Computers Architecture and Technology

This paper provides a technical review of position and speed sensorless methods for controlling Brushless Direct Current (BLDC) motor drives, including the background analysis using sensors, limitations and advances. The performance and reliability of BLDC motor drivers have been improved because the conventional control and sensing techniques have been improved through sensorless technology. Then, in this paper sensorless advances are reviewed and recent developments in this area are introduced with their inherent advantages and drawbacks, including the analysis of practical implementation issues and applications. The study includes a deep overview of state-of-the-art back-EMF sensing methods, which includes Terminal Voltage Sensing, Third Harmonic Voltage Integration, Terminal Current Sensing, Back-EMF Integration and PWM strategies. Also, the most relevant techniques based on estimation and models are briefly analysed, such as Sliding-mode Observer, Extended Kalman Filter, Model Reference Adaptive System, Adaptive observers (Full-order and Pseudoreduced-order) and Artificial Neural Networks.

Método para determinar la velocidad angular en un motor conmutado mecánicamente midiendo únicamente la corriente que circula por el mismo. El método se basa en la determinación de los instantes en los que se van produciendo las ondulaciones de la componente alterna. Con esta información estima el valor de la frecuencia ripple con ella el valor de la velocidad angular del motor. Ambas magnitudes están directamente relacionadas debido a que por cada giro del motor se producen un número concreto de ondulaciones en la corriente que depende del número de polos y delgas del motor, entendiendo como ondulación a cada periodo de la componente alterna de la corriente. La detección de la ondulación se realiza mediante un registro de desplazamiento de longitud w en el que se van almacenando las últimas w muestras de la corriente del motor. Si se cumple que el valor máximo de la corriente se encuentra en la posición central del registro se supone que se acaba de detectar el pico y por tanto se detecta la ondulación registrando el instante en el que se ha producido la detección. Dependiendo del valor de w se consigue filtrar en mayor o menor medida el ruido que aparece en la corriente.

Currently, for many applications, it is necessary to know the speed and position of motors. This can be achieved using mechanical sensors coupled to the motor shaft or using sensorless techniques. The sensorless techniques in brushed dc motors can be classified into two types: 1) techniques based on the dynamic brushed dc motor model and 2) techniques based on the ripple component of the current. This paper presents a new method, based on the ripple component, for speed and position estimation in brushed dc motors, using support vector machines. The proposed method only measures the current and detects the pulses in this signal. The motor speed is estimated by using the inverse distance between the detected pulses, and the position is estimated by counting all detected pulses. The ability to detect ghost pulses and to discard false pulses is the main advantage of this method over other sensorless methods. The performed tests on two fractional horsepower brushed dc motors indicate that the method works correctly in a wide range of speeds and situations, in which the speed is constant or varies dynamically.

The detection of position and speed in BLDC motors without using position sensors has meant many efforts for the last decades. The aim of this paper is to develop a sensorless technique for detecting the position and speed of BLDC motors, and to overcome the drawbacks of position sensorbased methods by improving the performance of traditional approaches oriented to motor phase voltage sensing. The position and speed information is obtained by computing the derivative of the terminal phase voltages regarding to a virtual neutral point. For starting-up the motor and implementing the algorithms of the detection technique, a FPGA board with a real-time processor is used. Also, a versatile hardware has been developed for driving BLDC motors through pulse width modulation (PWM) signals. Delta and wye winding motors have been considered for evaluating the performance of the designed hardware and software, and tests with and without load are performed. Experimental results for validating the detection technique were attained in the range 5-1500 rpm and 5-150 rpm under no-load and full-load conditions, respectively. Specifically, speed and position square errors lower than 3 rpm and between 10º-30º were obtained without load. In addition, the speed and position errors after full-load tests were around 1 rpm and between 10º-15º, respectively. These results provide the evidence that the developed technique allows to detect the position and speed of BLDC motors with low accuracy errors at starting-up and over a wide speed range, and reduce the influence of noise in position sensing, which suggest that it can be satisfactorily used as a reliable alternative to position sensors in precision applications.

En la actualidad, millones de dispositivos de Internet of Things (IoT) están presentes en nuestra vida diaria. Dichos dispositivos, con permanente comunicación a Internet, necesitan de normas o estándares para poder establecer conexión, tanto a nivel de conectividad como de transferencia de datos. Debido a la existencia de una gran cantidad de protocolos se está generando un verdadero problema de interoperabilidad, lo cual dificulta la conexión entre dispositivos, o redes de éstos, y su interacción de forma remota. La necesidad de establecer un protocolo estándar que sea capaz de cubrir todas las necesidades que requieren estos dispositivos es uno de los retos más relevantes en las ciencias de la computación en la actualidad.
En este trabajo se pretende aplicar estándares procedentes de las ciencias geoespaciales a dispositivos IoT con el objetivo de dotar de interoperabilidad a una red de monitorización de calidad del aire desplegada en la ciudad de Madrid. Para ello, se define una metodología para la integración de dicha red de monitorización utilizando el estándar OGC SensorThings API y su aplicabilidad a otras redes con este tipo de dispositivos IoT. Como ejemplo de uso de dicho estándar y de la interoperabilidad de la red se ha desarrollado un cliente web para visualizar los datos producidos por la red de calidad del aire y garantizar su compatibilidad con otras redes desarrolladas sobre el estándar OGC SensorThings API.

Desarrollo de un sistema móvil de control de instalaciones de energía solar fotovoltaica que integra todos los equipos necesarios para llevar a cabo esta labor de manera eficiente y automatizada. El prototipo, integrado en un vehículo todoterreno, dispone de un procesador de datos, un sistema de comunicación remota y un método de localización y antirrobo GPS. Responsable del proyecto: José Carlos Gamazo, coordinador del departamento de Ingeniería Electrónica, Telecomunicaciones e I+D+i de la empresa Enerman.

Innovador sistema antirrobo de paneles fotovoltaicos basado en la identificación, detección y localización de cada módulo fotovoltaico en lugar de la tradicional vigilancia integral del huerto solar.
Responsable del proyecto: José Carlos Gamazo, coordinador del departamento de Ingeniería Electrónica, Telecomunicaciones e I+D+i de la empresa Enerman.

This paper provides a technical review of position and speed sensorless methods for controlling Brushless Direct Current (BLDC) motor drives, including the background analysis using sensors, limitations and advances. The performance and reliability of BLDC motor drivers have been improved because the conventional control and sensing techniques have been improved through sensorless technology. Then, in this paper sensorless advances are reviewed and recent developments in this area are introduced with their inherent advantages and drawbacks, including the analysis of practical implementation issues and applications. The study includes a deep overview of state-of-the-art back-EMF sensing methods, which includes Terminal Voltage Sensing, Third Harmonic Voltage Integration, Terminal Current Sensing, Back-EMF Integration and PWM strategies. Also, the most relevant techniques based on estimation and models are briefly analysed, such as Sliding-mode Observer, Extended Kalman Filter, Model Reference Adaptive System, Adaptive observers (Full-order and Pseudoreduced-order) and Artificial Neural Networks.

The detection of position and speed in BLDC motors without using position sensors has meant many efforts for the last decades. The aim of this paper is to develop a sensorless technique for detecting the position and speed of BLDC motors, and to overcome the drawbacks of position sensorbased methods by improving the performance of traditional approaches oriented to motor phase voltage sensing. The position and speed information is obtained by computing the derivative of the terminal phase voltages regarding to a virtual neutral point. For starting-up the motor and implementing the algorithms of the detection technique, a FPGA board with a real-time processor is used. Also, a versatile hardware has been developed for driving BLDC motors through pulse width modulation (PWM) signals. Delta and wye winding motors have been considered for evaluating the performance of the designed hardware and software, and tests with and without load are performed. Experimental results for validating the detection technique were attained in the range 5-1500 rpm and 5-150 rpm under no-load and full-load conditions, respectively. Specifically, speed and position square errors lower than 3 rpm and between 10º-30º were obtained without load. In addition, the speed and position errors after full-load tests were around 1 rpm and between 10º-15º, respectively. These results provide the evidence that the developed technique allows to detect the position and speed of BLDC motors with low accuracy errors at starting-up and over a wide speed range, and reduce the influence of noise in position sensing, which suggest that it can be satisfactorily used as a reliable alternative to position sensors in precision applications.