Title :
Active power line conditioner with a neural network control
Author :
Chen, Yaow-ming ; Connell, Robert M O
Author_Institution :
Dept. of Electr. Eng., Missouri Univ., Columbia, MO, USA
Abstract :
Harmonics in a power system can lead to communication interference, transformer heating, or solid-state device malfunctions. Active power line conditioners (APLC) are one important way to achieve harmonic reduction. The purpose of this paper is to propose a novel voltage-type APLC, which cancels line current harmonics by injecting a compensation current. The proposed APLC consists of a variable DC voltage source, an inverter, and a neural network controller that is trained with the genetic algorithm and backpropagation. Computer simulations for two load current test cases show that the neural network can provide switch control signals for the proposed APLC to generate compensation currents that reduce line current total harmonic distortions significantly
Keywords :
backpropagation; compensation; control system analysis computing; control system synthesis; genetic algorithms; harmonic distortion; industrial power systems; neurocontrollers; optimal control; power system analysis computing; power system control; power system harmonics; active power line conditioner; backpropagation training; compensation current injection; computer simulation; control design; control simulation; genetic algorithm; harmonic reduction methods; industrial power system harmonics; inverter; line current harmonics cancellation; neural network control; total harmonic distortion; variable DC voltage source; Communication system control; Genetic algorithms; Heating; Interference; Inverters; Neural networks; Power system harmonics; Solid state circuits; Switches; Voltage control;
Journal_Title :
Industry Applications, IEEE Transactions on