Automated diagnosis of power system failures requires understanding complex causal relationships between protection device misoperations and system-level effects. This work is innovative because it combines temporal fault propagation graphs with discrete event system models to enable reasoning about both fault causes and timing constraints. The TCD reasoning algorithm provides systematic hypothesis generation for improving system diagnostics.
This work presents a Temporal Causal Diagram (TCD) approach for diagnosing failures in power system protection devices. The methodology models fault propagation as TFPG models integrated with Timed Discrete Event Systems (TDES) to capture temporal relationships between failure modes and their observable effects. The approach includes a TCD reasoning algorithm that generates hypotheses about system failures based on observed anomalies.
The TCD approach successfully diagnoses failures in a three-bus power system protected by distance relays and circuit breakers. Testing demonstrates the methodology's ability to identify fault propagation paths and distinguish between multiple possible failure scenarios. The reasoning algorithm generates plausible hypotheses ranked by consistency with observed system behavior.
@article{Mahadevan2015,
author = {Mahadevan, Nagabhushan and Dubey, Abhishek and Chhokra, Ajay and Guo, Huangcheng and Karsai, Gabor},
journal = {IEEE} Instrum. Meas. Mag.},
title = {Using temporal causal models to isolate failures in power system protection devices},
year = {2015},
number = {4},
pages = {28--39},
volume = {18},
abstract = {We introduced the modeling paradigm of Temporal Causal Diagrams (TCD) in this paper. TCDs capture fault propagation and behavior (nominal and faulty) of system components. An example model for the power transmission systems was also described. This TCD model was then used to develop an executable simulation model in Simulink/ Stateflow. Though this translation of TCD to an executable model is currently done manually, we are developing model templates and tools to automate this process. Simulations results (i.e., event traces) for a couple of single and multi-fault scenarios were also presented. As part of our future work, we wish to test and study the scalability of this approach towards a larger power transmission system taking into account a far richer set of protection elements. Further, we wish to consider more realistic event traces from the fault scenarios including missing, inconsistent and out-of-sequence alarms and events.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/journals/imm/MahadevanDCGK15},
contribution = {lead},
doi = {10.1109/MIM.2015.7155770},
file = {:Mahadevan2015-Using_temporal_causal_models_to_isolate_failures_in_power_system_protection_devices.pdf:PDF},
keywords = {fault diagnosis, temporal causal diagrams, power systems, protection devices, timed discrete event systems, failure propagation},
project = {cps-reliability,smart-energy},
tag = {platform,power},
timestamp = {Sun, 28 May 2017 01:00:00 +0200},
url = {https://doi.org/10.1109/MIM.2015.7155770}
}