IoT Based monitoring and control of fluid transportation using machine learning

It is important to concentrate on monitoring and control of the pipeline transportation system before the failure resulting in fatal accidents. To enhance the supervision performances, the SCADA (Supervisory Control and Data Acquisition) platform is incorporated with IoT by utilizing the NB-IOT module holding a high-level engineering interface. In the proposed methodology, SCADA with the LQR-PID controller serves as Local Intelligence. When the local intelligence fails to react proactively during risk occurrences, immediately its performance is deactivated by the webserver through the NB (Narrow Band)-IoT module. For experimental real-time validation of the proposed work, a lab-scale DCS (Distributed Control System) based fluid transportation system is undertaken where flow and pressure prevail to be the most influencing parameters during risk occurrences in the pipelines. Also, the performance analyses are validated experimentally using unsupervised K-means clustering to identify abnormality caused by blockage and crack in the pipeline on the cloud-stored data

Application of a data-driven fuzzy control design to a wind turbine benchmark model

In general, the modelling of wind turbines is a challenging task, since they are complex dynamic systems, whose aerodynamics are nonlinear and unsteady. Accurate models should contain many degrees of freedom, and their control algorithm design must account for these complexities. However, these algorithms must capture the most important turbine dynamics without being too complex and unwieldy, mainly when they have to be implemented in real-time applications. The first contribution of this work consists of providing an application example of the design and testing through simulations, of a data-driven fuzzy wind turbine control. In particular, the strategy is based on fuzzy modelling and identification approaches to model-based control design. Fuzzy modelling and identification can represent an alternative for developing experimental models of complex systems, directly derived directly from measured input-output data without detailed system assumptions. Regarding the controller design, this paper suggests again a fuzzy control approach for the adjustment of both the wind turbine blade pitch angle and the generator torque. The effectiveness of the proposed strategies is assessed on the data sequences acquired from the considered wind turbine benchmark. Several experiments provide the evidence of the advantages of the proposed regulator with respect to different control methods

PV System Design and Performance

Photovoltaic solar energy technology (PV) has been developing rapidly in the past decades, leading to a multi-billion-dollar global market. It is of paramount importance that PV systems function properly, which requires the generation of expected energy both for small-scale systems that consist of a few solar modules and for very large-scale systems containing millions of modules. This book increases the understanding of the issues relevant to PV system design and correlated performance; moreover, it contains research from scholars across the globe in the fields of data analysis and data mapping for the optimal performance of PV systems, faults analysis, various causes for energy loss, and design and integration issues. The chapters in this book demonstrate the importance of designing and properly monitoring photovoltaic systems in the field in order to ensure continued good performance

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

Applications of Mathematical Models in Engineering

The most influential research topic in the twenty-first century seems to be mathematics, as it generates innovation in a wide range of research fields. It supports all engineering fields, but also areas such as medicine, healthcare, business, etc. Therefore, the intention of this Special Issue is to deal with mathematical works related to engineering and multidisciplinary problems. Modern developments in theoretical and applied science have widely depended our knowledge of the derivatives and integrals of the fractional order appearing in engineering practices. Therefore, one goal of this Special Issue is to focus on recent achievements and future challenges in the theory and applications of fractional calculus in engineering sciences. The special issue included some original research articles that address significant issues and contribute towards the development of new concepts, methodologies, applications, trends and knowledge in mathematics. Potential topics include, but are not limited to, the following: Fractional mathematical models; Computational methods for the fractional PDEs in engineering; New mathematical approaches, innovations and challenges in biotechnologies and biomedicine; Applied mathematics; Engineering research based on advanced mathematical tools

Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above