Power system low frequency oscillation monitoring and analysis based on multi-signal online identification

Yuan, Ye; Sun, Yuanzhang; Cheng, Lin; Chen, Gang; Wang, Peng

doi:10.1007/s11431-010-4066-5

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…Prony analysis can be extended to a set of signals sharing the same oscillation modes (multiple‐signals Pronyanalysis), which is the case of oscillation modes in a power system. Multiple signal gives better estimation of oscillation modes …”

Section: Small Signal Stability Analysismentioning

confidence: 99%

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Multi‐objective optimal PMUs placement for online assessment of small‐signal stability

Tarif

Amrane

Chabane

2019

Int Trans Electr Energ Syst

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Summary The current paper presents a multi‐objective approach for the optimal placement of phasor measurement units (PMUs) for small‐signal stability assessment of a power system. The proposed methodology is based on a multi‐signal Prony analysis of the voltage amplitude, voltage angle, and frequency signals measured by the PMUs. A Pareto Archive Differential Evolution (PADE) algorithm is used to determine the best location of PMUs minimizing the PMUs number and maximizing the signal‐to‐noise ratio (SNR) subject to full system constraints. The capability of the proposed approach has been demonstrated through several cases studies, where it gives an accurate estimation of electromechanical modes when confronted with modal analysis under various scenarios, disturbances, and measurement noise. Additionally, the PADE algorithm is compared with nondominated sorting approach (NSGA)‐III, multi‐objective differential evolution (MODE), and multi‐objective particle swarm optimization (MOPSO) algorithms, and the superiority of PADE algorithm for solving the optimal PMUs placement (OPP) problem is demonstrated.

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Section: Small Signal Stability Analysismentioning

confidence: 99%

“…Multiple signal gives better estimation of oscillation modes. 47 Given a set of M signals y m (t) = 1, … , M system (11) is rewritten as…”

Section: Multiple-signals Prony Analysismentioning

confidence: 99%

Multi‐objective optimal PMUs placement for online assessment of small‐signal stability

Tarif

Amrane

Chabane

2019

Int Trans Electr Energ Syst

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“…[1,4] is based on eqs. (5)- (7). However, since the synchronous torque and the damping torque derived in these references are not theoretically sound, the PSS design based on the lag-angle compensation is not theoretically sound either.…”

Section: Clarification On Pss Designmentioning

confidence: 99%

“…Consequently the transient stability problem becomes very serious. To enhance the transient stability of the large units, the fast-response high-gain exciters began being widely applied in the power system, which causes another serious problem, i.e., the low frequency oscillation [1][2][3][4][5][6][7]. Power System Stabilizer (PSS) is an auxiliary excitation control technique in order to eliminate the low frequency oscillation happening in the power system [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Renewed investigation on Power System Stabilizer design

Jin

Wang

Zhang

2011

Sci. China Technol. Sci.

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Power System Stabilizer (PSS) was proposed during 1960s to solve the low frequency oscillation problem raised by the wide application of the high-gain fast-response exciters. The fundamentals of PSS design lie in the angle compensation to increase the damping torque, which, since then, has become an important principle in designing the various power system dampers, such as SVC, TCSC, UPFC. Although many papers have been dedicated to the application of this principle, it is interesting to note that in the real industry applications PSS parameters have to be carefully tuned on site in spite of its mature design theory. So does the classical PSS design theory really meet the PSS design demand? By combining the frequency domain and the time domain analysis, this paper reinvestigates the basic idea behind the classical PSS design theory. The paper clarifies the concepts of the synchronous torque as well as the damping torque and proves that the classical PSS design principles based on these concepts are not theoretically sound. Then the paper discusses the Linear Optimal Controller Design method and analyzes its relations with the conventional PID design. By doing so the paper reveals the real mechanism of the PSS and proposes to use more systematic and advanced control tools to enhance the controller performance.power system stabilizer, low frequency oscillation, power system dynamics Citation:

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“…Nevertheless, the information captured by WAMS requires advanced techniques to extract and estimate the LFO oscillatory parameters and approximate mode shapes (AMS). At present, many methods have been used to analyze the LFO off-line and on-line based on the measured data [6,7], including discrete Fourier transform (DFT) [8], Kalman filter [9], Prony method [10], Wavelet transform (WT) [11], stochastic subspace identification (SSI) [12] and Hilbert-Huang transform (HHT) [13,14]. Each of them has its own characteristics and application areas.…”

Section: Introductionmentioning

confidence: 99%

A novel method for analyzing dominant oscillation mode based on improved EMD and signal energy algorithm

Yang

Rehtanz

et al. 2011

Sci. China Technol. Sci.

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In this paper, a novel method that integrates the improved empirical mode decomposition (EMD) and signal energy algorithm is proposed to estimate the dominant oscillation parameters and corresponding mode shape. Firstly, the EMD with symmetrical extrema extension (SEE) is utilized to decompose the measured data from wide area measurement system (WAMS) into a finite set of intrinsic mode functions (IMFs). Then, the signal energy algorithm is used to calculate the approximate oscillation parameters of the IMFs. The nodes involved the dominant oscillation mode are classified based on the calculated frequency and reasonable threshold. Furthermore, for the dominant oscillation mode, the IMF with maximum mean amplitude is defined as the reference. Next, the relative phases (RPs) between the reference IMF and other IMFs are calculated in order to identify the negative and positive oscillation groups. According to the values of RPs, the coherent group and corresponding node contribution factor (NCF) can be identified, and the dominant approximate mode shape (AMS) can also be determined. The efficiency of the proposed approach is tested by applying it to synthetic signal and measured data from the simulation model. low frequency oscillation, wide area measurement system, symmetrical extrema extension, signal energy algorithm, total oscillation energy, node oscillation energy, node contribution factor Citation:Yang D C, Rehtanz C, Li Y, et al. A novel method for analyzing dominant oscillation mode based on improved EMD and signal energy algorithm.

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Power system low frequency oscillation monitoring and analysis based on multi-signal online identification

Cited by 14 publications

References 15 publications

Multi‐objective optimal PMUs placement for online assessment of small‐signal stability

Multi‐objective optimal PMUs placement for online assessment of small‐signal stability

Renewed investigation on Power System Stabilizer design

A novel method for analyzing dominant oscillation mode based on improved EMD and signal energy algorithm

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