Assessment of unintentional islanding operations in distribution networks with large induction motors

This paper is aimed at assessing the impact of unintentional islanding operations (IOs) in the presence of large induction motors (IMs) within distribution networks (DNs). When a fault occurs,followingthecircuitbreaker(CB)faultclearing,theIMsacttransientlyasgenerators,duetoits inertia, until the CB reclosing takes place. The present work is the outcome of a project carried out in a small DN, where ¿eld measurements were recorded over two years. This paper provides a detailed description of the test system, a selected list of ¿eld measurements, and a discussion on modeling guidelinesusedtocreatethemodeloftheactualpowersystem. Themaingoalistovalidatethesystem model by comparing ¿eld measurements with simulation results. The comparison of simulations and ¿eld measurements prove the appropriateness of the modeling guidelines used in this work and highlight the high accuracy achieved in the implemented three-phase Matlab/Simulink modelPostprint (published version

Design and Implementation of a Centralized Disturbance Detection System for Smart Microgrids

RÉSUMÉ L’excursion de fréquence et de tension sont parmi les défis nombreux qui se posent aux microréseaux. La détection des perturbations peut être effectuée par le système de surveillance centralisé de micro-réseaux qui utilise des données de synchrophasor rapportées à partir de différents noeuds. Les réseaux de communication de synchrophasor présentent des retards et des Pertes de paquets qui peuvent détériorer l’intégrité des données et donc compromettre la fiabilité des systèmes de surveillance et de contrôle des micro-réseaux intelligents. Ce mémoire présente un nouveau concentrateur de données de vecteurs de phase avancé (APDC) capable de contrer les manques de la communication et d’améliorer la qualité des ressources de la production décentralisée (DER) dans les micro-réseaux. L’APDC proposé utilise un système de compensation adaptatif pour obtenir une estimation efficace des éléments de données manquants. L’estimateur adaptatif utilise le taux de changement d’éléments de données pour choisir entre l’estimateur LMMSE et un estimateur basé sur les dérivés pour prédire les valeurs futures des éléments de données. Si, à un instant donné, les éléments de données synchrophasors de certaines unités de mesure de phasor (PMU) manquent, les valeurs estimées sont utilisées pour compenser les données manquantes. En outre, une unité de surveillance est proposée pour détecter de manière fiable les excursions en fréquence et identifier les DERs affectés par les îlotages. L’unité de surveillance utilise un algorithme de détection centralisé élaboré qui traite les données de fréquence pour distinguer entre l’îlotage possible des DERs et les perturbations du réseau de distribution. L’APDC proposé est développé sur la plate-forme OpenPDC en temps réel et sa performance est évaluée à l’aide d’une configuration expérimentale comprenant trois PMUs, un réseau de télécommunications, des interrupteurs, et un concentrateur de données de vecteurs de phase classique (PDC). Les résultats expérimentaux confirment une intégrité des données de haut niveau dans les conditions normales et perturbées des micro- réseaux. Des études sur l’effet du bruit de mesure montrent que l’APDC proposé est même efficace en présence de bruits sévères. De plus, une détection rapide et fiable des événements d’îlotage est obtenue en raison de l’amélioration considérable du temps de détection même en cas de pertes de données sévères et de bruit de mesure. Enfin, la performance de l’APDC proposé est comparée à une méthode d’estimation existante. Les résultats montrent l’avantage important de l’APDC, en particulier dans des conditions perturbées.----------ABSTRACT Microgrids are subject to various disturbances such as voltage transients and frequency excursions. Disturbance detection can be performed by a microgrid centralized monitoring system that employs synchrophasor data reported from different nodes within the microgrid. Synchrophasor communication networks exhibit delays and packet dropout that can undermine the data integrity and hence compromise the reliability of the monitoring and control systems of the smart microgrids. In this thesis, an advanced phasor data concentrators (APDC) is proposed that is capable of counteracting the communication impairments and improving the quality of monitoring of distributed energy resources (DERs) in microgrids. The proposed APDC utilizes an adaptive compensation scheme to achieve an efficient estimate of missing data elements. The adaptive estimator employs the rate of change of data elements to choose between the vector linear minimum mean square error (LMMSE) and the derivative-based estimators to predict the future values of data elements. Whenever the synchrophasor data elements of some phasor measurement units (PMU) are missing, the estimated values are used to compensate for the missing data. Moreover, a monitoring unit is proposed to reliably detect frequency excursions and identify the DERs affected by islanding events. The monitoring unit utilizes an elaborate centralized detection algorithm that processes frequency data to distinguish between possible islanding of DERs and disturbances occurred within the host grid. The proposed APDC is developed on a real-time OpenPDC platform and its performance is evaluated using an experimental setup including three PMUs, communication links, switches, and a conventional phasor data concentrator (PDC). The experimental results confirm a high-level data integrity under both normal and disturbed conditions. Studies on the effect of measurement noise show that the proposed APDC is even efficient in the presence of noise. Moreover, fast and reliable detection of islanding events is achieved even under severe data losses and measurement noise. Finally, the performance of the proposed APDC is compared with a recently proposed estimation method that shows the significant advantage of the APDC, especially under disturbed conditions

A novel assessment of unintentional islanding operations in distribution networks

This thesis aims to investigate an unexpected islanding operation (IO) which has been identified in a real distribution network. The process of recording and processing the data obtained from the field measurements in the distribution network (DN) has been the starting point of this research. It has to be underlined that this IO raised a problem and became a major challenge for the distribution operator. Therefore, the aim of this thesis is twofold; solving a real problem as well as further enhance the current research studies about IOs in DNs. IOs have been object of study during the recent years due to the rapid proliferation of the distributed generation (DG) within the so-called smart grids (SGs). Commonly, the power of these DG resources ranges between hundreds of kW and few MW and are allocated at either low voltage or medium voltage levels. One of the significant issues that these resources are raising is, undoubtedly, the IOs. These situations occur when a portion of the grid operates in parallel with the main grid following a disconnection. Thereby IOs, where the DG is energising the grid after a CB opening, must be identified and tripped in the minimum time possible. Failure to do so, the list of hurdles may include; power quality (PQ) disturbances (e.g., frequency and voltage out of range), a safety hazard for the network personnel or out-of-phase reclosings. That is the reason why the research towards the anti-islanding protection methods has elicited great interest. Fundamentally, the substantial improvement of this thesis lies in the fact that, in this IO, there are no DG resources, but large induction motors. In fact, the grid remains energised after the CB disconnection due to the induction motors (IMs) which transiently, act as generators. The island begins with the CB operation and ends when the CB recloses the circuit to restore the electrical supply. This rapid reclosing operation is widely adopted in DNs to avoid manual operations in self-extinguished faults and typically ranges between 0.5 and 1s. Given the fact that usually IOs are originated in the presence of DG, indeed, this IO is utterly unexpectedly for the DSO. Due to the phenomenon mentioned above, the specific goals of this thesis are described down below: 1. The first goal of this thesis focuses on developing a model suitable for validation purposes. To make a proper model validation, the simulations results obtained with this model will be compared with those obtained from field measurements. Thus, once the model has been validated, a thorough investigation regarding the most influential factors will be carried out. 2. The second goal of this thesis falls within the scope of the PQ. During the IO mentioned above, a new voltage sag topology is observed. Consequently, the efforts will be focused on modelling this new type of sag. 3. The third goal of this thesis emerges from the protective point of view. Once the IO has been defined and characterised, the need for identifying and preventing it becomes the main concern. In such a way, the third pillar of the thesis is targeted at implementing a suitable tool to prevent this particular IO. Besides, this new tool will be compared with the currently available methods for ID developed for scenarios with DG.Aquesta tesi te com a objectiu investigar una operació en illa no intencional, que ha set identificada en una xarxa de distribució real. El procés de registre i processament de les dades obtingudes a partir de les mesures de camp en la xarxa de distribució, ha estat el punt de partida d’aquesta investigació. Cal subratllar que aquesta operació en illa va plantejar un problema i es va convertir en un repte important per l’operador de distribució. Per tant, l'objectiu d'aquesta tesi és doble; resoldre un problema real, així com millorar els estudis de recerca actuals sobre les illes no intencionals en xarxes de distribució elèctrica. El fenomen de les illes dins una xarxa elèctrica, han estat objecte d’estudi durant els darrers anys a causa de la ràpida proliferació de la generació distribuïda. Habitualment, la potència d’aquests recursos distribuïts oscil·la entre centenars de kW i pocs MW i s’assignen a nivells de baixa tensió o mitja tensió. Una de les qüestions importants que plantegen aquests recursos és, sens dubte, les illes. Aquestes situacions es produeixen quan una part de la xarxa elèctrica funciona en paral·lel amb la xarxa principal després d’una desconnexió. Per això, les illes no intencionals es donen quan la generació distribuïda energitza la xarxa després de la obertura d’un interruptor. Principalment, l’objectiu es identificar aquesta situació i desconnectar dites fonts en el mínim temps possible. En el cas de que això no succeeixi, els següents disturbis poden produir-se; pertorbacions de la qualitat de potència (PQ) (per exemple, freqüència i tensió fora del rang), un perill per a la seguretat del personal de la xarxa o bé reconnexions fora de fase. Aquesta és la raó per la qual la investigació vers els mètodes de protecció “anti-islanding” han despertat un gran interès. Essencialment, la millora substancial d’aquesta tesi rau en el fet que, en aquesta illa, no hi ha recursos energètics distribuïts, sinó grans motors d’inducció. Així, la xarxa elèctrica continua energitzada després de la desconnexió del interruptor a causa dels motors d’inducció, que actuen de forma transitòria com a generadors. L’illa comença amb l’obertura del interruptor i finalitza quan aquest tanca el circuit per restablir el subministrament elèctric. Aquesta operació de reconnexió ràpid es freqüent en xarxes de distribució per evitar operacions manuals en faltes temporals i generalment oscil·la entre 0,5 i 1s. Tenint en compte que generalment les illes tenen l'origen en presència de generació distribuïda , realment, la illa elèctrica objecte d’aquesta tesi és inesperada per l’operador de distribució. A causa del fenomen esmentat anteriorment, els objectius específics d'aquesta tesi es descriuen a continuació: 1. El primer objectiu d'aquesta tesi se centra a desenvolupar un model adequat per la validació. Per fer una validació adequada del model, es compararan els resultats de les simulacions obtinguts amb aquest model amb els obtinguts de les mesures de camp. Així, un cop validat el model, es durà a terme una investigació completa sobre els factors més influents. 2. El segon objectiu d'aquesta tesi entra dins de l'àmbit d'aplicació del PQ. Durant l’esmentada illa, s’observa una nova topologia de forat de tensió. En conseqüència, els esforços se centraran en modelar aquest nou tipus de forat. 3. El tercer objectiu d'aquesta tesi s’emmarca en el punt de vista de proteccions. Un cop definida i caracteritzada l’illa, la necessitat d’identificar-la i prevenir-la esdevé la principal preocupació. D’aquesta manera, el tercer pilar de la tesi té com a objectiu la implementació d’una eina adequada per prevenir aquesta particular illa. A més, es compararà aquesta nova eina amb els actuals mètodes utilitzats per a identificar les illes en escenaris amb generació distribuïda

A Universal Islanding Detection Technique for Distributed Generation Using Pattern Recognition

In the past, distribution systems were characterized by a unidirectional power flow where power flows from the main power generation units to consumers. However, with changes in power system regulation and increasing incentives for integrating renewable energy sources, Distributed Generation (DG) has become an important component of modern distribution systems. However, when a portion of the system is energized by one or more DG and is disconnected from the grid, this portion becomes islanded and might cause several operational and safety issues. Therefore, an accurate and fast islanding detection technique is needed to avoid these issues as per IEEE Standard 1547-2003 [1]. Islanding detection techniques are dependent on the type of the DG connected to the system and can achieve accurate results when only one type of DG is used in the system. Thus, a major challenge is to design a universal islanding technique to detect islanding accurately and in a timely manner for different DG types and multiple DG units in the system. This thesis introduces an efficient universal islanding detection method that can be applied to both Inverter-based DG and Synchronous-based DG. The proposed method relies on extracting a group of features from measurements of the voltage and frequency at the Point of Common Coupling (PCC) of the targeted island. The Random Forest (RF) classification technique is used to distinguish between islanding and non-islanding situations with the goals of achieving a zero Non-Detection Zone (NDZ), which is a region where islanding detection techniques fail to detect islanding, as well as avoiding nuisance DG tripping during non-islanding conditions. The accuracy of the proposed technique is evaluated using a cross-validation technique. The methodology of the proposed islanding detection technique is shown to have a zero NDZ, 98% accuracy, and fast response when applied to both types of DGs. Finally, four other classifiers are compared with the Random Forest classifier, and the RF technique proved to be the most efficient approach for islanding detection

Design of Wireless Communication Networks for Cyber-Physical Systems with Application to Smart Grid

Cyber-Physical Systems (CPS) are the next generation of engineered systems in which computing, communication, and control technologies are tightly integrated. On one hand, CPS are generally large with components spatially distributed in physical world that has lots of dynamics; on the other hand, CPS are connected, and must be robust and responsive. Smart electric grid, smart transportation system are examples of emerging CPS that have significant and far-reaching impact on our daily life. In this dissertation, we design wireless communication system for CPS. To make CPS robust and responsive, it is critical to have a communication subsystem that is reliable, adaptive, and scalable. Our design uses a layered structure, which includes physical layer, multiple access layer, network layer, and application layer. Emphases are placed on multiple access and network layer. At multiple access layer, we have designed three approaches, namely compressed multiple access, sample-contention multiple access, and prioritized multiple access, for reliable and selective multiple access. At network layer, we focus on the problem of creating reliable route, with service interruption anticipated. We propose two methods: the first method is a centralized one that creates backup path around zones posing high interruption risk; the other method is a distributed one that utilizes Ant Colony Optimization (ACO) and positive feedback, and is able to update multipath dynamically. Applications are treated as subscribers to the data service provided by the communication system. Their data quality requirements and Quality of Service (QoS) feedback are incorporated into cross-layer optimization in our design. We have evaluated our design through both simulation and testbed. Our design demonstrates desired reliability, scalability and timeliness in data transmission. Performance gain is observed over conventional approaches as such random access

Real-time Prediction of Cascading Failures in Power Systems

Blackouts in power systems cause major financial and societal losses, which necessitate devising better prediction techniques that are specifically tailored to detecting and preventing them. Since blackouts begin as a cascading failure (CF), an early detection of these CFs gives the operators ample time to stop the cascade from propagating into a large-scale blackout. In this thesis, a real-time load-based prediction model for CFs using phasor measurement units (PMUs) is proposed. The proposed model provides load-based predictions; therefore, it has the advantages of being applicable as a controller input and providing the operators with better information about the affected regions. In addition, it can aid in visualizing the effects of the CF on the grid. To extend the functionality and robustness of the proposed model, prediction intervals are incorporated based on the convergence width criterion (CWC) to allow the model to account for the uncertainties of the network, which was not available in previous works. Although this model addresses many issues in previous works, it has limitations in both scalability and capturing of transient behaviours. Hence, a second model based on recurrent neural network (RNN) long short-term memory (LSTM) ensemble is proposed. The RNN-LSTM is added to better capture the dynamics of the power system while also giving faster responses. To accommodate for the scalability of the model, a novel selection criterion for inputs is introduced to minimize the inputs while maintaining a high information entropy. The criteria include distance between buses as per graph theory, centrality of the buses with respect to fault location, and the information entropy of the bus. These criteria are merged using higher statistical moments to reflect the importance of each bus and generate indices that describe the grid with a smaller set of inputs. The results indicate that this model has the potential to provide more meaningful and accurate results than what is available in the previous literature and can be used as part of the integrated remedial action scheme (RAS) system either as a warning tool or a controller input as the accuracy of detecting affected regions reached 99.9% with a maximum delay of 400 ms. Finally, a validation loop extension is introduced to allow the model to self-update in real-time using importance sampling and case-based reasoning to extend the practicality of the model by allowing it to learn from historical data as time progresses

Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples