Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale

ANALYSIS OF THE INFLUENCE OF LOOSE TERMINATION LEVEL WITH INCREASING TERMINATION TEMPERATURE OF ELECTRICAL EQUIPMENT USING INFRARED THERMOGRAPHY

The use of electrical energy in the industrial is very important to drive production machines. Machine maintenance is carried out so that the lifetime of the machine becomes longer so that the production process continues. Temperature is one of the most common indicators of the structural health of equipment and components. This means that the main symptoms of damage to machines and equipment can be indicated by increasing the temperature of the equipment. One of the symptoms of an increase in equipment temperature is due to a loose cable termination. The use of infrared thermography to measure the temperature rise due to loose termination is one of the methods in electrical machine maintenance. Previous studies using infrared thermography to measure the temperature rise due to loose termination have been carried out by many researchers, but these studies did not show a correlation between the level of loose termination and the increase in temperature but only stated that the loose termination caused an increase in temperature. This study focuses on finding the relationship between the level of loose termination compared to standard torque and the increase in temperature at the termination point. This is very useful as a quick overview in determining the level of urgency in maintenance activity, that percentage of loose termination of a certain value below the standard will give an increase in temperature of a certain value as well. This study resulted, for the torque setting condition 67% below the standard (loose) the temperature increase at the terminal was 13.4% - 15.6%, for the torque setting condition 33% below the standard temperature increase at the terminal of 12.3% -13.8% which mean that the increase in temperature is directly proportional to the level of slack termination and is also directly proportional to the increase in the motor speed. If the termination torque level is lower and the motor speed is increased, the terminal temperature rise will be drastically rise

Utilizing Geographic Information Systems for Condition-Based Maintenance on the Energy Distribution Grid

The energy distribution grid is a critical infrastructure challenged with shifting requirements induced by the skyrocketing importance of green energy. Particularly, legacy assets—such as medium-voltage switchgear cabinets and circuit breakers—need to be maintained to prevent energy outages and reduce resource consumption. While related research has abundantly presented algorithms for condition-based maintenance, no design knowledge is available to prescribe how an information system for this purpose ought to be designed. In a design science research project, we develop an information system for condition-based maintenance of legacy assets in the medium voltage distribution grid that utilizes geospatial data. Our design integrates Enterprise Resource Planning (ERP) functionality with Geographic Information Systems (GIS) and a Machine Learning System (MLS) for predicting outages. We demonstrate a current proof-of-concept and conclude by presenting a set of theoretical hypotheses that can guide the evaluation once the system is available

Structural health monitoring for offshore wind turbine foundations through unsupervised and semi supervised machine learning methods

The current climate crisis requires a shift towards renewable energies. Wind energy generation will play a major role. Offshore wind energy can provide greater output due to more predictable weather conditions compared to onshore wind energy and has one of the lowest lifecycle greenhouse gas emissions for any source of energy. Some of the difficulties in their operation and maintenance lie in the difficulty of accessing the site. Although remote monitoring has become standard in the industry, structural health monitoring and predictive maintenance still present some challenges. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of the available data. Furthermore, when there is no historical precedent of a type of damage, those methods cannot be used. In addition, offshore wind turbines work under a wide variety of environmental conditions and regions of operation involving unknown input excitation given by the wind and waves. Finally, supervised approaches rely on correctly labelling data, which is not possible in production conditions. Considering the difficulties, the stated strategy in this work is based on unsupervised and semi-supervised approaches and it works under different operating and environmental conditions based only on the output vibration data gathered by accelerometer sensors. The proposed strategy has been tested through experimental laboratory tests on a down-scaled model. This project applies spectral entropy, a non-standard parameter in vibration analysis, to the studied models. Overall accuracies of 93,88% for Isolation Forest (a semi-supervised method), and 88,67% for One Class Support Vector Machine (a non-supervised method) can be achieved. The accuracies of both models increase to up to 100% when trained against a larger dataset of healthy samples, however achieving these results requires retuning for features and hyperparameters. For all of this, the use of non-supervised and semi-supervised machine learning models is a realistic approach to structural health monitoring of offshore wind turbines and has obtained promising results when tested against an experimental dataset.La crisi climàtica actual requereix un gir cap a les energies renovables. La generació d'energia eòlica hi jugarà un paper important. L'energia eòlica marina pot proporcionar una major producció degut a condicions climàtiques més previsibles en comparació amb l'energia eòlica terrestre i té una de les emissions de gasos d'efecte hivernacle de cicle de vida més baixes en comparació amb qualsevol font d'energia. Algunes de les dificultats en el seu funcionament i manteniment radiquen en la dificultat d'accés al lloc. Si bé la monitorització remot s'ha volgut estàndard a la indústria, la monitorització de la salut estructural i el manteniment predictiu encara presenta algunes dificultats. Normalment, la majoria o totes les dades disponibles són de l’operació regular, per tant els mètodes enfocats en la utilització de les dades precedents a falles acabant utilitzant només un petit subconjunt de les dades disponibles. A més, quan no hi ha antecedents històrics d'un tipus de dany, no es poden utilitzar aquests mètodes. Encara, les turbines eòliques marines funcionen en una amplia varietat de condicions ambientals i regions d'operació que involucren una excitació d'entrada desconeguda proporcionada pel vent i les onades. Finalment, els enfocaments supervisats es basen en l'etiquetatge correcte de les dades, que no és possible en condicions de producció. Tenint en compte les dificultats, l'estratègia establerta en aquest treball es basa en enfocaments no supervisats i semi-supervisats i funciona sota diferents condicions ambientals i operatives basant-se únicament en les dades de vibració de sortida recopilades pels acceleròmetres. L’estratègia ha estat provada a través d’assajos experimentals de laboratori en un model a escala reduïda. Aquest projecte aplica l'entropia espectral, un paràmetre no estàndard en l'anàlisi de vibracions, als models estudiats. Es poden aconseguir precisions generals del 93,88 % per a ‘Isolation Forest’ (un mètode semi supervisat) i del 88,67 % per a ‘One Class Support Vector Machine’ (un mètode no supervisat). Les precisions dels dos models augmenten fins al 100 % quan s'entrenen amb un conjunt de dades més grans de mostres sanes; tanmateix, per aconseguir aquests resultats és necessari tornar a ajustar les ‘features’ i els hiperparàmetres. Per tot això, l’ús de models no supervisats i semi supervisats és un enfoc realista per la monitorització estructural de les turbines de vent marines obtenint resultats prometedors quan s’ha provat contra un conjunt de dades experimental.La actual crisis climática requiere un giro hacia las energías renovables. La generación de energía eólica jugará un papel importante. La energía eólica marina puede proporcionar una mayor producción debido a las condiciones climáticas más predecibles en comparación con la energía eólica terrestre y tiene una de las emisiones de gases de efecto invernadero de ciclo de vida más bajas en comparación cualquier fuente de energía. Algunas de las dificultades en su funcionamiento y mantenimiento radican en la dificultad de acceso al sitio. Si bien el monitoreo remoto se ha vuelto estándar en la industria, el monitoreo de la salud estructural y el mantenimiento predictivo aún presenta algunos desafíos. Normalmente, la mayoría o todos los datos disponibles son de operación regular, por lo que los métodos que se enfocan en los datos que conducen a fallas terminan usando solo un pequeño subconjunto de los datos disponibles. Además, cuando no existe un antecedente histórico de un tipo de daño, no se pueden utilizar esos métodos. Por añadido, las turbinas eólicas marinas funcionan en una amplia variedad de condiciones ambientales y regiones de operación que involucran una excitación de entrada desconocida proporcionada por el viento y las olas. Finalmente, los enfoques supervisados se basan en el etiquetado correcto de los datos, que no es posible en condiciones de producción. Teniendo en cuenta las dificultades, la estrategia establecida en este trabajo se basa en enfoques no supervisados y semi supervisados y funciona bajo diferentes condiciones operativas y ambientales basadas solo en los datos de vibración de salida recopilados por los sensores del acelerómetro. La estrategia propuesta ha sido probada a través de pruebas experimentales de laboratorio en un modelo a escala reducida. Este proyecto aplica la entropía espectral, un parámetro no estándar en el análisis de vibraciones, a los modelos estudiados. Se pueden lograr precisiones generales del 93,88 % para ‘Isolation Forest’ (un método semi supervisado) y del 88,67 % para ‘One Class Support Vector Machine’ (un método no supervisado). Las precisiones de ambos modelos aumentan hasta un 100 % cuando se entrenan con un conjunto de datos más grande de muestras sanas; sin embargo, para lograr estos resultados es necesario volver a ajustar las ‘features’ y los hiperparámetros. Por todo esto, el uso de modelos de aprendizaje automático no supervisados y semi supervisados es un enfoque realista para el monitoreo de la salud estructural de las turbinas eólicas marinas y ha obtenido resultados prometedores cuando se prueba con un conjunto de datos experimental.Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminan

Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges

This systematic review meticulously explores the transformative impact of digital technologies on the grid planning, grid operations, and energy market dynamics of power distribution grids. Utilizing a robust methodological framework, over 54,000 scholarly articles were analyzed to investigate the integration and effects of artificial intelligence, machine learning, optimization, the Internet of Things, and advanced metering infrastructure within these key subsections. The literature was categorized to show how these technologies contribute specifically to grid planning, operation, and market mechanisms. It was found that digitalization significantly enhances grid planning through improved forecasting accuracy and robust infrastructure design. In operations, these technologies enable real-time management and advanced fault detection, thereby enhancing reliability and operational efficiency. Moreover, in the market domain, they support more efficient energy trading and help in achieving regulatory compliance, thus fostering transparent and competitive markets. However, challenges such as data complexity and system integration are identified as critical hurdles that must be overcome to fully harness the potential of smart grid technologies. This review not only highlights the comprehensive benefits but also maps out the interdependencies among the planning, operation, and market strategies, underlining the critical role of digital technologies in advancing sustainable and resilient energy systems

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202