Laboratório de Sistemas Eléctricos Industriais 
Papers in International Journals with Referee or Books

Communications in scientific meetings with Referee

Technical Reports

Automatic Language Control of Electrical Drives. Background and Applications 
A new formal language concept for automatic control of electrical drives is presented. The proliferation of these systems promises improved efficiency and reliability, reduced maintenance and operative costs, and a more environment friendly operation. However, they exhibit considerable complexities related with their behaviour such as large uncertainties at structural and parameter levels, multidimensionality, and strong mutual interactions. The integration of these systems with information processing techniques allows a better adaptative modelling of their behaviour. The electrical drive system is assumed as a linguistic source producing a certain language. A developed grammatical inference algorithm is used in order to extract the productions that govern a grammar representing that language. The formalism of the developed formal language based control algorithm is presented and experimental results are discussed. 
Supervision Language Control of Electromechanical Drives 
The aim of this paper is to introduce a formal language based approach to the automatic supervision control of electromechanical drives. Modern applications often involve electromechanical drives with high dynamical complexity. The proliferation of these systems promises improved efficiency and reliability, reduced maintenance and operative costs, and a more environment friendly operation. However, they exhibit considerable complexities related with their behavior such as large uncertainties at a structural and parameter levels, multidimensionality, and strong mutual interactions. The integration of these systems with information processing techniques allows a better adaptative modeling of their behavior and thus better supervision concepts. Formal language techniques have been used in the past to study autonomous dynamical systems. Assuming the drive system as a linguistic source, producing a certain language, the modeling framework is based on a formulation in terms of contextdependent grammars, distinguishing between information generated by the system and by the input control. Making use of the formal language descriptive characteristics the grammatical supervision algorithm governs the overall behavior of the drive. The formalism of the formal language based supervision control algorithm is presented and experimental results are discussed. 
SVM Voltage Control implementation using lowcost reconfigurable FPGA system 
This paper describes a full VHDL implementation of a voltage SpaceVector Modulation in a FPGA reconfigurable system. A VHDL code was specially developed and optimized based on a finite state machine, which has been proven a more flexible and integrated environment for performing the SVM. A FPGA XC4005XL from Xilinx is used to allow a high economical solution. MATLAB functions automatically optimize and generate a configuration bitstream for the FPGA. Experimental results confirm the validity of the proposed methodology. 
Design and Implementation of a Laboratory Pseudo‑Master Oscilloscope Network 
This paper presents the design, development and implementation of a laboratory pseudomaster oscilloscope network. The purpose of the network is to centralize the data from a series of several laboratory working benches into only one regular PC. The decision to store the experimental data is taken in each working bench, running the oscilloscopes as pseudomaster units. In order to cover large interbenches distances the RS485 protocol was adopted to establish the network over several oscilloscopes. A management software was developed to manage the data flow through all the network, the storage, in different folders, and visualization of the data obtain from the oscilloscopes. The stored data can be sent directly to a printer or embedded in other software applications. The potential of the developed system is shown, namely regarding to flexibility, cost and performance, in the scope of laboratory working quality. 
Formal Language Modelling of a Switched Relutance Machine 
The Switched Reluctance Machine is being widely spread among industrial and domestic applications, replacing other electrical machines. However its modelling and control present important difficulties, due to complex system nonlinearities. In this paper it is shown how the use of formal language technique can help in the modelling of a Switched Reluctance Machine, overcoming these difficulties. Experimental tests with a lab prototype are performed in order to obtain a realistic and automatic machine model. 
A Network Distribution Power System Fault Location Based on Neural Eigenvalue Algorithm 
A new approach to fault location for distribution network power system is presented. This approach uses the Eigenvalue and an artificial neural network based learning algorithm. The neural network is trained to map the nonlinear relationship existing between fault location and characteristic Eigenvalue. The proposed approach is able to identify, to classify and to locate different types of faults such as: singlelinetoground, doublelinetoground, doubleline and threephase. Using the Eigenvalue as neural network inputs the proposed algorithm is able to locate the fault distance. The results presented show the effectiveness of the proposed algorithm for correct fault diagnosis and fault location on a distribution power system networks. 
A neural space vector fault location for parallel doublecircuit distribution lines 
A new approach to fault location for parallel doublecircuit distribution power lines is presented. This approach uses the Clarke–Concordia transformation and an artificial neural network based learning algorithm. The α, β, 0 components of double line currents resulting from the Clarke–Concordia transformation are used to characterize different states of the system. The neural network is trained to map the nonlinear relationship existing between fault location and characteristic eigenvalue. The proposed approach is able to identify and to locate different types of faults such as: phasetoearth, phasetophase, twophasetoearth and threephase. Using the eigenvalue as neural network inputs the proposed algorithm locates the fault distance. Results are presented which shows the effectiveness of the proposed algorithm for a correct fault location on a parallel doublecircuit distribution line. 
Implementation of an OnLine Learning Speed Controller for a Switched Reluctance Machine 
In the classical speed control of electrical machines it is usual to have linear PID controllers. This kind of controllers can present good results near a given operating point, where the machine may be considered as a linear system. However, the classical PID controller may have some limitations if it is planed to be used in a wide working region. In this case a good alternative can be the learning controllers using fuzzy logic and neural networks.In this paper it is presented and mainly discussed a neurofuzzy learning controller for speed regulation of an 8/6 Switched Reluctance Machine. Experimental results are presented and discussed showing how the controller can learn in real time the “good” rules. The results obtained with a classical PID controller are shown as a reference speed controller for the analysis. 
Performance of a Four Phase Switched Reluctance Motor Speed Control Based On an Adaptive Fuzzy System: Experimental Tests, Analysis and Conclusions 
In this paper, the controller’s tuning and performance of a speed controller prototype for switched reluctance motor is presented by a number of significative experimental tests. The system uses an online learning mechanism to acquire and modify, if needed, the “good” fuzzy control rules. Experimental essays are analysed and discussed to reveal some advantages of having a learning speed controller to the SR machine, and also the drawbacks that using these controllers can introduce to the drive system and possible ways to overcome them. 
Metodologia de Parametrização de um Controlador NeuroFuzzy de Velocidade para uma Máquina de Relutância Variável  In the classical speed control of electrical machines it is usual to have linear PID controllers. This kind of controllers can present good results near a given operating point, where the machine can be considered as a linear system. However, the classical PID controller has some limitations if it is planned to be used in a wide working region of electrical machines. In this case a good alternative can be the adaptive controllers using fuzzy logic and neural networks. One problem is tunning all parameters of the controller. This work presents the applied method and experimental results. 
Implementation of a NeuroFuzzy Speed Controller for a Switched Reluctance Machine  In the classical speed control of electrical machines it is usual to have linear PID controllers. This kind of controllers can present good results near a given operating point, where the machine can be considered as a linear system. However, the classical PID controller has some limitations if it is planed to be used in a wide working region of electrical machines. In this case a good alternative can be the “intelligent” controllers using fuzzy logic and neural networks. 
Obtaining the Magnetic Characteristics of an 8/6Switched Reluctance Machine: FEM Analysis and Experimental Tests 
This paper describes the stepbystep procedure for modeling an 8/6 switched reluctance machine. Starting from establishing the geometry of the machine, a nite element modeling approach is initially developed to compute the [1]ux linkage/current/rotor position relationship extracting proper and mutual inductances, also its torque and eld distribution characteristics. Experimental tests are performed to obtain the characteristics. Experimental tests are performed to obtain the magnetic characteristics of themachine, comparing, correcting, and discussing the results obtained with those of FEM analysis. 
This paper describes the systematic procedure for designing a MOSFET power converter for an 8/6switched reluctance machine. Starting from establishing its topology and subsystems, a Matlab simulation approach is initially developed to design its driving circuits, test different converter operating modes, and phase current controllers. Experimental tests are performed to verify the converter functionality and performance, comparing, correcting and discussing the results obtained with simulation analysis.  
On the use of Reactive Power as an Endogenous Variable in Shortterm Load Forecasting 
In the last decades, shortterm load forecasting (STLF)
has been the object of particular attention in the power systems field.
STLF has been applied almost exclusively to the generation sector, based
on variables, which are transversal to most models. Among the most
significant variables we can find load, expressed as active power (MW),
as well as exogenous variables, such as weather and economy related ones,
although the latter are applied in larger forecasting horizons than STLF. 
Estimating load diagrams in electricity substations  Load forecast is dealt with in this paper from the point of view of distribution networks. The issues associated with network management, as load dispatch and network reconfiguration, under quality of service constraints, require reliable shortterm (next hour) load forecasts. Maintenance issues and eventual power purchase decisions within liberalised electricity markets require, among others, reliable nextday load forecasts. Substations in electricity distribution networks are usually monitored to such an extent that power and energy data is available at dispatch centres with a reasonable spatial and time detail, as provided by SCADA systems spread throughout the networks. This availability allows forecast models to be tailored to specific networks, in such a way that it is possible to grasp the particular load behaviour and provide forecasts with a good level of confidence. The paper deals with a methodological approach to the use of artificial neural networks (ANN) as forecast models. It makes use of a procedural sequence for the preprocessing phase that allows capturing certain predominant relations among certain different sets of available data, providing a more solid basis to decisions regarding the composition of the input vector to ANN. The methodological approach is discussed and a real life case study is used for illustrating the defined steps, the ANN and the quality level of the results. 
Load Forecast Using Trend Information ANN Process Reconstruction 
The algorithms for shortterm load forecast (STLF), especially within the nexthour horizon, belong to a group of methodologies that aim to render more effective the actions of planning, operating and controlling electric energy systems (EES). In the context of the progressive liberalisation of the electricity sector, unbundling of the previous monopolistic structure emphasizes the need for load forecast, particularly at the network level. Methodologies such as artificial neural networks (ANN) have been widely used in nexthour load forecast. Designing an ANN requires, amongst other things, the proper choice of input variables, avoiding overfitting and an unnecessarily complex input vector (IV). This may be achieved by trying to reduce the arbitrariness in the choice of endogenous variables. At a first stage, we have applied the mathematical techniques of processreconstruction to the underlying stochastic process, using coding and block entropies to characterize the measure and memory range. At a second stage, the concept of consumption trend in homologous days of previous weeks has been used. The possibility to include weatherrelated variables in the IV has also been analyzed, the option finally being to establish a model of the nonweather sensitive type. The paper uses a reallife case study. 
A New Control Strategy Based On Optimised SmoothTorque Current Waveforms for Switched Reluctance Motors 
With the purpose of improving the dynamic performance of the Switched Reluctance Motor (SRM) one analyses its operation using MMF waveforms imposed in the linear region of its magnetic characteristics. Three factors are important in the control strategy: the dwell angle concerning the phase current and its location in the phase inductance profile; the phase current profiling; and the current control suitable to the application of the adopted strategy. In this paper, the first two points are analysed and a methodology is proposed to reduce the torque ripple and attenuate the phase voltage, by modelling current waveforms with specific criteria. 
Network Operating Characteristics Based on Imposed MMF Waveforms for Switched Reluctance Generators 
With the propose of improving the dynamic performance of the Switched Reluctance Generator (SRG) one analyses its operation using magnetomotive force (MMF) waveforms imposed in the linear region of the magnetic characteristics. In this paper, based on the proposed location of the dwell angle in the phase inductance profile, supported by energetic criteria, the SRG network operating characteristics were analysed for two typical cases: feeding a passive (ohmic) load and connected to a power network (infinite busbar). 
A Methodology Based on EnergyConversion Diagrams to Improve Switched Reluctance Generators Control  With the aim of improving the dynamic performance of the Switched Reluctance Generator one analyses its operation using magnetomotive force (MMF) waveforms imposed in the linear region of its magnetic characteristics. Three factors are important in the control strategy of a SRG: the dwell angle concerning the phase current and its location in the inductance profile; the phase current profile; and the current control. In this paper, the first point is analyzed and a methodology based on energetic aspects is proposed in order to achieve higher efficiency. 
Entropy Analysis on Electrical Drives Language Modeling  This paper discusses the relationship between the memory range of a controlled dynamical system and the maximum type of allowed productions in a grammatical learning algorithm. This is an important issue when the performance of the learning process is discussed. The shorter the maximum type of allowed productions is the faster the learning process will be. However small values assigned for the maximum type of allowed productions can damage the learning process, being the obtained grammar an insufficient model of the electrical drive. The memory range of a dynamical system can be achieved by using mathematical techniques of processreconstruction, using coding and block entropies. These modeling techniques allow a precise knowledge of the electrical drive dynamics, fundamental topic in modern control approaches. 
The Switched Reluctance Generator for Wind Power Conversion  The use of wind energy has become increasingly important as a renewable energy source, and therefore there is an increasing interest in exploiting it using a Switched Reluctance Machine (SRM) as a generator and optimise its characteristics in this domain. It is in this context that this paper analyzes the generator mode of the SRM 1) in a direct coupling to the turbine shaft and 2) coupled to the shaft through a gearbox. This paper is aimed at analyzing and proposing an alternative technical solution for wind power conversion, regarding the electrical generator system. 
O Gerador Eléctrico de Relutância Comutada como Alternativa aos Geradores Clássicos nos Aproveitamentos de Energia Eólica  A redução de emissões gasosas nefastas para o meio ambiente e a diminuição da dependência do petróleo como fonte primária de energia, são as principais razões que conduzem ao interesse crescente nas energias renováveis. De entre os vários tipos de energias renováveis existentes, a eólica é onde recai maior atenção, reflectindose naturalmente na investigação e desenvolvimento, particularmente nos sistemas electromecânicos e seus componentes, entre os quais os geradores eléctricos. Actualmente, as máquinas síncrona e assíncrona dominam o panorama das aplicações de energia eólica, no entanto, a Máquina Eléctrica de Relutância Comutada (MERC) tem sido alvo de investigação e é apontada como alternativa válida nestes aproveitamentos de energia. A despeito da MERC estar mais divulgada como motor, quer em aplicações de carácter industrial quer doméstico, como gerador existem aplicações na indústria aeronáutica e ainda, em menor número, trabalhos no domínio de geradores em aproveitamentos de energia eólica . 
A Simple PID Controller with Adaptive Parameter in a dsPIC; Case of Study  The main goal of this work consists in the development and implementation of a discrete PID controller with fast response and parameters adaptation capability, in an automatic way. This controller is based on a classic PID where a parameters adaptation algorithm was associated in order to control a process. This PID do not require any kind of adjustment or calibration from the operator. For the parameters adaptation one fuzzy system with a TakagiSugeno inference mechanism was chosen and some simplification of this system of decreasing the processing time and the controller response (250ms), in order to control fast processes without losing stability. The developed algorithm was implemented in a recent dsPIC30F. 
An Adaptive Learning Rate Approach for an OnLine NeuroFuzzy Speed Controller Applied to a Switched Reluctance Machine  The mostly used neurofuzzy motor speed control systems are time consuming and have an high computation effort when the speed reference changes gradually and the system has to learn the new operating point most of the time. In this cases a degradation of the system performance is evident has is demonstrated by experimental results in this paper. To surpass these effects, a decision and adaptation algorithm of the learning rate applied to the neurofuzzy control’s approach is proposed. The adaptive learning rate algorithm with the controller is tested and compared in the speed control system for an 8/6 switched reluctance motor by experimental tests. The proposed solution is explained, tested and the experimental tests are presented and discussed. 
A NeuroFuzzy Multilayer Weights Approach for an OnLine Learning Speed Controller Applied to a Switched Reluctance Machine: Why and How to Use  The most used neurofuzzy motor speed control systems are time consuming and have an high computation effort when the speed reference changes suddenly and the system, most of the time, has to learn this new operating point. In this case a degradation of the system performance is evident, as is demonstrated by our experimental results in this paper. To surpass these effects, a new neurofuzzy multilayer control’s approach is proposed. The multilayer controller is tested and compared in the speed control system for an 8/6 switched reluctance motor by experimental tests. The proposed solution is explained, tested and the experimental results are presented and discussed. 
OnLine Diagnosis of ThreePhase Induction Motor Using an Eigenvalue alfa beta–Vector Approach  An online diagnosis of threephase induction motor using an automatic algorithm based on Park’s vector approach is presented. This algorithm uses the Eigenvalue approach and allows an interpretation of the ab–vector. In fact, stator current ab–vector patterns are first obtained, then the Eigenvalue algorithm is used to see if the motor is healthy or not. Experimental results are presented in order to show the effectiveness of the proposed method. 
On the Use of Matlab/Simulink as a Tool for the Study of Power Systems Transient  This paper presents a methodology for the modelling of power systems on the transient analysis. In this methodology the MATLAB/SIMULINK" program is used. This program width their non linear blocks can be advantageously used for different kind of variable situations on electrical power networks, such as fault diagnosis and stability control. Furthermore, the modelling effort and required simulation times are smaller than the conventional simulators, such as EMTP. Since state space models are used, this simulation tool is useful to study the transient phenomena and easier to compare with power systems laboratory results. This paper summarizes the used method and gives some examples. 
A Neural Space Vector Fault Location for Parallel DoubleCircuit Distribution Lines  A new approach to fault location for parallel doublecircuit distribution power lines is presented. This approach uses the ClarkeConcordia transformation and an artificial neural network based learning algorithm. The a,b,0 components of double line currents resulting from the ClarkeConcordia transformation are used to characterize different states of the system. The neural network is trained to map the nonlinear relationship existing between fault location and characteristic Eigenvalue. The proposed approach is able to identify and to locate different types of faults such as: PhasetoEarth, PhasetoPhase, TwoPhasetoEarth and ThreePhase. Using the Eigenvalue as neural network inputs the proposed algorithm locates the fault distance. Results are presented which shows the effectiveness of the proposed algorithm for a correct fault location on a parallel doublecircuit distribution line. 
An Average Values Global Model for the Switched Reluctance Machine  The main subject of this paper is to present a new simplified global model for the Switched Reluctance Machine (SRM), useful, namely, for the command and control analysis of this kind of system. The concepts of power and energy are used in the global model construction, with the definition of global parameters and variables. This new global model presents an advantage over the classical detailed one, which is the reduction of the number of dynamic equations. The timedependent global parameters disadvantage, which appears in the model construction process, is overcome with the consideration of average values for global variables and global parameters, allowing the representation of the machine nondetailed behavior. An Average Values Global Model is obtained. An 8/6 SRM with four phases, eight stator poles and six rotor poles is characterized and used for illustration of the system behavior, regarding its variables evolution. In the SRM command it is assumed that the m.m.f. is imposed, which means that the system current is controlled. 
A Model for the Switched Reluctance Machine with Global Parameters and Global Variables  O objectivo principal deste artigo é apresentar um modelo para a Máquina Eléctrica de Relutância Comutada, útil na análise do comando e controlo deste tipo de máquina. Utilizando os conceitos de potência e energia construiuse um modelo com variáveis e parâmetros globais, que serão definidos no artigo. Este modelo tem a vantagem formal sobre o modelo detalhado tradicional de ter um reduzido número de equações dinâmicas. Esta abordagem pode ser vista como um caminho inicial no sentido de uma maior simplificação do modelo da máquina recorrendo a aproximações na determinação dos parâmetros e variáveis. Para ilustrar o comportamento do sistema, através da evolução dos vários parâmetros e variáveis, é utilizada uma máquina 8/6, com quarto fases, oito pólos no estator e seis pólos no rotor. No comando da máquina será assumido que a força magnetomotriz é imposta, o que significa que a corrente do sistema será controlada. Isto é útil na optimização do comportamento da máquina. 
FixedFrequency Active Current Controller and LowSensitivity Voltage Regulator for a Voltage Sourced BuckBoost Type Rectifier  We present an active fixedfrequency input current controller and a lowsensitivity output voltage regulator for a voltagesource buckboost type rectifier operating in the continuous current mode. The current controller enforces the input line current to track the desired sinusoidal waveform in phase with the input source voltage, ensuring a nearunity powerfactor operation of the rectifier. The current control strategy actively shapes the input current allowing a near sinusoidal input current even with high variations in the DC inductor current. A lowsensitivity output voltage regulator modulating the amplitude of the sinusoidal reference for the current controller is also proposed. Both the current controller and the voltage regulator are based on slidingmode theory. Experimental results are presented and the performance compared to that obtained using a conventional Proportional Integral (PI) output voltage controller. 
A Criteria for Designing Switched Reluctance Motors with Torque Ripple Reduction  With the goal of improving the dynamic performance of a Switched Reluctance Motor (SRM), it is important to find control strategies that reduce the machine torque ripple. In this way the settlement of a reference MMF synchronised with the rotor position, in conjunction with a suitable design of the SRM, allow to minimize the torque ripple, maintaining the torque nominal level of this type of machine. In this paper, an attention is devoted to the SRM design, concerning especially the relation between the number of stator and rotor poles. It is defined a criteria, based in the construction parameters of the machine, that indicates, a priori, which are the machine configurations that allow the minimization of torque ripple. 
Shortterm load forecast using trend information and process reconstruction  The algorithms for shortterm load forecast (STLF), especially within the nexthour horizon, belong to a group of methodologies that aim to render more effective the actions of planning, operating and controlling electric energy systems (EES). In the context of the progressive liberalization of the electricity sector, unbundling of the previous monopolistic structure emphasizes the need for load forecast, particularly at the network level. Methodologies such as artificial neural networks (ANN) have been widely used in nexthour load forecast. Designing an ANN requires the proper choice of input variables, avoiding overfitting and an unnecessarily complex input vector (IV). This may be achieved by trying to reduce the arbitrariness in the choice of endogenous variables. At a first stage, we have applied the mathematical techniques of processreconstruction to the underlying stochastic process, using coding and block entropies to characterize the measure and memory range. At a second stage, the concept of consumption trend in homologous days of previous weeks has been used. The possibility to include weatherrelated variables in the IV has also been analysed, the option finally being to establish a model of the nonweather sensitive type. The paper uses a reallife case study. 
Unsupervised NeuralNetwork Based Algorithm for an OnLine Diagnosis of ThreePhase Induction Motor Stator Fault  In this paper an automatic algorithm based an unsupervised neuralnetwork for an on‑line diagnostics of threephase induction motor stator fault is presented. This algorithm uses the alfa‑beta stator currents as input variables. Then a fully automatic unsupervised method is applied in which a hebbianbased unsupervised neuralnetwork is used to extract the principal components of the stator current data. These main directions are used to decide where the fault occurs and a relationship between the current components is calculated to verify the severity of the fault. One of the characteristics of this method, given its unsupervised nature, is that it does not need a prior identification of the system. The proposed methodology has been experimentally tested on a 1kW induction motor. The obtained experimental results show the effectiveness of the proposed method. 
Entropy Based Choice of a Neural Network Drive Model  The design of a
neural network requires, among other things, a proper choice of input
variables, avoiding over fitting and an unnecessarily complex input
vector. This may be achieved by trying to reduce the arbitrariness in
the choice of the input layer. This paper discusses the relation between the memory range of a particular controlled dynamical system (induction drive) and the dimension of the neural network input vector. Mathematical techniques of processreconstruction of the underlying process, using coding and block entropies to characterize the measure and memory range, were applied. These modeling techniques provide a precise knowledge of the drive dynamics, a fundamental requirement in modern control approaches. 
WPEC  A New Web Tool for the Power Electronics Learning  This paper presents a new web tool for the power electronics learning. This tool has several goals. The first goal is to teach power electronics through interactive animations. To support the student’s learning in a more active way a chat option is also provided. This interactive option allows the students to communicate with the teacher and with other students. In order to test and verify the students knowledge an option with questions are also provided. This option provides several oriented questions in order to identify some problems in the learning of the students. This last tool will give to the students a path where they should focus their study. 
Shortterm load forecasting based on ANN applied to electrical distribution Substations  The shortterm load forecasting (STLF) algorithms belong to the set of methodologies which aim to furnish more effectiveness in planning, operation and conduction in electric energy systems (EES). The presence of a deregulated environment reinforces the need of forecast, particularly in distribution networks. Actions like network management, load dispatch and network reconfiguration, under quality of service constraints, require reliable shortterm (next hour) load forecasts. Artificial neural networks (ANN) are widely used in this horizon of prevision, with satisfactory results. The construction of an “efficient” ANN goes through, among, other factors, the construction of an “efficient” input vector, in order to avoid over fitting problems and keeping the global simplicity of the model. This paper deals with a methodological approach, in order to provide more solid basis decision regarding the composition of the input vector, namely, in the choice of the number of the contiguous values of the principle variable (active power). In a first approach it was established a search for any “chains with complete connections”, in the active power signal, based on Gibbs measure, and a relative entropy analysis. It was introduced the concept of “consumption tendency” in past homologous days. It was also analyzed the correlation between the consumption and the climatic data, having been established a nonweather sensitive model. The methodological approach is discussed and compared with another input vector. The model was tested in a real life case study for illustration of defined steps. 