{'fraction_non_alphanumeric': 0.013278008298755186, 'fraction_numerical': 0.005809128630705394, 'mean_word_length': 5.554347826086956, '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}}
Distributed State Estimation for Distribution Grid with Sparse Measurements

This paper presents a distributed state estimation solution for estimating time-varying unknown loads in a distribution grid. The state estimation is performed using unscented Kalman filter (UKF) based on quasi-static power flow model of the grid and sparse measurements. A key challenge is the scale of the distribution grid, with several thousand nodes and loads, making a centralized estimation solution impractical. To address this, a distributed estimation scheme is proposed by partitioning the grid into several smaller sections. A UKF is then designed and implemented for each partition, while explicitly accounting for the boundary conditions on voltage and power flow continuity between neighboring sections. The proposed approach is practical to implement and scalable for any size grid state estimation problem. The application of proposed solution is demonstrated in simulation studies based on the IEEE 123 node test grid. Initial application of the distributed estimation to the IEEE 8500 node reference grid using five partitions shows promising computation results where centralized estimation is not tractable.