Online Learning-Based ANN Controller for a Grid-Interactive Solar PV System
Source abstract
Abstract
The technology transformation of industry 4.0 comprises computers, power converters such as variable speed devices, and microprocessors, which distract from the quality of power. The integration of distribution-generation technologies, such as solar photovoltaic (PV) and wind systems with source grids, frequently uses power converters, which increases the issues with power quality. DSTATCOM is the FACTS device most proficient in recompensing current-related power quality concerns. A model of DSTATCOM with an ANN controller was developed and implemented using a backpropagation online learning-based algorithm for balanced non-linear loads. This algorithm minimized the mathematical burden and the complications of control. It demonstrated a dynamic role in improving the quality of the power at the grid. The algorithm was implemented in MATLAB using an ANN model controller and the results were validated with an experimental set-up using an FPGA controller.
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Research context
OpenAlex topics: Photovoltaic System Optimization Techniques · Microgrid Control and Optimization · Smart Grid Energy Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University, Hyderabad 500085, Telangana, India
Department of Electrical and Electronics Engineering, SR University Warangal, Telangana 506371, India
Department of Electrical and Electronics Engineering, Mahatma Gandhi Institute of Technology, Hyderabad 500075, Telangana, India
Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634, USA
Department of Electrical and Electronics Engineering, University of Ryukyus, Okinawa 903-0213, Japan
References (21)
Show source-provided references
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