybrid microgrid. To give robustness to improved dynamic voltage stability of the microgrid, a voltage stabilizer is proposed and applied to the doubly fed induction generator (DFIG) installed in ac part. Furthermore, a particle swarm optimization (PSO) solution is proposed to optimize the various control gains among various converters in order to quickly restore and stabilize the voltage of both ac and dc parts under the different disturbances. The achieved results verify the controllers robustness and optimization algorithm efficiency.
PSO Algorithm for an Optimal Power Controller in a Microgrid
IOP Conference Series: Earth and Environmental Science
This paper presents the Particle Swarm Optimization (PSO) algorithm to improve the quality of the power supply in a microgrid. This algorithm is proposed for a real-time selftuning method that used in a power controller for an inverter based Distributed Generation (DG) unit. In such system, the voltage and frequency are the main control objectives, particularly when the microgrid is islanded or during load change. In this work, the PSO algorithm is implemented to find the optimal controller parameters to satisfy the control objectives. The results show high performance of the applied PSO algorithm of regulating the microgrid voltage and frequency.
2019
Distributed battery systems (DBS) are widely used in microgrids to compensate imbalanced active and reactive power flows, and for stabilizing bus voltages of microgrids with a high penetration of renewable energy sources (RES). In this paper, a particle-swarm-optimization (PSO) method is adopted as the secondary control of DBS in a standalone AC microgrid. The fitness function of the PSO algorithm contains the parameters of the bus voltage deviations and the power losses on the distribution lines. Through iteration, the bus voltage deviations and the power losses on the distribution lines are reduced, simultaneously. Importantly, the State-of-Charges (SOC) of the battery packs are also being taken into consideration and the battery packs are controlled by local controllers to prevent deep-discharge and overcharge. Both results from Matlab simulation and Real-Time Digital Simulator (RTDS) validate the effectiveness of the proposed control scheme in concurrently reducing the distribution power losses and meeting bus voltage regulations in AC microgrids.