Frequency Prediction of Synchronous Generators in a Multi-Machine Power System with a Photovoltaic Plant Using a Cellular Computational Network

Abstract

Power systems with synchronous generators and solar photovoltaic (PV) experience frequency and power fluctuations due to high variability of PV power. Automatic generation control is implemented to control power outputs of the generators and stabilize the system frequency. It is desirable with increasing levels of PV penetration to have foresight of frequency fluctuations to empower advanced control systems. A new methodology is presented in this paper for predicting frequency of synchronous generators in a power system with solar PV. A cellular computational network (CCN) is used to perform the frequency prediction over multi-time scale. CCNs are decentralized and distributed computing paradigms. Thus, CCNs are suitable for fast prediction of frequency of synchronous generators distributed spatially across a power system. The inputs to cells of the CCN are derived from phasor measurement unit (PMU) measurements of frequency and voltage phasor at the respective generator buses. Past, current and predicted measurements enable multi-timescale predictions of synchronous generator frequencies in a power system. Typical multi-time scale frequency predictions using the CCN are illustrated on a two-area four machine power system with solar PV integrated.