Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems.

Area of Science:

• Electrical Engineering
• Control Systems
• Optimization

Background:

• Accurate state estimation is crucial for reliable power system operation.
• Centralized methods face scalability challenges in large-scale power grids.
• Existing distributed methods may not fully mitigate outlier effects or computational burden.

Purpose of the Study:

• To propose a novel distributed state estimation method for large-scale power systems.
• To enhance computational efficiency and robustness against outliers.
• To validate the method's performance on standard power system test cases.

Main Methods:

• A distributed state estimation approach based on modified Partitioned Moving Horizon Estimation (mPMHE).
• Power systems are partitioned into local areas for distributed computation.
• Local optimization problems are solved using local measurements and neighboring estimates, incorporating state constraints.

Main Results:

• The proposed mPMHE method achieves comparable state estimation accuracy to centralized approaches.
• Significant reduction in overall computation load compared to traditional methods.
• Demonstrated effectiveness in mitigating the influence of outliers through constraint handling.

Conclusions:

• The distributed mPMHE method offers an efficient and robust solution for large-scale power system state estimation.
• This approach enhances scalability and computational feasibility for modern power grids.
• The method provides a practical alternative for real-time grid monitoring and control.