Projeto de controladores para sistemas chaveados com aplicações em conversores cc-cc

Pós-graduação em Engenharia Elétrica - FEIS

Demand-responsive decentralized urban traffic control : part 1, single-intersection policies /

"February 1982."

Magnetic control of industrial motors /

Part 1. Alternating-current control devices and assemblies.--Part 2. Alternating-current controllers.--Part 3. Direct-current controllers.

Control system components /

Mode of access: Internet.

Controladores repetitivos aplicados a filtros ativos de potência sob variações de carga e de frequência da rede

This work presents the development and modification of techniques to reduce the effects of load variation and mains frequency deviation in repetitive controllers applied to active power filters. To minimize the effects of aperiodic signals resulting from the connection or disconnection of non-linear loads is developed a technique which recognizes linear and nonlinear loads, and operates to reset the controller only when the error due to the transition of considerable value, and the transition is from non-linear to linear load. An algorithm to adapt the gain of the repetitive controller, based on a sigmoid function adaptation, in order to minimize the effects caused by random noise in the measurement system is also used. This work also analyzes the effects of frequency variation and presents the main methods to cope with this situation. Some solutions are the change in the number of samples per period and the variation of the sampling rate. The first has the advantage of using linear design techniques and results in a time invariant system. The second method changes the sampling frequency and leads to a time variant system that demands a difficult analysis of stability. The proposed algorithms were tested using the methods of truncation of the number of samples and the method of changing the sampling rate of the system to compensate possible frequency variations of the grid. Experimental results are presented to validate the proposal.

A New Control Architecture for Robust Controllers in Rear Electric Traction Passenger HEVs

It is well known that control systems are the core of electronic differential systems (EDSs) in electric vehicles (EVs)/hybrid HEVs (HEVs). However, conventional closed-loop control architectures do not completely match the needed ability to reject noises/disturbances, especially regarding the input acceleration signal incoming from the driver's commands, which makes the EDS (in this case) ineffective. Due to this, in this paper, a novel EDS control architecture is proposed to offer a new approach for the traction system that can be used with a great variety of controllers (e. g., classic, artificial intelligence (AI)-based, and modern/robust theory). In addition to this, a modified proportional-integral derivative (PID) controller, an AI-based neuro-fuzzy controller, and a robust optimal H-infinity controller were designed and evaluated to observe and evaluate the versatility of the novel architecture. Kinematic and dynamic models of the vehicle are briefly introduced. Then, simulated and experimental results were presented and discussed. A Hybrid Electric Vehicle in Low Scale (HELVIS)-Sim simulation environment was employed to the preliminary analysis of the proposed EDS architecture. Later, the EDS itself was embedded in a dSpace 1103 high-performance interface board so that real-time control of the rear wheels of the HELVIS platform was successfully achieved.

Controller structure and design of firing angle controllers for (unit connected) HVDC systems

This paper investigates the control of a HVDC link, fed from an AC source through a controlled rectifier and feeding an AC line through a controlled inverter. The overall objective is to maintain maximum possible link voltage at the inverter while regulating the link current. In this paper the practical feedback design issues are investigated with a view of obtaining simple, robust designs that are easy to evaluate for safety and operability. The investigations are applicable to back-to-back links used for frequency decoupling and to long DC lines. The design issues discussed include: (i) a review of overall system dynamics to establish the time scale of different feedback loops and to highlight feedback design issues; (ii) the concept of using the inverter firing angle control to regulate link current when the rectifier firing angle controller saturates; and (iii) the design issues for the individual controllers including robust design for varying line conditions and the trade-off between controller complexity and the reduction of nonlinearity and disturbance effects

A method of switch selection for an electric vehicle drive

The key to reducing cost of electric vehicles is integration. All too often systems such as the motor, motor controller, batteries and vehicle chassis/body are considered as separate problems. The truth is that a lot of trade-offs can be made between these systems, causing an overall improvement in many areas including total cost. Motor controller and battery cost have a relatively simple relationship; the less energy lost in the motor controller the less energy that has to be carried in the batteries, hence the lower the battery cost. A motor controller’s cost is primarily influenced by the cost of the switches. This paper will therefore present a method of assessing the optimal switch selection on the premise that the optimal switch is the one that produces the lowest system cost, where system cost is the cost of batteries + switches.

Controller synthesis of a bidirectional inductive power interface for electric vehicles

Bidirectional Inductive Power Transfer (IPT) systems are preferred for Vehicle-to-Grid (V2G) applications. Typically, bidirectional IPT systems consist of high order resonant networks, and therefore, the control of bidirectional IPT systems has always been a difficulty. To date several different controllers have been reported, but these have been designed using steady-state models, which invariably, are incapable of providing an accurate insight into the dynamic behaviour of the system A dynamic state-space model of a bidirectional IPT system has been reported. However, currently this model has not been used to optimise the design of controllers. Therefore, this paper proposes an optimised controller based on the dynamic model. To verify the operation of the proposed controller simulated results of the optimised controller and simulated results of another controller are compared. Results indicate that the proposed controller is capable of accurately and stably controlling the power flow in a bidirectional IPT system.

Fuzzy Expert Approach with Curtailed Controllers for Improved Reactive Power Dispatch

This paper addresses the problem of curtailing the number of control actions using fuzzy expert approach for voltage/reactive power dispatch. It presents an approach using fuzzy set theory for reactive power control with the purpose of improving the voltage profile of a power system. To minimize the voltage deviations from pre-desired values of all the load buses, using the sensitivities with respect to reactive power control variables form the basis of the proposed Fuzzy Logic Control (FLC). Control variables considered are switchable VAR compensators, On Load Tap Changing (OLTC) transformers and generator excitations. Voltage deviations and controlling variables are translated into fuzzy set notations to formulate the relation between voltage deviations and controlling ability of controlling devices. The developed fuzzy system is tested on a few simulated practical Indian power systems and modified IEEE-30 bus system. The performance of the fuzzy system is compared with conventional optimization technique and results obtained are encouraging. Results obtained for a modified IEEE-30 bus test system and a 205-node equivalent EHV system a part of Indian southern grid are presented for illustration purposes. The proposed fuzzy-expert technique is found suitable for on-line applications in energy control centre as the solution is obtained fast with significant speedups with few number of controllers.

Design, specification, and synthesis of aircraft electric power systems control logic

Cyber-physical systems integrate computation, networking, and physical processes. Substantial research challenges exist in the design and verification of such large-scale, distributed sensing, ac- tuation, and control systems. Rapidly improving technology and recent advances in control theory, networked systems, and computer science give us the opportunity to drastically improve our approach to integrated flow of information and cooperative behavior. Current systems rely on text-based spec- ifications and manual design. Using new technology advances, we can create easier, more efficient, and cheaper ways of developing these control systems. This thesis will focus on design considera- tions for system topologies, ways to formally and automatically specify requirements, and methods to synthesize reactive control protocols, all within the context of an aircraft electric power system as a representative application area.

This thesis consists of three complementary parts: synthesis, specification, and design. The first section focuses on the synthesis of central and distributed reactive controllers for an aircraft elec- tric power system. This approach incorporates methodologies from computer science and control. The resulting controllers are correct by construction with respect to system requirements, which are formulated using the specification language of linear temporal logic (LTL). The second section addresses how to formally specify requirements and introduces a domain-specific language for electric power systems. A software tool automatically converts high-level requirements into LTL and synthesizes a controller.

The final sections focus on design space exploration. A design methodology is proposed that uses mixed-integer linear programming to obtain candidate topologies, which are then used to synthesize controllers. The discrete-time control logic is then verified in real-time by two methods: hardware and simulation. Finally, the problem of partial observability and dynamic state estimation is ex- plored. Given a set placement of sensors on an electric power system, measurements from these sensors can be used in conjunction with control logic to infer the state of the system.

Energy Saving - Another Perspective for Parameter Optimization of P and PI Controllers

Modern control methods like optimal control and model predictive control (MPC) provide a framework for simultaneous regulation of the tracking performance and limiting the control energy, thus have been widely deployed in industrial applications. Yet, due to its simplicity and robustness, the conventional P (Proportional) and PI (Proportional–Integral) control are still the most common methods used in many engineering systems, such as electric power systems, automotive, and Heating, Ventilation and Air Conditioning (HVAC) for buildings, where energy efficiency and energy saving are the critical issues to be addressed. Yet, little has been done so far to explore the effect of its parameter tuning on both the system performance and control energy consumption, and how these two objectives are correlated within the P and PI control framework. In this paper, the P and PI controllers are designed with a simultaneous consideration of these two aspects. Two case studies are investigated in detail, including the control of Voltage Source Converters (VSCs) for transmitting offshore wind power to onshore AC grid through High Voltage DC links, and the control of HVAC systems. Results reveal that there exists a better trade-off between the tracking performance and the control energy through a proper choice of the P and PI controller parameters.

Adaptive intelligent energy management system of plug-in hybrid electric vehicle

Efficient energy management in hybrid vehicles is the key for reducing fuel consumption and emissions. To capitalize on the benefits of using PHEVs (Plug-in Hybrid Electric Vehicles), an intelligent energy management system is developed and evaluated in this paper. Models of vehicle engine, air conditioning, powertrain, and hybrid electric drive system are first developed. The effect of road parameters such as bend direction and road slope angle as well as environmental factors such as wind (direction and speed) and thermal conditions are also modeled. Due to the nonlinear and complex nature of the interactions between PHEV-Environment-Driver components, a soft computing based intelligent management system is developed using three fuzzy logic controllers. The crucial fuzzy engine controller within the intelligent energy management system is made adaptive by using a hybrid multi-layer adaptive neuro-fuzzy inference system with genetic algorithm optimization. For adaptive learning, a number of datasets were created for different road conditions and a hybrid learning algorithm based on the least squared error estimate using the gradient descent method was proposed. The proposed adaptive intelligent energy management system can learn while it is running and makes proper adjustments during its operation. It is shown that the proposed intelligent energy management system is improving the performance of other existing systems. © 2014 Elsevier Ltd.

Performance evaluation on electronic control systemsfor field sprayers using programmable logic controllers

The aim of this study was to develop a laboratory method for time response evaluation on electronically controlled spray equipment using Programmable Logic Controllers (PLCs). For that purpose, a PLC controlled digital drive inverter was set up to drive an asynchronous electric motor linked to a centrifugal pump on a experimental sprayer equipped with electronic flow control. The PLC was operated via RS232 serial communication from a PC computer. A user program was written to control de motor by adjusting the following system variables, all related to the motor speed: time stopped; ramp up and ramp down times, time running at a given constant speed and ramp down time to stop the motor. This set up was used in conjunction with a data acquisition system to perform laboratory tests with an electronically controlled sprayer. Time response for pressure stabilization was measured while changing the pump speed by +/-20%. The results showed that for a 0.2 s ramp time increasing the motor speed, as an example, an AgLogix Flow Control system (Midwest Technologies Inc.) took 22 s in average to readjust the pressure. When decreasing the motor speed, this time response was down to 8 s. General results also showed that this kind of methodology could make easier the definition of standards for tests on electronically controlled application equipment.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.