Evaluation of Manufactured Product Performance Using Neural Networks

This paper discusses some of the several successful applications of neural networks which have made them a useful simulation tool. After several years of neglect, confidence in the accuracy of neural networks began to grow from the 1980s with applications in power, control and instrumentation and robotics to mention a few. Several successful industrial implementations of neural networks in the field of electrical engineering will be reviewed and results of the authors’ research in the areas of food security and health will also be presented. The research results will show that successful neural simulation results using Neurosolutions software also translated to successful realtime implementation of cost-effective products with reliable overall performance of up to 90%

Neural Networks as a Tool for Product Manufacturing Innovation in Africa

-This paper highlights the numerous advantages of process simulation using neural networks. Apart from reviewing some successful industrial applications of neural networks (specifically in the field of electrical engineering), results of the authors' research in the areas of food security and health will also be presented. The research results will show that successful neural simulation results using Neurosolutions software also translated to successful realtime implementation of cost-effective products with reliable overall performance of up to 90%

Control Theory in Engineering

The subject matter of this book ranges from new control design methods to control theory applications in electrical and mechanical engineering and computers. The book covers certain aspects of control theory, including new methodologies, techniques, and applications. It promotes control theory in practical applications of these engineering domains and shows the way to disseminate researchers’ contributions in the field. This project presents applications that improve the properties and performance of control systems in analysis and design using a higher technical level of scientific attainment. The authors have included worked examples and case studies resulting from their research in the field. Readers will benefit from new solutions and answers to questions related to the emerging realm of control theory in engineering applications and its implementation

Estimação de parâmetros do motor de indução trifásico com o uso de redes neurais recorrentes

Nowadays, induction motor drive uses vector-control to get faster answer torque. To use flux estimation, direct measurement can be used with Hall sensors or another measurement technique or flux estimation by measurement of the stator voltage and current. The direct flux measurement is expensive and the process accuracy may not be enough and yet stator flux estimation process eliminates flux and speed sensors, decreasing cost and augment system reliability. For stator flux estimation this work uses the strategy of programmable cascaded low pass filter (PCLPF), implemented by recurrent-neural-network and training with Kalman Filter. The PCLPF method permits ideal voltage integration, from extremely low frequency to high frequency field-weakening range. Implementation of the filter, based on neural network is simpler with good performance and presenting faster performances by means of DSP (Signal Digital Processor). The use of the Kalman Filter as an RNN training algorhyth has shown good results as far as data quantity and total training time and concerned. Besides the measurement of the stator voltage and current, the motor parameters necessary for flux estimation using the direct vector control oriented through the stator flux, is the impedance equivalent to the stator winding of with the resistenace is significant. This work presents the stator resistance estimation, using Extended Kalman Filter, making torque and stator flux estimation more accurate. Later on, estimation of other parameters of an induction motor will be conducted, such as: simultaneous rotor and stator resistance and rotor inductance, by using the concept of EKF and also the rotor speed and resistance by means of the RNN and the EKF training. The estimations proposed above have been confirmed by means of the simulations results.Atualmente no acionamento de motores de indução é utilizado o controle vetorial para obter uma resposta rápida de torque. Para avaliação do fluxo pode-se utilizar o sensoriamento direto no entreferro, através de sondas de efeito Hall ou de outra técnica de medida ou realizar estimação do fluxo, medindo a tensão e a corrente do estator e através de processamentos realizar a estimação. O sensoriamento direto do fluxo tem alto custo e o sistema de medição pode não apresentar o desempenho necessário, já no processo de estimação do fluxo, os sensores de fluxo e velocidade são eliminados, diminuindo assim o custo e aumentando a confiabilidade do sistema. Para a estimação de fluxo neste trabalho, é usada a estratégia do Filtro Passa-Baixa em Cascata Programável (PCLPF-Programmable Cascaded Low-pass Filter), com implementação baseada em Redes Neurais Recorrente (RNN) treinada por Filtro de Kalman. O PCLPF permite a integração ideal da tensão, desde freqüências extremamente baixas até altas freqüências na escala de enfraquecimento de campo. A implementação do filtro, baseada em redes neurais, é simples, tem bom desempenho e pode apresentar execuções mais rápidas por processador digital de sinal (DSP). O uso do Filtro de Kalman como algoritmo de treinamento da RNN tem mostrado bons resultados em termos de quantidade de dados e tempo total de treinamento. Além da medição da tensão e da corrente do estator, o parâmetro do motor necessário para estimação do fluxo, utilizando o conceito do controle vetorial direto orientado através do fluxo do estator, é a impedância equivalente ao enrolamento do estator, do qual a resistência representa parte significativa. Este trabalho apresenta a estimação da resistência do estator usando um Filtro de Kalman Estendido (EKF), tornando assim, os valores da estimação do fluxo do estator e do torque mais precisos. Posteriormente, será realizada a estimação de outros parâmetros de um motor de indução, tais como: resistência do rotor; resistência do estator e indutância do rotor simultaneamente, através do emprego do conceito de EKF, e também a estimação da velocidade e resistência do rotor simultânea usando RNN e treinamento por EKF. As estimações propostas foram comprovadas através de resultados de simulações

Sensorless control of surface mounted permanent magnet machine using fundamental PWM excitation

This thesis describes the development of a sensorless control method for a surface mounted permanent magnet synchronous machine drive system. The saturation saliency in the machine is tracked from the stator current transient response to the fundamental space vector PWM (pulse width modulation) excitation. The rotor position and speed signals are obtained from measurements of the stator current derivative during the voltage vectors contained in the normal fundamental PWM sequence. In principle, this scheme can work over a wide speed range. However, the accuracy of the current derivative-measurements made during narrow voltage vectors reduces. This is because high frequency current oscillations exist after each vector switching instant, and these take a finite time to die down. Therefore, in this thesis, vector extension and compensation schemes are proposed which ensure correct current derivative measurements are made, even during narrow voltage vectors, so that any induced additional current distortion is kept to a minimum. The causes of the high frequency switching oscillations in the AC drive system are investigated and several approaches are developed to reduce the impact of these oscillations. These include the development of a novel modification to the IGBT gate drive circuit to reduce the requirement for PWM vector extension. Further improvements are made by modifications to the current derivative sensor design together with their associated signal processing circuits. In order to eliminate other harmonic disturbances and the high frequency noise appearing in the estimated position signals, an adaptive disturbance identifier and a tracking observer are incorporated to improve the position and speed signals. Experimental results show that the final sensorless control system can achieve excellent speed and position control performance

Power Converter of Electric Machines, Renewable Energy Systems, and Transportation

Power converters and electric machines represent essential components in all fields of electrical engineering. In fact, we are heading towards a future where energy will be more and more electrical: electrical vehicles, electrical motors, renewables, storage systems are now widespread. The ongoing energy transition poses new challenges for interfacing and integrating different power systems. The constraints of space, weight, reliability, performance, and autonomy for the electric system have increased the attention of scientific research in order to find more and more appropriate technological solutions. In this context, power converters and electric machines assume a key role in enabling higher performance of electrical power conversion. Consequently, the design and control of power converters and electric machines shall be developed accordingly to the requirements of the specific application, thus leading to more specialized solutions, with the aim of enhancing the reliability, fault tolerance, and flexibility of the next generation power systems

Advance control of a synchronous reluctance motor drive

This thesis investigates two predictive control algorithms designed to enhance the performance of a synchronous reluctance motor drive. In particular, a finite-control set solution approach has been followed. In particular, this thesis proposes the inclusion of integral terms into the cost function to ensure zero steady-state errors thus compensating for any model inaccuracy. In addition, a control effort term is also considered in the online optimization definition to achieve a quasi-continuous time digital controller given the high achievable ratio between the sampling frequency and the average switching frequency. After a comprehensive simulation study showing the advantages of the proposed approach over the conventional predictive controller solution over a wide range of operating conditions, several experimental test results are reported. The effectiveness of the proposed control approach, including a detailed analysis of the effect of the load and speed variations, is thus fully verified providing useful guidelines for the design of a direct model predictive controller of synchronous reluctance motor drives. In addition, this thesis investigates an innovative duty cycle calculation method for a continuous-control set model predictive control. The formulation of the duty cycles, as well as the introduction of integral terms, enable good reference tracking performance with zero steady-state error at fixed switching frequency over the whole current operating range. Low current ripple with smooth and fast dynamics are achievable, making the proposed control algorithm suitable as a valid alternative in synchronous reluctance motor drives over the established control methods. Simulations and experimental results show the effectiveness and the advantages of the proposed control algorithm over the benchmark

Advances in Rotating Electric Machines

It is difficult to imagine a modern society without rotating electric machines. Their use has been increasing not only in the traditional fields of application but also in more contemporary fields, including renewable energy conversion systems, electric aircraft, aerospace, electric vehicles, unmanned propulsion systems, robotics, etc. This has contributed to advances in the materials, design methodologies, modeling tools, and manufacturing processes of current electric machines, which are characterized by high compactness, low weight, high power density, high torque density, and high reliability. On the other hand, the growing use of electric machines and drives in more critical applications has pushed forward the research in the area of condition monitoring and fault tolerance, leading to the development of more reliable diagnostic techniques and more fault-tolerant machines. This book presents and disseminates the most recent advances related to the theory, design, modeling, application, control, and condition monitoring of all types of rotating electric machines