Neural Network (NN) Controller for Unified Power Quality Conditioner (UPQC)

The Neural Network (NN) controller for Unified Power Quality Conditioner (UPQC) serves as a critical operational component within the UPQC system architecture. This intelligent controller continuously monitors and analyzes grid power quality parameters through real-time data acquisition, typically implemented using sensor interfaces and analog-to-digital converters. The core algorithm employs neural network models - potentially using multilayer perceptron (MLP) or recurrent neural network (RNN) architectures - to process voltage/current waveforms and detect power quality issues such as harmonics, sags, swells, and flickers. Upon identifying disturbances, the controller executes corrective actions by generating PWM signals for power converters, often implemented through embedded C/C++ code with precise timing control using microcontroller interrupts. Beyond primary regulation functions, the NN controller incorporates advanced features including fault detection algorithms that analyze pattern deviations using statistical methods, event logging mechanisms that store timestamped data in non-volatile memory, and communication protocols (such as Modbus TCP/IP or CAN bus) for system integration. The implementation typically involves training neural networks with historical power quality data using backpropagation algorithms, then deploying the optimized model for real-time inference. This comprehensive functionality ensures optimal UPQC system performance, reliability, and extended operational lifespan through adaptive power quality management.