1. Deep neural network based MPPT algorithm and PR controller based SMO for grid connected PV system.
- Author
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Srinivasan, Rajendiran and Ramalingam Balamurugan, Chinnapettai
- Subjects
- *
PHOTOVOLTAIC power systems , *RECURRENT neural networks , *BALD eagle , *TRACKING algorithms , *ALGORITHMS - Abstract
In a grid connected photovoltaic (PV) system, the maximum power will be tracked by the conventional Perturb and Observe (P&O) algorithm. It generally produces high oscillations around maximum power point, and it fails to attain extreme power under prompt environmental conditions. To overcome these issues, this work proposes a deep neural network (DNN) based Maximum Power Point tracking (MPPT) algorithm and PR controller-based SMO for PV grid-connected system. The DNN consists of many algorithms in that here we use RNN in MPPT algorithm for tracking the maximum power. The Recurrent Neural Network (RNN) hidden layer has more number of layers. The deep network weight function is optimised by meta-heuristic-based Bald Eagle Search (BES) Optimisation. The proposed tracked RNN based Maximum Power Point (MPP) is passed through the DC/DC buck-boost converter. The inverter is tuned by Proportional Resonant (PR) controller, and it has two gain parameters like a proportional and integral gain. The PR controller gain parameters are tuned by the global optimisation algorithm of Spider Monkey Optimisation (SMO) to achieve better performance. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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