Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

Journal Article · 07 January 2022 · Sensors

DOI: https://doi.org/10.3390/s22020458 · OSTI ID:2471553

El Mrabet, Zakaria  ^[1]; Sugunaraj, Niroop  ^[1]; Ranganathan, Prakash ^[1]; Abhyankar, Shrirang ^[2]

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1. Univ. of North Dakota, Grand Forks, ND (United States)
2. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 2471553
• Journal Information: Sensors, Journal Name: Sensors Journal Issue: 2 Vol. 22; ISSN 1424-8220
• Publisher: MDPI AGCopyright Statement
• Country of Publication: United States
• Language: English