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Considering that a unit has four states 1, 2, 3, and 4 with corresponding performance level P1 = 0, P4 = P nom and the values of P2, P3 can be obtained by the apportioning method [13], ( p_{{j_{m} }} (t) ) is a probability and the process is in state j m at time instant t ≥ 0.
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Therefore, the apportioning method [23] is used to reduce the ACPT, by which each level is apportioned among the assigned discrete states.
We can obtain state values and the corresponding probabilities of each interval using the apportioning method.
A technique, called the apportioning method [4], is usually used to create steady-state multi-state generating unit models based on real world statistical data for generating units.
Four to six states can reflect the characteristics of wind speed and load [19], so we can choose four-state models for wind farms in each sub-period by an apportioning method.
Firstly, a principal component analysis (PCA) combined with a hierarchal clustering algorithm is used to achieve the salient and time-varying patterns of wind power, then a sequential UGF equivalent model of wind power output is established by an apportioning method.
First, we looked at the universities' graduation rate multiplied by historical FTE enrollment (note that this was the sole apportioning method used in last year's list).
This is the probability apportioning method (PAM version 4).
In this way, a concept of derating-adjusted forced outage rates (DAFOR) is proposed to describe the derated state [13], and then an apportioning method is used to calculate the derated state and its outage probability [14].
An apportioning method [13] is used to establish the sequential UGF of the wind power output.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com