Active Fault-Tolerance in Wireless Networked Control Systems

In a Wireless Networked Control System (WNCS), several nodes or components of the system may communicate over the common network that connects them together. Thus, there may be communication taking place between the sensors and the controller nodes, among the controllers themselves, among the sensors themselves, among the actuator themselves, and between the controller and the actuator nodes. The purpose of this communication is to improve the performance of the control system. The performance may be a measurable quantity defined in terms of a performance criterion, as in the case of optimal control or estimation, or it may be a qualitative measure described as a desired behaviour. Each node of the WNCS may act as a decision maker, making control as well as communication decisions. The presence of a network brings in constraints in the design of the control system, as information between the various decision makers must be exchanged according to the rules and dynamics of the network. Our goal is to quantify some of these constraints, and design the control system together with the communication system so as both do their best given the constraints. This work in no way attempts to suggest the best way to design a communication network that suits the needs of a particular control system, but some of the results obtained here may be used in conjunction with other results in forming an understanding as to how to proceed in the design of such systems in the future. The work proposes a novel real-time communication protocol based on the Time Division Multiple Access (TDMA) strategy, which has built-in tolerance against the network-induced effects like lost packets, assuring a highly deterministic and reliable behaviour of the overall networked control system, thus allowing the use of classical control design methods with WNCS. Determinism in the transmission times, for sending and for receiving, is assured by a communication schedule that is dynamically updated based on the conditions of the network and the propagation environment. An advanced experimentation platform has been developed, called WiNC, which demonstrates the efficiency of the protocol with two well-known laboratory benchmarks that have very different dynamics, namely the three-tank system and the inverted pendulum system. Wireless nodes belonging to both systems are coordinated and synchronized by a master node, namely the controller node. The WiNC platform uses only open source software and general-purpose (commercial, off-the shelf) hardware, thus making it with a minimal investment (low cost) a flexible and easily extendable research platform for WNCS. And considering the general trend towards the adoption of Linux as a real-time operating system for embedded system in automation, the developed concepts and algorithms can be ported with minimum effort to the industrial embedded devices which already run Linux

Application of real-time simulation for hydropower plants monitoring

The global emission of CO2 has increased in the sector of power generation from countries outside the OECD, particularly in China which comprises two-third of the share. Meanwhile, OECD countries focus their effort in reducing their emission, where the industry area declined their emission by a quarter. Consequently, governments set up energy policies where renewable-based energies such as solar or wind energies, are highlighted. The drawback is the dependency of environment factors which generates power variations in the network leading to instabilities and a blackout in the worst case. The use of hydropower is an excellent complement to versatile renewable energy, capable of compensating these fluctuations and even be used as an energy storage. In Switzerland, hydropower is the most available resources and is constantly under development to increase capacity and energy-efficiency. Nevertheless, a lot of effort is put to extend lifetime of power generating equipment as much as possible in order to optimize the best timing for replacement or refurbishment, which requires efficient and flexible tools such as real-time simulations, which are widely used with the rapid development of computation technology. The objective of this present thesis is to study the feasibility of a multi-physics model-based real-time simulation with SIMSEN for an existing hydroelectric power plant. The concept of such system consists of using a validated model and achieves a real-time simulation taking into account boundary conditions such as water level of upstream reservoir, voltage of power network, but also set point of control system including turbine guide vane opening and the excitation system of generator. The system would enable to detect potential dysfunctions if the behavior of the power plant shows significant discrepancies in the simulation-measurement comparison during the real-time simulation. The study is divided into two main parts, which are the set-up of a test bench and the implementation of the real-time system in the power plant. The first part describes the methodology for setting-up a small-scale power unit which simulates similar behavior as a large scale one. The model includes the parameters from experimental tests used for parameters identification of synchronous machine. Two validations tests are presented: (i) a load variation (ii) sudden three-phase short circuit. The model validation is achieved comparing offline simulation results with measurement, followed by a validation in real-time with similar tests. The set-up of the test bench is concluded by a demonstration of feasibility in monitoring application by detection of a dysfunction during an opening failure of a circuit-breaker. The second part explains the modeling of a 72MW hydroelectric power plant, which includes the modeling of hydraulic components such as pipe, surge tank and Pelton turbine besides the modeling of generator. Two measurement campaigns were organized. The first consisted in collecting data in order to validate the present model. The second consisted of implementing the model running in real-time in a general-purpose computer, in the power plant and performed variations of active and reactive powers

Power System Digital Twins and Real-Time Simulations in Modern Grids

Power systems are in a state of constant change with new hardware, software and applications affecting their planning, operation, and maintenance. Power system control centers are also evolving through new technologies and functionalities to adapt to current needs. System control rooms have moved from fully manual to automated operations, from analog to digital, and have become an embedded and complex information, communication, computation and control system. Digital twins are virtual representations of physical systems, assets and/or processes. They are enabled through software, hardware and data integration, and allow real-time monitoring, controlling, prediction, optimization, and improved decision-making. Consequently, digital twins arise as a technology capable of incorporating existing control systems along with new ones to collect, classify, store, retrieve and disseminate data for the future generation of control centers. Power system digital twins (PSDTs) can uplift how data from power grids and their equipment is processed, providing operators new ways to visualize and understand the information. Nevertheless, complexity and size of modern power systems narrow the scope a current digital twin can have. Furthermore, the services provided are limited to only certain phenomena and/or applications. This thesis addresses the need for a flexible and versatile solution that is also robust and adaptable for monitoring, operating and planning future power systems. The modular design for implementation of the next generation of PSDTs is proposed based on grid applications and/or services they can provide. From a modeling perspective, this thesis also distinguishes how real-time simulations enable the design, development, and operation of a PSDT. First, the need for enhanced power system modeling and simulation techniques is established. Moreover, the necessity of expanding to a more complete and varied open-source library of power system models is identified. The thesis continues by designing, developing, and testing models of inverter-based resources that can be used by the industry and researchers when developing PSDTs. Furthermore, the first-of-its-kind synthetic grid with a longitudinal structure, the S-NEM2300-bus benchmark model, based on the Australian National Electricity Market is created. The synthetic grid is, finally, used to illustrate the first steps towards implementing a practical PSDT

Interconnected Services for Time-Series Data Management in Smart Manufacturing Scenarios

xvii, 218 p.The rise of Smart Manufacturing, together with the strategic initiatives carried out worldwide, have promoted its adoption among manufacturers who are increasingly interested in boosting data-driven applications for different purposes, such as product quality control, predictive maintenance of equipment, etc. However, the adoption of these approaches faces diverse technological challenges with regard to the data-related technologies supporting the manufacturing data life-cycle. The main contributions of this dissertation focus on two specific challenges related to the early stages of the manufacturing data life-cycle: an optimized storage of the massive amounts of data captured during the production processes and an efficient pre-processing of them. The first contribution consists in the design and development of a system that facilitates the pre-processing task of the captured time-series data through an automatized approach that helps in the selection of the most adequate pre-processing techniques to apply to each data type. The second contribution is the design and development of a three-level hierarchical architecture for time-series data storage on cloud environments that helps to manage and reduce the required data storage resources (and consequently its associated costs). Moreover, with regard to the later stages, a thirdcontribution is proposed, that leverages advanced data analytics to build an alarm prediction system that allows to conduct a predictive maintenance of equipment by anticipating the activation of different types of alarms that can be produced on a real Smart Manufacturing scenario

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

A service orientated architecture and wireless sensor network approach applied to the measurement and visualisation of a micro injection moulding process. Design, development and testing of an ESB based micro injection moulding platform using Google Gadgets and business processes for the integration of disparate hardware systems on the factory shop floor

Factory shop floors of the future will see a significant increase in interconnected devices for monitoring and control. However, if a Service Orientated Architecture (SOA) is implemented on all such devices then this will result in a large number of permutations of services and composite services. These services combined with other business level components can pose a huge challenge to manage as it is often difficult to keep an overview of all the devices, equipment and services. This thesis proposes an SOA based novel assimilation architecture for integrating disparate industrial hardware based processes and business processes of an enterprise in particular the plastics machinery environment. The key benefits of the proposed architecture are the reduction of complexity when integrating disparate hardware platforms; managing the associated services as well as allowing the Micro Injection Moulding (µIM) process to be monitored on the web through service and data integration. An Enterprise Service Bus (ESB) based middleware layer integrates the Wireless Sensor Network (WSN) based environmental and simulated machine process systems with frontend Google Gadgets (GGs) based web visualisation applications. A business process framework is proposed to manage and orchestrate the resulting services from the architecture. Results from the analysis of the WSN kits in terms of their usability and reliability showed that the Jennic WSN was easy to setup and had a reliable communication link in the polymer industrial environment with the PER being below 0.5%. The prototype Jennic WSN based µIM process monitoring system had limitations when monitoring high-resolution machine data, therefore a novel hybrid integration architecture was proposed. The assimilation architecture was implemented on a distributed server based test bed. Results from test scenarios showed that the architecture was highly scalable and could potentially allow a large number of disparate sensor based hardware systems and services to be hosted, managed, visualised and linked to form a cohesive business process

Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року

Second International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters (ICSF 2021). Kryvyi Rih, Ukraine, May 19-21, 2021.Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року