OscilloWatch

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PMU data monitoring tool that detects poorly damped oscillations in power systems in real time. Made for my master's thesis.

Divides a signal into segments of data samples, which are analyzed using the Hilbert-Huang transform (HHT) and an oscillation detection algorithm, to detect and characterize oscillatory modes from the Hilbert spectrum. Raises alarms for sustained oscillations with poor damping that are detected.

Please note that this is meant as a proof of concept and something that can be built upon to become a useful tool for grid operators. It does not have a proper user interface and is not ready for deployment 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), which is required for real-time analysis, 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 the settings are explained in the Settings page in the wiki. The settings are split into basic settings, which most users likely need to check/change, and advanced settings, which are intended for advanced users with deeper knowledge on 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, with basic examples intended as introductions. To learn how to use the application, I recommend starting with the example scripts in the examples folder and modifying them to fit your needs.

Signal Snapshot Analysis Mode

Analyzes a pre-given signal (such as a synthetic signal or pre-recorded PMU data) 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.

from OscilloWatch.OWSettings import OWSettings
from OscilloWatch.SignalSnapshotAnalysis import SignalSnapshotAnalysis

# input_signal is a list or array with 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.

from OscilloWatch.OWSettings import OWSettings
from OscilloWatch.RealTimeAnalysis import RealTimeAnalysis

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. Each result that can be stored is explained in the Results page in the wiki. They are split into basic and advanced results, similar to the settings. By default, only the basic results are included. 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.