Robust harmonic detection, classification and compensation method for electric drives based on the sparse fast Fourier transform and the Mahalanobis distance

Abstract

This work presents a method for the robust detection, classification and possible compensation of harmonics in electric drives during real-time operation, with the aim of providing a framework for monitoring and diagnostic without the need of additional hardware. The detection is performed with a sparse fast Fourier transform algorithm, for its lower computational effort when the signals are sparse (which, by definition, contain few meaningful spectral lines). The classification is performed on the results of the signal frequency analysis by means of the Mahalanobis distance concept, improving the robustness and noise rejection properties of the method. The compensation part relies on a family of regulators in parallel, each operating in the rotating reference frame of the harmonic to be cancelled. The theoretical background is followed by a discussion on the implementation and the interaction of the three blocks for a successful real-time operation. The system was tested in laboratory and proved to fulfil the requirements, by running in parallel to a vector control for synchronous machines. It was also found that the method is a useful tool to determine the presence of unknown harmonics in an electric drive system, thus potentially providing early warnings of unexpected failures.

A Harmonic and Interharmonic Detection Method for Power Systems Based on Enhanced SVD and the Prony Algorithm

“…Specifically, S(i) is replaced by S (i) and E(i) is replaced by E (i) (E (i) = X(i) − S (i)). Substituting S (i) and E (i) into Equation (5), the Estimated Signal-to-Noise Ratio (ESNR) can be obtained:…”

“…This lea increase of 𝑆 ′ (𝑖) and a gentle decrease of |𝑋(𝑖) − 𝑆 ′ (𝑖)| with an increasing 𝑘 ing in a gentle increase in ESNR. Figure 2a illustrates the curve of accurate SNR obtained by Equation (5). When the order k is less than eight, the system is in a state of over-denoising, where noise signals are filtered out along with a large number of noiseless signals.…”

“…At present, the detection of harmonics and interharmonics is mainly divided into frequency domain analysis and time-domain analysis. Frequency domain analysis mainly refers to Fourier transform, which is simple in method and fast in computation, but suffers from spectrum leakage and the fence effect [5][6][7]. Time-domain analysis includes Wave Transform (WT) [8][9][10], Empirical Mode Decomposition (EMD) [11][12][13], Hilbert-Huang Transform (HHT) [14][15][16][17][18], the Prony algorithm [19][20][21][22], etc.…”

An improved harmonics detection method based on sliding disrete Fourier transform for three-phase grid-tie inverter system

“…Various harmonic detection methods have been researched in the literatures [1,2,3,4,5,6,7,8,9,10,11,12], which can be generally categorized into timedomain and frequency-domain methods. Typical time-domain methods based on the instantaneous reactive power theory (pq-theory), which only estimate the fundamental signal and detect the rest harmonics as a whole.…”

“…The major drawback of the above Fourier-based harmonic detection methods, as evaluated in [7,8,9,10,11,12], is the slow dynamic responses, requiring at least one-cycle settling time and it must occupy all sample point of a fundamental cycle.…”