Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

To improve the monitoring of the electrical power grid, it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. From the results of the best structure of the models, the hyperparameters are evaluated and the wavelet transform is used to obtain an enhanced model. The contribution of this paper is related to the improvement of well-established models using the wavelet transform, thus obtaining hybrid models that can be used for several applications. The results showed that using the wavelet transform leads to an improvement in all the used models, especially the wavelet ANFIS model, which had a mean RMSE of 1.58 × 10−3, being the model that had the best result. Furthermore, the results for the standard deviation were 2.18 × 10−19, showing that the model is stable and robust for the application under study. Future work can be performed using other components of the distribution power grid susceptible to contamination because they are installed outdoors.N/

Energy storage systems and grid code requirements for large-scale renewables integration in insular grids

This thesis addresses the topic of energy storage systems supporting increased penetration of renewables in insular systems. An overview of energy storage management, forecasting tools and demand side solutions is carried out, comparing the strategic utilization of storage and other competing strategies. Particular emphasis is given to energy storage systems on islands, as a new contribution to earlier studies, addressing their particular requirements, the most appropriate technologies and existing operating projects throughout the world. Several real-world case studies are presented and discussed in detail. Lead-acid battery design parameters are assessed for energy storage applications on insular grids, comparing different battery models. The wind curtailment mitigation effect by means of energy storage resources is also explored. Grid code requirements for large-scale integration of renewables are discussed in an island context, as another new contribution to earlier studies. The current trends on grid code formulation, towards an improved integration of distributed renewable resources in island systems, are addressed. Finally, modeling and control strategies with energy storage systems are addressed. An innovative energy management technique to be used in the day-ahead scheduling of insular systems with Vanadium Redox Flow battery is presented.Esta tese aborda a temática dos sistemas de armazenamento de energia visando o aumento da penetração de energias renováveis em sistemas insulares. Uma visão geral é apresentada acerca da gestão do armazenamento de energia, ferramentas de previsão e soluções do lado da procura de energia, comparando a utilização estratégica do armazenamento e outras estratégias concorrentes. É dada ênfase aos sistemas de armazenamento de energia em ilhas, como uma nova contribuição no estado da arte, abordando as suas necessidades específicas, as tecnologias mais adequadas e os projetos existentes e em funcionamento a nível mundial. Vários casos de estudos reais são apresentados e discutidos em detalhe. Parâmetros de projeto de baterias de chumbo-ácido são avaliados para aplicações de armazenamento de energia em redes insulares, comparando diferentes modelos de baterias. O efeito de redução do potencial de desperdício de energia do vento, recorrendo ao armazenamento de energia, também é perscrutado. As especificidades subjacentes aos códigos de rede para a integração em larga escala de energias renováveis são discutidas em contexto insular, sendo outra nova contribuição no estado da arte. As tendências atuais na elaboração de códigos de rede, no sentido de uma melhor integração da geração distribuída renovável em sistemas insulares, são abordadas. Finalmente, é estudada a modelação e as estratégias de controlo com sistemas de armazenamento de energia. Uma metodologia de gestão de energia inovadora é apresentada para a exploração de curto prazo de sistemas insulares com baterias de fluxo Vanádio Redox

Machine Learning and Data Mining Applications in Power Systems

This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries

IMPROVEMENT OF POWER QUALITY OF HYBRID GRID BY NON-LINEAR CONTROLLED DEVICE CONSIDERING TIME DELAYS AND CYBER-ATTACKS

Power Quality is defined as the ability of electrical grid to supply a clean and stable power supply. Steady-state disturbances such as harmonics, faults, voltage sags and swells, etc., deteriorate the power quality of the grid. To ensure constant voltage and frequency to consumers, power quality should be improved and maintained at a desired level. Although several methods are available to improve the power quality in traditional power grids, significant challenges exist in modern power grids, such as non-linearity, time delay and cyber-attacks issues, which need to be considered and solved. This dissertation proposes novel control methods to address the mentioned challenges and thus to improve the power quality of modern hybrid grids.In hybrid grids, the first issue is faults occurring at different points in the system. To overcome this issue, this dissertation proposes non-linear controlled methods like the Fuzzy Logic controlled Thyristor Switched Capacitor (TSC), Adaptive Neuro Fuzzy Inference System (ANFIS) controlled TSC, and Static Non-Linear controlled TSC. The next issue is the time delay introduced in the network due to its complexities and various computations required. This dissertation proposes two new methods such as the Fuzzy Logic Controller and Modified Predictor to minimize adverse effects of time delays on the power quality enhancement. The last and major issue is the cyber-security aspect of the hybrid grid. This research analyzes the effects of cyber-attacks on various components such as the Energy Storage System (ESS), the automatic voltage regulator (AVR) of the synchronous generator, the grid side converter (GSC) of the wind generator, and the voltage source converter (VSC) of Photovoltaic (PV) system, located in a hybrid power grid. Also, this dissertation proposes two new techniques such as a Non-Linear (NL) controller and a Proportional-Integral (PI) controller for mitigating the adverse effects of cyber-attacks on the mentioned devices, and a new detection and mitigation technique based on the voltage threshold for the Supercapacitor Energy System (SES). Simulation results obtained through the MATLAB/Simulink software show the effectiveness of the proposed new control methods for power quality improvement. Also, the proposed methods perform better than conventional methods

The Development of an assistive chair for elderly with sit to stand problems

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyStanding up from a seated position, known as sit-to-stand (STS) movement, is one of the most frequently performed activities of daily living (ADLs). However, the aging generation are often encountered with STS issues owning to their declined motor functions and sensory capacity for postural control. The motivated is rooted from the contemporary market available STS assistive devices that are lack of genuine interaction with elderly users. Prior to the software implementation, the robot chair platform with integrated sensing footmat is developed with STS biomechanical concerns for the elderly. The work has its main emphasis on recognising the personalised behavioural patterns from the elderly users’ STS movements, namely the STS intentions and personalised STS feature prediction. The former is known as intention recognition while the latter is defined as assistance prediction, both achieved by innovative machine learning techniques. The proposed intention recognition performs well in multiple subjects scenarios with different postures involved thanks to its competence of handling these uncertainties. To the provision of providing the assistance needed by the elderly user, a time series prediction model is presented, aiming to configure the personalised ground reaction force (GRF) curve over time which suggests successful movement. This enables the computation of deficits between the predicted oncoming GRF curve and the personalised one. A multiple steps ahead prediction into the future is also implemented so that the completion time of actuation in reality is taken into account

Contribution au pronostic de durée de vie des systèmes piles à combustible PEMFC

This thesis work aims to provide solutions for the limited lifetime of Proton Exchange Membrane Fuel Cell Systems (PEM-FCS) based on two complementary disciplines:A first approach consists in increasing the lifetime of the PEM-FCS by designing and implementing a Prognostics & Health Management (PHM) architecture. The PEM-FCS are essentially multi-physical systems (electrical, fluid, electrochemical, thermal, mechanical, etc.) and multi-scale (time and space), thus its behaviors are hardly understandable. The nonlinear nature of phenomena, the reversibility or not of degradations and the interactions between components makes it quite difficult to have a failure modeling stage. Moreover, the lack of homogeneity (actual) in the manufacturing process makes it difficult for statistical characterization of their behavior. The deployment of a PHM solution would indeed anticipate and avoid failures, assess the state of health, estimate the Remaining Useful Lifetime (RUL) of the system and finally consider control actions (control and/or maintenance) to ensure operation continuity.A second approach proposes to use a passive hybridization of the PEMFC with Ultra Capacitors (UC) to operate the fuel cell closer to its optimum operating conditions and thereby minimize the impact of aging. The UC appear as an additional source to the PEMFC due to their high power density, their capacity to charge/discharge rapidly, their reversibility and their long life. If we take the example of fuel cell hybrid electrical vehicles, the association between a PEMFC and UC can be performed using a hybrid of active or passive type system. The overall behavior of the system depends on both, the choice of the architecture and the positioning of these elements in connection with the electric charge. Today, research in this area focuses mainly on energy management between the sources and embedded storage and the definition and optimization of a power electronic interface designated to adjust the flow of energy between them. However, the presence of power converters increases the source of faults and failures (failure of the switches of the power converter and the impact of high frequency current oscillations on the aging of the PEMFC), and also increases the energy losses of the entire system (even if the performance of the power converter is high, it nevertheless degrades the overall system).Les travaux de cette thèse visent à apporter des éléments de solutions au problème de la durée de vie des systèmes pile à combustible (FCS – Fuel Cell System) de type à « membrane échangeuse de protons » (PEM – Proton Exchange Membrane) et se décline sur deux champs disciplinaires complémentaires :Une première approche vise à augmenter la durée de vie de celle-ci par la conception et la mise en œuvre d'une architecture de pronostic et de gestion de l'état de santé (PHM – Prognostics & Health Management). Les PEM-FCS, de par leur technologie, sont par essence des systèmes multi-physiques (électriques, fluidiques, électrochimiques, thermiques, mécaniques, etc.) et multi-échelles (de temps et d'espace) dont les comportements sont difficilement appréhendables. La nature non linéaire des phénomènes, le caractère réversible ou non des dégradations, et les interactions entre composants rendent effectivement difficile une étape de modélisation des défaillances. De plus, le manque d'homogénéité (actuel) dans le processus de fabrication rend difficile la caractérisation statistique de leur comportement. Le déploiement d'une solution PHM permettrait en effet d'anticiper et d'éviter les défaillances, d'évaluer l'état de santé, d'estimer le temps de vie résiduel du système, et finalement, d'envisager des actions de maîtrise (contrôle et/ou maintenance) pour assurer la continuité de fonctionnement. Une deuxième approche propose d'avoir recours à une hybridation passive de la PEMFC avec des super-condensateurs (UC – Ultra Capacitor) de façon à faire fonctionner la pile au plus proche de ses conditions opératoires optimales et ainsi, à minimiser l'impact du vieillissement. Les UCs apparaissent comme une source complémentaire à la PEMFC en raison de leur forte densité de puissance, de leur capacité de charge/décharge rapide, de leur réversibilité et de leur grande durée de vie. Si l'on prend l'exemple des véhicules à pile à combustible, l'association entre une PEMFC et des UCs peut être réalisée en utilisant un système hybride de type actif ou passif. Le comportement global du système dépend à la fois du choix de l'architecture et du positionnement de ces éléments en lien avec la charge électrique. Aujourd'hui, les recherches dans ce domaine se focalisent essentiellement sur la gestion d'énergie entre les sources et stockeurs embarqués ; et sur la définition et l'optimisation d'une interface électronique de puissance destinée à conditionner le flux d'énergie entre eux. Cependant, la présence de convertisseurs statiques augmente les sources de défaillances et pannes (défaillance des interrupteurs du convertisseur statique lui-même, impact des oscillations de courant haute fréquence sur le vieillissement de la pile), et augmente également les pertes énergétiques du système complet (même si le rendement du convertisseur statique est élevé, il dégrade néanmoins le bilan global)

Optimal adaptive neuro-fuzzy inference system architecture for time series forecasting with calendar effect

This paper discusses a procedure for model selection in ANFIS for time series forecasting with a calendar effect. Calendar effect is different from the usual trend and seasonal effects. Therefore, when it occurs, it will affect economic activity during that period and create new patterns that will result in inaccurate forecasts for decision making if not considered. The focus is on the model selection strategy to find the appropriate input variable and the number of membership functions (MFs) based on the Lagrange Multiplier (LM) test. The ARIMAX stochastic model is used at the preprocessing stage to capture calendar variations in the data. The calendar effect observed is the Eid al-Fitr holiday in Indonesia, a country with the largest Muslim population in the world. The data of Tanjung Priok port passengers used as a case study. The result shows that hybrid ARIMAX-ANFIS based on the LM test can be an effective procedure for model selection in ANFIS for time series with calendar effect forecasting. Empirical results show that the use of the calendar effect variable provides more accurate predictions as indicated by smaller RMSE and MAPE values than without the calendar effect variable

Artificial intelligence techniques for ground fault line selection in power systems: State-of-the-art and research challenges

In modern power systems, efficient ground fault line selection is crucial for maintaining stability and reliability within distribution networks, especially given the increasing demand for energy and integration of renewable energy sources. This systematic review aims to examine various artificial intelligence (AI) techniques employed in ground fault line selection, encompassing artificial neural networks, support vector machines, decision trees, fuzzy logic, genetic algorithms, and other emerging methods. This review separately discusses the application, strengths, limitations, and successful case studies of each technique, providing valuable insights for researchers and professionals in the field. Furthermore, this review investigates challenges faced by current AI approaches, such as data collection, algorithm performance, and real-time requirements. Lastly, the review highlights future trends and potential avenues for further research in the field, focusing on the promising potential of deep learning, big data analytics, and edge computing to further improve ground fault line selection in distribution networks, ultimately enhancing their overall efficiency, resilience, and adaptability to evolving demands

Wind Energy Harvesting and Conversion Systems: A Technical Review

Wind energy harvesting for electricity generation has a significant role in overcoming the challenges involved with climate change and the energy resource implications involved with population growth and political unrest. Indeed, there has been significant growth in wind energy capacity worldwide with turbine capacity growing significantly over the last two decades. This confidence is echoed in the wind power market and global wind energy statistics. However, wind energy capture and utilisation has always been challenging. Appreciation of the wind as a resource makes for difficulties in modelling and the sensitivities of how the wind resource maps to energy production results in an energy harvesting opportunity. An opportunity that is dependent on different system parameters, namely the wind as a resource, technology and system synergies in realizing an optimal wind energy harvest. This paper presents a thorough review of the state of the art concerning the realization of optimal wind energy harvesting and utilisation. The wind energy resource and, more specifically, the influence of wind speed and wind energy resource forecasting are considered in conjunction with technological considerations and how system optimization can realise more effective operational efficiencies. Moreover, non-technological issues affecting wind energy harvesting are also considered. These include standards and regulatory implications with higher levels of grid integration and higher system non-synchronous penetration (SNSP). The review concludes that hybrid forecasting techniques enable a more accurate and predictable resource appreciation and that a hybrid power system that employs a multi-objective optimization approach is most suitable in achieving an optimal configuration for maximum energy harvesting