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Nonetheless, the PV system evolves a clear fuel source, is a fluctuating source of energy subsequently it has to ensure its reliability, which can be got through the Knowledge about the reliability sensing parameters such voltage, current, power factor. But when it comes to sense knowledge about the reliability parameters of the PV system over the large cover area there arise the recurrence problem. Therefore, this scenario forces the PV system to have reliability recurrently sensing prominent smart grid strategy. Hitherto, those recurrent inputs are being clustered and the parameters have been sensed but the accurate measuring is lacking due to the squander of essential data and due to the increased memory utilization for recurrence operation. Regarding this issue, this work presentedAdvanced Artificial intelligent RAP-NN (Recurrent Approximated Predictive – Neural Network) for Prominent Smart Grid Measurements which avoids increased utilization of memory by including Long Short Term Memory for recurrently updating values and also the accurate measurement of the parameters are done by fourth-order Runge-Kutta approximation strategy which in turn gives very accurateprediction parameters readily yields better power quality measurement.

Prominent smart grid measurements by RAP-NN to warrant the reliability of the PV system