oedisi-hif-identification
Science Score: 54.0%
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Low similarity (13.3%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: openEDI
- License: bsd-3-clause
- Language: Python
- Default Branch: main
- Size: 1.56 MB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Project Title
A Data-Driven Approach for High-Impedance Fault Localization in Distribution Systems
Appendix
https://arxiv.org/abs/2311.15168
NREL Software Record
SWR-24-13
Usage
```javascript
Pick some high impedance fault data for classification
filepaths = ['IEEE123PVL1C1B5HIF.csv', 'IEEE123PVL1C1B34HIF.csv', 'IEEE123PV_L1C1B45HIF.csv'] ```
The high impedance fault (HIF) data for the IEEE 123-bus system can be downloaded from here.
The user can select the HIF data from a few locations for the classification. The algorithm first processes the data and apply piecewise linear approximation to the voltage-current trajectory. Following that, the simplified function features are collected as inputs and SVM is utilized for the HIF identification.
When SVM is used for the HIF identification task, the learning output is the fault location label (e.g., bus number), and we propose utilizing the features from the approximation functions as the input. Specifically, for the piecewise linear approximation, the SVM input can be constructed as:
math
x_{\mathcal{L}} = \{s_1, s_2, s_3\}
which consists of slope rates for all segments in the piecewise linear function.
Since quadratic functions contain more information than linear functions, SVM inputs under quadratic approximation will be lifted to a higher-dimensional space:
math
x_{\mathcal{Q}} = \{(m_1,n_1),(m_2,n_2),(m_3,n_3)\}
which include both quadratic and linear coefficients for each piece of the function.
Examples
The performance of the piecewise approximation and SVM algorithm for HIF identification tasks can be validated using the IEEE 123-node test feeder. The test feeder is simulated in EMTP-ATP (ElectroMagnetic Transients Program, version Alternative Transients Program) with 14 rooftop photovoltaic (PV) units incorporated. In this model, the loading condition ({0.4, 1.0} per unit) and PV capacity ({0.4, 0.6, 0.8, 1.0} per unit) are varied for different steady-state scenarios. The HIF simulations span across 66 locations in total, with 63 internal nodes as well as 3 adjacent nodes outside the feeder.
The classification of two faults at buses 7 and 64 using the Gaussian kernel as an example is shown below:
Owner
- Name: Open Energy Data Initiative
- Login: openEDI
- Kind: organization
- Website: https://openei.org/wiki/Open_Energy_Data_Initiative_(OEDI)
- Repositories: 7
- Profile: https://github.com/openEDI
The OEDI Data Lake is a centralized repository of high-value energy research datasets aggregated from the U.S. Department of Energy’s Programs and Offices.
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
High Impedance Fault Localization Algorithm Using Support
Vector Machine
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Yuqi
family-names: Zhou
affiliation: NREL
- given-names: Yuqing
family-names: Dong
affiliation: NREL
- given-names: Rui
family-names: Yang
affiliation: NREL
identifiers:
- type: doi
value: 10.48550/arXiv.2311.15168
- type: other
value: SWR-24-13
description: NREL Software Record
repository-code: 'https://github.com/openEDI/oedisi-hif-identification'
abstract: >-
Accurate and quick identification of high-impedance faults
is critical for the reliable operation of distribution
systems. Unlike other faults in power grids, HIFs are very
difficult to detect by conventional overcurrent relays due
to the low fault current. Although HIFs can be affected by
various factors, the voltage current characteristics can
substantially imply how the system responds to the
disturbance and thus provides opportunities to effectively
localize HIFs. In this work, we propose a data-driven
approach for the identification of HIF events. To tackle
the nonlinearity of the voltage current trajectory, first,
we formulate optimization problems to approximate the
trajectory with piecewise functions. Then we collect the
function features of all segments as inputs and use the
support vector machine approach to efficiently identify
HIFs at different locations. Numerical studies on the IEEE
123-node test feeder demonstrate the validity and accuracy
of the proposed approach for real-time HIF
identification.
keywords:
- Systems and Control
- Machine Learning
- Distribution System Algorithms
- OEDISI
license: BSD-3-Clause
GitHub Events
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- Fork event: 1
Last Year
- Fork event: 1
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| zhouyuqisconvex | 1****x | 11 |
| Joseph McKinsey | 3****y | 2 |
| Joseph McKinsey | j****y@n****v | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 7 months ago
Dependencies
- python 3.12.1-slim-bullseye build
- matplotlib *
- numpy *
- pandas *
- scikit-learn *