A power system stability is essential for maintaining the power system oscillation frequency within a small and acceptable interval around its nominal frequency. Hence, it is necessary to study and control the frequency for stable operation of a power system by knowing the characteristics within a power system. One approach is to understand the effectiveness of frequency and damping characteristics of generators in power systems. Hence, the simulation analysis of IEEE 118-bus power system is used for this study. The analysis includes theoretical analysis with a mathematical approach and simulation studies of swing equation to determine the characteristics of damped single-machine infinite bus, which is represented as a generator connects to a large network system with a small signal disturbance by line losses. Additionally, mathematical derivation of Prony analysis is presented in order to estimate the frequency and damping ratio of the simulation results. In the end, the results demonstrate that the frequency and damping characteristics of generators are highly dependent on the system inertia constant. Therefore, the higher inertia constant is a critical factor to ensure the system is more stable. / Master of Science / A power system’s stability is dependent on maintaining the oscillation frequency within a small and acceptable variance of its normal frequency. In order to control the frequency for the stable operation of a power system, it is necessary to study the characteristics within a power system. One approach is to study the effectiveness of frequency and damping characteristics of generators in power systems. For this study, the simulation analysis of the IEEE 118-bus power system will be used. This includes a mathematical approach and simulation studies of swing equation. These will determine the characteristics of damped single-machine infinite bus. This is represented as a generator connected to a large network system with a small signal disturbance caused by line losses. Additionally, the mathematical basis of Prony analysis is presented in order to estimate the frequency and damping ratio of the simulation results. In the end, the results demonstrate that the frequency and damping characteristics of generators are highly dependent on the system inertia constant. Therefore, a high inertia constant is critical to the stability of the system.

The radical changes that are currently occurring in the nature of power systems means that in the future it may no longer be possible to guarantee security of supply using offline security assessment and planning. The increased uncertainty, particularly the reduction and variation in system inertia that will be faced in the future must be overcome through the use of adaptive online solutions for ensuring system security. The introduction of synchronised measurement technology means that the wide area real time measurements that are necessary to implement these online actions are now available.The objective of the research presented in this thesis was to create methods for predicting the post-disturbance frequency behaviour of a power system with the intent of contributing to the development of real time adaptive corrective control for future power systems. Such a prediction method would generate an online prediction based on wide area measurements of frequency and active power that are recorded within the period of approximately one second after a disturbance to the active power balance of the system. Predictions would allow frequency control to respond more quickly and efficiently as it would no longer be necessary to wait for the system frequency behaviour to violate pre-determined thresholds.The research presented in this thesis includes the creation of an online method for the simultaneous detection of the time at which a disturbance occurred in a power system, or area of a power system, and the estimation of the inertia of that system, or area. An existing prediction method based on approximate models has been redesigned to eliminate its dependence on offline information. Furthermore, the thesis presents the novel application of pattern classification theory to frequency prediction and a five class example of pattern classification is implemented.