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Artificial intelligence for the control of hybrid power filters

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dc.contributor.advisor Prof. J.D. van Wyk; Prof. I.S. Shaw. en_US
dc.contributor.author Van Schoor, George
dc.date.accessioned 2012-08-15T07:14:07Z
dc.date.available 2012-08-15T07:14:07Z
dc.date.issued 2012-08-15
dc.date.submitted 2000-07
dc.identifier.uri http://hdl.handle.net/10210/5757
dc.description D.Ing. en_US
dc.description.abstract Training data are developed for an ANN controlling a laboratory scale HPC. Special attention is given to the development of a cost function to determine the optimal state of the HPC for a particular input state. The cost function uses the reactive power compensation efficiency (77Q), the distortion compensation efficiency (rid) and the losses in the HPC (PLHP0 as optimisation parameters. After a process of optimisation the ANN is trained with a randomised training set of size 2000. A 5:10:6 ANN topology representing 5 input layer neurons, 10 hidden layer neurons and 6 output layer neurons is used. The optimisation results in shorter training times as well as more effective training. A laboratory scale experiment, which practically proves that an ANN can make meaningful choices in terms of HPC control, is conducted. The adaptive behaviour of the ANN controller for the HPC is evaluated by means of the interactive integrated state space model. It is found that the ANN controller can sensibly adapt its output under conditions of line impedance change as well as conditions of load changes of users sharing the same point of coupling as the consumer being compensated. The conclusion from this research is that it is viable to apply AI in the control of an HPC. A non-linear, time-varying system such as this ideally lends itself to the application of ANN control. The total cost of the HPC is expected to be minimised while minimum standards in terms of compensation are still maintained. The performance of such an ANN controller is however strongly dependent on the integrity of the training data. Using an actual system to set up the training data would be the ideal in refining the ANN model. Devising a strategy to continually update the training of the ANN to ensure the relevancy with respect to the dynamic range of the ANN is recommended as an area for further research. en_US
dc.language.iso en en_US
dc.subject Artificial intelligence. en_US
dc.subject Capacitors. en_US
dc.title Artificial intelligence for the control of hybrid power filters en_US
dc.type Thesis en_US

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