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The controller includes a neuro-fuzzy system with power error and its derivative as inputs.
The results data have spectral bandwidth of 10 nm and the absolute power error is calibrated to be less than 0.5%.
The result is that the power clamp meters specified accuracy (0.40%) has almost the same value as the power error due to the influence of phase coil effect (0.42%).
The results show errors below 50 MHz in the 59% of measurements carried out in a range from 1 to 11 GHz and an average power error of 0.34 dB.
To show the potentials of the procedure, I present some design examples of positive and negative lenses where the tangential power error is virtually eliminated for all gaze directions (up to 40°).
1) Distributed control using power error of single wind plants as reference input.
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The real and reactive power errors are eliminated by directly calculating the rotor control voltages through DSMC.
The inputs for the proposed control scheme are the DC voltage and reactive power errors at the converter station and the active and reactive power errors at the inverter station of the voltage-source converter-based high voltage direct current transmission (VSC-HVDC) link.
Section 2 presents the linear model with exponential power errors and the associated likelihood function.
We have developed Bayesian reference analysis for linear models with exponential power errors.
We can see that both Student-t and exponential power errors fits are robust against outliers.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com