Department of Electrical and Information Engineering, Faculty of Engineering, University of Ruhuna, Galle 80000, Southern Province, Sri Lanka.
Department of Electrical and Computer Engineering, Faculty of Information and Communication Technology, University of Calgary, Calgary, AB T5J0N3, Canada.
Sensors (Basel). 2020 Jan 20;20(2):567. doi: 10.3390/s20020567.
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.
物联网 (IoT) 可以显著增强当今电力电网基础设施的各个方面，从而实现可靠、高效和安全的下一代智能电网 (SGs)。然而，恶劣和复杂的电网基础设施和环境降低了通过物联网平台传播的信息的准确性。特别是，由于测量误差、量化误差和传输误差，信息会被损坏。这会导致电网中的重大系统故障和不稳定。传统上，冗余信息测量和重传用于消除噪声通信网络中的错误。然而，这些技术会消耗过多的资源，如能源和信道容量，并增加网络延迟。因此，我们提出了一种新的统计信息融合方法，不仅适用于结构链和基于树的传感器网络，也适用于 SG 环境中的无结构双向图噪声无线传感器网络。我们通过使用广泛的仿真来比较所述参数与几种分布式估计算法，评估了所提出方法的准确性、节能、融合复杂性和延迟，并提出了几种 SG 应用。结果证明，所提出的方法在所有考虑的网络中都优于其他融合技术，具有更好的整体性能。在智能电网通信环境下，所提出的方法保证了在所有融合准确性、复杂性和能耗方面的最佳性能。还通过考虑所有主要误差，为结构网络中汇节点处最终聚合值的方差推导出了分析上限。
