US 11,894,679 B2
Methods, devices, and systems for distributed monitoring based adaptive diagnosis of power anomalies in a power network
Saurabh Pathak, Bahraich District (IN); Fiaz Shaik, Macherla (IN); Saiful Haq, Lucknow (IN); and Vaibhav S. Devalalikar, Barshi (IN)
Assigned to EATON INTELLIGENT POWER LIMITED, Dublin (IE)
Filed by Eaton Intelligent Power Limited, Dublin (IE)
Filed on Oct. 19, 2020, as Appl. No. 17/074,082.
Prior Publication US 2022/0123552 A1, Apr. 21, 2022
Int. Cl. H02J 3/00 (2006.01); H02J 13/00 (2006.01); H04L 67/12 (2022.01)
CPC H02J 3/001 (2020.01) [H02J 13/00002 (2020.01); H02J 13/00012 (2020.01); H04L 67/12 (2013.01)] 11 Claims
OG exemplary drawing
 
1. A method for detecting an anomaly in a power network, the method comprising: determining a baseline power usage in the power network; receiving data indicative of an active power usage in the power network; detecting an anomaly based on a difference between the baseline power usage and the active power usage; isolating a fault for an element in the power network, responsive to detecting the anomaly; transmitting fault isolation information indicating the fault to a user device; determining a feedback based on the anomaly; and modifying the baseline power usage based on the feedback, wherein the element is at a lower level hierarchy of the power network than the anomaly,
wherein isolating the fault comprises: identifying, with a main anomaly detection circuit, a main anomaly in a first element of a main meter hierarchy of the power network comprising a plurality of first elements; searching, with a lower anomaly detection circuit, for a lower anomaly in a plurality of second elements of the lower level hierarchy that is lower than the main meter hierarchy, wherein the plurality of second elements are associated with the first element; and identifying, with the lower anomaly detection circuit, a second element of the plurality of second elements associated with the lower anomaly,
wherein determining the feedback based on the anomaly comprises: generating data related to the active power usage and the anomaly; and triggering generation of the feedback based on the data related to the active power usage and the anomaly.