Modeling for harmonic analysis of ac offshore wind power plants

This Ph.D. dissertation presents the work carried out on the modeling, for harmonic analysis, of AC offshore wind power plants (OWPP). The studies presented in this Ph.D. thesis are oriented to two main aspects regarding the harmonic analysis of this type of power system. The first aspect is the modeling and validation of the main power components of an AC offshore wind power plant. Special emphasis is focused on the modeling of wind turbines, power transformers, submarine cables, and the interaction between them. A proposal of a wind turbine harmonic model is presented in this dissertation to represent the behavior of a wind turbine and its harmonics, up to 5 kHz. The distinctive structure of this model consists of implementing a voltage source containing both the fundamental component and the harmonics emitted by the converter. For the case of transformer and submarine cables, the frequency-dependent behavior of certain parameters is modeled for frequencies up to 5 kHz as well. The modeling of the frequency-dependent characteristics, due to skin and proximity effect, is achieved by means of Foster equivalent networks for time-domain simulations. Regarding the interaction between these power components, two complementary modeling approaches are presented. These are the Simulink®-based model and an analytical sequence network model of the passive components of the OWPP. A description of model development and parameterization is carried out for both modeling approaches considering a scenario that is defined according to a real offshore wind power plant. On the other hand, the second aspect of this Ph.D. thesis is oriented to the analysis of the issues that appear in offshore wind power plants in relation to harmonic amplification risk, compliance of grid codes in terms of harmonics and power factor, and the design of effective solutions to improve the harmonic emission of the facility. The technical solutions presented in this Ph.D. thesis cover aspects regarding modulation strategies, design of the connection filter of the grid side converter and management of the operation point of the grid side converter of wind turbines. This last by means of changing the setpoint of certain variables. As inferred, these are solutions from the perspective of the wind turbine manufacturer

Power and Energy Student Summit 2019: 9 – 11 July 2019 Otto von Guericke University Magdeburg ; Conference Program

The book includes a short description of the conference program of the "Power and Energy Student Summit 2019". The conference, which is orgaized for students in the area of electric power systems, covers topics such as renewable energy, high voltage technology, grid control and network planning, power quality, HVDC and FACTS as well as protection technology. Besides the overview of the conference venue, activites and the time schedule, the book includes all papers presented at the conference

Neural Network Fault Recognition in Power Systems with High Penetrations of Inverter-Based Resources

The growing demand for renewable energy resources (RER) has led to increased integration of inverter-based resources (IBRs), into existing power distribution and transmission networks. However, RER locations are often not ideally suited for direct integration, necessitating a restructuring of the grid from a traditional radial network to a more complex mesh network topology. This transition presents challenges in terms of protection and coordination, as IBRs exhibit atypical responses to power system anomalies compared to conventional synchronous generation. To address these challenges and support existing power system protection infrastructure, this work explores the incorporation of machine learning algorithms. Specifically, an optimized convolutional neural network (CNN) is developed for real-time application in power system protection schemes. The focus is on prioritizing key performance metrics such as recall, specificity, speed, and the reduction of computational resources required for effective protection. The machine learning model is trained to differentiate between healthy system dynamics and hazardous conditions, such as faults, in the presence of IBRs. By analyzing data retrieved from an IEEE 34-bus 24kV distribution network, the model's application is demonstrated and its performance is evaluated. A photovoltaic (PV) source was incorporated into the IEEE 34-bus distribution feeder model at the end of the feeder. By adding a PV source at the end of the feeder, IBR characteristics, such as its response to system anomalies can be monitored through the model. Once the modified IEEE 34-bus distribution feeder model with the PV source was set up, various system anomalies were simulated to create a diverse dataset for training the machine learning (ML) model. These anomalies included; load rejection - a sudden and complete removal of load from the distribution network, simulating a scenario where a significant portion of the load disconnects from the grid, load addition - a sudden and significant increase in load demand, representing a scenario where new loads are connected to the grid, islanding - a scenario where the distribution feeder becomes electrically isolated from the main grid, with the PV source acting as a microgrid and supplying power to the local loads, and various types of faults, such as short-circuits or ground faults, occurring at different locations along the distribution line. To create a diverse dataset, model parameters were varied through 50 different iterations of each simulated anomaly scenario. These parameters included the PV system's capacity, the location of the anomaly on the feeder, the severity and duration of the anomaly, and other relevant grid parameters. For each iteration and anomaly scenario, the responses of the system were recorded, including voltage levels, current flows, and other relevant synchorphasors at the PV source's point of common coupling (PCC). These responses formed the dataset for training the ML model. The accumulated dataset was then used to train the various ML models, including the optimized convolutional neural network (CNN), to identify patterns and hidden characteristics in the data corresponding to different system anomalies. The training process involved feeding the model with input data from the various iterations and scenarios, along with corresponding labels indicating the type of anomaly present. By exposing the ML model to diverse scenarios and varying parameters, the model learns to generalize its understanding of system dynamics and accurately distinguish between healthy system states and hazardous conditions. The models in this work were specifically trained to recognize the various fault characteristics on the system. The trained model's ability to process time-series data and recognize anomalies from the accumulated dataset enhances power system protection infrastructure's capability to respond rapidly and accurately to various grid disturbances, ensuring the reliable and stable operation of the distribution network, especially in the presence of PV and other IBRs. The results show that the optimized CNN outperforms traditional machine learning models used in time-series data analysis. The model's speed and reliability make it an effective tool for identifying hidden characteristics in power system data without the need for extensive manual analysis or rigid programming of existing protection relays. This capability is particularly valuable as power grids integrate a higher penetration of IBRs, where traditional protection infrastructure may not fully account for their unique responses. The successful integration of the optimized CNN into power system protection infrastructure enhances the grid's ability to detect and respond to anomalies, such as faults, in a more efficient and accurate manner. By leveraging machine learning techniques, power system operators can better adapt to the challenges posed by the increasing presence of IBRs and ensure the continued stability and reliability of the distribution network

Application of the cascaded multilevel inverter as a shunt active power filter

Abstract unavailable please refer to PD

University Physics Volume 2

University Physics is a three-volume collection that meets the scope and sequence requirements for two- and three-semester calculus-based physics courses. Volume 1 covers mechanics, sound, oscillations, and waves. Volume 2 covers thermodynamics, electricity and magnetism, and Volume 3 covers optics and modern physics. This textbook emphasizes connections between theory and application, making physics concepts interesting and accessible to students while maintaining the mathematical rigor inherent in the subject. Frequent, strong examples focus on how to approach a problem, how to work with the equations, and how to check and generalize the result.https://commons.erau.edu/oer-textbook/1002/thumbnail.jp

Mechanical Engineering

The book substantially offers the latest progresses about the important topics of the "Mechanical Engineering" to readers. It includes twenty-eight excellent studies prepared using state-of-art methodologies by professional researchers from different countries. The sections in the book comprise of the following titles: power transmission system, manufacturing processes and system analysis, thermo-fluid systems, simulations and computer applications, and new approaches in mechanical engineering education and organization systems

EUROSENSORS XVII : book of abstracts

Fundação Calouste Gulbenkien (FCG).Fundação para a Ciência e a Tecnologia (FCT)