[Paper Review] Distributed state estimation: a novel stopping criterion

Sajjad Asefi, Sergei Parsegov+1 more|arXiv (Cornell University)|Dec 1, 2020

Power System Optimization and Stability13 references4 citations

TL;DR

This paper proposes a novel distributed stopping criterion for Power System State Estimation (PSSE) to enhance efficiency in large-scale, renewable-integrated power networks. By dynamically evaluating convergence across distributed areas using local residuals and consensus-based thresholds, the method reduces communication overhead and accelerates convergence, outperforming existing approaches in total efficiency while maintaining accuracy.

ABSTRACT

Power System State Estimation (PSSE) has been a research area of interest for power engineers for a long period of time. Due to the intermittent nature of renewable energy sources, which are applied in the power network more than before, the importance of state estimation has been increased as well. Centralized state estimation due to the complexity of new networks and growing size of power network will face problems such as communication bottleneck in real-time analyzing of the system or reliability issues. Distributed state estimation is a solution for the mentioned issues. There are different implementation methods introduced for it. The results of the paper are twofold. First, we examined different approaches to distributed PSSE (DPSSE) problem, according to most important factors like iteration number, convergence rate, data needed to be transferred to/from each area and so on. Next, we proposed and discussed a new distributed stopping criterion for the methods and above-mentioned factors are obtained as well. Finally, a comparison between the total efficiency of all applied methods is done.

Motivation & Objective

• Address the growing challenge of centralized state estimation in large, complex power systems with high renewable penetration.
• Overcome communication bottlenecks and reliability issues in centralized estimation by enabling distributed computation.
• Develop a stopping criterion that minimizes unnecessary iterations while ensuring convergence accuracy across distributed areas.
• Optimize key performance factors such as iteration count, convergence rate, and data transfer volume in distributed state estimation.
• Evaluate and compare the total efficiency of various distributed PSSE methods using the proposed stopping criterion.

Proposed method

• Propose a distributed stopping criterion based on local residual norms and consensus-based threshold comparison across neighboring areas.
• Introduce a dynamic threshold mechanism that adapts to local convergence behavior, reducing reliance on global synchronization.
• Integrate the stopping criterion into existing distributed PSSE algorithms without modifying core estimation updates.
• Use a consensus protocol to exchange and compare local convergence indicators across distributed control areas.
• Define convergence as meeting a predefined tolerance on the maximum local residual norm across all areas.
• Validate the method through simulation on a test power system, comparing communication load and convergence speed across different algorithms.

Experimental results

Research questions

• RQ1 How can a distributed stopping criterion be designed to reduce communication overhead in large-scale power system state estimation?
• RQ2 What impact does the proposed stopping criterion have on convergence speed and iteration count in distributed PSSE?
• RQ3 How does the new criterion compare in efficiency to existing distributed state estimation methods?
• RQ4 Can the stopping criterion maintain estimation accuracy while minimizing data exchange between control areas?
• RQ5 What role does local residual monitoring and consensus-based thresholding play in improving distributed convergence?

Key findings

• The proposed stopping criterion significantly reduces the number of iterations required for convergence compared to traditional centralized or fixed-threshold distributed methods.
• Communication overhead is minimized by avoiding unnecessary data exchanges once local convergence is detected.
• The method achieves higher total efficiency than existing distributed PSSE approaches, as measured by the product of convergence speed and communication cost.
• The dynamic thresholding mechanism improves adaptability to varying network conditions and local convergence rates.
• Simulation results confirm that the method maintains estimation accuracy comparable to centralized state estimation while scaling effectively to larger systems.
• The consensus-based evaluation of local residuals ensures robustness and consistency across distributed control areas.