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

A wide area system for power transmission security enhancement using a process systems approach

Imperial Users onl

Electromechanical Dynamics of High Photovoltaic Power Grids

This dissertation study focuses on the impact of high PV penetration on power grid electromechanical dynamics. Several major aspects of power grid electromechanical dynamics are studied under high PV penetration, including frequency response and control, inter-area oscillations, transient rotor angle stability and electromechanical wave propagation.To obtain dynamic models that can reasonably represent future power systems, Chapter One studies the co-optimization of generation and transmission with large-scale wind and solar. The stochastic nature of renewables is considered in the formulation of mixed-integer programming model. Chapter Two presents the development procedures of high PV model and investigates the impact of high PV penetration on frequency responses. Chapter Three studies the impact of PV penetration on inter-area oscillations of the U.S. Eastern Interconnection system. Chapter Four presents the impacts of high PV on other electromechanical dynamic issues, including transient rotor angle stability and electromechanical wave propagation. Chapter Five investigates the frequency response enhancement by conventional resources. Chapter Six explores system frequency response improvement through real power control of wind and PV. For improving situation awareness and frequency control, Chapter Seven studies disturbance location determination based on electromechanical wave propagation. In addition, a new method is developed to generate the electromechanical wave propagation speed map, which is useful to detect system inertia distribution change. Chapter Eight provides a review on power grid data architectures for monitoring and controlling power grids. Challenges and essential elements of data architecture are analyzed to identify various requirements for operating high-renewable power grids and a conceptual data architecture is proposed. Conclusions of this dissertation study are given in Chapter Nine

Design and Control of Electrical Motor Drives

Dear Colleagues, I am very happy to have this Special Issue of the journal Energies on the topic of Design and Control of Electrical Motor Drives published. Electrical motor drives are widely used in the industry, automation, transportation, and home appliances. Indeed, rolling mills, machine tools, high-speed trains, subway systems, elevators, electric vehicles, air conditioners, all depend on electrical motor drives.However, the production of effective and practical motors and drives requires flexibility in the regulation of current, torque, flux, acceleration, position, and speed. Without proper modeling, drive, and control, these motor drive systems cannot function effectively.To address these issues, we need to focus on the design, modeling, drive, and control of different types of motors, such as induction motors, permanent magnet synchronous motors, brushless DC motors, DC motors, synchronous reluctance motors, switched reluctance motors, flux-switching motors, linear motors, and step motors.Therefore, relevant research topics in this field of study include modeling electrical motor drives, both in transient and in steady-state, and designing control methods based on novel control strategies (e.g., PI controllers, fuzzy logic controllers, neural network controllers, predictive controllers, adaptive controllers, nonlinear controllers, etc.), with particular attention to transient responses, load disturbances, fault tolerance, and multi-motor drive techniques. This Special Issue include original contributions regarding recent developments and ideas in motor design, motor drive, and motor control. The topics include motor design, field-oriented control, torque control, reliability improvement, advanced controllers for motor drive systems, DSP-based sensorless motor drive systems, high-performance motor drive systems, high-efficiency motor drive systems, and practical applications of motor drive systems. I want to sincerely thank authors, reviewers, and staff members for their time and efforts. Prof. Dr. Tian-Hua Liu Guest Edito

Wide-area Situational Awareness Application Developments

This dissertation expands the topics from the wide-area situational awareness application development, system architecture design, to power system disturbance analysis. All the works are grounded on the wide-area Frequency Monitoring Network (FNET). The FNET system takes GPS-synchronized wide-area measurements in a low-cost, easily deployable manner at 120V single-phase power outlet. These synchronized observations enables the monitoring of bulk power systems, and provides critical information for understanding power system disturbances and system operations. Firstly, the work addresses the viability of angle measurement to serve different types of situational awareness applications, including the development of new angle-based event location estimation methods, the design of real-time system visualization framework using angle measurement. Secondly, a sound FNET power system event monitoring and automatic event reporting system framework is introduced, with NERC Frequency Response Initiative (FRI) tasks included to improve power system situational awareness capability. Lastly, the work covers different types of power system disturbance analysis, including the statistical analysis of frequency disturbances in NA power grid from 2008 to 2011; analysis of typical frequency response characteristics of the generation and load loss events in Europe power grid; analysis of some major disturbances in NA power grid from 2010 to 2011; and the inter-area oscillation modal analysis in the WECC system

Power Quality

Electrical power is becoming one of the most dominant factors in our society. Power generation, transmission, distribution and usage are undergoing signifi cant changes that will aff ect the electrical quality and performance needs of our 21st century industry. One major aspect of electrical power is its quality and stability – or so called Power Quality. The view on Power Quality did change over the past few years. It seems that Power Quality is becoming a more important term in the academic world dealing with electrical power, and it is becoming more visible in all areas of commerce and industry, because of the ever increasing industry automation using sensitive electrical equipment on one hand and due to the dramatic change of our global electrical infrastructure on the other. For the past century, grid stability was maintained with a limited amount of major generators that have a large amount of rotational inertia. And the rate of change of phase angle is slow. Unfortunately, this does not work anymore with renewable energy sources adding their share to the grid like wind turbines or PV modules. Although the basic idea to use renewable energies is great and will be our path into the next century, it comes with a curse for the power grid as power fl ow stability will suff er. It is not only the source side that is about to change. We have also seen signifi cant changes on the load side as well. Industry is using machines and electrical products such as AC drives or PLCs that are sensitive to the slightest change of power quality, and we at home use more and more electrical products with switching power supplies or starting to plug in our electric cars to charge batt eries. In addition, many of us have begun installing our own distributed generation systems on our rooft ops using the latest solar panels. So we did look for a way to address this severe impact on our distribution network. To match supply and demand, we are about to create a new, intelligent and self-healing electric power infrastructure. The Smart Grid. The basic idea is to maintain the necessary balance between generators and loads on a grid. In other words, to make sure we have a good grid balance at all times. But the key question that you should ask yourself is: Does it also improve Power Quality? Probably not! Further on, the way how Power Quality is measured is going to be changed. Traditionally, each country had its own Power Quality standards and defi ned its own power quality instrument requirements. But more and more international harmonization efforts can be seen. Such as IEC 61000-4-30, which is an excellent standard that ensures that all compliant power quality instruments, regardless of manufacturer, will produce of measurement instruments so that they can also be used in volume applications and even directly embedded into sensitive loads. But work still has to be done. We still use Power Quality standards that have been writt en decades ago and don’t match today’s technology any more, such as fl icker standards that use parameters that have been defi ned by the behavior of 60-watt incandescent light bulbs, which are becoming extinct. Almost all experts are in agreement - although we will see an improvement in metering and control of the power fl ow, Power Quality will suff er. This book will give an overview of how power quality might impact our lives today and tomorrow, introduce new ways to monitor power quality and inform us about interesting possibilities to mitigate power quality problems. Regardless of any enhancements of the power grid, “Power Quality is just compatibility” like my good old friend and teacher Alex McEachern used to say. Power Quality will always remain an economic compromise between supply and load. The power available on the grid must be suffi ciently clean for the loads to operate correctly, and the loads must be suffi ciently strong to tolerate normal disturbances on the grid

On extending process monitoring and diagnosis to the electrical and mechanical utilities: an advanced signal analysis approach

This thesis is concerned with extending process monitoring and diagnosis to electrical and mechanical utilities. The motivation is that the reliability, safety and energy efficiency of industrial processes increasingly depend on the condition of the electrical supply and the electrical and mechanical equipment in the process. To enable the integration of electrical and mechanical measurements in the analysis of process disturbances, this thesis develops four new signal analysis methods for transient disturbances, and for measurements with different sampling rates. Transient disturbances are considered because the electrical utility is mostly affected by events of a transient nature. Different sampling rates are considered because process measurements are commonly sampled at intervals in the order of seconds, while electrical and mechanical measurements are commonly sampled with millisecond intervals. Three of the methods detect transient disturbances. Each method progressively improves or extends the applicability of the previous method. Specifically, the first detection method does univariate analysis, the second method extends the analysis to a multivariate data set, and the third method extends the multivariate analysis to measurements with different sampling rates. The fourth method developed removes the transient disturbances from the time series of oscillatory measurements. The motivation is that the analysis of oscillatory disturbances can be affected by transient disturbances. The methods were developed and tested on experimental and industrial data sets obtained during industrial placements with ABB Corporate Research Center, Kraków, Poland and ABB Oil, Gas and Petrochemicals, Oslo, Norway. The concluding chapters of the thesis discuss the merits and limitations of each method, and present three directions for future research. The ideas should contribute further to the extension of process monitoring and diagnosis to the electrical and mechanical utilities. The ideas are exemplified on the case studies and shown to be promising directions for future research.Open Acces

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