Renewable Energy and Future Power Systems

This book covers advanced technologies for renewable energy and power systems, including neural network applications, deep learning, multilevel inverters, solid-state transformers, and fault detection. It also discusses the role of artificial intelligence and machine learning in power systems.

Format: Paperback / softback
Length: 260 pages
Publication date: 29 March 2022
Publisher: Springer Verlag, Singapore

This comprehensive book delves into cutting-edge technologies for applications in renewable energy and power systems. It encompasses a wide range of topics, including neural network applications in power electronics, deep learning applications in power systems, design and simulation of multilevel inverters, solid-state transformers, neural network applications for fault detection in power electronics, and much more. Moreover, the book explores the crucial role of artificial intelligence in power systems and the use of machine learning for renewable energy. With its extensive coverage and in-depth analysis, this book is a valuable resource for researchers, professionals, and technocrats seeking to advance their knowledge and expertise in power systems, power distribution, and grid operations.

Introduction:
The field of renewable energy and power systems has witnessed significant advancements in recent years, driven by the need for sustainable and efficient energy sources. In this context, advanced technologies have emerged as key tools in addressing the challenges faced by these systems. This book aims to provide a comprehensive overview of these advanced technologies, their applications, and their potential impact on the future of renewable energy and power systems.

Neural Network Applications in Power Electronics:
Neural networks have gained widespread popularity in the field of power electronics due to their ability to learn and adapt to complex patterns. In power electronics, neural networks are used for various applications, such as power factor correction, voltage regulation, and power management. One of the key applications of neural networks in power electronics is in the area of power factor correction. Power factor correction is a process that improves the efficiency of power distribution by reducing the amount of reactive power consumed. Neural networks can learn the relationship between the input voltage and current and adjust the switching frequency of power electronics devices to achieve a better power factor. Another application of neural networks in power electronics is in the area of voltage regulation. Voltage regulation is a process that maintains a constant voltage level at the output of power electronics devices. Neural networks can learn the dynamics of power systems and adjust the output voltage of power electronics devices to maintain a stable voltage level.

Deep Learning Applications in Power Systems:
Deep learning is a subset of machine learning that involves the use of neural networks with multiple layers to learn complex patterns. In power systems, deep learning is used for various applications, such as fault detection, load forecasting, and power system optimization. Fault detection is one of the most important applications of deep learning in power systems. Deep learning algorithms can analyze large amounts of data and identify patterns that indicate the presence of faults in power systems. This can help power system operators detect and isolate faults quickly, reducing the downtime and the impact of outages on the grid. Load forecasting is another important application of deep learning in power systems. Load forecasting is the process of predicting the future demand for electricity. Deep learning algorithms can analyze historical data and make accurate predictions about future electricity demand, which can help power system operators optimize their generation and distribution systems.

Design and Simulation of Multilevel Inverters:
Multilevel inverters are a type of power electronics device that converts DC power into AC power. They are used in a wide range of applications, such as solar power systems, wind power systems, and electric vehicles. The design and simulation of multilevel inverters are important for improving their efficiency and performance. Multilevel inverters are designed to convert DC power into multiple levels of AC power. Each level of AC power has a different voltage and frequency, which can be used to optimize the efficiency of the inverter. Design and simulation of multilevel inverters can help researchers and engineers optimize the design of these devices, reducing their size, weight, and cost.

Solid-State Transformers:
Solid-state transformers are a type of power electronics device that uses solid-state materials to convert electrical power. They are used in a wide range of applications, such as power distribution, power generation, and power conditioning. Solid-state transformers are known for their high efficiency, low noise, and low loss. They are also resistant to environmental factors, such as temperature and humidity. Design and simulation of solid-state transformers can help researchers and engineers optimize the design of these devices, improving their efficiency and performance.

Neural Network Applications for Fault Detection in Power Electronics:
Neural networks are also used for fault detection in power electronics. Fault detection is a process that identifies and isolates faults in power systems. Neural networks can analyze large amounts of data and identify patterns that indicate the presence of faults. This can help power system operators detect and isolate faults quickly, reducing the downtime and the impact of outages on the grid.

Artificial Intelligence in Power Systems:
Artificial intelligence is playing an increasingly important role in power systems. It is used for various applications, such as power system monitoring, control, and optimization. Artificial intelligence algorithms can analyze large amounts of data and make accurate predictions about the behavior of power systems. This can help power system operators optimize their operations and improve the efficiency of their systems.

Machine Learning for Renewable Energy:
Machine learning is also used for renewable energy applications. Machine learning algorithms can analyze data from renewable energy sources, such as solar panels and wind turbines, and make predictions about their performance. This can help renewable energy operators optimize their operations and improve the efficiency of their systems.

Conclusion:
In conclusion, this book provides a comprehensive overview of advanced technologies for applications in renewable energy and power systems. It covers a wide range of topics, including neural network applications in power electronics, deep learning applications in power systems, design and simulation of multilevel inverters, solid-state transformers, neural network applications for fault detection in power electronics, and artificial intelligence in power systems. The book also discusses the important role of machine learning for renewable energy. With its extensive coverage and in-depth analysis, this book is a valuable resource for researchers, professionals, and technocrats seeking to advance their knowledge and expertise in power systems, power distribution, and grid operations.

Weight: 427g
Dimension: 235 x 155 (mm)
ISBN-13: 9789813367555
Edition number: 1st ed. 2021