Robust estimation of power system harmonics using a hybrid firefly based recursive least square algorithm

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

doi:10.1016/j.ijepes.2016.01.046

Cited by 44 publications

(20 citation statements)

References 15 publications

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“…Therefore, there are lN values in the frequency grid and Eqs. (5) and (6) are evaluated dividing the argument of the trigonometric functions by l. Thus, it is possible to reduce the interval in the frequency grid and to increase the range of frequencies available in the output spectrum. Thus, a more significant number of frequency components can be accurately estimated.…”

Section: Dictionary Designmentioning

confidence: 99%

“…overheating effect on energy conductors, decreasing the lifetime of electrical appliances and causing interferences in AC grid systems [4]. In turn, the interharmonics components generate effects such as flickering in lighting systems and computer monitors, sub-synchronous oscillations, and voltage fluctuations [1], [2], [5].…”

Section: Introductionmentioning

confidence: 99%

“…The method presented in [5] uses an optimization algorithm known as Firefly Algorithm (FA) to estimate the initial parameters applied to the Recursive Least Square (RLS) algorithm, which evaluates amplitudes and phases of the time-varying signal adaptively. Despite presenting adequate results, the FA-RLS algorithm has limited precision, and the definition of the parameters is a challenge in the case of time-varying signals.…”

Section: Introductionmentioning

confidence: 99%

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An Algorithm Based on Sparse Decomposition for Estimating Harmonic and Interharmonic Components of Stationary Signals in Power Systems

2019

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Many methods for estimating frequency components of stationary signals in power systems are based on the Discrete Fourier Transform. These methods have a fixed frequency resolution which depends on the sampling frequency and the number of samples of the signal, making it difficult to estimate interharmonics. This paper presents an algorithm for estimating harmonics and interharmonics of power system signals using the signal sparse decomposition technique with an overcomplete dictionary. Discrete Trigonometric Transforms have been analyzed for building this dictionary. The l-fold method has also been applied to the dictionary, which has allowed the adjustment of the frequency grid of the output spectrum. The algorithm proposed is called Harmonics and Interharmonics components Estimation based on Signal Sparse Decomposition, and it was assembled using a dictionary formed by atoms of Discrete Cosine and Discrete Sine Transforms of type II. Three synthetic signals containing harmonic and interharmonics distortions with different noise conditions were used to test the algorithm. The proposed method presented better results in the estimation of harmonic and interharmonics than Discrete Fourier Transform, Matrix Pencil Method and Fast Matching Pursuit algorithms. The results demonstrated robustness to noise and adequate estimation of the interharmonics when the frequency grid is adjusted correctly. INDEX TERMS Discrete trigonometric transform, harmonics and interharmonics estimation, overcomplete dictionary, power quality signal analysis, signal sparse decomposition.

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Section: Dictionary Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Algorithm Based on Sparse Decomposition for Estimating Harmonic and Interharmonic Components of Stationary Signals in Power Systems

2019

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Reverse harmonic load flow analysis using an evolutionary technique

Vedik

Shiva

Harish*³

2020

SN Appl. Sci.

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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.

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“…In order to assess the power system distortion level, the traditional procedures have been designed on three main methodological lines: Discrete Fourier Transform (DFT), Wavelet Transforms (WT) [5,6], and recursive-based methods. The two former ones are purely second-order methods, and the latter incorporate many procedures, such as the Kalman filter [7], neural networks [8], the gravity search algorithm [9], the biogeography hybridized algorithm [10], the hybrid firefly algorithm [11], etc. The convergence of the recursive methods is related to signal conditions, so the accuracy changes.…”

Section: Introductionmentioning

confidence: 99%

Application of Spectral Kurtosis to Characterize Amplitude Variability in Power Systems’ Harmonics

et al. 2019

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The highly-changing concept of Power Quality (PQ) needs to be continuously reformulated due to the new schemas of the power grid or Smart Grid (SG). In general, the spectral content is characterized by their averaged or extreme values. However, new PQ events may consist of large variations in amplitude that occur in a short time or small variations in amplitude that take place continuously. Thus, the former second-order techniques are not suitable to monitor the dynamics of the power spectrum. In this work, a strategy based on Spectral Kurtosis (SK) is introduced to detect frequency components with a constant amplitude trend, which accounts for amplitude values’ dispersion related to the mean value of that spectral component. SK has been proven to measure frequency components that follow a constant amplitude trend. Two practical real-life cases have been considered: electric current time-series from an arc furnace and the power grid voltage supply. Both cases confirm that the more concentrated the amplitude values are around the mean value, the lower the SK values are. All this confirms SK as an effective tool for evaluating frequency components with a constant amplitude trend, being able to provide information beyond maximum variation around the mean value and giving a progressive index of value dispersion around the mean amplitude value, for each frequency component.

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Robust estimation of power system harmonics using a hybrid firefly based recursive least square algorithm

Cited by 44 publications

References 15 publications

An Algorithm Based on Sparse Decomposition for Estimating Harmonic and Interharmonic Components of Stationary Signals in Power Systems

An Algorithm Based on Sparse Decomposition for Estimating Harmonic and Interharmonic Components of Stationary Signals in Power Systems

Reverse harmonic load flow analysis using an evolutionary technique

Application of Spectral Kurtosis to Characterize Amplitude Variability in Power Systems’ Harmonics

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