A distributed networked approach for fault detection of large-scale systems

Networked systems present some key new challenges in the development of fault diagnosis architectures. This paper proposes a novel distributed networked fault detection methodology for large-scale interconnected systems. The proposed formulation incorporates a synchronization methodology with a filtering approach in order to reduce the effect of measurement noise and time delays on the fault detection performance. The proposed approach allows the monitoring of multi-rate systems, where asynchronous and delayed measurements are available. This is achieved through the development of a virtual sensor scheme with a model-based re-synchronization algorithm and a delay compensation strategy for distributed fault diagnostic units. The monitoring architecture exploits an adaptive approximator with learning capabilities for handling uncertainties in the interconnection dynamics. A consensus-based estimator with timevarying weights is introduced, for improving fault detectability in the case of variables shared among more than one subsystem. Furthermore, time-varying threshold functions are designed to prevent false-positive alarms. Analytical fault detectability sufficient conditions are derived and extensive simulation results are presented to illustrate the effectiveness of the distributed fault detection technique

Model predictive control for microgrid functionalities: review and future challenges

ABSTRACT: Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.info:eu-repo/semantics/publishedVersio

Special issue on smart interactions in cyber-physical systems: Humans, agents, robots, machines, and sensors

In recent years, there has been increasing interaction between humans and non‐human systems as we move further beyond the industrial age, the information age, and as we move into the fourth‐generation society. The ability to distinguish between human and non‐human capabilities has become more difficult to discern. Given this, it is common that cyber‐physical systems (CPSs) are rapidly integrated with human functionality, and humans have become increasingly dependent on CPSs to perform their daily routines.The constant indicators of a future where human and non‐human CPSs relationships consistently interact and where they allow each other to navigate through a set of non‐trivial goals is an interesting and rich area of research, discovery, and practical work area. The evidence of con- vergence has rapidly gained clarity, demonstrating that we can use complex combinations of sensors, artificial intelli- gence, and data to augment human life and knowledge. To expand the knowledge in this area, we should explain how to model, design, validate, implement, and experiment with these complex systems of interaction, communication, and networking, which will be developed and explored in this special issue. This special issue will include ideas of the future that are relevant for understanding, discerning, and developing the relationship between humans and non‐ human CPSs as well as the practical nature of systems that facilitate the integration between humans, agents, robots, machines, and sensors (HARMS).Fil: Kim, Donghan. Kyung Hee University;Fil: Rodriguez, Sebastian Alberto. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaFil: Matson, Eric T.. Purdue University; Estados UnidosFil: Kim, Gerard Jounghyun. Korea University

Overview of methods to analyse dynamic data

This book gives an overview of existing data analysis methods to analyse the dynamic data obtained from full scale testing, with their advantages and drawbacks. The overview of full scale testing and dynamic data analysis is limited to energy performance characterization of either building components or whole buildings. The methods range from averaging and regression methods to dynamic approaches based on system identification techniques. These methods are discussed in relation to their application in following in situ measurements: -measurement of thermal transmittance of building components based on heat flux meters; -measurement of thermal and solar transmittance of building components tested in outdoor calorimetric test cells; -measurement of heat transfer coefficient and solar aperture of whole buildings based on co-heating or transient heating tests; -characterisation of the energy performance of whole buildings based on energy use monitoring

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Secure Data Management and Transmission Infrastructure for the Future Smart Grid

Power grid has played a crucial role since its inception in the Industrial Age. It has evolved from a wide network supplying energy for incorporated multiple areas to the largest cyber-physical system. Its security and reliability are crucial to any country’s economy and stability [1]. With the emergence of the new technologies and the growing pressure of the global warming, the aging power grid can no longer meet the requirements of the modern industry, which leads to the proposal of ‘smart grid’. In smart grid, both electricity and control information communicate in a massively distributed power network. It is essential for smart grid to deliver real-time data by communication network. By using smart meter, AMI can measure energy consumption, monitor loads, collect data and forward information to collectors. Smart grid is an intelligent network consists of many technologies in not only power but also information, telecommunications and control. The most famous structure of smart grid is the three-layer structure. It divides smart grid into three different layers, each layer has its own duty. All these three layers work together, providing us a smart grid that monitor and optimize the operations of all functional units from power generation to all the end-customers [2]. To enhance the security level of future smart grid, deploying a high secure level data transmission scheme on critical nodes is an effective and practical approach. A critical node is a communication node in a cyber-physical network which can be developed to meet certain requirements. It also has firewalls and capability of intrusion detection, so it is useful for a time-critical network system, in other words, it is suitable for future smart grid. The deployment of such a scheme can be tricky regarding to different network topologies. A simple and general way is to install it on every node in the network, that is to say all nodes in this network are critical nodes, but this way takes time, energy and money. Obviously, it is not the best way to do so. Thus, we propose a multi-objective evolutionary algorithm for the searching of critical nodes. A new scheme should be proposed for smart grid. Also, an optimal planning in power grid for embedding large system can effectively ensure every power station and substation to operate safely and detect anomalies in time. Using such a new method is a reliable method to meet increasing security challenges. The evolutionary frame helps in getting optimum without calculating the gradient of the objective function. In the meanwhile, a means of decomposition is useful for exploring solutions evenly in decision space. Furthermore, constraints handling technologies can place critical nodes on optimal locations so as to enhance system security even with several constraints of limited resources and/or hardware. The high-quality experimental results have validated the efficiency and applicability of the proposed approach. It has good reason to believe that the new algorithm has a promising space over the real-world multi-objective optimization problems extracted from power grid security domain. In this thesis, a cloud-based information infrastructure is proposed to deal with the big data storage and computation problems for the future smart grid, some challenges and limitations are addressed, and a new secure data management and transmission strategy regarding increasing security challenges of future smart grid are given as well

Secure Data Management and Transmission Infrastructure for the Future Smart Grid

Power grid has played a crucial role since its inception in the Industrial Age. It has evolved from a wide network supplying energy for incorporated multiple areas to the largest cyber-physical system. Its security and reliability are crucial to any country’s economy and stability [1]. With the emergence of the new technologies and the growing pressure of the global warming, the aging power grid can no longer meet the requirements of the modern industry, which leads to the proposal of ‘smart grid’. In smart grid, both electricity and control information communicate in a massively distributed power network. It is essential for smart grid to deliver real-time data by communication network. By using smart meter, AMI can measure energy consumption, monitor loads, collect data and forward information to collectors. Smart grid is an intelligent network consists of many technologies in not only power but also information, telecommunications and control. The most famous structure of smart grid is the three-layer structure. It divides smart grid into three different layers, each layer has its own duty. All these three layers work together, providing us a smart grid that monitor and optimize the operations of all functional units from power generation to all the end-customers [2]. To enhance the security level of future smart grid, deploying a high secure level data transmission scheme on critical nodes is an effective and practical approach. A critical node is a communication node in a cyber-physical network which can be developed to meet certain requirements. It also has firewalls and capability of intrusion detection, so it is useful for a time-critical network system, in other words, it is suitable for future smart grid. The deployment of such a scheme can be tricky regarding to different network topologies. A simple and general way is to install it on every node in the network, that is to say all nodes in this network are critical nodes, but this way takes time, energy and money. Obviously, it is not the best way to do so. Thus, we propose a multi-objective evolutionary algorithm for the searching of critical nodes. A new scheme should be proposed for smart grid. Also, an optimal planning in power grid for embedding large system can effectively ensure every power station and substation to operate safely and detect anomalies in time. Using such a new method is a reliable method to meet increasing security challenges. The evolutionary frame helps in getting optimum without calculating the gradient of the objective function. In the meanwhile, a means of decomposition is useful for exploring solutions evenly in decision space. Furthermore, constraints handling technologies can place critical nodes on optimal locations so as to enhance system security even with several constraints of limited resources and/or hardware. The high-quality experimental results have validated the efficiency and applicability of the proposed approach. It has good reason to believe that the new algorithm has a promising space over the real-world multi-objective optimization problems extracted from power grid security domain. In this thesis, a cloud-based information infrastructure is proposed to deal with the big data storage and computation problems for the future smart grid, some challenges and limitations are addressed, and a new secure data management and transmission strategy regarding increasing security challenges of future smart grid are given as well

Energy efficient wireless sensor network protocols for monitoring and prognostics of large scale systems

In this work, energy-efficient protocols for wireless sensor networks (WSN) with applications to prognostics are investigated. Both analytical methods and verification are shown for the proposed methods via either hardware experiments or simulation. This work is presented in five papers. Energy-efficiency methods for WSN include distributed algorithms for i) optimal routing, ii) adaptive scheduling, iii) adaptive transmission power and data-rate control --Abstract, page iv