2,042 research outputs found
Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids
Smart grid is a large complex network with a myriad of vulnerabilities,
usually operated in adversarial settings and regulated based on estimated
system states. In this study, we propose a novel highly secure distributed
dynamic state estimation mechanism for wide-area (multi-area) smart grids,
composed of geographically separated subregions, each supervised by a local
control center. We firstly propose a distributed state estimator assuming
regular system operation, that achieves near-optimal performance based on the
local Kalman filters and with the exchange of necessary information between
local centers. To enhance the security, we further propose to (i) protect the
network database and the network communication channels against attacks and
data manipulations via a blockchain (BC)-based system design, where the BC
operates on the peer-to-peer network of local centers, (ii) locally detect the
measurement anomalies in real-time to eliminate their effects on the state
estimation process, and (iii) detect misbehaving (hacked/faulty) local centers
in real-time via a distributed trust management scheme over the network. We
provide theoretical guarantees regarding the false alarm rates of the proposed
detection schemes, where the false alarms can be easily controlled. Numerical
studies illustrate that the proposed mechanism offers reliable state estimation
under regular system operation, timely and accurate detection of anomalies, and
good state recovery performance in case of anomalies
Optimal Observer Synthesis for Microgrids With Adaptive Send-on-Delta Sampling Over IoT Communication Networks
State estimation is one of the main challenges in the microgrids, due to the complexity of the system dynamics and the limitations of the communication network. In this regard, a novel real-time event-based optimal state estimator is introduced in this paper, which uses the proposed adaptive send-on-delta (SoD) non-uniform sampling method over wireless sensors networks. The proposed estimator requires low communication bandwidth and incurs lower computational resource cost. The threshold for the SoD sampler is made adaptive based on the average communication link delay, which is computed in a distributed form using the event-based average consensus protocol. The SoD non-uniform signal sampling approach reduces the traffic over the wireless communication network due to the events transmitted only when there is a level crossing in the measurements. The state estimator structure is extended on top of the traditional Kalman filter with the additional stages for the fusion of the received events. The error correction stage is further improved by optimal reconstruction of the signals using projection onto convex sets (POCS) algorithm. Finally, an Internet of things (IoT) experimental platform based on LoRaWAN and IEEE 802.11 (WiFi) protocols is developed to analyse the performance of the state estimator for the IEEE 5 Bus case study microgrid
Distributed Target Tracking with Fading Channels over Underwater Wireless Sensor Networks
This paper investigates the problem of distributed target tracking via
underwater wireless sensor networks (UWSNs) with fading channels. The
degradation of signal quality due to wireless channel fading can significantly
impact network reliability and subsequently reduce the tracking accuracy. To
address this issue, we propose a modified distributed unscented Kalman filter
(DUKF) named DUKF-Fc, which takes into account the effects of measurement
fluctuation and transmission failure induced by channel fading. The channel
estimation error is also considered when designing the estimator and a
sufficient condition is established to ensure the stochastic boundedness of the
estimation error. The proposed filtering scheme is versatile and possesses wide
applicability to numerous real-world scenarios, e.g., tracking a maneuvering
underwater target with acoustic sensors. Simulation results demonstrate the
effectiveness of the proposed filtering algorithm. In addition, considering the
constraints of network energy resources, the issue of investigating a trade-off
between tracking performance and energy consumption is discussed accordingly.Comment: 12 pages, 6 figures, 6 table
IoT-Based Cyber-Physical Communication Architecture: Challenges and Research Directions
In order to provide intelligent services, the Internet of Things (IoT) facilitates millions of smart cyber-physical devices to be enabled with network connectivity to sense, collect, process, and exchange information. Unfortunately, the traditional communication infrastructure is vulnerable to cyber attacks and link failures, so it is a challenging task for the IoT to explore these applications. In order to begin research and contribute into the IoT-based cyber-physical digital world, one will need to know the technical challenges and research opportunities. In this study, several key technical challenges and requirements for the IoT communication systems are identified. Basically, privacy, security, intelligent sensors/actuators design, low cost and complexity, universal antenna design, and friendly smart cyber-physical system design are the main challenges for the IoT implementation. Finally, the authors present a diverse set of cyber-physical communication system challenges such as practical implementation, distributed state estimation, real-time data collection, and system identification, which are the major issues require to be addressed in implementing an efficient and effective IoT communication system
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