Model-based Fault-tolerant Control to Guarantee the Performance of a Hybrid Wind-Diesel Power System in a Microgrid Configuration

AbstractThis paper presents a comparison of two different adaptive control schemes for improving the performance of a hybrid wind-diesel power system in an islanded microgrid configuration against the baseline controller, IEEE type 1 automatic voltage regulator (AVR). The first scheme uses a model reference adaptive controller (MRAC) with a proportional-integral-derivative (PID) controller tuned by a genetic algorithm (GA) to control the speed of the diesel engine (DE) for regulating the frequency of the power system and uses a classical MRAC for controlling the voltage amplitude of the synchronous machine (SM). The second scheme uses a MRAC with a PID controller tuned by a GA to control the speed of the DE, and a MRAC with an artificial neural network (ANN) and a PID controller tuned by a GA for controlling the voltage amplitude of the SM. Different operating conditions of the microgrid and fault scenarios in the diesel engine generator (DEG) were tested: 1) decrease in the performance of the diesel engine actuator (40% and 80%), 2) sudden connection of 0.5 MW load, and 3) a 3-phase fault with duration of 0.5seconds. Dynamic models of the microgrid components are presented in detail and the proposed microgrid and its fault-tolerant control (FTC) are implemented and tested in the Simpower Systems of MATLAB/Simulink® simulation environment. The simulation results showed that the use of ANNs in combination with model-based adaptive controllers improves the FTC system performance in comparison with the baseline controller

Robust nonlinear trajectory controllers for a single-rotor UAV with particle swarm optimization tuning

This paper presents the utilization of robust nonlinear control schemes for a single-rotor unmanned aerial vehicle (SR-UAV) mathematical model. The nonlinear dynamics of the vehicle are modeled according to the translational and rotational motions. The general structure is based on a translation controller connected in cascade with a P-PI attitude controller. Three different control approaches (classical PID, Super Twisting, and Adaptive Sliding Mode) are compared for the translation control. The parameters of such controllers are hard to tune by using a trial-and-error procedure, so we use an automated tuning procedure based on the Particle Swarm Optimization (PSO) method. The controllers were simulated in scenarios with wind gust disturbances, and a performance comparison was made between the different controllers with and without optimized gains. The results show a significant improvement in the performance of the PSO-tuned controllers.Peer ReviewedPostprint (published version

Nonlinear Robust H-Infinity PID Controller for the Multivariable System Quadrotor

This paper presents the methodology of design of a nonlinear robust controller for attitude regulation and its implementation in an experimental platform of an unmanned aerial vehicle (UAV) quadrotor. Details on the kinematic and dynamic modeling based on the Euler-Lagrange formalism are provided, as well as the particulars of the design of a nonlinear robust H-infinity PID controller to regulate the rotational moments. The performance and effectiveness of the proposed controller are tested in a simulation and an experimental platform. The performance of the proposed controller is compared with a conventional PID controller by using the integral square error (ISE) as performance parameter. Experimental results help to demonstrate the correct operation of the system for real-time applications in the presence of unmodeled dynamics and the uncertainties of the parameters

A review of optimal control techniques applied to the energy management and control of microgrids

This paper presents a literature review on optimal control techniques for energy management and control of microgrids. A classification of references linked with the design and development of optimal energy management systems (EMS) is made, considering mainly the objective functions to be solved and also the optimization techniques used for solving optimal control problems (OCPs) related with reliable operations of microgrids. A hierarchical control architecture for the development of EMS is the most commonly found in literature, which implies the necessity of a telecommunications infrastructure to communicate a distributed control layer with an upper layer, where the optimization of the microgrid operation is done. Typically, this layer is developed at an entity called microgrid central controller (MGCC). A general architecture for optimal EMS is provided and analyzed in detail, as well as its future perspectives.Londo

Cognitive granular-based path planning and tracking for intelligent vehicle with multi-segment bezier curve stitching

Unmanned vehicles are currently facing many difficulties and challenges in improving safety performance when running in complex urban road traffic environments, such as low intelligence and poor comfort performance in the driving process. The real-time performance of vehicles and the comfort requirements of passengers in path planning and tracking control of unmanned vehicles have attracted more and more attentions. In this paper, in order to improve the real-time performance of the autonomous vehicle planning module and the comfort requirements of passengers that a local granular-based path planning method and tracking control based on multisegment Bezier curve splicing and model predictive control theory are proposed. Especially, the maximum trajectory curvature satisfying ride comfort is regarded as an important constraint condition, and the corresponding curvature threshold is utilized to calculate the control points of Bezier curve. By using low-order interpolation curve splicing, the planning computation is reduced, and the real-time performance of planning is improved, compared with one-segment curve fitting method. Furthermore, the comfort performance of the planned path is reflected intuitively by the curvature information of the path. Finally, the effectiveness of the proposed control method is verified by the co-simulation platform built by MATLAB/Simulink and Carsim. The simulation results show that the path tracking effect of multisegment Bezier curve fitting is better than that of high-order curve planning in terms of real-time performance and comfort

Design of model-based controllers applied to a solid-state low voltage dc breaker

This paper presents the methodology of design of model-based sliding mode control (SMC) algorithms applied to power electronic dc-dc converters, which are part of the components of a solid-state low voltage dc breaker (SLVDB). The power converters used in the tested schemes of the SLVDB are the boost and sepic dc-dc converters. Accurate disconnection times, user-configured, are achieved with the proposed controllers, as well as a complete minimization of the transient recovery voltage (TRV) in the breaker terminals. Details of the performance of two SLVDB configurations are analyzed and compared in order to establish the best design comprising complexity vs. performance. MATLAB simulations support the results and provide a reasonable picture of the operation of the SLVDB.Montevide

Adapted D∗Lite to Improve Guidance, Navigation and Control of a Tail-Actuated Underwater Vehicle in Unknown Environments

Biomimetic Autonomous Underwater Vehicles (BAUVs) navigate aquatic environments by mimicking natural propellants from fish species. These vehicles move part(s) of their bodies using various mechanisms to propel and swim forward or laterally. Their main goal is to follow and adjust defined paths to reach a target autonomously. Local path planning is of paramount importance during navigation tasks due to unexpected obstacles. Moreover, path planning strategies should consider the environment's information obtained by the vehicle during its mission, as well as its dynamics and mechanical limitations, to define new routes properly. This article presents the development of a waypoint generator based on the D*Lite algorithm. The proposed planner considers a frontal-short-sighted and tail-actuated BAUV with motion constraints to adjust the vehicle's path towards a target coordinate. By identifying obstacles, the planner adjusts and defines inner waypoints inside the vehicle's vision range by considering closeness to obstacles found and BAUV's current position. The developed strategy reduces collision risks due to the discrimination of nodes near obstacles, prioritizing broad hallways and safer swimming distances between the vehicle's current position and inner waypoints. The effectiveness of the proposed algorithm is simulated using the BAUV's hydrodynamics model and by adding a waypoint tracking controller to correct the vehicle's swimming performance inside three scenarios. The vehicle can reach the goal by properly defining inner waypoints while safely avoiding collisions, narrow hallways, and sharp turns

Adaptive quasi-sliding mode control based on a recursive weighted least square estimator for a DC motor

This paper presents the methodology of design of a discrete-time adaptive quasi-sliding mode controller (QSMC) based on a recursive weighted least square (RWLS) estimator for a dc motor. The proposed control scheme allows handling the classic problem of a QSMCs, which is the steady-state error due to the use of a saturation function instead of a switching function in the sliding mode control (SMC) algorithm. The use of linear and nonlinear signal references helps to show the closed-loop performance of the control system and its tracking capabilities. Experimental results show a better performance of the RWLS-QSMC algorithm applied on the speed control of a dc motor than a classic SMC.Buenos Aire

Optimal Energy Management for Stable Operation of an Islanded Microgrid

This paper presents a methodology on the design of an optimal predictive control scheme applied to an islanded microgrid. The controller manages the batteries energy and performs a centralized load shedding strategy to balance the load and generation within the microgrid, and to keep the stability of the voltage magnitude. A nonlinear model predictive control (NMPC) algorithm is used for processing a data set composed of the batteries state of charge, the distributed energy resources (DERs) active power generation, and the forecasted load. The NMPC identifies upcoming active power unbalances and initiates automated load shedding over noncritical loads. The control strategy is tested in a medium voltage distribution system with DERs. This control strategy is assisted by a distribution monitoring system, which performs real-time monitoring of the active power generated by the DERs and the current load demand at each node of the microgrid. Significant performance improvement is achieved with the use of this control strategy over tested cases without its use. The balance between the power generated by the DERs and the load demand is maintained, while the voltage magnitude is kept within the maximum variation margin of pm 5\% recommended by the standard ANSI C84.1-1989

Model-based fault-tolerant control to guarantee the performance of a hybrid wind-diesel power system in a microgrid configuration

This paper presents a comparison of two different adaptive control schemes for improving the performance of a hybrid wind-diesel power system in an islanded microgrid configuration against the baseline controller, IEEE type 1 automatic voltage regulator (AVR). The first scheme uses a model reference adaptive controller (MRAC) with a proportional-integral-derivative (PID) controller tuned by a genetic algorithm (GA) to control the speed of the diesel engine (DE) for regulating the frequency of the power system and uses a classical MRAC for controlling the voltage amplitude of the synchronous machine (SM). The second scheme uses a MRAC with a PID controller tuned by a GA to control the speed of the DE, and a MRAC with an artificial neural network (ANN) and a PID controller tuned by a GA for controlling the voltage amplitude of the SM. Different operating conditions of the microgrid and fault scenarios in the diesel engine generator (DEG) were tested: 1) decrease in the performance of the diesel engine actuator (40% and 80%), 2) sudden connection of 0.5 MW load, and 3) a 3-phase fault with duration of 0.5 seconds. Dynamic models of the microgrid components are presented in detail and the proposed microgrid and its faulttolerant control (FTC) are implemented and tested in the Simpower Systems of MATLAB/Simulink® simulation environment. The simulation results showed that the use of ANNs in combination with model-based adaptive controllers improves the FTC system performance in comparison with the baseline controller. © 2013 The Authors. Published by Elsevier B.V