Abstract
A low-cost analog neural network control scheme for the inverters of Uninterruptible Power Supplies (UPS) is proposed to achieve low total harmonics distortion (THD) output voltage and good dynamic response. Such a scheme is based on learning control law from representative example patterns obtained from two simulation models. One is a multiple-feedback-loop controller for linear loads, and the other is a novel idealized load-current-feedback controller specially designed for nonlinear loads. Example patterns for various loading conditions are used in the off-line training of a selected neural network. When the training is completed, the neural network is used to control the UPS inverter on-line. A simple analog hardware is built to implement the proposed neural network controller, an optimized PI controller is built as well. Experimental results show that the proposed neural-network-controlled inverter achieves lower THD and better dynamic responses than the PI-controlled inverter does.
Original language | English |
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Title of host publication | IECON Proceedings (Industrial Electronics Conference) |
Publisher | IEEE |
Pages | 779-784 |
Number of pages | 6 |
Publication status | Published - 1 Dec 1999 |
Event | The 25th Annual Conference of the IEEE Industrial Electronics Society (IECON'99) - San Jose, CA, United States Duration: 29 Nov 1999 → 3 Dec 1999 |
Conference
Conference | The 25th Annual Conference of the IEEE Industrial Electronics Society (IECON'99) |
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Country/Territory | United States |
City | San Jose, CA |
Period | 29/11/99 → 3/12/99 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering