An ANN Based MPPT for Power Monitoring in Smart Grid using Interleaved Boost Converter

Abstract

The energy sector is highly concerned about the rapid growth in power utilization which causes an imbalance between supply and demand. A demand-side energy management technique has to be developed to prevent substantial supply-side shortages and improve energy efficiency. The current trend in energy management is to lower the price of electricity without limiting use, instead preferring to minimize power usage during peak hours. To address the issue mentioned earlier and balance the overall system, a flexible and portable system that can serve a wide range of customers is necessary. Hence, a grid-connected hybrid system's improved power monitoring technique is proposed to generate and maintain constant DC voltage. An Artificial Neural Network (ANN) based Maximum Power Point Tracking (MPPT) technique is introduced in the smart grid using an interleaved boost converter to achieve power quality improvement with the aid of the Internet of Things (IoT). The PV voltage is improved with an interleaved boost converter, and optimum reliability is obtained with the aid of the ANN approach. The proposed technique can offer a robust and economical solution for tracking the maximum power, which guarantees regulated output and promotes the extraction of all available power from solar panels. By using a 3ϕ Voltage Source Inverter (VSI), the hybrid system's output is fed to the grid, and the sensors placed in the IoT module measure the generated power. From the results, it is demonstrated that the PV-based IoT approach aids in the compensation of grid stability, power quality issues, and harmonic reduction in the distribution network.

Keywords

• artificial neural network
• hybrid grid connected system
• internet of things
• maximum power point tracking
• voltage source inverter