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Thesis (D. Tech. (Engineering: Electric)) -- Central University of technology, 2012 / Because power generation relies heavily on electricity demand, consumers are required to wisely manage their loads to consolidate the power utility‟s optimal power generation efforts. Consequently, accurate and reliable electric load forecasting systems are required. Prior to the present situation, there were various forecasting models developed primarily for electric load forecasting. Modelling short term load forecasting using artificial neural networks has recently been proposed by researchers. This project developed a model for short term load forecasting using a neural network. The concept was tested by evaluating the forecasting potential of the basic feedforward and the cascade forward neural network models. The test results showed that the cascade forward model is more efficient for this forecasting investigation. The final model is intended to be a basis for a real forecasting application. The neural model was tested using actual load data of the Bloemfontein reticulation network to predict its load for half an hour in advance. The cascade forward network demonstrates a mean absolute percentage error of less than 5% when tested using four years of utility data. In addition to reporting the summary statistics of the mean absolute percentage error, an alternate method using correlation coefficients for presenting load forecasting performance results are shown. This research proposes that a 6:1:1 cascade forward neural network can be trained with data from a month of a year and forecast the load for the same month of the following year. This research presents a new time series modeling for short term load forecasting, which can model the forecast of the half-hourly loads of weekdays, as well as of weekends and public holidays. Obtained results from extensive testing on the Bloemfontein power system network confirm the validity of the developed forecasting approach. This model can be implemented for on-line testing application to adopt a final view of its usefulness.

Thesis (MTech (Quality)--Cape Peninsula University of Technology, 2009 / There is a South African Government initiative to use State-owned Enterprises (SOE’s) to roll out a programme for the development and stimulation of local small businesses in South Africa. The state has requested SOE’s to set targets on a voluntary basis to increase trade with small businesses, with the purpose of developing small enterprises to eventually enhance skills transfer, training and employment. However, when large customers such as Eskom Nuclear Generation require ISO certification as a prerequisite for a supplier to provide goods and/or services to them, most small businesses are unable to comply. The requirement of ISO9000 compliance inhibits the ability of most small businesses to compete with their larger counterparts. Small businesses constitute as much as 90% of most world economies. They have many advantages to offer customers, such as a high level of flexibility, innovation and responsiveness to customer needs. These attributes can introduce healthy competition to the supply chain. Small businesses, by their very nature experience more risks, such as a higher vulnerability to volatile market forces and skills loss. In addition, they are generally less specialised. They are under continuous competitive pressure, and are generally not able to provide assurance of a sustainable product over a longer period. Although there is an imperative to develop and use small suppliers, they introduce higher risk to the supply chain. The primary research objective of this dissertation is to develop a robust model to identify risks inherent to small businesses, and to propose measures to mitigate such risks. A classification of problems with small suppliers that have occurred at Koeberg Nuclear Power Station over a period of 3 years (from June 2005 to May 2008), will form the basis of the research methodology. The anticipated findings of the research include the following. _ Several common critical issues of failure will be identified in the internal processes of small suppliers, with variations between types of suppliers, which will indicate which elements within the context of ISO9000 can be applied to address shortcoming in the suppliers’ processes. _ A matrix will be compiled from this by which the customer can identify the type of supplier, the types of risks inherent to that supplier, and which elements of ISO9000 the customer should insist upon to be adopted into an elementary quality management system of that small supplier. This should be executed as part of a larger supplier development programme.

Concerns about climate change brought about by the increasing usage of fossil fuels has made it imperative to develop sustainable energy usage based on renewable sources. Micro-hydel plants are an important source of renewable energy that can be exploited to supply requirements of local loads in remote locations while operating as an isolated source, or the larger network when operating in grid connected mode. The focus of this research is to develop an alternative topology to the one currently in use in micro-hydel power plants. While existing plants are based on a ballast-controlled, fixed-speed, operator-supervised model, the proposed work introduces a ballast-free, variable-speed generator capable of unsupervised operation. Conventional micro-hydel generators use o-the-shelf machines with the purported aim of reducing costs. They run at a fixed speed, maintaining constant electrical load by switch-ing a plant-situated ballast load to compensate for consumer load changes. Although the intention is to have a simplified control scheme and reduced costs, the conventional plants end up being expensive since the balance-of-system costs are increased. The plant re-quires supervision by a trained operator and frequent maintenance, failing which the reliability suers. The cost and maintenance reduction possible is analysed by comparing the proposed topology with a typical well designed conventional micro-hydel plant. The proposed topology takes the characteristics of the turbine into account, and by running at variable speed, ensures that only as much power is generated as required by the consumer load. This eliminates the ballast load and associated problems present in conventional plants. The generator can be connected to the grid, if present, enabling the available power to be fully utilized. The behavior of a hydraulic turbine operating at a fixed head and discharge rate with no flow control is analyzed. Based on the turbine characteristics, a generator topology is developed, which operates in a speed range dictated by the characteristics of the turbine. Continual supervision is unnecessary since the operation of the generator is within safe limits at all times. A simple emulator that can mimic the steady state and dynamic behaviour of the turbine is developed to test the proposed generator. The two-machine wound rotor generator proposed has an auxiliary exciter similar to a conventional brushless alternator with the additional provision for bidirectional power transfer. The shaft mounted rotor side electronics facilitate brushless operation, and to-gether with the stator side controllers form an embedded system that does away with having to tune the plant in-situ. The control scheme is evaluated for expected perfor-mance in dierent operating modes. The thesis also discusses an optimization of the synchronous speed of the generator with respect to the turbine characteristics. This minimizes the bidirectional slip power transfer requirements of the rotor side converters and leads to the lowest rating for the auxiliary machine. The proposed generator can then operate like a conventional synchronous gen-erator in the grid connected mode with a simplified control scheme.

MSc (Statistics) / Department of Statistics / Forecasting of electricity peak demand levels is important for decision makers in Eskom. The overall objective of this study was to develop medium term load forecasting models which will help decision makers in Eskom for planning of the operations of the utility company. The frequency table of hourly daily demands was carried out and the results show that most peak loads occur at hours 19:00 and 20:00, over the period 2009 to 2013. The study used generalised additive models with and without tensor product interactions to forecast electricity demand at 19:00 and 20:00 including daily peak electricity demand. Least absolute shrinkage and selection operator (Lasso) and Lasso via hierarchical interactions were used for variable selection to increase the model interpretability by eliminating irrelevant variables that are not associated with the response variable, this way also over tting is reduced. The parameters of the developed models were estimated using restricted maximum likelihood and penalized regression. The best models were selected based on smallest values of the Akaike information criterion (AIC), Bayesian information criterion (BIC) and Generalized cross validation (GCV) along with the highest Adjusted R2. Forecasts from best models with and without tensor product interactions were evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE). Operational forecasting was proposed to forecast the demand at hour 19:00 with unknown predictor variables. Empirical results from this study show that modelling hours individually during the peak period results in more accurate peak forecasts compared to forecasting daily peak electricity demand. The performance of the proposed models for hour 19:00 were compared and the generalized additive model with tensor product interactions was found to be the best tting model. / NRF