Hybrid FPGA and Neural Network Architecture for Real-Time Power Quality Monitoring

The increasing integration of nonlinear loads and renewable energy sources has intensified power quality disturbances in modern electrical grids. This paper presents a hybrid FPGA and Neural Network architecture for real-time power quality monitoring with high speed and accuracy. The proposed system combines the parallel processing capability of Field-Programmable Gate Arrays (FPGAs) with the intelligent pattern recognition ability of Artificial Neural Networks (ANNs). The FPGA performs high-speed signal acquisition, preprocessing, and feature extraction from voltage and current waveforms. Extracted features are supplied to the neural network for automatic classification of power quality events. The architecture effectively identifies disturbances such as voltage sag, swell, harmonics, transients, interruptions, and flicker. Hardware acceleration significantly reduces processing latency, enabling real-time operation in smart grid environments. The neural network improves classification accuracy by learning complex nonlinear patterns from training data. The proposed design offers scalability, reliability, and low power consumption for embedded monitoring applications. Experimental evaluation demonstrates superior performance compared to conventional software-based monitoring techniques. The hybrid implementation achieves fast response times while maintaining high detection precision under varying operating conditions. The system is suitable for industrial power systems, renewable energy integration, and smart grid monitoring. The proposed architecture provides an efficient and cost-effective solution for continuous power quality assessment. Overall, the integration of FPGA hardware with neural network intelligence enhances the speed, accuracy, and reliability of real-time power quality monitoring systems.