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

Space station automation of common module power management and distribution, volume 2

The new Space Station Module Power Management and Distribution System (SSM/PMAD) testbed automation system is described. The subjects discussed include testbed 120 volt dc star bus configuration and operation, SSM/PMAD automation system architecture, fault recovery and management expert system (FRAMES) rules english representation, the SSM/PMAD user interface, and the SSM/PMAD future direction. Several appendices are presented and include the following: SSM/PMAD interface user manual version 1.0, SSM/PMAD lowest level processor (LLP) reference, SSM/PMAD technical reference version 1.0, SSM/PMAD LLP visual control logic representation's (VCLR's), SSM/PMAD LLP/FRAMES interface control document (ICD) , and SSM/PMAD LLP switchgear interface controller (SIC) ICD

Space station automation of common module power management and distribution

The purpose is to automate a breadboard level Power Management and Distribution (PMAD) system which possesses many functional characteristics of a specified Space Station power system. The automation system was built upon 20 kHz ac source with redundancy of the power buses. There are two power distribution control units which furnish power to six load centers which in turn enable load circuits based upon a system generated schedule. The progress in building this specified autonomous system is described. Automation of Space Station Module PMAD was accomplished by segmenting the complete task in the following four independent tasks: (1) develop a detailed approach for PMAD automation; (2) define the software and hardware elements of automation; (3) develop the automation system for the PMAD breadboard; and (4) select an appropriate host processing environment

A review of networked microgrid protection: Architectures, challenges, solutions, and future trends

The design and selection of advanced protection schemes have become essential for the reliable and secure operation of networked microgrids. Various protection schemes that allow the correct operation of microgrids have been proposed for individual systems in different topologies and connections. Nevertheless, the protection schemes for networked microgrids are still in development, and further research is required to design and operate advanced protection in interconnected systems. The interconnection of these microgrids in different nodes with various interconnection technologies increases the fault occurrence and complicates the protection operation. This paper aims to point out the challenges in developing protection for networked microgrids, potential solutions, and research areas that need to be addressed for their development. First, this article presents a systematic analysis of the different microgrid clusters proposed since 2016, including several architectures of networked microgrids, operation modes, components, and utilization of renewable sources, which have not been widely explored in previous review papers. Second, the paper presents a discussion on the protection systems currently available for microgrid clusters, current challenges, and solutions that have been proposed for these systems. Finally, it discusses the trend of protection schemes in networked microgrids and presents some conclusions related to implementation

Time domain analysis of switching transient fields in high voltage substations

Switching operations of circuit breakers and disconnect switches generate transient currents propagating along the substation busbars. At the moment of switching, the busbars temporarily acts as antennae radiating transient electromagnetic fields within the substations. The radiated fields may interfere and disrupt normal operations of electronic equipment used within the substation for measurement, control and communication purposes. Hence there is the need to fully characterise the substation electromagnetic environment as early as the design stage of substation planning and operation to ensure safe operations of the electronic equipment. This paper deals with the computation of transient electromagnetic fields due to switching within a high voltage air-insulated substation (AIS) using the finite difference time domain (FDTD) metho

Power System Stability Analysis using Neural Network

This work focuses on the design of modern power system controllers for automatic voltage regulators (AVR) and the applications of machine learning (ML) algorithms to correctly classify the stability of the IEEE 14 bus system. The LQG controller performs the best time domain characteristics compared to PID and LQG, while the sensor and amplifier gain is changed in a dynamic passion. After that, the IEEE 14 bus system is modeled, and contingency scenarios are simulated in the System Modelica Dymola environment. Application of the Monte Carlo principle with modified Poissons probability distribution principle is reviewed from the literature that reduces the total contingency from 1000k to 20k. The damping ratio of the contingency is then extracted, pre-processed, and fed to ML algorithms, such as logistic regression, support vector machine, decision trees, random forests, Naive Bayes, and k-nearest neighbor. A neural network (NN) of one, two, three, five, seven, and ten hidden layers with 25%, 50%, 75%, and 100% data size is considered to observe and compare the prediction time, accuracy, precision, and recall value. At lower data size, 25%, in the neural network with two-hidden layers and a single hidden layer, the accuracy becomes 95.70% and 97.38%, respectively. Increasing the hidden layer of NN beyond a second does not increase the overall score and takes a much longer prediction time; thus could be discarded for similar analysis. Moreover, when five, seven, and ten hidden layers are used, the F1 score reduces. However, in practical scenarios, where the data set contains more features and a variety of classes, higher data size is required for NN for proper training. This research will provide more insight into the damping ratio-based system stability prediction with traditional ML algorithms and neural networks.Comment: Masters Thesis Dissertatio

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