Title :
Active power line conditioner with a neural network control
Author :
Chen, Yaow-ming ; O´Connell, Robert M.
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 method of achieving harmonic reduction. The purpose of this paper is to propose a novel voltage-type APLC which cancels harmonic currents 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 net can provide switch control signals for the proposed APLC to generate compensation currents that reduce line current THD significantly
Keywords :
DC-AC power convertors; backpropagation; compensation; genetic algorithms; harmonic distortion; invertors; neurocontrollers; optimal control; power system control; power system harmonics; active power line conditioner; backpropagation; compensation current injection; computer simulation; genetic algorithm; harmonic current cancellation; harmonics compensation; inverter; line current THD; neural net training; neural network control; switch control signals; variable DC voltage source; Communication system control; Genetic algorithms; Heating; Interference; Inverters; Neural networks; Power system harmonics; Solid state circuits; Switches; Voltage control;
Conference_Titel :
Industry Applications Conference, 1996. Thirty-First IAS Annual Meeting, IAS '96., Conference Record of the 1996 IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-3544-9
DOI :
10.1109/IAS.1996.563888