Development of the future generation of smart high voltage connectors and related components for substations, with energy autonomy and wireless data transmission capability

The increased dependency on electricity of modern society, makes reliability of power transmission systems a key point. This goal can be achieved by continuously monitoring power grid parameters, so possible failure modes can be predicted beforehand. It can be done using existing Information and Communication Technologies (ICT) and Internet of Things (10T) technologies that include instrumentation and wireless communication systems, thus forming a wireless sensor network (WSN). Electrical connectors are among the most critical parts of any electrical system and hence, they can act as nodes of such WSN. Therefore, the fundamental objective of this thesis is the design, development and experimental validation of a self-powered IOT solution for real-time monitoring of the health status of a high-voltage substation connector and related components of the electrical substation. This new family of power connectors is called SmartConnector and incorporates a thermal energy harvesting system powering a microcontroller that controls a transmitter and several electronic sensors to measure the temperature, current and the electrical contact resistance (ECR) of the connector. These measurements are sent remotely via a Bluetooth 5 wireless communication module to a local gateway, which further transfers the measured data to a database server for storage as well as further analysis and visualization. By this way, after suitable data processing, the health status of the connector can be available in real-time, allowing different appealing functions, such as assessing the correct installation of the connector, the current health status or its remaining useful life (RUL) in real-time. The same principal can also be used for other components of substation like spacers, insulators, conductors, etc. Hence, to prove universality of this novel approach, a similar strategy is applied to a spacer which is capable of measuring uneven current distribution in three closely placed conductors. This novel IOT device is called as SmartSpacer. Care has to be taken that this technical and scientific development has to be compatible with existing substation bus bars and conductors, and especially to be compatible with the high operating voltages, i.e., from tens to hundreds of kilo-Volts (kV), and with currents in the order of some kilo-pm peres (kA). Although some electrical utilities and manufacturers have progressed in the development of such technologies, including smart meters and smart sensors, electrical device manufacturers such as of substation connectors manufacturers have not yet undertaken the technological advancement required for the development of such a new family of smart components involved in power transmission, which are designed to meet the future needs.La mayor dependencia de la electricidad de la sociedad moderna hace que la fiabilidad de los sistemas de transmisión de energía sea un punto clave. Este objetivo se puede lograr mediante la supervisión continua de los parámetros de la red eléctrica, por lo que los posibles modos de fallo se pueden predecir de antemano. Se puede hacer utilizando las tecnologías existentes de Tecnologías de la Información y la Comunicación (1CT) e Internet de las cosas (lo T) que incluyen sistemas de instrumentación y comunicación inalámbrica, formando así una red de sensores inalámbricos (WSN). Los conectores eléctricos se encuentran entre las partes más críticas de cualquier sistema eléctrico y, por lo tanto, pueden actuar como nodos de dicho VVSN. Por lo tanto, el objetivo fundamental de esta tesis es el diseño, desarrollo y validación experimental de una solución IOT autoalimentada para la supervisión en tiempo real del estado de salud de un conector de subestación de alta tensión y componentes relacionados de la subestación eléctrica. Esta nueva familia de conectores de alimentación se llama SmartConnector e incorpora un sistema de recolección de energía térmica que alimenta un microcontrolador que controla un transmisor y varios sensores electrónicos para medir la temperatura, la corriente y la resistencia del contacto eléctrico (ECR) del conector. Esta nueva familia de conectores de alimentación se llama SmartConnector e incorpora un sistema de recolección de energía térmica que alimenta un microcontrolador que controla un transmisor y varios sensores electrónicos para medir la temperatura, la corriente y la resistencia al contacto eléctrico (ECR) del conector. De esta manera, después del procesamiento de datos adecuado, el estado de salud del conector puede estar disponible en tiempo real, permitiendo diferentes funciones atractivas, como evaluar la correcta instalación del conector, el estado de salud actual o su vida útil restante (RUL) en tiempo real. El mismo principio también se puede utilizar para otros componentes de la subestación como espaciadores, aislantes, conductores, etc. Por lo tanto, para demostrar la universalidad de este enfoque novedoso, se aplica una estrategia similar a un espaciador, que es capaz de medir la distribución de corriente desigual en tres conductores estrechamente situados. Hay que tener cuidado de que este desarrollo técnico y científico tenga que sea compatible con las barras y "busbars" de subestación existentes, y sobre todo para ser compatible con las altas tensiones de funcionamiento, es decir, de decenas a cientos de kilovoltios (kV), y con corrientes en el orden de algunos kilo-Amperes (kA). Aunque algunas empresas eléctricas y fabricantes han progresado en el desarrollo de este tipo de tecnologías, incluidos medidores inteligentes y sensores inteligentes, los fabricantes de dispositivos eléctricos, como los fabricantes de conectores de subestación, aún no han emprendido el avance tecnológico necesario para el desarrollo de una nueva familia de componentes intel

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

Towards an automated photogrammetry-based approach for monitoring and controlling construction site activities

The construction industry has a poor productivity record, which was predominantly ascribed to inadequate monitoring of how a project is progressing at any given time. Most available approaches do not offer key stakeholders a shared understanding of project performance in real-time, which as a result failed to identify any project slippage on the original schedule. This study reports on the development of a novel automated system for monitoring, updating and controlling construction site activities in real-time. The proposed system seeks to harness advances in close-range photogrammetry, BIM and computer vision to deliver an original approach that is capable of continuous monitoring of construction activities, with the progress status determinable, at any given time, throughout the construction stage.The research adopted a sequential mixed approach strategy pursuant to the design science standard processes in three stages. The first stage involved interviews within a focus group setting with seven carefully selected construction professionals. Their answers were analysed and provided "the informed-basis for the development of the automated system” for detecting and notifying delays in construction projects. The second stage involved development of ‘proof of the concept’ in a pilot project case study with nine potential users of the proposed automated system. Face-to-face interviews were conducted to evaluate and verify the effectiveness of the developed prototype, which as a result was continuously refined and improved according to the users’ comments and feedbacks. Within this stage the prototype to be tested and evaluated by a representative of construction professionals was developed. Subsequently a sub-stage of the system’s development sought to test and validate the final version of the system in the context of a real-life construction project in Dubai whereby an online survey is administered to 40 users, a representative sample of potential system users. The third stage addressed the conclusion, limitations and recommendations for further research studies for the proposed system.The findings of the study revealed that once the system installed and programmed, it does not require any expertise or manual intervention. This is mainly due to all the processes of the system being fully automated and the data collection, interpretations, analysis and notifications are automatically processed without any human intervention. Consequently, human errors and subjectivity are eliminated, and accordingly the system achieved a significantly high level of accuracy, automation and reliability. The system achieved a level of accuracy of 99.97% for horizontal construction elements and exceeded 99.70% for vertical elements. The findings also highlighted that this developed system is inexpensive, easy to operate and its accuracy excels that of current systems sought to automate monitoring and updating of progress status’ for construction projects. The distinctive features of the proposed system assisted the site team to complete the project 61 days ahead of its contractual completion date with a 9% time saving and 3% cost saving.The proposed system has the potential to identify any deviation from as-planned construction schedules, and prompt actions taken in response to the automatic notification system, which informs decision-makers via emails and SMS

WOFEX 2021 : 19th annual workshop, Ostrava, 1th September 2021 : proceedings of papers

The workshop WOFEX 2021 (PhD workshop of Faculty of Electrical Engineer-ing and Computer Science) was held on September 1st September 2021 at the VSB – Technical University of Ostrava. The workshop offers an opportunity for students to meet and share their research experiences, to discover commonalities in research and studentship, and to foster a collaborative environment for joint problem solving. PhD students are encouraged to attend in order to ensure a broad, unconfined discussion. In that view, this workshop is intended for students and researchers of this faculty offering opportunities to meet new colleagues.Ostrav

Technology and Management Applied in Construction Engineering Projects

This book focuses on fundamental and applied research on construction project management. It presents research papers and practice-oriented papers. The execution of construction projects is specific and particularly difficult because each implementation is a unique, complex, and dynamic process that consists of several or more subprocesses that are related to each other, in which various aspects of the investment process participate. Therefore, there is still a vital need to study, research, and conclude the engineering technology and management applied in construction projects. This book present unanimous research approach is a result of many years of studies, conducted by 35 well experienced authors. The common subject of research concerns the development of methods and tools for modeling multi-criteria processes in construction engineering

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters