The Applications of Artificial Neural Network Methodology in Converter-Interfaced Renewable Energy System: A Review
Abstract
Converter-interfaced renewable energy system (CiRES) must operate reliably, yet dynamic and uncertain conditions, including external disturbances, circuit parameter variations, nonlinear loads, security threats, etc., often introduce strong nonlinearity and intermittency. Artificial neural networks (ANNs) are increasingly adopted to address these challenges and are widely applied in CiRES. This paper presents a structured overview of ANN applications across multiple domains in CiRES, including adaptive control, anomaly detection, energy and battery management, impedance identification, maximum power point tracking and power quality improvement. By organizing these applications in a modular CiRES framework, this review highlights cross-module interactions and points to opportunities for intelligent coordination and resilient system design under practical sensing and computational constraints.
Fields of Study & Subjects
Microgrid Control and Optimization
Advanced Battery Technologies Research
HVDC Systems and Fault Protection
Publication details
- Journal / source
- IEEE Internet of Things Journal
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 13
- Issue
- 10
- Pages
- 20498–20520
- Publication date
- 9 March 2026
- Language
- EN