AC voltage regulation of a bidirectional high-frequency link converter using a deadbeat controller

This paper presents a digital controller for AC voltage regulation of a bidirectional high-frequency link (BHFL) inverter using Deadbeat control. The proposed controller consists of inner current loop, outer voltage loop and a feed-forward controller, which imposes a gain scheduling effect according to the reference signal to compensate the steady-state error of the system. The main property of the proposed controller is that the current- and the voltage-loop controllers have the same structure, and use the same sampling period. This simplifies the design and implementation processes. To improve the overall performance of the system, additional disturbance decoupling networks are employed. This takes into account the model discretization effect. Therefore, accurate disturbance decoupling can be achieved, and the system robustness towards load variations is increased. To avoid transformer saturation due to low frequency voltage envelopes, an equalized pulse width modulation (PWM) technique has been introduced. The proposed controller has been realized using the DS1104 digital signal processor (DSP) from dSPACE. Its performances have been tested on a one kVA prototype inverter. Experimental results showed that the proposed controller has very fast dynamic and good steady-state responses even under highly nonlinear loads

Performance Evaluation of Fuel Cell and Microturbine as Distributed Generators in a Microgrid

This paper presents dynamic models of distributed generators (DG) and investigates dynamic behaviour of the DG units within a microgrid system. The DG units include micro turbine, fuel cell and the electronically interfaced sources. The voltage source converter is adopted as the electronic interface which is equipped with its controller to maintain stability of the microgrid during small signal dynamics. This paper also introduces power management strategies and implements the DG load sharing concept to maintain the microgrid operation in standalone, grid-connected and islanding modes of operation. The results demonstrate the operation and performance of the microturbine and SOFC as distributed generators in a microgrid. Keywords: Microgrid, Distributed Generation, Microturbine, Fuel Cel

Smart control architecture for microgrid application

This research proposes non-linear control architectures dedicated towards improving transient response, reliability and computational burden for grid connected inverters applicable for ac micro-grids. Also this work proposes an optimization procedure applied to a small microgrid to reduce the billing cost for power incorporating battery degradation mechanism. Three works are discussed in this research that discusses methodologies to improve the operation of a three phase grid connected inverters. The first work discusses a globally stable estimation architecture for estimating the plant parameters for a grid connected inverter during its operation. Then a Lyapunov based control architecture is utilized and online parameter update scheme is used to optimize the controller performance. The second work discusses a Lyapunov based control architecture during a contingency that the grid voltage sensor fails. In this work an internal model based grid voltage estimation architecture has been proposed which successfully estimates the grid voltage and controls the grid current. The last work shows a methodology to optimally utilize a battery in a microgrid based on Markov Decision Process. Dynamic algorithm is used to solve the problem so that the cost is minimized at the end of the day. Furthermore, in this research detailed stability analysis of the first two works along with the controller design has been presented. Also in this work, battery degradation is modelled empirically and the overall cost function is obtained for the optimization of billing cost for a small microgrid. Detailed plant modeling, controller design, simulation and experimental results are presented for all of the proposed schemes --Abstract, page iv

Inverter PQ Control With Trajectory Tracking Capability For Microgrids Based On Physics-informed Reinforcement Learning

The increasing penetration of inverter-based resources (IBRs) calls for an advanced active and reactive power (PQ) control strategy in microgrids. To enhance the controllability and flexibility of the IBRs, this paper proposed an adaptive PQ control method with trajectory tracking capability, combining model-based analysis, physics-informed reinforcement learning (RL), and power hardware-in-the-loop (HIL) experiments. First, model-based analysis proves that there exists an adaptive proportional-integral controller with time-varying gains that can ensure any exponential PQ output trajectory of IBRs. These gains consist of a constant factor and an exponentially decaying factor, which are then obtained using a model-free deep reinforcement learning approach known as the twin delayed deeper deterministic policy gradient. With the model-based derivation, the learning space of the RL agent is narrowed down from a function space to a real space, which reduces the training complexity significantly. Finally, the proposed method is verified through numerical simulation in MATLAB-Simulink and power HIL experiments in the CURENT center.With the physics-informed learning method, exponential response time constants can be freely assigned to IBRs, and they can follow any predefined trajectory without complicated gain tuning

Management of Islanded Operation of Microgirds

Distributed generations with continuously growing penetration levels offer potential solutions to energy security and reliability with minimum environmental impacts. Distributed Generations when connected to the area electric power systems provide numerous advantages. However, grid integration of distributed generations presents several technical challenges which has forced the systems planners and operators to account for the repercussions on the distribution feeders which are no longer passive in the presence of distributed generations. Grid integration of distributed generations requires accurate and reliable islanding detection methodology for secure system operation. Two distributed generation islanding detection methodologies are proposed in this dissertation. First, a passive islanding detection technique for grid-connected distributed generations based on parallel decision trees is proposed. The proposed approach relies on capturing the underlying signature of a wide variety of system events on a set of critical system parameters and utilizes multiple optimal decision tress in a parallel network for classification of system events. Second, a hybrid islanding detection method for grid-connected inverter based distributed generations combining decision trees and Sandia frequency shift method is also proposed. The proposed method combines passive and active islanding detection techniques to aggregate their individual advantages and reduce or eliminate their drawbacks. In smart grid paradigm, microgrids are the enabling engine for systematic integration of distributed generations with the utility grid. A systematic approach for controlled islanding of grid-connected microgrids is also proposed in this dissertation. The objective of the proposed approach is to develop an adaptive controlled islanding methodology to be implemented as a preventive control component in emergency control strategy for microgrid operations. An emergency power management strategy for microgrid autonomous operation subsequent to inadvertent islanding events is also proposed in this dissertation. The proposed approach integrates microgrid resources such as energy storage systems, demand response resources, and controllable micro-sources to layout a comprehensive power management strategy for ensuring secure and stable microgrid operation following an unplanned islanding event. In this dissertation, various case studies are presented to validate the proposed methods. The simulation results demonstrate the effectiveness of the proposed methodologies

Microgrids/Nanogrids Implementation, Planning, and Operation

Today’s power system is facing the challenges of increasing global demand for electricity, high-reliability requirements, the need for clean energy and environmental protection, and planning restrictions. To move towards a green and smart electric power system, centralized generation facilities are being transformed into smaller and more distributed ones. As a result, the microgrid concept is emerging, where a microgrid can operate as a single controllable system and can be viewed as a group of distributed energy loads and resources, which can include many renewable energy sources and energy storage systems. The energy management of a large number of distributed energy resources is required for the reliable operation of the microgrid. Microgrids and nanogrids can allow for better integration of distributed energy storage capacity and renewable energy sources into the power grid, therefore increasing its efficiency and resilience to natural and technical disruptive events. Microgrid networking with optimal energy management will lead to a sort of smart grid with numerous benefits such as reduced cost and enhanced reliability and resiliency. They include small-scale renewable energy harvesters and fixed energy storage units typically installed in commercial and residential buildings. In this challenging context, the objective of this book is to address and disseminate state-of-the-art research and development results on the implementation, planning, and operation of microgrids/nanogrids, where energy management is one of the core issues