Case Study: Small Signal Spectrum Capturing

Grid Oscillation Challenges for Australian WMZ

The West Murray Zone (WMZ), spanning parts of Victoria and New South Wales, is a critical area within the Australian National Electricity Market (NEM), noted for its low system strength. This region has seen significant investment in solar and wind generation in recent years.

Operational challenges in the WMZ persist for AEMO, Network Service Providers, and Generator Developers, particularly following power system oscillations observed in 2019. Advanced, detailed modelling is now essential for technical assessments in these weak grid areas.

VECTO System - Electric Grid Sub-synchronous Oscillations

Since August 20, 2020, AEMO has intermittently observed sub-synchronous oscillations in the West Murray area (VIC/NSW), initially identified following a line trip. In response, AEMO developed a bespoke monitoring tool at Red Cliffs Terminal Station (RCTS) 220 kV, capturing intervals with sub-synchronous voltage oscillations, typically around 19 Hz with varying magnitudes.

Insufficient PMU Sampling Rate Challenges

Analysis revealed the highest magnitude oscillations near Red Cliffs. On August 25, 2021, AEMO held an industry briefing to present several instances of these oscillations and subsequently released sample data for stakeholders to analyse.

The challenge was that when using conventional PMU devices, the highest academic oscillation frequency detectable must be < 25Hz (to satisfy Nyquist criteria1). The collected data was therefore of poor quality and not accurate.

Solved: High Resolution PMU Sampling Data

In the response to the AEMO Industry Briefing, CT LAB developed a small signal stability algorithm using a GPS synchronised 1-second wide window of raw sine wave data to calculate a harmonic spectrum from DC to 60Hz at 1Hz resolution. This spectrum gave us the ability to accurately detect the occurrence of oscillations. When combined with our large Pre- and post- waveform buffers, we were able to accurately record the much needed EMT data2 (50kHz GPS synchronised waveform data with ±100ns absolute time accuracy).

From the recorded data we identified that:

  • The oscillations happen in very short bursts (burps). 
  • The time of occurrence could also be correlated to network loading conditions.
  • We were also able to identify the oscillation phasor (amplitude and phase angle) at various locations. Unfortunately the pilot project scope was too small to investigate the propagation of the oscillation using the synchronised phasors.

At the same time we also implemented a low-cost oscillation detection mechanism to be used throughout the network to detect the presence and frequency of the oscillations. This algorithm detects the amplitude and frequency of the highest 1-sec spectrum oscillation over a 10-min period. The user can access the data as 2 x 10-min trends. The one trend will indicate the amplitude of the oscillation (identify whether oscillations are present). The second trend indicates the frequency of the highest oscillation.

In addition to the 1Hz resolution spectrum we also implemented the recording of a 0.1Hz resolution spectrum using a 10-s wide GPS synchronised window of raw data. This time we gave the user the choice to select a 6Hz frequency span to be monitored (ae. 6Hz-12Hz in 0.1Hz steps).

A novice new mechanism to accurately detect and investigate sub harmonic oscillations is now available to industry.

Sources:

Footnotes:

  1. Nyquist Criteria:
    • The Nyquist-Shannon sampling theorem states that to accurately reconstruct a signal, it must be sampled at least twice the highest frequency present in the signal.
    • For example, to accurately detect an oscillation frequency of 25 Hz, the data must be sampled at a minimum of 50 samples per second (50 Hz). ↩︎
  2. EMT (Electromagnetic Transient) Data refers to high-resolution, time-domain data used to model and analyse transient phenomena in electric power systems. This type of data captures the rapid changes in electrical parameters such as voltage and current, typically with high sampling rates. ↩︎

Additional Notes:

Analysing Small Signal Oscillations in a Power Grid

Context: Monitoring and analysing small signal oscillations within an electric power grid, crucial for ensuring grid stability.

Key Points:

  1. Conventional PMU Devices:
    • Phasor Measurement Units (PMUs) are devices used in power grids to measure electrical waves.They provide real-time data on voltage and current, which is essential for monitoring grid stability and detecting oscillations.
  2. Oscillation Frequency:
    • Oscillations in the power grid can indicate issues with stability. Small signal oscillations refer to minor fluctuations in the grid’s electrical parameters.
    • Detecting these oscillations accurately is crucial for preventing larger stability problems.
  3. Nyquist Criteria:
    • The Nyquist-Shannon sampling theorem states that to accurately reconstruct a signal, it must be sampled at least twice the highest frequency present in the signal.
    • For example, to accurately detect an oscillation frequency of 25 Hz, the data must be sampled at a minimum of 50 samples per second (50 Hz).
  4. Challenge with Conventional PMUs:
    • Conventional PMUs have limitations in their sampling rates, typically up to 50 Hz.
    • According to the Nyquist criteria, this restricts the maximum detectable oscillation frequency to less than 25 Hz.
    • If oscillations higher than this frequency occur, conventional PMUs cannot detect them accurately, leading to poor quality and inaccurate data.

Detailed Breakdown:

  1. Nyquist Criteria in Detail:
    • The Nyquist rate is the minimum sampling rate required to avoid aliasing, which occurs when higher frequency components of the signal are misrepresented as lower frequency components due to insufficient sampling.
    • To prevent aliasing, the sampling frequency (fs) must be greater than twice the highest frequency component (fmax) of the signal: fs > 2 \times f_{max} .
  2. Impact on Data Quality:
    • If the sampling rate is too low, the PMU may miss critical oscillations, especially those occurring at frequencies higher than half the sampling rate.
    • This results in incomplete or inaccurate data, making it challenging to monitor and manage the grid effectively.

Summary:

The challenges presented at the West Murray Zone (WMZ) highlighted the limitation of conventional PMUs in detecting small signal oscillations above 25 Hz due to the Nyquist criteria. As a result, the collected data lacked accuracy and quality, impeding effective grid stability monitoring.

References:

High Resolution EMT Data in the Context of Electric Power Grids

Definition: EMT (Electromagnetic Transient) Data refers to high-resolution, time-domain data used to model and analyze transient phenomena in electric power systems. This type of data captures the rapid changes in electrical parameters such as voltage and current, typically with high sampling rates.

Contextual Importance: In the context of electric power grid stability and small signal oscillations, EMT data is crucial for accurately detecting and analysing fast, transient events that can impact the stability of the grid.

Key Points:

  1. High Resolution:
    • EMT data is collected at very high sampling rates, often in the range of several kilohertz (kHz), compared to traditional PMU data which is sampled at 30-60 Hz.
    • This high resolution allows for the capture of fast, transient events and small signal oscillations (SSO‘s) that conventional PMUs may miss.
  2. Detailed Analysis:
    • The data provides detailed insights into the dynamic behaviour of the power grid.
    • It is essential for modelling electromagnetic transients and analysing their effects on system stability and performance.
  3. Applications:
    • Transient Stability Analysis: Understanding how the system responds to faults and disturbances.
    • Small Signal Stability: Identifying and mitigating oscillations that could lead to instability.
    • System Protection: Designing protection schemes that respond effectively to transient events.
  4. Comparison with PMU Data:
    • PMU Data: Provides synchrophasor measurements at lower sampling rates, suitable for steady-state and slowly varying phenomena.
    • EMT Data: Captures high-frequency components and fast transients, offering a more granular view of system dynamics.

Example:

Consider a scenario where a power grid experiences a sudden fault. Conventional PMUs might capture the event with limited detail due to lower sampling rates. VECTO System EMT data, with its higher resolution, can provide a much clearer picture of the transient response, helping engineers develop more effective mitigation strategies.

References:

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