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Power Factor Correction

Power Factor Correction

The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.

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The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...

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Maximum Correntropy with Variable Center Unscented Kalman Filter for Robust Power System State Estimation.

Zhenglong Sun¹, Chuanlin Liu¹, Siyuan Peng^1,2

¹Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Northeast Electric Power University, Ministry of Education, Jilin 132012, China.

Entropy (Basel, Switzerland)

|April 23, 2022

Summary

A new Maximum Correntropy with Variable Center Unscented Kalman Filter (MCVUKF) improves power system state estimation by adapting correntropy. An enhanced version (En-MCVUKF) further boosts accuracy by mitigating bad data impacts.

Keywords:

correntropy with variable center power system state estimation robustness unscented Kalman filter

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Area of Science:

Electrical Engineering
Control Systems
Signal Processing

Background:

Robust Kalman filters with correntropy loss enhance power system stability by mitigating abnormal situations.
Existing correntropy-based filters use fixed-center Gaussian kernels, limiting effectiveness in practical power system forecasting-aided state estimation (PSSE).

Purpose of the Study:

To propose a novel Maximum Correntropy with Variable Center Unscented Kalman Filter (MCVUKF) for improved PSSE.
To develop an enhanced MCVUKF (En-MCVUKF) that further suppresses bad data influence for increased accuracy.

Main Methods:

Developed MCVUKF by replacing the fixed-center Gaussian kernel with a variable-center correntropy in an unscented Kalman filter framework.
Introduced En-MCVUKF by incorporating an exponential function of the innovation vector to adjust the covariance matrix, targeting bad data suppression.

Main Results:

MCVUKF demonstrated improved performance in PSSE by utilizing a more adaptable correntropy kernel.
En-MCVUKF showed superior accuracy in PSSE, effectively mitigating the impact of bad data on the innovation vector.
Simulations on IEEE 14, 30, and 57-bus systems confirmed the superiority of MCVUKF and En-MCVUKF over existing methods.

Conclusions:

The proposed MCVUKF offers a more robust and adaptable approach to PSSE compared to traditional methods.
En-MCVUKF provides enhanced accuracy and reliability in PSSE by effectively handling bad data.
These advanced Kalman filter techniques significantly improve the stability and accuracy of power systems.