Search

The increase in the demand for a reliable electricity supply by the utilities and consumers has necessitated the evaluation of the reliability of power systems. A reliable electricity supply is characterized by no or minimal duration and frequency of supply outages. This has triggered the necessity of using renewable energy sources (RESs) with optimization methods for reliability improvement of electricity systems and reduction of greenhouse gas (GHG) emissions. The main objective of this study is to optimize micro-grid systems operations, improve reliability, reduce emissions and balance the demand and supply of energy through RESs and battery energy storage system (BESS). The adaptive model predictive control (AMPC) method is used to address the issues of micro-grid operation. The AMPC algorithm solves the optimization problem of disturbance prediction in a micro-grid with different types of RESs and BESS integration. This optimization problem considers different constraints for minimum operating costs in different case scenarios. The financial viability of the proposed method is investigated. Solar photovoltaic (Solar PV), wind plant (WP) and BESSs are used with the AMPC method to investigate the impacts of annual real interest rates on cost and emission parameters, quantification of the emission oxides from different case scenarios, reduction of the cost of electricity (Ccoe), and power system reliability improvement. A modified Roy Billinton Test System (RBTS) is used to confirm the reliability enhancement and financial feasibility of the system. Case studies are used to confirm the proposed methods using climatic data for the city of Pietermaritzburg (29.37° S and 30.23° E), South Africa. The results obtained establish that the incorporation of RESs and BESSs using the AMPC method gives satisfactory outcomes.

The majority of faults on electric power systems are traceable to distribution networks due to the exposure of the networks to different adverse climatic conditions, among other factors. Power outages due to climatic factors are inevitable since a major part of a distribution network is made up of overhead lines and exposed to different unfavorable weather conditions. Climatic factors need to be fully considered when designing a power system model in order to achieve a meaningful system reliability improvement. The projection of power system outage requires a model with high accuracy, taking climatic conditions into consideration. Conventional models rely only on fault data and do not consider climatic factors, and so they have very low accuracy. This research proposes a computational intelligence model using historical fault and climatic data sets of Newcastle, South Africa. Three models are developed in this paper: the trend projection (TP) model, and two artificial neural network (ANN) models (Models 1 and 2). The performance of the models are examined using statistical parameters. The results of the ANN models were satisfactory unlike the trend projection model; thereby illustrating the efficacy of the computational methods.

Reliability of the electric grid structure for the transmission and distribution of power from the generating plants to the consumers, is an essential requirement for the reliability of electric supply. The components of the grid is exposed to weather events which cause faults to the grid’s structures. There is an expectation that as climate change alters the severity and number of weather events, electricity supplies and electricity grid reliability are expected to be affected. Computational intelligence models have been proven to be excellent predictive models for electricity reliability problems in previous studies. But there has not been sufficient literature reporting their applications in weather related electricity outage forecasting problems. In this study, weather related electricity outage was forecasted using artificial neural networks (ANNs) with back-propagation algorithm. Real-life data sets of the city of Pietermaritzburg, South Africa, was used to investigate the performance of the ANN model and was compared with a conventional model – exponential smoothing (ES). The ANN model gave satisfactory results as compared to the ES model. The result is a demonstration of the robustness of computation techniques.

Most transmission lines are overhead, spanning possibly thousands of kilometers and are exposed to different climatic conditions. and therefore prone to faults. This paper develops accurate, fast and reliable algorithms that can classify and locate faults on transmission lines. These reduce outage time. Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN) are used to implement the algorithms because of their ability to learn during a wide range of conditions. The ANN inputs are the voltage and current signals. These were measured at one terminal of the line. The test system is the 29-bus Great Britain transmission network. The algorithm sensitivity is investigated with varying fault impedance and inception angle, and different locations on the transmission line.

Occurrence of faults in power systems is unavoidable but their timely recognition and location enhances the reliability and security of supply; thereby resulting in economic gain to consumers and power utility alike. Distribution Network (DN) is made smarter by the introduction of sensors and computers into the system. In this paper, detection and classification of faults in DN using Artificial Neural Network (ANN) is emphasized. This is achieved through the employment of Back Propagation Algorithm (BPA) of the Feed Forward Neural Network (FFNN) using three phase voltages and currents as inputs. The simulations were carried out using the MATLAB® 2017a. ANN with various hidden layers were analyzed and the results authenticate the effectiveness of the method.

The security and reliability of supply is often affected due to fault occurrence in electrical power Distribution Networks (DN). In the conventional DN, faults location takes more than the expected time, which results in economic losses to power utility companies as well as consumers. However, the advent of Intelligent Electronic Devices (IEDs) and recent advances in Information and Communication Technology (ICT) has made DN better, safer and smarter. In this paper, we present the outcome of simulation experiments carried out to locate faults in a DN. The IEEE 13 Node Test Feeder was simulated in SIMULINK with different fault conditions and the fault data acquired were utilized to develop an ANN classification model. The outcome of the experiments shows that the ANN based classification model is effective in locating faults on distribution lines with satisfactory performances.