Iot Based Solar Fault Identification Using Ann Classification
|International Journal of Electronics and Communication Engineering|
|© 2021 by SSRG - IJECE Journal|
|Volume 8 Issue 2|
|Year of Publication : 2021|
|Authors : G.Narmadha, Sakthivel.B|
How to Cite?
G.Narmadha, Sakthivel.B, "Iot Based Solar Fault Identification Using Ann Classification," SSRG International Journal of Electronics and Communication Engineering, vol. 8, no. 2, pp. 8-11, 2021. Crossref, https://doi.org/10.14445/23488549/IJECE-V8I2P103
Solar plants should be checked for perfect power yield. This recovers productive power production from power plants while detecting for faulty photovoltaic (PV), associations, sprinkle amassed on panels getting down yield some of the issues are also executed. According to this issue, an automated IOT based solar power checking framework is proposed, which is considered that the power of solar is updated through the internet. In this proposed work, an Arduino-based framework is presented that is incorporated with the sensors such as Light Dependent Resistor (LDR), Current Transformer (CT), and Potential Transformer (PT) sensors, respectively. These sensors are used for estimating boundaries to screen solar panels. In this work, by using a parameter of LDR sensor power and panel estimated voltage, the solar power is evaluated. Here, the machine learning algorithm artificial neural network (ANN) is presented in this work which is utilized to detect and order fault precisely. Thus, the framework continually detects the PV, and the power yield is observed IOT framework over the web. The IOT connection shows these boundaries to the client utilizing a successful GUI and furthermore cautions the client when the yield falls underneath explicit cutoff points. This makes distantly observing solar plants exceptionally simple and guarantees the best power yield.
IOT, ANN, LDR, PT Sensor, Solar fault, PV.
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