Protection of Electrical Power Systems with Full Penetration of Converter-Interface Generation

Mención Internacional en el título de doctorSince the advent of generation with converter-interface, mainly wind and solar photovoltaic (PV), power system operators have deal with some problems to maintain system stability and security. However, due to its low penetration in the system, it had barely any consequences and its study lack of interest. But over the years the generation scheme has changed, and converter-interface generators have been increasing their presence due to their low energy costs and policies against climate change. When the penetration rate is 100 %, protection systems have detection problems in the overcurrent scheme and pick-up problems in the distance scheme, jeopardising the safety of the electrical power system. This thesis proposes to use the Wavelet transform analysis method to solve these problems in full penetration scenarios of converter-interface generation. It can detect high and low frequency variations in voltage and current signals, and classify them in time and magnitude when they occur. In order to be able to propose a satisfactory solution, this thesis has carried out a study of the main key factors to be considered for fault detection. Analysing the differences between synchronous generators and generators with converter-interface, and the consequences of each of them for the protection systems. Describing the main converter control architectures and defining the equivalent model of converter short-circuit. Introducing the different types of faults in power systems. And describing the fundamental criteria for protection, and the most common protection schemes. The model used to obtain the results and check the feasibility of the proposal is the IEEE nine-bus system in a ring layout. It has been modelled including all power system elements (transmission lines, transformers, and loads) and both generation technologies (synchronous generators and converter-interface generators). In addition, the converter control strategy and its current limiting have also been considered. The results show a correct and immediate fault detection.Desde la aparición de los sistemas de generación de energía eléctrica con interfaz de convertidor electrónico, mayoritariamente eólica y solar fotovoltaica, los operadores de red han tenido que lidiar con los diferentes problemas que estos provocan para mantener la estabilidad y la seguridad del sistema. Aunque debido a su baja penetración en el sistema apenas tenía consecuencias y su estudio carecía de interés. Pero con el paso de los años ha ido cambiando el esquema de generación y los generadores con interfaz de convertidor electrónico han ido incrementando su presencia debido a sus bajos costes de la energía y a las políticas de lucha contra el cambio climático. Cuando se alcanzan niveles de penetración del 100 %, los sistemas de protección tienen problemas de detección en el esquema de sobrecorriente y de arranque en el esquema de distancia, poniendo en riesgo la seguridad del sistema eléctrico. Esta tesis propone utilizar el método de análisis de la transformada de Wavelet para solventar estos problemas en escenarios con máxima penetración de generación con interfaz de convertidor. El cual permite detectar variaciones de alta y baja frecuencia en las señales de tensión y de corriente, y clasificarlas tanto en tiempo como en tamaño cuando se producen. Para poder presentar una solución con garantías de ser satisfactoria, en esta tesis se ha realizado un estudio de los principales factores clave para tener en cuenta para la detección de faltas. Analizando las diferencias entre generadores síncronos y generadores con interfaz de convertidor electrónico, y qué consecuencias tiene cada uno de ellos para los sistemas de protección. Describiendo las principales arquitecturas de control de convertidores y definiendo los modelos equivalentes de cortocircuito del convertidor. Presentando los diferentes tipos de faltas en los sistemas eléctricos. Y describiendo los criterios fundamentales de las protecciones y los esquemas de protección más comunes. El modelo utilizado para la obtención de los resultados y comprobar la viabilidad de la propuesta es el sistema de nueve nudos del IEEE dispuesto en anillo. El cual ha sido modelado incluyendo todos los elementos del sistema (líneas de transmisión, transformadores y cargas) y ambas tecnologías de generación (generadores síncronos y generadores con interfaz de convertidor electrónico). Además, también se ha tenido en cuenta la estrategia de control del convertidor y su limitación de corriente. Los resultados muestran una correcta e inmediata detección de la falta.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidenta: Mónica Chinchilla Sánchez.- Secretario: Joaquín Eloy-García Carrasco.- Vocal: Roberto Lorenzo Alves Baraciart

Fault Detection and Classification using Wavelet and ANN in DFIG and TCSC Connected Transmission Line

This paper presents fault detection and classification using Wavelet and ANN based methods in a DFIG-based series compensated system. The state-of-the art methods include Wavelet transform, Fourier transform, and Wavelet-neuro fuzzy methods-based system for fault detection and classification. However, the accuracy of these state-of-the-art methods diminishes during variable conditions such as changes in wind speed, high impedance faults, and the changes in the series compensation level. Specifically, in Wavelet transform based methods, the threshold values need to be adapted based on the variable field conditions. To solve this problem, this paper has proposed a Wavelet-ANN based fault detection method where Wavelet is used as an identifier and ANN is used as a classifier for detecting various fault cases. This methodology is also effective under SSR condition. The proposed methodology is evaluated on various fault and non-fault cases generated on an IEEE first benchmark model under varying compensation levels from 20% to 55%, impedance faults, and wind velocity from 6m/sec to 10m/sec using MATLAB/Simulink, OPALRT(OP4510) manufactured real-time digital simulator environment, Arduino board I/O ports communicating with external PC in which ANN model dumped, using Arduino support package of MATLAB. The preliminary results are compared with the state-of-the-art fault detection method, where the proposed method shows robust performance under varying field conditions

Machine Learning assisted Digital Twin for event identification in electrical power system

The challenges of stable operation in the electrical power system are increasing with the infrastructure shifting of the power grid from the centralized energy supply with fossil fuels towards sustainable energy generation. The predominantly RES plants, due to the non-linear electronic switch, have brought harmonic oscillations into the power grid. These changes lead to difficulties in stable operation, reduction of outages and management of variations in electric power systems. The emergence of the Digital Twin in the power system brings the opportunity to overcome these challenges. Digital Twin is a digital information model that accurately represents the state of every asset in a physical system. It can be used not only to monitor the operation states with actionable insights of physical components to drive optimized operation but also to generate abundant data by simulation according to the guidance on design limits of physical systems. The work addresses the topic of the origin of the Digital Twin concept and how it can be utilized in the optimization of power grid operation.Die Herausforderungen für den zuverfässigen Betrieb des elektrischen Energiesystems werden mit der Umwandlung der Infrastruktur in Stromnetz von der zentralen Energieversorgung mit fossilen Brennstoffen hin zu der regenerativen Energieeinspeisung stetig zugenommen. Der Ausbau der erneuerbaren Energien im Zuge der klimapolitischen Zielsetzung zur CO²-Reduzierung und des Ausstiegs aus der Kernenergie wird in Deutschland zügig vorangetrieben. Aufgrund der nichtlinearen elektronischen Schaltanlagen werden die aus EE-Anlagen hervorgegangenen Oberschwingungen in das Stromnetz eingebracht, was nicht nur die Komplexität des Stromnetzes erhöht, sondern auch die Stabilität des Systems beeinflusst. Diese Entwicklungen erschweren den stabilen Betrieb, die Verringerung der Ausfälle und das Management der Netzschwankungen im elektrischen Energiesystem. Das Auftauchen von Digital Twin bringt die Gelegenheit zur Behebung dieser Herausforderung. Digital Twin ist ein digitales Informationsmodell, das den Zustand des physikalischen genau abbildet. Es kann nicht nur zur Überwachung der Betriebszustände mit nachvollziehbarem Einsichten über physischen Komponenten sondern auch zur Generierung der Daten durch Simulationen unter der Berücksichtigung der Auslegungsgrenze verwendet werden. Diesbezüglich widmet sich die Arbeit zunächste der Fragestellung, woher das Digital Twin Konzept stammt und wie das Digitan Twin für die Optimierung des Stromnetzes eingesetzt wird. Hierfür werden die Perspektiven über die dynamische Zustandsschätzung, die Überwachung des des Betriebszustands, die Erkennung der Anomalien usw. im Stromnetz mit Digital Twin spezifiziert. Dementsprechend wird die Umsetzung dieser Applikationen auf dem Lebenszyklus-Management basiert. Im Rahmen des Lebenszyklusschemas von Digital Twin sind drei wesentliche Verfahren von der Modellierung des Digital Twins zur deren Applizierung erforderlich: Parametrierungsprozess für die Modellierung des Digital Twins, Datengenerierung mit Digital Twin Simulation und Anwendung mit Machine Learning Algorithmus für die Erkennung der Anomalie. Die Validierung der Zuverlässigkeit der Parametrierung für Digital Twin und der Eventserkennung erfolgt mittels numerischer Fallstudien. Dazu werden die Algorithmen für Online und Offline zur Parametrierung des Digital Twins untersucht. Im Rahmen dieser Arbeit wird das auf CIGRÉ basierende Referenznetz zur Abbildung des Digital Twin hinsichtlich der Referenzmessdaten parametriert. So sind neben der Synchronmaschine und Umrichter basierende Einspeisung sowie Erreger und Turbine auch regler von Umrichter für den Parametrierungsprozess berücksichtigt. Nach der Validierung des Digital Twins werden die zahlreichen Simulationen zur Datengenerierung durchgeführt. Jedes Event wird mittels der Daten vo Digital Twin mit einem "Fingerprint" erfasst. Das Training des Machine Learning Algorithmus wird dazu mit den simulierten Daten von Digital Twin abgewickelt. Das Erkennungsergebnis wird durch die Fallstudien validiert und bewertet

Islanding Detection in Micro-grids using Sum of Voltage and Current Wavelet Coefficients Energy before the Main Circuit Breaker Side

This paper presents wavelet based islanding detection in distributed generation (DG) interfaced to the microgrid. Also a new fast method is developed for islanding detection based on measuring the utility currents and voltages signals processed by discrete wavelet transform. These currents and voltages signals are measured before the main circuit breaker of microgrid network and their features extracted by discrete wavelet transform. These features are sum of wavelet coefficients energy and are used for distinguishing the islanding conditions from non-islanding ones. Because of changing in measuring point of currents and voltages signals from point of common coupling (PCC) in traditional methods to before the main circuit breaker in proposed method, this new method detects the islanding conditions faster than the other methods. The proposed method has been examined under various scenarios; including mains supply faults, various one, two, or three phases' grid faults, and changes of rate of produced energy on IEEE 1547 anti-islanding test system. The numerical studies show the feasibility and applicability of the proposed method with satisfactory results

The application of advanced signal processing techniques to the condition monitoring of electrical machine drive systems

Includes bibliographical references (leaves 128-129).The thesis examines the use of two time-frequency domain signal processing tools in its application to condition monitoring of electrical machine drive systems. The mathematical and signal processing tools which are explored are wavelet analysis and a non-stationary adaptive signal processing algorithm. Four specific applications are identified for the research. These applications were specifically chosen to encapsulate important issues in condition monitoring of variable speed drive systems. The main aim of the project is to highlight the need for fault detection during machine transients and to illustrate the effectiveness of incorporating and adapting these new class of algorithms to detect faults in electrical machine drive systems during non-stationary conditions

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

A New Relaying Method for Third Zone Distance Relay Blocking During Power Swings

Due to the increasing complexity of modern bulk power systems, the power swing identification, blocking, and protection have become more challenging than they used to be. Among various transmission line protection methods, distance relays are the most commonly used type. One of the advantages of using distance relays is the zoned protection which provides redundancy. However, the additional redundancy comes with a problem that it increases the probability of incorrect operation. For example, the undesired operation of the third zone distance protection during power swing scenarios has been attributed as one of the major causes for creating large-scale blackouts. Some research works in the literature investigate proper identification of stable and unstable power swing conditions. Most research works dwell on identification of power swing conditions but do not address how the scheme could be used for blocking the third zone of distance relays during stable power swings. Also, the current power swing detection schemes are often very complex to implement for a relaying engineer or are not fast enough for blocking the third zone distance element. This research proposes a reliable and fast methodology for the third zone blocking (TZB) during power swings. The new mathematical formulations and derivations are based on sound time tested power system theory and are simpler to understand for a relaying engineer to implement this technique. The algorithm proposed in the research can prevent unnecessary tripping of distance relays during power swings. The algorithm also overcomes the shortcomings of the conventional power swing identification methods when applied for the third zone blocking. A first zero-crossing (FZC) concept is introduced as the criteria for identifying stable power swing or out-of-step phenomena. The analysis is based on system stability point of view and utilizes power-angle equations. The proposed algorithm could be applied at every discrete time interval or time step of a distance relay to detect power swing points. It could also be applied to any transmission line in the power system by finding an equivalent single machine infinite bus (SMIB) configuration individually for each line on a real-time basis, which is one of the primary advantages of the proposed method. In the thesis work, the proposed technique is first demonstrated using a simple single machine infinite bus system. The TZB algorithm is then tested using a modified Western Electricity Coordinating Council (WSCC) power system configuration using Power System Analysis Toolbox (PSAT) simulations. The code is written in MATLAB. The TZB method is then further analyzed using electromagnetic simulations with Real-Time Digital Simulator (RTDS) on WSCC system. The proposed method uses small time step simulations (50 μs) to take various aspects of power system complexity into consideration, such as different harmonics presents in the system, synchronous machine operation at different speeds, travelling wave representation of transmission lines instead of purely lumped parameter representation, etc. The investigations as mentioned above and the results show that the proposed TZB scheme is a straightforward and reliable technique, involving only a few calculation steps, and could be applied to any power system configuration. The main novelty of this technique is that it does not require a priori stability study to find the relay settings unlike conventional power swing identification or distance relay blocking techniques. The inputs to the relay are basic electrical quantities which could be easily measured locally on any transmission line. The local measurements would make the implementation of the proposed TZB simpler for relaying applications compared to Wide Area Measurement System (WAMS) based techniques. In a WAMS based relaying technique - the cost associated with the communication network, reliability of the communication network, impact of communication delay on relay, etc all become factors for actual industry use

An effective passive islanding detection algorithm for distributed generations

Different issues will be raised and highlighted by emerging distributed generations (DGs) into modern power systems in which the islanding detection is the most important. In the islanding situation, a part of the system which consists of at least one DG, passive grid, and local load, becomes fully separated from the main grid. Several detection methods of islanding have been proposed in recent researches based on measured electrical parameters of the system. However, islanding detection based on local measurements suffers from the non-detection zone (NDZ) and undesirable detection during grid-connected events. This paper proposes a passive islanding detection algorithm for all types of DGs by appropriate combining the measured frequency, voltage, current, and phase angle and their rate of changes at the point of common coupling (PCC). The proposed algorithm detects the islanding situation, even with the exact zero power mismatches. Proposed algorithm discriminates between the islanding situation and non-islanding disturbances, such as short circuit faults, capacitor faults, and load switching in a proper time and without mal-operation. In addition, the performance of the proposed algorithm has been evaluated under different scenarios by performing the algorithm on the IEEE 13-bus distribution system.fi=vertaisarvioitu|en=peerReviewed