Hierarchical Clustering based optimal PMU placement for power system fault observability

Area of Science:

• Electrical Engineering
• Power Systems Analysis
• Network Optimization

Background:

• Optimal placement of phasor measurement units (PMUs) is crucial for power system observability, especially during fault conditions.
• Existing methods for fault observability are computationally intensive and complex for large networks.

Purpose of the Study:

• To introduce a novel algorithm for determining optimal PMU placement that considers network fault observability.
• To simplify the complex analysis and reduce the computational burden associated with fault observability.

Main Methods:

• Network fault simulation to capture post-fault voltage changes (ΔV).
• Development of a Faulted Connectivity Matrix (FCM) using ΔV to represent network topology during faults.
• Application of Pearson correlation coefficient and Hierarchical Clustering to identify correlated buses and zone the network.
• Identification of optimal PMU locations within clusters using defined placement rules.

Main Results:

• The proposed algorithm effectively determines optimal PMU locations considering fault conditions.
• Demonstrated simplicity and effectiveness across various fault scenarios and standard test systems.
• Successfully addresses the challenges of achieving full network observability during faults.

Conclusions:

• The developed algorithm offers a straightforward and efficient approach to optimal PMU placement for fault observability.
• The method significantly reduces computational complexity, making it suitable for large-scale power systems.
• The findings highlight the algorithm's practical applicability and effectiveness in enhancing power system reliability.