D.Ing / It is every person’s social responsibility to ensure that electrical energy is used as efficiently as possible. This is as a result of the considerable fossil fuels that are currently required to generate electricity. These fuels are available in limited supply on Earth and result in air pollution when consumed in the electrical energy generation process. Moreover, as scarcity increases, not just in fuel reserves, but also in electricity infrastructure such as servitudes, generation capacity etc, the costs of electricity also rises. This then brings about an opportunity to reduce input costs if the electrical energy is utilized as efficiently as possible. This can however only be done by the application of a suitable strategy. This thesis develops an electrical load management (ELM) strategy which may be effective in reducing input costs, by reducing electrical energy costs. This strategy has it’s foundation in tried-and-tested ELM strategies (albeit called by other names such as demand-side management (DSM) and Energy Management (EM)) developed by the world’s foremost utility research organization called EPRI over a number of decades, thereby ensuring, to some extent, the success of the proposed strategy. The strategy has been tested, in its constituent parts, in a real world environment in the South African mining industry. The examples of the sub-elements that have been tested in the industry are the artificial neural network (ANN) for short-term forecasting; the statistical regression technique for short-term load forecasting; the analysis of the external factors affecting the electricity supply industry and also the comparison of electricity tariffs in the mining industry. The validity of the strategy is further enhanced by the involvement of Technology Managers within the mining industry which have been involved with ELM in the mining industry for a number of years. Their input was solicited via an in-depth survey which was conducted in this industry. This survey represents the ELM strategy currently in existence of: - 62 shafts or open pit operations, 44 process plants and 5 smelter operations. The largest mining groups in South Africa were involved in this survey so that this survey represents: amongst others, 40% of the gold mining industry, 62% of the platinum mining industry and 95% of the diamond mining industry. The collective experience represented by the survey is equivalent to 67 man-years in ELM in the mining industry. Electricity tariffs are the means by which benefits for electrical load management are obtained. It thus warranted an analysis of all the factors affecting the electricity tariffs and in particular the factors affecting the price of electricity. To this end the Electricity Supply Industry (ESI) was analyzed in-depth and proactively to identify the external factors which may affect the price of electricity. Production intrusions may not be tolerated in the mining industry and as these intrusions have been the major cause for abandoning such ELM strategies previously, an electrical load model with production correlation was developed in this research which affords production a very high priority in the ELM strategy. Moreover, this load model, which is a key element of the ELM strategy in this thesis, forecasts the electrical efficiency of a mine in the near future. The effect of this efficiency forecast is to give management a real-time and proactive tool by which to make decisions. This approach avoids potentially large inefficiencies on the overall mine load such as when the electrical efficiency was only checked at the end of each month. This model may be used either in real-time control mode or in simulation mode to test various ELM initiatives before they are implemented. The model has either a statistical-regression based load-forecasting algorithm or an Artificial Neural Network (ANN) load-forecasting algorithm at its core. The choice of which forecasting methodology is used is determined by the value of the Pearson’s rank correlation coefficient for a set of test data. The latest prevailing ELM technologies have also been incorporated into a matrix for easy identification. The matrix should assist with the implementation of this ELM strategy. Not all of the technologies found in the matrix result in control of the mining load for ELM initiatives such as: “peak-clipping”, “load-shifting” or “valley-filling”. Some of these technologies result in “conservation” of the electrical energy by the application of newer and more efficient techniques to perform the necessary activities found on a typical mine (drilling, ventilation, cooling etc.). A complete strategy for ELM in the South African mining industry is thus developed in this thesis which overcomes two of the most serious pitfalls associated with previous strategies. These pitfalls being, the inadequate focus on production in those strategies and also the lack of real-time, efficiency-forecasting of the overall mine load. The strategy also focuses the potential Electrical Load Manager on the key steps of this process, by means of an intuitive, step-by-step approach. It is grounded in the demand-side management (DSM) experiences of the past, enhanced by actual case studies of the sub-elements in the mining industry and has been ratified by the involvement of very experienced Technology Managers active in ELM in South African mining industry.

M. Phil. / Presently, ESKOM is rated as the fifth largest utility in the world that generates and distributes electricity power to their consumers at the lowest price per kilowatt-hour (kW.h). As a utility, ESKOM is the largest supplier of electrical energy in South Africa and is currently generating and distributing on demand to approximately 3000 consumers. This represents 92% of the South African market. ESKOM was selected as the utility supplying electrical energy for the purpose of this study. ESKOM's objective is to provide the means and systems by which the consumer can be satisfied with electricity at the most cost-effective manner. In order to integrate the consumers into these objectives, ESKOM took a decision in 1994 to change the supply tariff from active power (kW) to apparent power (kVA) for a number of reasons: To establish a structure whereby the utility and the consumer can control the utilisation of electrical power supply to the consumer. To utilise demand and control through power factor correction and implementation of load management systems. To identify some cost implications of electrical power factor correction and load management. Consumers with kW maximum demand tariff options had little or no financial incentives to improve their low power factor (PF) by reducing their reactive current supply. Switching to (kVA) maximum demand will involve steps to be taken to ensure that the reactive component is kept to a minimum with maximum power factor. ESKOM has structured various tariff rates and charges with unique features that would accommodate the consumers in their demand side management and load cost requirements, which, when applied, will result in an efficient and cost effective load profile. These tariffs are designed to guide consumers automatically into an efficient way of using electrical power, as it is designed to recover both the capital investment and the operating cost within two to three years after installation of power factor correction equipment. ESKOM's concept of Time-of-use (TOU) periods for peak, standard and off-peak times during week, Saturday and Sunday periods is discussed as load management. Interruptible loads can be scheduled or shed to suit lower tariff rates and to avoid maximum demand charge. The concept of load management will change the operation pattern of the consumer's electricity demand whereby the consumer will have immediate technical and financial benefits. In the last chapter of this dissertation, a hypothetical case study addresses and concludes on some of the technical and cost implications of electrical power factor correction and load management as a successful and profitable solution to optimize electrical power supply to the consumer. By implementing the above, ESKOM ensures that the consumer utilizes the electrical power supply to its optimum level at the lowest cost per kilowatthour (kW.h) generated.

M.Ing. (Electrical and Electronic Engineering) / Long transmission lines in power system require high line loading in order to lower voltage limits due to line losses. For relatively long lines, line charging is high and thus higher voltage limits reached at low loading. It follows then that it is a challenge to maintaining the voltages between the acceptable limits for relatively long lines. This dissertation highlights the problems experienced when load varying from very low to very high is supplied by very long parallel lines of different impedance characteristic. When the load is extremely high, there are low voltages experienced which are solved by use of shunt capacitors and/or adding more lines. When the load is extremely low, there are high voltages experienced which are solved by use of shunt reactors and/or switching some of the lines off. The type of solutions to this two loading extremes as indicated above, can be problematic, in that; new lines requires servitudes which can take too long, shunt capacitors and reactors in this type of the network is not desirable since the introduction of too many of these devices have maintenance implications and they would require continuous switching to maintain acceptable voltages, resulting in complicated operation of the network. This research proposes the use of a phase shifting transformer located on one of two parallel corridors supplying power to a load located remotely from the rest of the system. The transformer is able to rearrange the active power flows to vary loadings of the corridors and the improvements in voltage regulation can be realised during both low and high load conditions.

D.Phil. (Electrical and Electronic Engineering) / Load forecasting is a necessary and an important task for both the electrical consumer and electrical supplier. Whilst many studies emphasize the importance of determining the future demand, few papers address both the forecasting algorithm and computational resources needed to offer a turnkey solution to address the load forecasting problem. The major contribution that, this paper identified is a turnkey load forecasting algorithm. A turnkey forecasting solution is defined by a comprehensive solution that incorporates both the algorithm and processing elements needed to execute the algorithm in the most effective and efficient manner. An electrical consumer, namely the operator of a rapid railway system was faced with a problem of having to forecast the notified network demand and energy consumption. The forecast period was expected to be between a very short term window for maintenance reasons and long term for the requirements warranted by the electrical supplier. The problem was addressed by firstly reviewing the most common forms of load forecasting for which there are two types. These are statistically based methods and methods based upon artificial intelligence. The basic principle of a statistical approach is to approximate or define a curve that best defines the relationship between the load and its parameters. Regression and similar day approach methods use the defined correlation of past values in order to forecast the future behaviour. In other words the future load forecast is forecasted by observing the behaviour of the factors that influenced the load behaviour in the past. The underlying factors that influence the final load may be identified by means of a top down drill down approach. In this way both the load factors and influential variables may be identified. This paper makes use of relevance trees to create a structure of load and influential variables. For a regression forecasting model, the behaviour of the load is modelled according to weather and non-weather variables. The load may be stochastic or deterministic, linear or nonlinear. One of the biggest problems with statistical models is the lack of generality. One model may yield more acceptable results over another model simply because of the sensitivity of the model to one load element that defines the model significantly. Regression type forecast models are an example of this where the elements that define the load are broadly divided into weather and non-weather elements. It is important that the correlation curve reflects the true correlation between the load and its elements. The recursive properties of a statistical based techniques (Kalman filter) allows that the relationship be refined. For methods such as neural networks, the relationship between the load elements that define the future load behaviour is learnt by presenting a series of patterns and then a forecast model is derived. Rigorous mathematical equations are replaced with an artificial neural network where the load curve is learnt. Unlike a statistical based approach (ARMA models), the load does not first need to be defined as a stochastic or deterministic series. In terms of a stochastic approach (non stationery process), the load first would have to be brought to a stationery process. For artificial neural networks, such processes are eliminated and the future forecast is derived faster in terms of a turnkey approach (tested solution). Artificial Neural Networks (ANN) has gained momentum since the eighties. Specifically in the area of forecasting, neural networks have become a common application. In this thesis, data from a railway operator was used to train the neural network and then future data is forecasted. Two embedded processing elements were then evaluated in terms of speed, memory and ability to execute complex mathematical functions (libraries). These were namely a Complex Programmable Logic Device (CPLD) and microcontroller (MCU). The ANN forecasting algorithm was programmed on both a MCU and PLD and compared by means of timing models and hardware platform testing. The most ideal turnkey solution was found to be the ANN algorithm residing on a PLD. The accuracy and speed results surpassed that of a MCU.

The focus of the study was on the relationship between employee engagement strategies and intention-to-stay of engineering professionals at a power station. The investigated engagement strategies or drivers in the model adopted for this study included: leadership style, remuneration structure, physical and emotional work climate, nature of work and career development opportunities. The purpose of the study was to investigate and contribute to a better understanding of a voluntary turnover problem of engineering professionals at a power station by considering the influence of engagement on intention-to-stay. The research was conducted on a sample of 65 engineering professionals employed at a power station. The findings of the study revealed that only one variable, namely nature of work, was significantly related to employee engagement. It was also established that of the five engagement strategies investigated, nature of work and leadership style (transformational) were significantly related to intention-to-stay. A positive relationship between employee engagement and intention-to-stay was proven in this study.

M.Ing.

D.Phil / The prediction of the expected transmission network loads as required for transmission network power flow studies, has become very important and much more complex than ten to twenty years ago. Therefore a single forecast is no longer the answer to the problem. The modelling of different long-term electrical forecasts makes it possible to compare a number of different forecasts. The modelling provides the further option that each expected load can be entered as a range and then the developed balancing algorithm checks for consensus (feasibility). If feasibility exists, then the different forecasts are reconciled (a feasible solution is determined). Factors such as international and national market trends, economical cycles, different weather patterns, climate cycles and demographic changes are studied. The factors that have significant impact on the transmission electrical loads are integrated in ten different forecasts. It thus gives more insight into the electrical industry and makes the forecast results more informative and therefore reduces the uncertainty in the future expected loads.

This thesis is a study of the Weatherford Municipal Light and Power Plant. An attempt has been made to trace the history of electric service in Weatherford, Texas, and to reveal why this present, and previous, service has culminated in a municipally owned system.

The effect of a thermal effluent on the attached algae of the Provo River, Utah, USA, was studied from 1975 to 1977. Data for macroscopic and microscopic algae were collected and analyzed. Diatoms, Cladophora glomerata, and Hydrurus foetidus dominated the flora. The thermal effluent significantly affected the algal flora in a section of river 100 to 135 meters long immediately below the discharge point. Cladophora growth was increased and Hydrurus was absent in this area. In addition, diatom production was often higher and diversity lower than in the rest of the river. Community structure was unique from all other adjacent areas. Small temperature increases which occurred as effluent and river waters mixed farther downstream were apparently not as important to the algal flora as other environmental factors.

M.S.