Abstract
Internet of Things (IoT) can significantly enhance various aspects of today’s electric power grid infrastructures for making reliable, efficient, and safe next-generation Smart Grids (SGs). However, harsh and complex power grid infrastructures and environments reduce the accuracy of the information propagating through IoT platforms. In particularly, information is corrupted due to the measurement errors, quantization errors, and transmission errors. This leads to major system failures and instabilities in power grids. Redundant information measurements and retransmissions are traditionally used to eliminate the errors in noisy communication networks. However, these techniques consume excessive resources such as energy and channel capacity and increase network latency. Therefore, we propose a novel statistical information fusion method not only for structural chain and tree-based sensor networks, but also for unstructured bidirectional graph noisy wireless sensor networks in SG environments. We evaluate the accuracy, energy savings, fusion complexity, and latency of the proposed method by comparing the said parameters with several distributed estimation algorithms using extensive simulations proposing it for several SG applications. Results prove that the overall performance of the proposed method outperforms other fusion techniques for all considered networks. Under Smart Grid communication environments, the proposed method guarantees for best performance in all fusion accuracy, complexity and energy consumption. Analytical upper bounds for the variance of the final aggregated value at the sink node for structured networks are also derived by considering all major errors.
Highlights
IntroductionNodes A, B, D, E, and F are leaf nodes of the tree that only transmit local measurements to parent nodes in Average and proposed methods of information fusion algorithms acting only as source nodes
We prove that the proposed statistical data fusion technique is such a fusion method that targets Specific Smart Grids (SGs) applications such as Home Energy Management (HMI), Advanced Metering Infrastructure (AMI), outage management, demand response management, asset management, distributed energy resource and storage, vehicle to grid energy transferring, electric vehicle charging, etc., in which latency requirement is higher than 300 ms [3]
We compare the MSEnetwork and the MSEsink node when the proposed statistical information fusion method; the Estimation Based Diffusion Kalman Filtering (EBDKF) and AVG method are employed in the presence of measurement errors, quantization errors, and transmission errors
Summary
Nodes A, B, D, E, and F are leaf nodes of the tree that only transmit local measurements to parent nodes in Average and proposed methods of information fusion algorithms acting only as source nodes. The intermediate nodes C, G, and H fuse incoming data from its child nodes with their own local measurements and forward the results to their parent nodes These nodes work as source and information fusion nodes. In contrast in EBDKF fusion technique, all the nodes aggregate data and sensor measurements due to inherent two-way communication present in diffusion techniques. For this technique there is no identifiable root node or leaf nodes unlike in Average or proposed method fusion techniques
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