Gravity Search Algorithm hybridized Recursive Least Square method for power system harmonic estimation

Singh, Santosh Kumar; Kumari, Deepika; Sinha, Nilotpal; Goswami, Arup Kumar; Sinha, Nidul

doi:10.1016/j.jestch.2017.01.006

Cited by 23 publications

(13 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition to the methods described above, many evolutionary optimisation‐based estimation algorithms were developed for harmonic analysis in the literature [3]. A selection of these algorithms can be found in [26–33]. Finally, some hybrid algorithms in the literature combine different approaches for harmonic analysis as in [34].…”

Section: Literature Survey Motivation and Contributionsmentioning

confidence: 99%

Performance limits for the amplitude estimation of power system harmonics & interharmonics

Altintasi

Orguner

Salor

2020

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

Section: Literature Survey Motivation and Contributionsmentioning

confidence: 99%

Performance limits for the amplitude estimation of power system harmonics & interharmonics

Altintasi

Orguner

Salor

2020

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

“…In recent years, the use of optimization algorithms for solving the engineering problems has been increasing [11][12]. Many hybrid optimization methods have also been applied for the harmonic analysis [13][14][15][16][17][18][19][20][21][22]. The amplitude and phase values of harmonics and sub-inter-harmonics estimated in [13][14][15][16] are more 5% of the actual values, which is higher than the maximum error rate specified in the standard [5].…”

Section: Introductionmentioning

confidence: 99%

“…In all optimization-based algorithms [13][14][15][16][17][18][19][20][21][22] that are based on KF or RLS, the amplitude and phase estimation is performed by assuming that the frequency of harmonic or sub-inter-harmonic components contained in the signal to be estimated are known. However, in real power systems, if the fundamental frequency and the frequencies of other harmonics and sub-inter-harmonics are not at their nominal values, then the amplitude and phase estimations made by these methods will be false.…”

Section: Introductionmentioning

confidence: 99%

Sine Cosine Algorithm Approaches for Directly Estimation of Power System Harmonics & Interharmonics Parameters

Altintasi

2021

IEEE Access

View full text Add to dashboard Cite

In order to maintain the power quality, parameters of harmonic and sub-inter-harmonic components in the signal must be estimated accurately with less computational complexity. In power systems, the value of fundamental frequency changes between 49.5Hz and 50.5Hz and this situation causes the frequency of other harmonic components to change. In order to make efficient amplitude and phase estimation, initially, frequencies must be estimated properly. In this study, the Sine Cosine Algorithm is applied for the estimation of harmonics and sub-inter-harmonics parameters in the power systems. Unlike other optimization-based methods in the literature, it is assumed that the frequencies of the harmonic and sub-inter-harmonics component in the signal are not known. The proposed algorithm has been tested on synthetic signals at different noisy environment. Given the results obtained, SCA can predict the frequencies, amplitudes, and phases of harmonics and sub-inter-harmonics with less than 5% error, which is the maximum error limit specified by the standard, and proposed algorithm estimates the actual parameters with a low error rate even in noisy environments. In addition, SCA has a better convergence performance index than optimization algorithms used in studies in the literature assuming that frequency is known. Performance of the proposed algorithm is also tested on the data obtained from electrical transmission system. SCA estimates the frequencies of harmonics with almost no error, and the amplitude and phase estimation values of each harmonic are in corroboration with the results obtained by the DFT method.

show abstract

“…In [19], a hybrid optimization technique based on the firefly algorithm and recursive least square (RLS) is suggested to estimate the magnitude and phase angle of electrical harmonics. Subsequently, RLS and biogeography-based optimization (BBO) [20,21], particle swarm optimization (PSO) and gradient decent (GD) method [22], weighted least squares (WLS) and SVD [23], genetic algorithm and least square method [24], genetic algorithm based on adaptive perceptron [25], gravity search algorithm (GSA) and RLS [26] have been suggested to estimate the harmonic voltage phasors at all the buses. It has been observed that this approach has improved the accuracy in estimating the harmonic states of the power system when compared to individual optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Reverse harmonic load flow analysis using an evolutionary technique

Vedik

Shiva

Harish*³

2020

SN Appl. Sci.

View full text Add to dashboard Cite

In the present paper, a new approach to estimate the harmonic distortions is proposed by considering the existence of harmonic sources in the power system. The algorithm employs a differential evolution technique by formulating the reverse harmonic load flow problem for each harmonic order and by considering a limited number of phasor measurement units (PMUs) deployed across the power system. The technique utilizes the measured voltage and current harmonics obtained using synchronized PMUs with known transmission network as input data. The proposed technique estimates the harmonic voltage phasors at the unmonitored buses of the power system with reasonable accuracy. The effectiveness and efficiency of the proposed method is validated by applying it to five bus and IEEE 14-bus test systems by considering uncertainty in measurement noise. It is shown that the proposed approach yields optimum solution and provides reliable and accurate harmonic state estimates.

show abstract

Gravity Search Algorithm hybridized Recursive Least Square method for power system harmonic estimation

Cited by 23 publications

References 16 publications

Performance limits for the amplitude estimation of power system harmonics & interharmonics

Performance limits for the amplitude estimation of power system harmonics & interharmonics

Sine Cosine Algorithm Approaches for Directly Estimation of Power System Harmonics & Interharmonics Parameters

Reverse harmonic load flow analysis using an evolutionary technique

Contact Info

Product

Resources

About