Search

Illovo is Africa’s largest sugar producer, within the Illovo group each operational entity has its own unique power system. Each mill faces various challenges with regards to layout, loading, operational conditions and it’s utility. It is of utmost importance that the protection settings applied to the electrical infrastructure are efficient, accurate and reliable. This paper presents the analysis and design of protection settings applied to overcurrent relays in an industrial network. This is achieved by analyzing the past protection model designed and comparing it with the current system, thus highlighting the deficiencies. A short circuit and load flow analysis is carried out on the current system, using the engineering software ETAP (Electrical Transient Analysis Program). The result produced are than used to correctly implement new relay co-ordination settings to achieve fault discrimination within the network.

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.