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<p>The modern power system is highly penetrated by Renewable Energy Sources (RES). The grid synchronisation demands high accuracy of phase, frequency, and magnitude matching with the grid. Frequency detection is necessary, as even a slight frequency fluctuation can have disastrous effects on the power system. Primitive low-frequency oscillation (LFO) detection methods lack accuracy due to several problems, including noise, PMU inaccuracy, poor detection resolution, and tough PMU tuning. To overcome this ineffectiveness, this paper presents the Ambient Stochastic Subspace Identification (ASSI) for low-frequency oscillation detection. It enables continuous monitoring of frequency and can effectively identify low-frequency oscillations without being affected by system disturbances like noise and PMU latency. Due to good compatibility with PMU and the low latency of data transfer, Ambient-SSI is best for large system monitoring, as it is a good fit for wide area monitoring systems as well. It basically makes the Toeplitz matrix and analyses the oscillation. Once the matrix is known, the state matrix and output matrix can be further solved to know the frequency and damping ratio. Once these quantities are known, oscillations can be decided. Ambient SSI suits the PMU configuration and is the best among all primitive methods such as MP, FFT, and ERA, due to the independence of system non-linearity.</p>

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Low frequency oscillation detection in the presence of renewable energy sources using ambient stochastic subspace identification technique

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月2日

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  • Nilesh Chothani,
  • Parth Vyas,
  • Choon Kit Chan,
  • Saurav Dixit,
  • Deekshant Varshney,
  • Chandrakant Sonawane

摘要

The modern power system is highly penetrated by Renewable Energy Sources (RES). The grid synchronisation demands high accuracy of phase, frequency, and magnitude matching with the grid. Frequency detection is necessary, as even a slight frequency fluctuation can have disastrous effects on the power system. Primitive low-frequency oscillation (LFO) detection methods lack accuracy due to several problems, including noise, PMU inaccuracy, poor detection resolution, and tough PMU tuning. To overcome this ineffectiveness, this paper presents the Ambient Stochastic Subspace Identification (ASSI) for low-frequency oscillation detection. It enables continuous monitoring of frequency and can effectively identify low-frequency oscillations without being affected by system disturbances like noise and PMU latency. Due to good compatibility with PMU and the low latency of data transfer, Ambient-SSI is best for large system monitoring, as it is a good fit for wide area monitoring systems as well. It basically makes the Toeplitz matrix and analyses the oscillation. Once the matrix is known, the state matrix and output matrix can be further solved to know the frequency and damping ratio. Once these quantities are known, oscillations can be decided. Ambient SSI suits the PMU configuration and is the best among all primitive methods such as MP, FFT, and ERA, due to the independence of system non-linearity.