Realidad Aumentada para mejorar la eficiencia energética del uso final en el marco de las Smart Grids

A medida que aumenta el número de instalaciones que adoptan un sistema de gestión de edificios bajo el paradigma de la Industria 4.0, es fundamental asegurar la buena salud de sus operaciones. La continuidad de las actividades y las operaciones ininterrumpidas son requisitos clave para cualquier edificio, para lo cual la supervisión sofisticada de la calidad y la fiabilidad del suministro de energía puede desempeñar un papel extremadamente importante. La submedición, a diferencia de la medición en el punto de conexión común, implica la medición del consumo de energía para unidades individuales o electrodomésticos en un complejo de edificios. El Internet de las cosas (IoT) está difundiéndose cada vez más por todos los aspectos de la vida, incluyendo la infraestructura energética de nuestros edificios. Los dispositivos IoT ofrecen muchas opciones de conectividad para ayudar a supervisar todos los activos energéticos importantes. Una red de dispositivos de Internet de las cosas, que permita la submedición de la calidad de la energía eléctrica dentro de toda la instalación, sería sumamente beneficiosa para la gestión del edificio, garantizando así la continuidad de las operaciones comerciales. Por tanto, en esta tesis, se describe un novedoso sensor de bajo costo, con tecnología IoT, para medir y analizar la calidad de la energía eléctrica a la entrada de cualquier electrodoméstico individual de corriente alterna (CA), que proporciona un sistema de detección y análisis tempranos que controla esas variables críticas dentro de las instalaciones y permite anticiparse a las fallas con alertas tempranas. Además, los parámetros de calidad de la energía registrados que se procesan en la Nube pueden ayudar a reducir el consumo de energía, ya que las perturbaciones de la calidad de la energía pueden analizarse automáticamente e incluso compararse con los valores estándar. Este sensor IoT propuesto ayudará a los usuarios a detectar la mayoría de las perturbaciones de régimen permanente de la calidad de la energía, así como los eventos, al tiempo que monitoriza el consumo de energía. Este sensor de calidad de energía de IoT está construido sobre la base de un microcontrolador que gestiona un Circuito Integrado (CI) de medición de energía a través de una Interfaz Periférica en Serie (SPI), aumentando sus capacidades originales mediante la inclusión de nuevas y sofisticadas funcionalidades de software. Además, se comunica de forma inalámbrica con una plataforma IoT en la Nube para permitir el almacenamiento y la supervisión de los diferentes eventos de calidad de la energía para toda la instalación. La realidad aumentada (AR) mejora la forma en que adquirimos, comprendemos y mostramos la información del mundo real sin distracciones. Estas tecnologías pueden ser utilizadas en diferentes aplicaciones e industrias ya que pueden incorporar visualizaciones específicas en una pantalla del mundo real. La realidad aumentada móvil (MAR) consiste esencialmente en superponer elementos virtuales sobre objetos reales en la pantalla, para dar un valor añadido y enriquecer la interacción con la realidad. La visualización de datos a través de AR y en plataformas basadas en la Nube permite a los equipos de operaciones tener acceso a la información en tiempo casi real para tomar decisiones rápidas y tener una respuesta rápida con respecto al uso de la energía, mientras que los equipos de mantenimiento se mantienen al tanto de la calidad de la energía de los aparatos y la fiabilidad necesaria mediante el uso de la IoT. En este documento se presenta una novedosa aproximación para visualizar la calidad de la energía eléctrica consumida por un electrodoméstico, con el fin de aumentar la conciencia sobre el ahorro energético, para ello se emplea una solución combinada de MAR con tecnologías de IoT.As the number of facilities adopting a Building Management System under the Industry 4.0 paradigm increases, it is critical to ensure the good health of their operations. Business continuity and uninterrupted operations are key requirements for any building, for which Power Quality and Supply Reliability sophisticated monitoring can play an extremely important role. Submetering, as opposed to point of common coupling metering, implies measuring power consumption for individual units or appliances in a building complex. An Internet of Things device network, which brings ubiquitous power quality submetering inside the entire facility, would be extremely beneficial for the management of the building thus ensuring seamless business operations. This work describes a novel low-cost Internet of Things sensor for measuring and analyzing power quality at the input of any individual Alternating Current (AC) appliance, providing an early detection and analysis system which controls those critical variables inside the facility and leads to anticipate faults with early-stage alerts. Moreover, the recorded power quality parameters that are processed in the Cloud can help to reduce energy consumption, as power quality disturbances can be automatically analyzed and even compared to standard values. The proposed Internet of Things sensor will help users to detect most power quality steady-state and events disturbances, while monitoring the energy consumption. This Internet of Things Power Quality sensor is built around a flexible microcontroller, which manages an energy metering Integrated Circuit (IC) through Serial Peripheral Interface (SPI), increasing its original capabilities by including new sophisticated software functionality. Additionally, it wirelessly communicates with a cloud-based Internet of Things Platform to allow the storage and supervision of the different power quality events for the entire facility. An example of the access to the data is also included. Augmented reality (AR) improves how we acquire, understand, and display information without distracting us from the real world. These technologies can be used in different applications and industries as they can incorporate domain-specific visualizations on a real-world screen. Mobile augmented reality (MAR) essentially consists of superimposing virtual elements over real objects on the screen of mobile devices (Smartphones, tablets and PDAs) to give added value and enrich the interaction with reality. In numerous plants, it is being used for maintenance and repair tasks, as well as training. The Internet of Things (IoT) is increasingly pervading every aspect of our lives, including the power infrastructure of our buildings. IoT-enabled devices offer many connectivity options for helping supervise all-important energy assets. Aggregating data to cloud-based platforms enables operations teams to have on-time information access to make fast decisions and have a fast response regarding energy use, while maintenance teams keep on top of the appliance power quality and reliability needed by using MAR. This thesis presents a novel approximation for visualizing appliance-related power quality to enhance awareness about the consumed electricity. A combined solution of MAR with IoT technologies is employed. Engineered solutions’ hands-free way to get data about surrounding appliances reduces the complexity, saves energy, and speeds up the operations. An innovative way to measure things at the right time leads to a competitive advantage

Industrial Augmented Reality As An Approach For Device Identification Within A Manufacturing Plant For Property Alteration Purposes

ThesisThe introduction of 3D computer graphics has led to an increase in the processing capacity of the computational units monumentally, along with speed, memory and transmission bandwidth. Augmented Reality (AR) has modelled remarkable progress towards real-world consumer applications. Considering the fact that mass production occurs daily in the manufacturing plants with large sums of wastage, caused either by human error, load-shedding (power outage), machine malfunction, or the time it takes the engineers to identify and fix the problem, are observed in high volumes. Therefore, the need to identify strategies and solutions to reduce such problems on-site with accurate data, rather than outsourcing or depending solely on the Supervisory Control and Data Acquisition (SCADA) system data, which might damage the integrity and economy of the manufacturing plant, needs to be developed and implemented. In a controlled network, identification and detection of a component in the process are difficult without prior knowledge and background in the design and implementation process. Thus, the concept of device identification with the aid of augmented reality, utilising markerless identifiers, such as machine vision, other than Quick Response Codes (QR codes) or Radio Frequency Identification (RFID), needs to be investigated. It is because of such reasons that the deployment of new types of technologies, such as “augmented reality” and “machine vision” need to further be investigated to obtain the device details, based on their positions and features within the indoor manufacturing plant to procure and commercialise this solution technology. This study proposes an optimal and efficient model, utilising machine vision application to detect and identify devices, based on their positions and features within the manufacturing plant with the aid of an augmented reality application for extending the device details. The study has outlined a machine vision application developed for object detection, based on colour and shape. Additionally, another method based on the augmented reality application was developed for the identification and augmentation of device details, based on the feature and position of the device within the indoor manufacturing plant. The study proved to be very successful in the identification and detection of objects, making use of machine vision algorithms, namely colour, shape and Canny Edge detection and the identification of devices (robotic arm and motors), based on their features and position within an indoor manufacturing environment set-up. For the optimal efficiency of this model, the Simultaneous Localisation and Mapping (SLAM) algorithm (ORB-SLAM) was used, in conjunction with the bundle adjustment algorithm as an alternative solution in the absence of the user built-in maps for the calculation of the device positions, based on the uncertainties of the exact locations within the indoor manufacturing environment set-up. However, some of the shortcomings were identified and addressed, such as the communication speed and the room’s light conditions, which impacted the sensing of the camera to detect the correct objects. These shortcomings were, however, addressed by conducting two studies, namely the day and night study to compare the best light settings and also to reduce the distance between the devices and the AR application to compensate for the communication speed issues. The scientific contribution of this study is the recognition of components by means of vision identification within such a process within an indoor manufacturing set-up. By means of identification, the user will have the capability to view and adjust the parameters of the process in a scaled plant. This contribution makes use of a modelled JPEG image. An AR image that the user can identify the devices apart from, relying on the SCADA system alone, was physically modelled on Blender3D for utilisation in Unity3D, as opposed to utilisation of any image and referencing it which would make the process tedious and reduce the processing speed. Subsequently, it has been depicted as part of a new knowledge contribution, that the identification of the devices can be achieved by placing the smartphone at any angle of the device (robotic arm or motor), and the detection and augmentation will be achieved without any change in the settings. As part of result validation, a video was taken and uploaded on YouTube to receive a user perspective on the developed AR application. After the video upload, a survey was shared with 20 individuals, together with the YouTube link to indicate a broader base evaluation. However, the results came back positive with the majority of the sample individuals recommending the adoption of the application and its utilisation in the scaled manufacturing plant. In addition to the results verification, a SCADA model was developed in National InstrumentsTM LabviewTM and was integrated with the AR application for evaluation purposes. The results showed that the AR application doesn’t require any alteration, despite utilising a different SCADA model in different software applications, provided that the array index is the same. Only when the array index differs, is it that alterations are necessary utilising the AR application in order to have the same array elements and avoid having a null index that might cause the application to crash or not to debug. It is therefore noted that the AR application is compatible and reliable for integration with other SCADA models without alteration requirements. The entire work outlined in this thesis was validated by two sets of physical experiments, namely GPS-based detection, and the ORB-SLAM, integrated with the Bundle Adjustment algorithm for feature and position detection. However, despite the prior knowledge of the GPS's inconsistent operation within a scaled indoor environment, it was necessary to perform the test to obtain more insight into this inconsistency and inaccurate data results