{'fraction_non_alphanumeric': 0.014198782961460446, 'fraction_numerical': 0.010141987829614604, 'mean_word_length': 5.450980392156863, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Fast decomposition-based state estimation using automatic graph partitioning and ADMM

State estimation is a critical function in the energy management system that processes measurements from the field and determines the most likely state of the power system. As more sensing is added and wider areas need to be estimated in real time, the computational speed of state estimation becomes increasingly more important. This paper proposes a scalable method that uses automatic graph partitioning to decompose a global state estimation problem into a series of smaller subproblems and forces these sub-problems to reach a consensus using the alternating direction method of multipliers (ADMM). The proposed method is demonstrated on the IEEE 14 bus, 57 bus, 118 bus, and 300 bus systems. Simulation results suggest that partitioning the global state estimation problem into smaller state estimation problems greatly improves the computational performance, even for a serial implementation.