Comparing Chi-square-Based Bad Data Detection Algorithms for Distribution System State Estimation

• Published in: Proceedings of the IEEE/PES Transmission and Distribution, Latin America Conference and Exposition pp. 1 - 5
• Main Authors: Angulo-Paniagua, Jam, Quiros-Tortos, Jairo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.09.2020
• Subjects: Bad data detection; Chi-square; Conferences; Covariance matrices; Data processing; Distribution networks; Measurement errors; Measurement uncertainty; residual covariance matrix; State estimation
• ISSN: 2472-9639
• DOI: 10.1109/TDLA47668.2020.9326241

The transition from passive to active distribution networks has led to a growing use of state estimators (SEs), particularly in applications that require high quality data (e.g., demand management and fault detection). To ensure accurate estimations, a bad data detection (BDD) algorithm capable of detecting errors is needed. While these algorithms exist, network operators require understanding the benefits and limitations of these approaches before adopting a solution for their SEs. This paper compares three Chi-square-based BDD algorithms (conventional and two residual covariance matrix-based) to evaluate their performance in terms of speed and effectiveness. Comparisons are carried out using the IEEE 37-bus test feeder with different bad data scenarios. Results highlight that the conventional algorithm is faster, but its filtering capabilities is more limited. On the other hand, they show that the residual covariance matrix-based algorithms require on average 1594% and 2661% more time to achieve a detection that in certain conditions can be 1.56 and 1.67 times better.