OscilloWatch 0.1.0

PMU data monitoring tool for giving early warnings for low-frequency oscillations in power systems.

OscilloWatch

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PMU data monitoring tool that detects and gives early warnings for poorly damped low-frequency oscillations in power systems.

Uses the Hilbert-Huang transform (HHT) on segments of data samples and uses an algorithm to find and characterize oscillatory modes from the Hilbert spectrum. Raises an alarm if a sustained with poor damping is found.

Made for my master's thesis, which describes how it works in great detail. It will be linked here when published.

Please note that this is meant as a proof of concept and something that can be built upon to become useful. It does not have a proper UI and is unlikely to be very useful in an actual control room its current state.

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Basic Usage

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Installation

To install with pip:

pip install OscilloWatch

pip install synchrophasor @ git+https://github.com/hallvar-h/pypmu

(Synchrophasor (pyPMU) must be installed manually because it uses a specific fork only accessible through direct GitHub link, which PyPI does not allow.)

You can also install it by downloading the zip file with the source code or by cloning with Git. The following dependencies must then be installed manually:

• numpy
• scipy
• matplotlib
• pyPMU (this fork)

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Settings

Settings are assigned by creating an OWSettings object with the desired settings as constructor parameters. All settings are explained here. They are split into basic settings, which most users likely need to check/change, and advanced settings, which is intended only for advanced users who know how the application works.

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Signal Analysis Modes

There are two options for analyzing a signal: Signal snapshot analysis and real-time analysis. Below are basic explanations for how to use them. See example scripts in the examples folder for practical examples.

Signal Snapshot Analysis Mode

Analyzes a pre-given signal as if it were analyzed in real time.

1. Obtain signal in a list or array.
2. Create OWSettings object with the settings you want.
3. Create SignalSnapshotAnalysis object with your OWSettings object and input signal as parameters.
4. Run analysis.

# input_signal contains the signal that will be analyzed

settings = OWSettings(
    segment_length_time=10,
    extension_padding_time_start=10,
    extension_padding_time_end=2,
    minimum_frequency=0.1
    minimum_amplitude=1e-4
)

sig_an = SignalSnapshotAnalysis(input_signal, settings)

sig_an.analyze_whole_signal()

Real-Time Analysis Mode

Connects to a PMU or PDC and analyzes the data in real time.

1. Create OWSettings with the settings you want, including details on the device you want to connect to.
2. Create RealTimeAnalysis object with your OWSettings object as a parameter.
3. Run analysis.

settings = OWSettings(
    ip="192.168.1.10",
    port=50000,
    sender_device_id=1410,
    pmu_id=1410,
    channel="VA",
    phasor_component="magnitude",

    segment_length_time=10,
    extension_padding_time_start=10,
    extension_padding_time_end=2,
    minimum_frequency=0.1
    minimum_amplitude=1e-4
)

rta = RealTimeAnalysis(settings)
rta.start()  # Infinite loop

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Results

Numerical results are stored in table form in a CSV file. Explanations for the results are given here. They are split into basic and advanced results, similar to the settings. By default, only the basic results are included by default. Advanced results can be included by enabling the setting include_advanced_results.

Additionally, results are stored in a PKL file with the Pickle library. This allows advanced users to access all variables, plots etc. from the analysis, and it can be used for the post-processing methods in the examples/post_processing folder.