A Machine Learning-Based Strategy For Predicting The Fault Recovery Duration Class In Electric Power Transmission System

Joyokusumo, Irfan; Putra, Handika; Fatchurrahman, Rifqi

doi:10.1109/ict-pep50916.2020.9249902

Cited by 1 publication

(2 citation statements)

References 23 publications

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“…These outage costs increased substantially depending on the time of year and outage duration, especially when they occur during winter. Thus, predicting faults in the system along with their duration is the first step towards reducing the number of unplanned outages and providing a prediction-based plan to the utility for deploying the appropriate maintenance crews and the sequence of operations [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…This approach was based on recurrent neural network (RNN) and was trained on three sources of historical data: outage report provided by Seattle City Light and 15 years of data, repair logs, and weather information. Another approach for predicting faults duration in transmission system was proposed in [6]. This approach was based on naive Bayes classifier (NBC) and support vector machine (SVM) and it was trained on nontemporary fault-type data including features such as substation, asset type, fault category, and outage start time.…”

Section: Introductionmentioning

confidence: 99%

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

Mrabet

Sugunaraj

Ranganathan

et al. 2022

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%