Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review

The protection of AC microgrids (MGs) is an issue of paramount importance to ensure their reliable and safe operation. Designing reliable protection mechanism, however, is not a trivial task, as many practical issues need to be considered. The operation mode of MGs, which can be grid-connected or islanded, employed control strategy and practical limitations of the power electronic converters that are utilized to interface renewable energy sources and the grid, are some of the practical constraints that make fault detection, classification, and coordination in MGs different from legacy grid protection. This article aims to present the state-of-the-art of the latest research and developments, including the challenges and issues in the field of AC MG protection. A broad overview of the available fault detection, fault classification, and fault location techniques for AC MG protection and coordination are presented. Moreover, the available methods are classified, and their advantages and disadvantages are discussed

ANOMALY INFERENCE BASED ON HETEROGENEOUS DATA SOURCES IN AN ELECTRICAL DISTRIBUTION SYSTEM

Harnessing the heterogeneous data sets would improve system observability. While the current metering infrastructure in distribution network has been utilized for the operational purpose to tackle abnormal events, such as weather-related disturbance, the new normal we face today can be at a greater magnitude. Strengthening the inter-dependencies as well as incorporating new crowd-sourced information can enhance operational aspects such as system reconfigurability under extreme conditions. Such resilience is crucial to the recovery of any catastrophic events. In this dissertation, it is focused on the anomaly of potential foul play within an electrical distribution system, both primary and secondary networks as well as its potential to relate to other feeders from other utilities. The distributed generation has been part of the smart grid mission, the addition can be prone to electronic manipulation. This dissertation provides a comprehensive establishment in the emerging platform where the computing resources have been ubiquitous in the electrical distribution network. The topics covered in this thesis is wide-ranging where the anomaly inference includes load modeling and profile enhancement from other sources to infer of topological changes in the primary distribution network. While metering infrastructure has been the technological deployment to enable remote-controlled capability on the dis-connectors, this scholarly contribution represents the critical knowledge of new paradigm to address security-related issues, such as, irregularity (tampering by individuals) as well as potential malware (a large-scale form) that can massively manipulate the existing network control variables, resulting into large impact to the power grid

Synchrophasor Data Analytics for Control and Protection Applications in Smart Grids

RÉSUMÉ Des réseaux intelligents sont des réseaux d’énergie fortement distribués où les technologies d’énergie et des services sont intégrés avec des informations, des communications et contrôlent des technologies. Puisque les sources d’énergie renouvelable deviennent plus efficaces et rentables, les réseaux intelligents peuvent livrer la puissance propre, durable, sécuritaire, et fiable aux consommateurs. Cependant, l’utilisation rapide de sources d’énergie renouvelable provoque des défis techniques en termes de surveillance, le contrôle et la protection des réseaux électriques. En fait, l’énergie renouvelable implique les phénomènes qui sont naturellement stochastiques comme la lumière du soleil et le vent. Donc, les réseaux intelligents devraient être capables de surveiller et répondre aux changements tant dans fournisseur d’énergie que dans la demande. L’évolution des réseaux électriques provoque aussi le déploiement de nombreuses unités de mesure sans précédent et d’intelligents appareils de mesure. En vertu des systèmes de communications, les signaux en temps réel et les données peuvent être échangés entre les composants des réseaux intelligents. Le flux de données en temps réel fournit une occasion unique pour des applications axées sur les données et des outils pour démultiplier la modernisation de réseaux et la résilience. Les unités de mesure de phaseur sont les dispositifs spécialisés qui acquièrent le phaseur synchronisé (synchrophasor) des données des réseaux électriques. L’analytique de données Synchrophasor peut potentiellement étre plus performant que des méthodes traditionnelles en termes de prise de décisions. Spécifiquement, l’analytique de données est des approches qualitatives/quantitatives et les algorithmes qui rassemblent et traitent des données pour en fin de compte améliorer la conscience situationnelle dans des réseaux électriques. Motivé par ce fait, cette thèse présente des solutions viables pour l’analytique de données synchrophasor dans le but d’améliorer la surveillance, le contrôle et la protection de réseaux de distribution. La thèse se concentre sur trois fonctionnalités qui sont portées de basé sur l’analytique de données synchrophasor: Détection de perturbation centralisée, surveillance de production décentralisée (PD) et la protection “backup” coordonnée. L’objectif de surveillance de perturbation est de réaliser la détection rapide et fiable de tension/des déviations de fréquence qui affectent la stabilité de réseau. La surveillance de PD est liée à la détection de la présence/absence de ressources énergétiques pour la gestion du flux de puissance.----------ABSTRACT Smart grids are highly distributed energy networks where energy technologies and services are integrated with information, communications and control technologies. As renewable energy sources are becoming more efficient and cost–effective, the smart grids can deliver safe, clean, sustainable and reliable power to consumers. However, the rapid utilization of renewable energy sources brings about technical challenges in terms of monitoring, control, and protection of power systems. In fact, renewable energy involves phenomena which are naturally stochastic such as sunlight and wind. Therefore, the smart grids should be capable of monitoring and responding to changes in both power supply and demand. The evolution of the power systems also gives rise to deployment of unprecedented number of measurement units and smart meters. By virtue of communications systems, real-time signals and data can be exchanged between components of the smart grids. The flow of real-time data provides a unique opportunity for data-driven applications and tools to leverage grid modernization and resiliency. Phasor measurement units are specialized devices that acquire synchronized phasor (synchrophasor) data from the power systems. Synchrophasor data analytics can potentially outperform traditional methods in terms of decision making. Specifically, data analytics are qualitative/quantitative approaches and algorithms that collect and process data to ultimately improve situational awareness in the power systems. Motivated by this fact, this thesis presents viable solutions for synchrophasor data analytics with the aim of improving monitoring, control and protection of power distribution grids. The thesis focuses on three functionalities that are carried out based on synchrophasor data analytics: Centralized disturbance detection, monitoring of distributed generation (DG) systems, and coordinated backup protection. The objective of disturbance monitoring is to achieve fast and reliable detection of voltage/frequency deviations that affect the network stability. The DG monitoring is concerned with detecting presence/absence of energy resources for management of the flow of power. Disturbance and DG monitoring tools pave the way for adaptive backup protection of active distribution networks. The adaptive backup protection scheme ensures the post-fault stability by detecting line faults within a permissible tolerance time. The coordination between control and backup protection systems leads to fast recovery of voltage/frequency and minimizes power outage. The efficacy and reliability of the developed methods and algorithms are validated by extensive computer simulations based on different benchmarks

LoRa Enabled Smart Inverters for Microgrid Scenarios with Widespread Elements

The introduction of low-power wide-area networks (LPWANs) has changed the image of smart systems, due to their wide coverage and low-power characteristics. This category of communication technologies is the perfect candidate to be integrated into smart inverter control architectures for remote microgrid (MG) applications. LoRaWAN is one of the leading LPWAN technologies, with some appealing features such as ease of implementation and the possibility of creating private networks. This study is devoted to analyze and evaluate the aforementioned integration. Initially, the characteristics of different LPWAN technologies are introduced, followed by an in-depth analysis of LoRa and LoRaWAN. Next, the role of communication in MGs with widespread elements is explained. A point-by-point LoRa architecture is proposed to be implemented in the grid-feeding control structure of smart inverters. This architecture is experimentally evaluated in terms of latency analysis and externally generated power setpoint, following smart inverters in different LoRa settings. The results demonstrate the effectiveness of the proposed LoRa architecture, while the settings are optimally configured. Finally, a hybrid communication system is proposed that can be effectively implemented for remote residential MG management

AN INTELLIGENT PASSIVE ISLANDING DETECTION AND CLASSIFICATION SCHEME FOR A RADIAL DISTRIBUTION SYSTEM

Distributed generation (DG) provides users with a dependable and cost-effective source of electricity. These are directly connected to the distribution system at customer load locations. Integration of DG units into an existing system has significantly high importance due to its innumerable advantages. The high penetration level of distributed generation (DG) provides vast techno-economic and environmental benefits, such as high reliability, reduced total system losses, efficiency, low capital cost, abundant in nature, and low carbon emissions. However, one of the most challenges in microgrids (MG) is the island mode operations of DGs. the effective detection of islanding and rapid DG disconnection is essential to prevent safety problems and equipment damage. The most prevalent islanding protection scheme is based on passive techniques that cause no disruption to the system but have extensive non-detection zones. As a result, the thesis tries to design a simple and effective intelligent passive islanding detection approach using a CatBoost classifier, as well as features collected from three-phase voltages and instantaneous power per phase visible at the DG terminal. This approach enables initial features to be extracted using the Gabor transform (GT) technique. This signal processing (SP) technique illustrates the time-frequency representation of the signal, revealing several hidden features of the processed signals to be the input of the intelligent classifier. A radial distribution system with two DG units was utilized to evaluate the effectiveness of the proposed islanding detection method. The effectiveness of the proposed islanding detection method was verified by comparing its results to those of other methods that use a random forest (RF) or a basic artificial neural network (ANN) as a classifier. This was accomplished through extensive simulations using the DigSILENT Power Factory® software. Several measures are available, including accuracy (F1 Score), the area under the curve (AUC), and training time. The suggested technique has a classification accuracy of 97.1 per cent for both islanded and non-islanded events. However, the RF and ANN classifiers\u27 accuracies for islanding and non-islanding events, respectively, are proven to be 94.23 and 54.8 per cent, respectively. In terms of the training time, the ANN, RF, and CatBoost classifiers have training times of 1.4 seconds, 1.21 seconds, and 0.88 seconds, respectively. The detection time for all methods was less than one cycle. These metrics demonstrate that the suggested strategy is robust and capable of distinguishing between the islanding event and other system disruptions

Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization

This study delves into the shift from centralized to decentralized approaches in the electricity industry, with a particular focus on how machine learning (ML) advancements play a crucial role in empowering renewable energy sources and improving grid management. ML models have become increasingly important in predicting renewable energy generation and consumption, utilizing various techniques like artificial neural networks, support vector machines, and decision trees. Furthermore, data preprocessing methods, such as data splitting, normalization, decomposition, and discretization, are employed to enhance prediction accuracy. The incorporation of big data and ML into smart grids offers several advantages, including heightened energy efficiency, more effective responses to demand, and better integration of renewable energy sources. Nevertheless, challenges like handling large data volumes, ensuring cybersecurity, and obtaining specialized expertise must be addressed. The research investigates various ML applications within the realms of solar energy, wind energy, and electric distribution and storage, illustrating their potential to optimize energy systems. To sum up, this research demonstrates the evolving landscape of the electricity sector as it shifts from centralized to decentralized solutions through the application of ML innovations and distributed decision-making, ultimately shaping a more efficient and sustainable energy future

Power Quality Management and Classification for Smart Grid Application using Machine Learning

The Efficient Wavelet-based Convolutional Transformer network (EWT-ConvT) is proposed to detect power quality disturbances in time-frequency domain using attention mechanism. The support of machine learning further improves the network accuracy with synthetic signal generation and less system complexity under practical environment. The proposed EWT-ConvT can achieve 94.42% accuracy which is superior than other deep learning models. The detection of disturbances using EWT-ConvT can also be implemented into smart grid applications for real-time embedded system development

Protection and Disturbance Mitigation of Next Generation Shipboard Power Systems

Today, thanks to modern advances mainly in the power electronics field, megawatt-level electric drives and magnetic levitation are being integrated into the marine power grids. These technologies operate based on Direct Current (DC) power which require Alternating Current (AC) to DC conversion within the current grid. Medium-voltage Direct Current (MVDC) and Flywheel Energy Storage Systems (FESS) are the next state-of-the-art technologies that researchers are leaning on to produce, convert, store, and distribute power with improved power quality, reliability, and flexibility. On the other hand, with the extensive integration of high-frequency power electronic converters, system stability analysis and the true system dynamic behaviors assessment following grid disturbances have become a serious concern for system control designs and protection. This dissertation first explores emerging shipboard power distribution topologies such as MVDC networks and FESS operation with charge and discharge dynamics. Furthermore, the important topic of how these systems perform in dynamic conditions with pulsed power load, faults, arc fault and system protection are studied. Secondly, a communication-based fault detection and isolation system controller that improves upon a directional AC overcurrent relay protection system is proposed offering additional protection discrimination between faults and pulsed-power Load (PPL) in MVDC systems. The controller is designed to segregate between system dynamic short-circuit fault and bus current disturbances due to a PPL. Finally, to validate the effectiveness of the proposed protection controller, different bus current disturbances are simulated within a time-domain electromagnetic transient simulation of a shipboard power system including a PPL system operating with different ramp rate profiles, pulse widths, peak powers, and fault locations. This overarching goal of this work is to address some of the critical issues facing the US Navy as warfighter mission requirements increase exponentially and move towards advanced and sophisticated pulsed power load devices such as high energy weapon systems, high energy sensor and radar systems. The analyses and proposed solutions in this dissertation support current shipbuilding industry priorities to improve shipboard power system reliability and de-risk the integration of new power system technologies for next generation naval vessels