Estimation of Low Frequency Oscillation Parameters Using Singular Value Decomposition Combined Group Search Optimizer

Xue, Zhengyi; Chen, Ziming; Ji, Tianyao; Li, Mengshi

doi:10.1080/15325008.2019.1582118

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…29 A distinct method is reported for evaluating small-signal strength by installing a PMU on the generational bus of a multi-machine system. 6 For the assessment of power system stability, especially small signal stability, several measurement-based techniques like Prony Analysis, 30 Kalman filter, 31 Matrix Pencil Algorithm, 32 Auto Regressive Moving Average, 33 Hankel Total Least Square technique, 34 Fast Fourier Transform, 35 Hilbert-Huang Transform, 36 Wavelet Transform, 36,37 and Singular Value Decomposition 38 have been applied in data acquisition to identify electromechanical oscillation patterns.…”

Section: 2mentioning

confidence: 99%

Multi‐objective precise phasor measurement locations to assess small‐signal stability using dingo optimizer

Logeshwari,

Abirami,

Subramanian

et al. 2023

Optim Control Appl Methods

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Small‐signal stability is an important task and key research in electrical engineering for networks. This research article focuses on the implementation of a multi‐objective approach for choosing an optimal location for Phasor Measurement Units (PMUs) to quantify a power system's small‐signal stability by maximizing the signal‐to‐noise ratio (SNR) in the system. The novelty of this research lies in the implementation of Dingo Optimization (DOX) technique along with the Prony Analysis (PA) approach for the assessment of small‐signal stability in standard grid networks. The voltage angle, amplitude and the range of frequencies are measured by the optimal placement of PMUs, which primarily focus on the multi‐signal PA. To achieve the objective of this research, DOX integrated with the multi‐signal PA approach is used to determine the ideal position for PMU placement by considering maximum redundancy and optimizing the signal to noise ratio to a maximum level. The effectiveness of the DOX strategy is established with improved accuracy and fewer disturbances by optimizing the electromechanical oscillations of the system. The implementation of the DOX approach for attaining the best value of the maximized SNR is obtained by analyzing a wide set of conditions, perturbations, and additive noise, which provides an accurate assessment of damping ratio (DR) and frequency (f) of electromechanical oscillations. Numerical results obtained from the standard IEEE test systems (14, 39, 57, 118, and 300 bus systems) are compared with the existing methods in the literature. The statistical indices demonstrate that under the highly limited optimization context selected, the intended optimizer functions satisfactorily.

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Section: 2mentioning

confidence: 99%

Multi‐objective precise phasor measurement locations to assess small‐signal stability using dingo optimizer

Logeshwari,

Abirami,

Subramanian

et al. 2023

Optim Control Appl Methods

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show abstract

“…In 2018, Nanivadekar and Kalekar [22] combine GSO with Grey Wolf Optimization (GWO) to allocate resources for cognitive radio systems. In [23], GSO is combined with Singular Value Decomposition (SVD) to estimate low-frequency oscillation parameters in electrical networks. Table 1 summarizes and compares these different works.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

“…[22] FA and GSO -Resource allocation of the Homogeneous cognitive radio system collaborative Xue and al. [23] SVD and GSO -Identification and determi-Heterogeneous nation of mode parameters collaborative in an oscillation event with several other metaheuristics and have been shown to give very high-performance results. ( 5) Complete methods such as filtering (or CP) are often unable to solve large complex real-world problems in a reasonable time.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

A Hybrid APM-CPGSO Approach for Constraint Satisfaction Problem Solving: Application to Remote Sensing

Ayadi¹,

Boulila²,

Farah³

2021

Preprint

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Constraint satisfaction problem (CSP) has been actively used for modeling and solving a wide range of complex real-world problems. However, it has been proven that developing efficient methods for solving CSP, especially for large problems, is very difficult and challenging. Existing complete methods for problem-solving are in most cases unsuitable. Therefore, proposing hybrid CSP-based methods for problem-solving has been of increasing interest in the last decades. This paper aims at proposing a novel approach that combines incomplete and complete CSP methods for problem-solving. The proposed approach takes advantage of the group search algorithm (GSO) and the constraint propagation (CP) methods to solve problems related to the remote sensing field. To the best of our knowledge, this paper represents the first study that proposes a hybridization between an improved version of GSO and CP in the resolution of complex constraint-based problems. Experiments have been conducted for the resolution of object recognition problems in satellite images. Results show good performances in terms of convergence and running time of the proposed CSP-based method compared to existing state-of-the-art methods.

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RETRACTED ARTICLE: Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications

Abualigah

2020

Neural Comput & Applic

160

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Estimation of Low Frequency Oscillation Parameters Using Singular Value Decomposition Combined Group Search Optimizer

Cited by 7 publications

References 19 publications

Multi‐objective precise phasor measurement locations to assess small‐signal stability using dingo optimizer

Multi‐objective precise phasor measurement locations to assess small‐signal stability using dingo optimizer

A Hybrid APM-CPGSO Approach for Constraint Satisfaction Problem Solving: Application to Remote Sensing

RETRACTED ARTICLE: Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications

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