Dynamic State Estimation of Power Systems With Quantization Effects: A Recursive Filter Approach.

Area of Science:

• Electrical Engineering
• Control Systems
• Signal Processing

Background:

• Power systems rely on accurate state estimation for reliable operation.
• Nonlinear measurements and quantization effects from devices like remote terminal units (RTUs) and phasor measurement units (PMUs) complicate state estimation.
• Traditional methods often use approximations that can degrade performance.

Purpose of the Study:

• To develop a recursive filter algorithm for power system state estimation.
• To address challenges posed by quantized nonlinear measurements.
• To guarantee and minimize the upper bound of the estimation error covariance.

Main Methods:

• Developed a recursive filter algorithm incorporating a logarithmic quantizer model.
• Treated linearization and quantization errors as norm-bounded uncertainties.
• Designed the filter within a robust recursive estimation framework.
• Tested the algorithm on the IEEE benchmark power system.

Main Results:

• The developed recursive filter effectively estimates power system states despite nonlinear and quantized measurements.
• The algorithm successfully guarantees and minimizes the estimation error covariance upper bound.
• Performance improvement is achieved by explicitly handling linearization and quantization errors as uncertainties.

Conclusions:

• The proposed robust recursive filter offers an effective solution for state estimation in power systems with challenging measurement characteristics.
• This approach enhances the reliability and performance of power system monitoring and control.
• The method provides a more accurate estimation compared to traditional approximation techniques.