Data-driven p-norms for estimating transmission loss coefficients in power systems

Area of Science:

• Electrical Engineering
• Power Systems Analysis
• Optimization Techniques

Background:

• Accurate modeling of transmission loss coefficients (B-coefficients) is crucial for efficient power system operation and planning.
• Existing methods may lack robustness across diverse operating conditions.
• Data-driven approaches offer potential for enhanced estimation accuracy.

Purpose of the Study:

• To introduce a novel convex methodology for estimating transmission loss coefficients (B-coefficients) in power systems.
• To enhance the accuracy and practical relevance of B-coefficient estimation using power system measurements.
• To develop an efficient semi-definite programming (SDP) model for B-coefficient derivation.

Main Methods:

• A data-driven approach utilizing power system measurements.
• A convex methodology employing semi-definite programming (SDP).
• Incorporation of varied operating conditions with uniform and Gaussian distributed active power injections and demand profiles.
• Leveraging p-norm formulations within the SDP model.

Main Results:

• The proposed SDP model efficiently derives B-coefficients.
• Evaluation across IEEE 14-, 39-, 57-, and 118-bus test systems demonstrated effectiveness.
• Achieved average estimation errors between [Formula: see text] and [Formula: see text] across diverse scenarios.
• The methodology proved robust under various operating conditions.

Conclusions:

• The novel convex methodology reliably estimates transmission loss coefficients.
• The approach contributes to improved accuracy in transmission loss modeling.
• This enhances the foundation for more efficient power system operations.