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Artificial neural network based dynamic dump load control for integrated standalone microgrid

Year: 2026

Type: article

Abstract: ANN control approach for DC-link stabilization and power flow coordination in a standalone hybrid microgrid formed by a 10 kWp photovoltaic array, a 20 kW wind turbine generator, and a 60 kWh battery storage system operating on a 600 V bus. A controllable dump-load branch is integrated into the design to dissipate surplus energy when the battery reaches its upper state-of-charge limit. The ANN dynamically generates converter duty ratios based on its inputs of DC-link error, its rate of change, and real-time renewable power. Simulation studies conducted in MATLAB demonstrate that the proposed controller maintains DC-link variations within ±2 %, reduces voltage ripple to approximately 3.2 V, and limits current distortion to 2.64 %, while a PI controller exhibits slower recovery, higher overshoot, and post-filter harmonics of about 3.82 %. The results confirm that ANN-based dump-load coordination ensures stable operation, prevents converter stress due to power surplus, and improves the overall utilization of renewable energy in standalone microgrid environments.

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/e3sconf/202669201020/pdf

Authors: Anand Kumar Myla, Srinivasa Rao Gorantla

Institution: Vignan's Foundation for Science, Technology & Research

Language: English

Topic: Microgrid Control and Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W7128062908

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