A Novel Amplitude-constrained Algorithm for Minimum Frequency Prediction

The prediction of minimum frequency in relation to a disturbed power system is an essential aim with regard to power system stability and security assessment. Exploiting the real-time data acquired from a wide-area measurement system, this paper develops and demonstrates an algorithm for predicting the minimum system frequency. The nonlinearity of the amplitude-limit in governor system is considered and its effect on the minimum frequency is quantified using the describing function (DF) method. A first-order model is obtained by fitting with the response of the fully detailed governor model in order to accommodate a variety of governor types. In order to improve the prediction accuracy, an index-based pre-selection of governors reaching their amplitude-limit after a disturbance is implemented and consequently the DF method is applied selectively. Case studies have been conducted on the reduced model of the Great Britain transmission system in order to demonstrate the algorithmic performance.