T. Mariprasath – författare

This book outlines the global opportunity to increase solar photovoltaic (PV) plant energy yields through modelling and analysis. Because it is endlessly available in Earth’s atmosphere, solar PV energy extraction is rising faster than all other renewable energy sources worldwide. Thus, technological improvements are needed to lower the cost of solar PV per watt every year. Since solar PV efficiency is low, modelling and analysis of boost converters, maximum power point tracing, solar PV cleaners, irradiation tracing systems, and panel tilt adjustments all help increase solar PV plant energy yield.Solar Photovoltaic System Modelling and Analysis covers topics such as:Relevance, types, and growth rate of renewable resourcesHow solar PV systems generate electricityPanel varieties and their importanceSolar PV designs and architecturesCharge controllers, including the latest technologiesBoost converter modelling and analysis in MATLABThe necessity of hybrid power plants (modelling and analysis)Designing a solar PV system including panel selection for a specific application, as well as converter and inverter estimation

This book provides in-depth information about the latest trends in transformer insulation design. This practical guide is prepared from a hands-on perspective, offering readers valuable insights into the trends in liquid dielectrics for transformer applications. Chapter 1 covers the necessity of alternate liquid dielectrics for transformers. Chapter 2 delves into the historical development of liquid dielectrics for transformer applications, drawing insights from reputable publications. It also explores the impact of nanoparticles on ester oil characteristics. In Chapter 3, the significance of spectroscopy analysis for investigating the ageing effect on both cellulosic insulating materials and oil samples is discussed. Chapter 4 describes material preparations followed by experimental analysis, estimating the degradation rate of solid and liquid dielectrics using spectroscopies. Chapter 5 discusses the importance of condition monitoring for transformers and its historical methods. Chapter 6 explores the methodology for hot spot indication and its application for assessing the transformer's condition. It covers real-time case studies as well. In Chapter 7, the book investigates the application of artificial intelligence in transformer insulation systems, leveraging machine learning algorithms to predict transformer insulation.

This is an essential resource for beginners and experienced practitioners in machine learning. This comprehensive guide covers a broad spectrum of machine learning topics, starting with an in-depth exploration of popular machine learning libraries. Readers will gain a thorough understanding of Scikit-learn, TensorFlow, PyTorch, Keras, and other pivotal libraries like XGBoost, LightGBM, and CatBoost, which are integral for efficient model development and deployment.The book delves into various neural network architectures, providing readers with a solid foundation in understanding and applying these models. Beginning with the basics of the Perceptron and its application in digit classification, it progresses to more complex structures such as multilayer perceptrons for financial forecasting, radial basis function networks for air quality prediction, and convolutional neural networks (CNNs) for image classification. Additionally, the book covers recurrent neural networks (RNNs) and their variants like long short-term memory (LSTM) and gated recurrent units (GRUs), which are crucial for time-series analysis and sequential data applications.Supervised machine learning algorithms are meticulously explained, with practical examples to illustrate their application. The book covers logistic regression and its use in predicting sports outcomes, decision trees for plant classification, random forests for traffic prediction, and support vector machines for house price prediction. Gradient boosting machines and their applications in genomics, AdaBoost for bioinformatics data classification, and extreme gradient boosting (XGBoost) for churn prediction are also discussed, providing readers with a robust toolkit for various predictive tasks.Unsupervised learning algorithms are another significant focus of the book, introducing readers to techniques for uncovering hidden patterns in data. Hierarchical clustering for gene expression data analysis, principal component analysis (PCA) for climate predictions, and singular value decomposition (SVD) for signal denoising are thoroughly explained. The book also explores applications like robot navigation and network security, demonstrating the versatility of these techniques.Natural language processing (NLP) is comprehensively covered, highlighting its fundamental concepts and various applications. The book discusses the overview of NLP, its fundamental concepts, and its diverse applications such as chatbots, virtual assistants, clinical NLP applications, and social media analytics. Detailed sections on text pre-processing, syntactic analysis, machine translation, text classification, named entity recognition, and sentiment analysis equip readers with the knowledge to build sophisticated NLP models.The final chapters of the book explore generative AI, including generative adversarial networks (GANs) for image generation, variational autoencoders for vibrational encoder training, and autoregressive models for time series forecasting. It also delves into Markov chain models for text generation, Boltzmann machines for pattern recognition, and deep belief networks for financial forecasting. Special attention is given to the application of recurrent neural networks (RNNs) for generation tasks, such as wind power plant predictions and battery range prediction, showcasing the practical implementations of generative AI in various fields.