Integrating Control and Fault-Tolerant Wireless Network Design for Small Modular Nuclear Reactors (Invited Paper)

Wang, Wen-Chen; D’Angelo, Christopher J.; Mossé, Daniel; Cole, Daniel G.

doi:10.1109/iri.2016.51

Cited by 5 publications

(6 citation statements)

References 19 publications

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“…Wireless control systems over multi-hop wireless sensor network have received significant attention in recent years. 14,15,16,17,18,19,20 Given that the control room is usually geographically distant from the sensors and actuators, wireless networks are good for place-and-play deployment due to the lack of wires (electrical or networking). However, wireless network delays to/from the control room and packet losses can induce serious errors in the control system, which is undesirable.…”

Section: Networked Control Systemsmentioning

confidence: 99%

Advanced I&C for Fault-Tolerant Supervisory Control of Small Modular Reactors

Cole¹

2018

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In this research, we have developed a supervisory control approach to enable automated control of SMRs. By design the supervisory control system has an hierarchical, interconnected, adaptive control architecture. A considerable advantage to this architecture is that it allows subsystems to communicate at different/finer granularity, facilitates monitoring of process at the modular and plant levels, and enables supervisory control. We have investigated the deployment of automation, monitoring, and data collection technologies to enable operation of multiple SMRs. Each unit's controller collects and transfers information from local loops and optimize that unit's parameters. Information is passed from the each SMR unit controller to the supervisory controller, which supervises the actions of SMR units and manage plant processes. The information processed at the supervisory level will provide operators the necessary information needed for reactor, unit, and plant operation. In conjunction with the supervisory effort, we have investigated techniques for fault-tolerant networks, over which information is transmitted between local loops and the supervisory controller to maintain a safe level of operational normalcy in the presence of anomalies. The fault-tolerance of the supervisory control architecture, the network that supports it, and the impact of faulttolerance on multi-unit SMR plant control has been a second focus of this research. To this end, we have investigated the deployment of advanced automation, monitoring, and data collection and communications technologies to enable operation of multiple SMRs. We have created a faulttolerant multi-unit SMR supervisory controller that collects and transfers information from local loops, supervise their actions, and adaptively optimize the controller parameters. The goal of this research has been to develop the methodologies and procedures for faulttolerant supervisory control of small modular reactors. To achieve this goal, we have identified the following objectives. These objective are an ordered approach to the research: I) Development of a supervisory digital I&C system II) Fault-tolerance of the supervisory control architecture III) Automated decision making and online monitoring.

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Section: Networked Control Systemsmentioning

confidence: 99%

Advanced I&C for Fault-Tolerant Supervisory Control of Small Modular Reactors

Cole¹

2018

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“…Integration and co-design of network and control system are effective for WCSs both when exploring network topological conditions and control system stability [25] or the integration of wireless network and control in NPPs [34,36,37]. In [16], the author proposes an algorithm on data link layer TDMA scheduling to achieve higher delivery ratio for emergency packets than regular packets.…”

Section: Related Workmentioning

confidence: 99%

“…If M 3 also gets lost, the induced delay D 3 becomes 0.4s and the controller will (re-)use M 1 . D is related to both network delay and the number of consecutive packet losses, which is a function of the packet delivery ratio (delivery ratio estimation and end-to-end worst-case delay estimation have been studied elsewhere [29,34]). n loss is estimated by the expected value of the network loss ratio (1-DR).…”

Section: Network Imperfection Modelmentioning

confidence: 99%

“…The steps are as follows. (1) for each average LSR value (LSR = 0.5, 0.51, 0.52, ..., 1.0), we estimate the delay induced delay D for each network configuration by applying the worst-case network delay (D networ k ) estimation (e.g., [29,30,34]) and DR estimation (e.g., [34]); (2) our algorithm searches all network configurations in a brute-force way (1,500 possibilities: 30 configurations and 50 LSR values) and finds the one with minimum induced delay, D; (3) we find a set of estimated optimal network topologies with minimum total induced delay D for each LSR value and store them in a lookup table T indexed by LSR value. Note that our algorithm is generic when we can estimate D for each configuration (how to estimate D is not the focus of this paper).…”

Section: Optimal Network Configurationsmentioning

confidence: 99%

“…Wireless control systems (WCS) are composed of controllers, sensors and actuators connected via a multi-hop wireless network [7,12,15,16,25,26,34]. Given that the control room is usually geographically distant from the sensors and actuators, wireless networks are good due to the lack of wires (electrical or networking).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Wireless Network Reconfiguration for Control System applied to a Nuclear Reactor Case Study

Wang

Mossé

Cole

et al. 2018

Proceedings of the 26th International Conference on Real-Time Networks and Systems

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Control systems using sensors and wireless networks are becoming more prevalent, due to its ease of deployment (no wires and no electricity) and maintenance (longer lifetime). However, interference and noise can cause delays and packet losses that can degrade control system performance, which leads us to find the optimal network configuration to minimize that impact. Another problem of wireless networks for control systems is caused by time-varying fault patterns, which motivates us to do network reconfiguration at runtime. We focus on the online wireless network reconfiguration in an embedded networked and cyber-physical system (CPS), with four main contributions: (1) the design and implementation of a novel network reconfiguration framework with offline and online components that consider time-correlated link failures; (2) a network imperfection model; (3) six online reconfiguration algorithms for wireless control system; (4) a case study with 12 hops and up to 50 nodes that controls a nonlinear small modular nuclear reactor. Our network imperfection model is accurate (with 0.993 Pearson correlation) and our online reconfiguration algorithms have smaller error and longer network lifetime than a state-of-theart static scheme.

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