M.Ing. (Electrical and Electronics) / The increased use of power electronic equipment in power networks prompted the development of various topologies to compensate for the distortion in the power networks. The various compensator topologies employ a vast range of converters for the compensation of the different non-active power components. The compensators are either designed to eliminate a specific non-active power component, or a combination of converters is used to simultaneously compensate for several non-active components. The choice of compensator depends largely on the type of load, the distortion levels in the power network, the effectiveness of the compensator and very importantly, the cost implications for the user. Under constant load conditions a particular compensator would suffice. It is however not the case when the load and the accompanied distortion varies with time, which is the case with present non-linear, dynamic high power loads on the network. In these cases,. a need for another compensator or compensation strategy, that is more effective in compensating the changing load condition, exists. It would therefore be advantageous to construct a single compensator from various converters -the hybrid compensator -, so as to enable the user to compensate effectively at all times the distortion caused by his load. In order to be able to operate such a hybrid compensator cost-effectively an intelligent control system capable of constantly monitoring the load and updating the compensation strategy, is needed. Keeping in mind that, with the technology available today, compensators can effectively operate for periods in excess of twenty years, it makes sound economical sense to operate the compensator as cost-optimally as possible. This dissertation investigates the development of an artificial neural network based controller for the cost-optimal control of a hybrid compensator. The hybrid compensator considered consists of the following: A 21 kVAR three phase FF-TCR compensator with LC-fiIters tuned at the 5th, 7th,11th and 13th harmonic frequencies and a 6 kVA three-phase dynamic power filter. The hybrid compensator is to be applied for the compensation of a 25 KVA non-linear load (Inductively loaded controlled rectifier). The above mentioned compensators have been modelled to agree with experimental pilot plants. The complete system with low-level controllers was simulated with EMTP (The Electromagnetic Transients Program). This simulation was used to verify the intelligent controller operation. The neural network based controller that is investigated, consists of a Backpropagation-trained neural network, that continuously analyses the load conditions, considers the operational characteristics and losses of the hybrid compensator and proposes a cost-optimal compensation strategy for the hybrid compensator. The modelling of the hybrid compensator's operational losses and characteristics to enable the cost-effective operation thereof is discussed. Special attention is given to the modelling of the cost-effective control strategy, in the training data used for the training of the neural network controller. The training of the neural network controller, and an evaluation of its behaviour when applied to two different hybrid compensator structures, is also given.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:4245 |
| Date | 10 March 2014 |
| Creators | Pretorius, Robert W. |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
| Rights | University of Johannesburg |
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