Field-winding fault detection in synchronous machines with static excitation through frequency response analysis.

Frequency Response Analysis is a well-known technique for the diagnosis of power transformers. Currently, this technique is under research for its application in rotary electrical machines. This paper presents significant results on the application of Frequency Response Analysis to fault detection in field winding of synchronous machines with static excitation. First, the influence of the rotor position on the frequency response is evaluated. Secondly, some relevant test results are shown regarding ground fault and inter-turn fault detection in field windings at standstill condition. The influence of the fault resistance value is also taken into account. This paper also studies the applicability of Frequency Response Analysis in fault detection in field windings while rotating. This represents an important feature because some defects only appear with the machine rated speed. Several laboratory test results show the applicability of this fault detection technique in field windings at full speed with no excitation current.

New On-Line Excitation-System Ground Fault Location Method Tested in a 106 MVA Synchronous Generator

In this paper, a novel excitation-system ground-fault location method is described and tested in a 106 MVA synchronous machine. In this unit, numerous rotor ground-fault trips took place always about an hour after the synchronization to the network. However, when the field winding insulation was checked after the trips, there was no failure. The data indicated that the faults in the rotor were caused by centrifugal forces and temperature. Unexpectedly, by applying this new method, the failure was located in a cable between the excitation transformer and the automatic voltage regulator. In addition, several intentional ground faults were performed along the field winding with different fault resistance values, in order to test the accuracy of this method to locate defects in rotor windings of large generators. Therefore, this new on-line rotor ground-fault detection algorithm is tested in high-power synchronous generators with satisfactory results.

Plataforma de simulación en Matlab-Simulink de un accionamiento regulado para emular aerogeneradores

En este proyecto se desarrolla un modelo de simulación de un accionamiento controlado que emula el comportamiento de una turbina eólica, el cual se ha llevado a cabo a través del programa para simulación Matlab/Simulink. Su desarrollo se ha estructurado de la siguiente forma: Tras una breve introducción a la energía eólica y a las máquinas eléctricas objeto de estudio en este proyecto, se procede a la caracterización y representación de dichas maquinas dentro de la plataforma de simulación virtual Simulink. Posteriormente se explican posibles estrategias de control de la máquina de inducción, las cuales son aplicadas para la realización de un control de velocidad. Asimismo, se realiza un control vectorial de par de la máquina de inducción de modo que permita un seguimiento efectivo del par de referencia demandado por el usuario, ante distintas condiciones. Finalmente, se añade el modelo de turbina eólica de manera que, definiendo los valores de velocidad de viento, ángulo de paso y velocidad del eje, permite evaluar el par mecánico desarrollado por la turbina. Este modelo se valida comprobando su funcionamiento para diferentes puntos de operación ante diversas condiciones del par de carga. Las condiciones de carga se establecen acoplando al modelo de la turbina, un generador síncrono de imanes permanentes conectado a una carga resistiva. ! II! ABSTRACT In this project, the simulation model of a controlled drive that emulates the behaviour of a wind turbine is developed. It has been carried out through the platform for multidomian simulation called Matlab/Simulink. Its development has been structured as follows: After a brief introduction to the wind energy and the electrical machines studied in this project, these machines are characterized and represented into the virtual simulation platform, Simulink. Subsequently, the possible control strategies for the induction machine are explained and applied in order to carry out a speed control. Additionally, a torque vector control of the induction machine is performed, so as to enable an effective monitoring of the reference torque requested by the user, under different conditions. Finally, the wind turbine model is implemented so as to assess the turbine mechanical torque, after defining the wind speed, the pitch angle and the shaft speed values. This model is validated by testing its functionality for different operating points under various load torques. The load conditions are set up by attaching a permanent magnets synchronous machine, with a resistive load, to the turbine model.

Co-simulation Methodologies for Hybrid and Electric Vehicle Dynamics

In recent decades, full electric and hybrid electric vehicles have emerged as an alternative to conventional cars due to a range of factors, including environmental and economic aspects. These vehicles are the result of considerable efforts to seek ways of reducing the use of fossil fuel for vehicle propulsion. Sophisticated technologies such as hybrid and electric powertrains require careful study and optimization. Mathematical models play a key role at this point. Currently, many advanced mathematical analysis tools, as well as computer applications have been built for vehicle simulation purposes. Given the great interest of hybrid and electric powertrains, along with the increasing importance of reliable computer-based models, the author decided to integrate both aspects in the research purpose of this work. Furthermore, this is one of the first final degree projects held at the ETSII (Higher Technical School of Industrial Engineers) that covers the study of hybrid and electric propulsion systems. The present project is based on MBS3D 2.0, a specialized software for the dynamic simulation of multibody systems developed at the UPM Institute of Automobile Research (INSIA). Automobiles are a clear example of complex multibody systems, which are present in nearly every field of engineering. The work presented here benefits from the availability of MBS3D software. This program has proven to be a very efficient tool, with a highly developed underlying mathematical formulation. On this basis, the focus of this project is the extension of MBS3D features in order to be able to perform dynamic simulations of hybrid and electric vehicle models. This requires the joint simulation of the mechanical model of the vehicle, together with the model of the hybrid or electric powertrain. These sub-models belong to completely different physical domains. In fact the powertrain consists of energy storage systems, electrical machines and power electronics, connected to purely mechanical components (wheels, suspension, transmission, clutch…). The challenge today is to create a global vehicle model that is valid for computer simulation. Therefore, the main goal of this project is to apply co-simulation methodologies to a comprehensive model of an electric vehicle, where sub-models from different areas of engineering are coupled. The created electric vehicle (EV) model consists of a separately excited DC electric motor, a Li-ion battery pack, a DC/DC chopper converter and a multibody vehicle model. Co-simulation techniques allow car designers to simulate complex vehicle architectures and behaviors, which are usually difficult to implement in a real environment due to safety and/or economic reasons. In addition, multi-domain computational models help to detect the effects of different driving patterns and parameters and improve the models in a fast and effective way. Automotive designers can greatly benefit from a multidisciplinary approach of new hybrid and electric vehicles. In this case, the global electric vehicle model includes an electrical subsystem and a mechanical subsystem. The electrical subsystem consists of three basic components: electric motor, battery pack and power converter. A modular representation is used for building the dynamic model of the vehicle drivetrain. This means that every component of the drivetrain (submodule) is modeled separately and has its own general dynamic model, with clearly defined inputs and outputs. Then, all the particular submodules are assembled according to the drivetrain configuration and, in this way, the power flow across the components is completely determined. Dynamic models of electrical components are often based on equivalent circuits, where Kirchhoff’s voltage and current laws are applied to draw the algebraic and differential equations. Here, Randles circuit is used for dynamic modeling of the battery and the electric motor is modeled through the analysis of the equivalent circuit of a separately excited DC motor, where the power converter is included. The mechanical subsystem is defined by MBS3D equations. These equations consider the position, velocity and acceleration of all the bodies comprising the vehicle multibody system. MBS3D 2.0 is entirely written in MATLAB and the structure of the program has been thoroughly studied and understood by the author. MBS3D software is adapted according to the requirements of the applied co-simulation method. Some of the core functions are modified, such as integrator and graphics, and several auxiliary functions are added in order to compute the mathematical model of the electrical components. By coupling and co-simulating both subsystems, it is possible to evaluate the dynamic interaction among all the components of the drivetrain. ‘Tight-coupling’ method is used to cosimulate the sub-models. This approach integrates all subsystems simultaneously and the results of the integration are exchanged by function-call. This means that the integration is done jointly for the mechanical and the electrical subsystem, under a single integrator and then, the speed of integration is determined by the slower subsystem. Simulations are then used to show the performance of the developed EV model. However, this project focuses more on the validation of the computational and mathematical tool for electric and hybrid vehicle simulation. For this purpose, a detailed study and comparison of different integrators within the MATLAB environment is done. Consequently, the main efforts are directed towards the implementation of co-simulation techniques in MBS3D software. In this regard, it is not intended to create an extremely precise EV model in terms of real vehicle performance, although an acceptable level of accuracy is achieved. The gap between the EV model and the real system is filled, in a way, by introducing the gas and brake pedals input, which reflects the actual driver behavior. This input is included directly in the differential equations of the model, and determines the amount of current provided to the electric motor. For a separately excited DC motor, the rotor current is proportional to the traction torque delivered to the car wheels. Therefore, as it occurs in the case of real vehicle models, the propulsion torque in the mathematical model is controlled through acceleration and brake pedal commands. The designed transmission system also includes a reduction gear that adapts the torque coming for the motor drive and transfers it. The main contribution of this project is, therefore, the implementation of a new calculation path for the wheel torques, based on performance characteristics and outputs of the electric powertrain model. Originally, the wheel traction and braking torques were input to MBS3D through a vector directly computed by the user in a MATLAB script. Now, they are calculated as a function of the motor current which, in turn, depends on the current provided by the battery pack across the DC/DC chopper converter. The motor and battery currents and voltages are the solutions of the electrical ODE (Ordinary Differential Equation) system coupled to the multibody system. Simultaneously, the outputs of MBS3D model are the position, velocity and acceleration of the vehicle at all times. The motor shaft speed is computed from the output vehicle speed considering the wheel radius, the gear reduction ratio and the transmission efficiency. This motor shaft speed, somehow available from MBS3D model, is then introduced in the differential equations corresponding to the electrical subsystem. In this way, MBS3D and the electrical powertrain model are interconnected and both subsystems exchange values resulting as expected with tight-coupling approach.When programming mathematical models of complex systems, code optimization is a key step in the process. A way to improve the overall performance of the integration, making use of C/C++ as an alternative programming language, is described and implemented. Although this entails a higher computational burden, it leads to important advantages regarding cosimulation speed and stability. In order to do this, it is necessary to integrate MATLAB with another integrated development environment (IDE), where C/C++ code can be generated and executed. In this project, C/C++ files are programmed in Microsoft Visual Studio and the interface between both IDEs is created by building C/C++ MEX file functions. These programs contain functions or subroutines that can be dynamically linked and executed from MATLAB. This process achieves reductions in simulation time up to two orders of magnitude. The tests performed with different integrators, also reveal the stiff character of the differential equations corresponding to the electrical subsystem, and allow the improvement of the cosimulation process. When varying the parameters of the integration and/or the initial conditions of the problem, the solutions of the system of equations show better dynamic response and stability, depending on the integrator used. Several integrators, with variable and non-variable step-size, and for stiff and non-stiff problems are applied to the coupled ODE system. Then, the results are analyzed, compared and discussed. From all the above, the project can be divided into four main parts: 1. Creation of the equation-based electric vehicle model; 2. Programming, simulation and adjustment of the electric vehicle model; 3. Application of co-simulation methodologies to MBS3D and the electric powertrain subsystem; and 4. Code optimization and study of different integrators. Additionally, in order to deeply understand the context of the project, the first chapters include an introduction to basic vehicle dynamics, current classification of hybrid and electric vehicles and an explanation of the involved technologies such as brake energy regeneration, electric and non-electric propulsion systems for EVs and HEVs (hybrid electric vehicles) and their control strategies. Later, the problem of dynamic modeling of hybrid and electric vehicles is discussed. The integrated development environment and the simulation tool are also briefly described. The core chapters include an explanation of the major co-simulation methodologies and how they have been programmed and applied to the electric powertrain model together with the multibody system dynamic model. Finally, the last chapters summarize the main results and conclusions of the project and propose further research topics. In conclusion, co-simulation methodologies are applicable within the integrated development environments MATLAB and Visual Studio, and the simulation tool MBS3D 2.0, where equation-based models of multidisciplinary subsystems, consisting of mechanical and electrical components, are coupled and integrated in a very efficient way.