Non‐linear dynamics based sub‐synchronous resonance index by using power system measurement data

Cho, Hwanhee; Oh, Sangwook; Nam, Suchul; Lee, Byongjun

doi:10.1049/iet-gtd.2018.5414

Cited by 17 publications

(16 citation statements)

References 18 publications

(31 reference statements)

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“…BPA's method is rather intuitive for the four types of natural oscillations, but forced oscillations, which can be observed in every frequency band, are difficult to identify. However, if the conditions of the measurement devices are sufficient to observe oscillatory behavior addressed in [16], time-series techniques, such as the maximum Lyapunov exponent for nearly real-time oscillation detection, are available [17].…”

Section: List Of Symbols -Time-series Representationmentioning

confidence: 99%

“…5 shows the test system configuration for the SSR study and Table 2 shows the parameters of the corresponding system. The conditions used in [17] have been applied in this paper.…”

Section: ) Ieee Second Benchmark System Of Ssr Computation Examplementioning

confidence: 99%

“…In [17], the relationship between system strength and MLE is demonstrated by changing Z Tr and Z L2 . The results can be summarized as follows: the effect of resonance is larger if the distance between the generator and transmission line is sufficiently close at a small transformer impedance Z Tr , and the short circuit ratio is higher.…”

Section: ) Ieee Second Benchmark System Of Ssr Computation Examplementioning

confidence: 99%

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Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation

2020

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Oscillations may cause both economic and technical problems such as a reduction in overall system reliability. Therefore, detecting and preventing oscillatory behavior that affects power systems is important. This paper proposes an oscillation recognition method that includes monitoring and extracting features in a recursive and sequential manner in a time-series measurement in power systems. We propose a geometric feature extraction process for recognizing oscillations by constructing an average system and Poincaré map for time-series measurement. The proposed process provides the features of a system's damping and frequency of oscillation, and the developed monitoring systems are based on nonlinear dynamics. The circulating oscillatory behavior is represented on a finite-integer-delay embedded time-series plane, extracted by a Poincaré map construction, and examined directly along the trajectory to monitor the features of the oscillation according to damping and frequency. Oscillatory behavior recognition is tested on IEEE's second benchmark system for subsynchronous resonance to verify the fast extraction of oscillation components. In addition, a case study for Korean power systems with a high penetration of renewable energy and application on actual measurement data is carried out to demonstrate the practical application of the process.

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Section: List Of Symbols -Time-series Representationmentioning

confidence: 99%

“…5 shows the test system configuration for the SSR study and Table 2 shows the parameters of the corresponding system. The conditions used in [17] have been applied in this paper.…”

Section: ) Ieee Second Benchmark System Of Ssr Computation Examplementioning

confidence: 99%

Section: ) Ieee Second Benchmark System Of Ssr Computation Examplementioning

confidence: 99%

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Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation

2020

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“…Other researchers improved the calculation efficiency of the maximal Lyapunov exponent in time series data [3,4]. The maximal Lyapunov exponent was also modified for applications in time series data on power systems [5,6]. The maximal Lyapunov exponent is a stability index that is strongly connected to the fluctuation of data.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the characteristics or stability of the oscillatory behavior of nonlinear systems differ from local stability, especially for time series data. There are some certain approaches (real-time or near-to-real-time) in large amounts of literature to detect local stability such as positively or negatively damped oscillations (for example, Voltage stability indices or maximum Lyapunov exponent [5,6]). However, there are few existing solutions or approaches in power systems or other applicable engineering field to detect uncertain response as marginal stability (mathematically defined but not practically).…”

Section: Introductionmentioning

confidence: 99%

Development of Floquet Multiplier Estimator to Determine Nonlinear Oscillatory Behavior in Power System Data Measurement

Choi¹,

Cho²,

Lee³

2019

Energies

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Measurement-based technology has been developed in the area of power transmission systems with phasor measurement units (PMU). Using high-resolution PMU data, the oscillatory behavior of power systems from general electromagnetic oscillations to sub-synchronous resonances can be observed. Studying oscillations in power systems is important to obtain information about the orbital stability of the system. Floquet multipliers calculation is based on a mathematical model to determine the orbital stability of a system with the existence of stable or unstable periodic solutions. In this paper, we have developed a model-free method to estimate Floquet multipliers using time series data. A comparative study between calculated and estimated Floquet multipliers has been performed to validate the proposed method. The results are provided for a sample three-bus power system network and the system integrated with a doubly fed induction generator.

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Improved subsynchronous oscillation detection method in a DFIG-based wind farm interfaced with a series-compensated network

Abdeen

Liu

2020

International Journal of Electrical Power & Energy Systems

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Non‐linear dynamics based sub‐synchronous resonance index by using power system measurement data

Cited by 17 publications

References 18 publications

Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation

Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation

Development of Floquet Multiplier Estimator to Determine Nonlinear Oscillatory Behavior in Power System Data Measurement

Improved subsynchronous oscillation detection method in a DFIG-based wind farm interfaced with a series-compensated network

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