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Connected and Autonomous Electric Vehicle Charging Infrastructure Integration to Microgrids in Future Smart Cities
Published in Hussein T. Mouftah, Melike Erol-Kantarci, Sameh Sorour, Connected and Autonomous Vehicles in Smart Cities, 2020
Mohammad Sadeghi, Melike Erol-Kantarci, Hussein T. Mouftah
The central control schemes are well-established and widely implemented in practice for decades. However, the increase in consumers, distributed energy generation and storing, and renewable energy sources make the central control approaches to be less effective. The central approaches are unable to operate and control the future microgrid systems due to the following: The increase in the number of users including the number of CAEVs integrated with microgrids imposes heavy computational load which cannot be handled with the central approach.It is difficult to expand the central control systems, whereas microgrids need to evolve and expand very fast.A single point of failure results in the failure of the whole system. Therefore, these control methods are more suitable for small-scale systems.High level of connectivity is necessary in central approaches, as each agent should be connected to the central unit individually which imposes enormous cost as the number of agents grows.
Upgrading Security
Published in Frank R. Spellman, The Drinking Water Handbook, 2017
Conduct physical security surveys and assess all remote sites connected to the SCADA network to evaluate their security. Any location that has a connection to the SCADA network is a target, especially unmanned or unguarded remote sites. Conduct a physical security survey and inventory access points at each facility that has a connection to the SCADA system. Identify and assess any source of information, including remote telephone/ computer network/fiberoptic cables, that could be tapped; radio and microwave links that are exploitable; computer terminals that could be accessed; and wireless local area network access points. Identify and eliminate single points of failure. The security of the site must be adequate to detect or prevent unauthorized access. Do not allow live network access points at remote, unguarded sites simply for convenience.
Robust Passive Fault Tolerant Control for Air Fuel Ratio Control of Internal Combustion Gasoline Engine for Sensor and Actuator Faults
Published in IETE Journal of Research, 2023
Arslan Ahmed Amin, Khalid Mahmood-ul-Hasan
Redundancy is one of the important characteristics of FTCS [21,22]. It is characterized into two types: hardware and analytical. Dual redundancy is an important scheme of hardware redundancy as shown in Figure 3 in which two components perform same function in parallel, one as main and other as backup. If fault appears in primary component, the operation is taken over by the standby component isolating the faulty one. Hardware redundancy plays an important role in eliminating single point of failure in the system [23,24]. A single point of failure means fault in a solo component causes complete system failure. In the Analytical Redundancy (AR), instead of adding additional hardware component, software-based algorithm is designed to produce parameter value as per defined model of the system. In event of fault in the component, a software value is used in the control algorithm. The AR approach can be used to save extra cost of hardware and space, however, accurate modelling of the system is essential for the AR design.
Automated fault detection for additive manufacturing using vibration sensors
Published in International Journal of Computer Integrated Manufacturing, 2021
Roberto Milton Scheffel, Antônio Augusto Fröhlich, Marco Silvestri
The Fault-Tolerant Trustful Space-Time Protocol (FT – TSTP) (Fröhlich et al. 2019; Scheffel and Fröhlich 2018a) transports data from devices (e.g. sensors, actuators, machines) to multiple gateways connected to an IT infrastructure. It is intended to deliver data to the application despite transient network and gateway failures (benign and malicious). FT-TSTP relies on node redundancy to achieve high data availability, modeling a kind of mesh network in which each device can act as a forwarder of messages originated by other devices. Nodes forward messages based on the distance to every gateway using a greedy, fully reactive, geographic routing algorithm. Gateway redundancy is employed to avoid single points of failure in the system.
Cyber-Physical Systems, a new formal paradigm to model redundancy and resiliency
Published in Enterprise Information Systems, 2020
Initially the intention of redundancy is the increasing reliability of the system but it can also present itself as modelling errors. Various methodologies exist in literature to face the redundancy optimization problems. (Aggarwal, Gupta, and Misra 1975) state to select the stage where redundancy is to be added, an heuristic criterion is introduced which takes into account the relative increment in reliability versus decrement in performance. (Dokhanchi, Hoxha, and Fainekos 2018) propose a framework for the elicitation and debugging of formal specifications for Cyber-Physical Systems through two debugging algorithms. One checks for erroneous or incomplete temporal logic specifications without considering the system and the other can be used for the analysis of reactive requirements with respect to system test traces. The user study establishes that requirement errors are common and that the debugging framework can resolve many insidious specification modelers’ errors. The redundancy study is also a way to prevent a single-point of failure, as stated by (Cardenas, Amin, and Sastry 2008) or hardware failure that is not considered in design (Wan et al. 2011) thus, practical systems may need to incorporate redundancy. The knowledge of the domain and application context may help to unify information presentation and permits subsystems reuse, so as to reduce information redundancy during the process of semantic interpretation in the agents. (Lin, Sedigh, and Miller 2010) define an ontology for the system domain to reduce information redundancy in the model and simplify the data interpretation procedure. The redundancy problem has been studied under multiple points of views, our approach focuses the structural evidence coming from the formal knowledge extracted by the data clustering. It has become increasingly crucial that cognitive representations of the models need to be created as a pre-requisite for assessing and intelligently managing the complexity, maneuvering through uncertain environments and eventually achieving the optimized outcomes (Chung-SheChung-Sheng, Darema, and Chang 2018).