Abstract
This paper proposes a novel approach to predict
power frequency by applying a state-space model to describe
the time-varying nature of power systems. It introduces the Expectation
maximization (EM) and prediction error minimization
(PEM) algorithms to dynamically estimate the parameters of the
model. This paper discusses how the proposed models can be
used to ensure the efficiency and reliability of power systems
in Frequency Monitoring Network (FNET), if serious frequency
fluctuation or measurement failure occur at some nodes; this is
achieved without requiring the exact model of complex power
systems. Our approach leads to an easy online implementation
with high precision and short response time that are key to
effective frequency control. We randomly pick a set of frequency
data for one power station in FNET and use it to estimate and
predict the power frequency based on past measurements. Several
computer simulations are provided to evaluate the method.
Simulation results showed that the proposed technique could
achieve good performance regarding the frequency monitoring
with very limited measurement input information.
