The smart grid environment comprises of the sensor for monitoring the environment for effective power supply, utilization and establishment of communication. However, the management of smart grid in the monitoring environment isa difficult process due to diversifieduser request in the sensor monitoring with the grid-connected devices. Presently, context-awaremonitoring incorporates effective management of data management and provision of services in two-way processing and computing. In a heterogeneous environment context-aware, smart grid exhibits significant performance characteristics with the grid-connected communication environment for effective data processing for sustainability and stability. Fault diagnoses in the automated system are formulated to diagnose the fault separately. This paper developed anoptimized power grid control model (OPGCM) model for fault detection in the control system model for grid-connected smart home appliances. OPGCM model uses the context-aware power-awarescheme for load management in grid-connected smart homes. Through the adaptive smart grid model,power-aware management is incorporated with the evolutionary programming model for context-awareness user communication. The OPGCM modelperforms fault diagnosis in the grid-connected control system initially, Fault diagnosis system comprises of a sequential process with the extraction of the statistical features to acquirea sustainable dataset with effective signal processing. Secondly, the features are extracted based on the sequential process for the acquired dataset with a reduction of dimensionality. Finally, the classification is performed with the deep learning model to predict or identify the fault pattern. With the OPGCM model, features are optimized with the whale optimization model to acquire features to perform fault diagnosis and classification. Simulation analysis expressed that the proposed OPGCM model exhibits ~16% improved classification accuracy compared with the ANN and HMM model.
With the advancement of Internet of Things (IoT), the network devices seem to be raising, and the cloud data centre load also raises; certain delay-sensitive services are not responded to promptly which leads to a reduced quality of service (QoS). The technique of resource estimation could offer the appropriate source for users through analyses of load of resource itself. Thus, the prediction of resource QoS was important to user fulfillment and task allotment in edge computing. This study develops a new manta ray foraging optimization with backpropagation neural network (MRFO-BPNN) model for resource estimation using quality of service (QoS) in the edge computing platform. Primarily, the MRFO-BPNN model makes use of BPNN algorithm for the estimation of resources in edge computing. Besides, the parameters relevant to the BPNN model are adjusted effectually by the use of MRFO algorithm. Moreover, an objective function is derived for the MRFO algorithm for the investigation of load state changes and choosing proper ones. To facilitate the enhanced performance of the MRFO-BPNN model, a widespread experimental analysis is made. The comprehensive comparison study highlighted the excellency of the MRFO-BPNN model.
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