Abstract
The goal of this project is to review and develop cost effective methods for preventive maintenance in power systems. As power system components age and deteriorate, power system reliability starts to decline and subsequently failure rates of the components will rise (R.Keith Mobley, 1990). With the application of preventive and predictive maintenance, the life expectancy of the system components is extended if not renewed (Frank Waterer, 2012). Utility companies strive to maintain a high level of system reliability, given the need and demand by customers for a reliable power supply. This report reviews maintenance practices used in the industry and suggests an effective approach to address the likely problems that may lead to system failure. The approaches and solutions reviewed in this report include preventive maintenance; by monitoring incipient fault that can lead to system outage or total failure. In this approach, the application of neural network algorithms and simulation was examined. Supervisory control and data acquisition (SCADA) system was examined as one of the effective ways to monitor power system equipment located some miles away from the substation or the monitoring center. The SCADA system is an intelligent method that has the capabilities of monitoring power system parameters in real time. This has created the opportunity for a quicker response, thereby reducing the downtime of the power network.