The dynamic performance of photovoltaic supplied DC motor fed from DC-DC converter and controlled by neural networks

Ahmed Hussein, Kotaro Hirasawa, Jinglu Hu, Junichi Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

44 Citations (Scopus)

Abstract

This paper presents an adaptive neural network controller (ANNC) that is used to control the speed of a separately excited dc motor deriving a centrifugal pump load and fed from photovoltaic (PV) generator through dc-dc buck-boost converter. The controller is also used to track the maximum power point (MPP) of the PV generator by controlling the converter duty ratio. Such kind of controllers must have two objective functions to perform these two tasks, but in this research the objective function related to the MPP is converted to a constrained for the second objective function by making some approximation in the system equations. An adaptive neural network identifier (ANNI), which emulates the dynamic behavior of the motor system, plays an important role in computing the system Jacobian and hence updating the weights and biases of the ANNC. The weights and biases of both networks are updated on line using BP algorithm with adaptive learning rate. The computation of the adaptive learning rate is based on the value of the speed error through an empirical formula to get faster response with less oscillation and minimum overshoot. The transient response of the motor speed, current and voltage for a step change in the reference speed and the insolation are presented.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages607-612
Number of pages6
Volume1
Publication statusPublished - 2002
Event2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States
Duration: May 12 2002May 17 2002

Other

Other2002 International Joint Conference on Neural Networks (IJCNN '02)
Country/TerritoryUnited States
CityHonolulu, HI
Period5/12/025/17/02

All Science Journal Classification (ASJC) codes

  • Software

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