IoT-based monitoring of smart grid using high-gain converter with optimized maximum power point tracking

• Published in: Electrical engineering Vol. 106; no. 3; pp. 2297 - 2311
• Main Authors: Nivedha, M., Titus, S.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-06-2024; Springer Nature B.V
• Subjects: Applications programs; Artificial neural networks; Economics and Management; Electric potential; Electric power systems; Electrical Engineering; Electrical Machines and Networks; Energy Policy; Engineering; High gain; Internet of Things; Maximum power tracking; Monitoring; Observability (systems); Original Paper; Parameters; Photovoltaic cells; Power Electronics; Sensors; Smart grid; Voltage; PV system; Random forest; Smart grid; GWO-ANN; IoT
• ISSN: 0948-7921; 1432-0487
• DOI: 10.1007/s00202-023-02070-4

The development of the smart grid (SG) offers a way to improve the generation of electrical energy as well as the corresponding transmission and distribution. Due to the versatile nature, the installing of SG consumes less area and time when compared to traditional grids. The major aim of SG is to provide controllability of assets and grid observability and enhance the security and performance of power system. An integration of internet of things (IoT) in smart grid ensures intelligent features with reduced human intervention, cost-effectiveness, and reliability. In order to communicate the useful information with the internet and other web applications, IoT-enabled sensors are widely utilized in the power grid system, which enables better grid management. A photovoltaic (PV) system and a high-gain integrated Luo converter are included in the IoT-based power monitoring system for the smart grid that is recommended as a result of these variables. The maximum solar power is tracked with a help of grey wolf optimized artificial neural network (GWO-ANN) which aids in enhanced operation of converter. The obtained converter output is applied to
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grid through a
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voltage source inverter (VSI). The parameters like temperature, intensity, converter voltage and current, and grid voltage and current are monitored by sensors and further stored in IoT webpage. The random forest (RF) classifier is used for the prediction of shortest path through which the monitored data are transmitted. The whole set-up contributes an improved monitoring of smart grid parameters which is proved by the generated results.