Power quality assessment is an important performance measurement in smart grids. Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters. Addressing this issue, in this study, we propose segregation of the power disturbance from regular values using one-class support vector machine (OCSVM). To precisely detect the power disturbances of a voltage wave, some practical wavelet filters are applied. Considering the unlimited types of waveform abnormalities, OCSVM is picked as a semi-supervised machine learning algorithm which needs to be trained solely on a relatively large sample of normal data. This model is able to automatically detect the existence of any types of disturbances in real time, even unknown types which are not available in the training time. In the case of existence, the disturbances are further classified into different types such as sag, swell, transients and unbalanced. Being light weighted and fast, the proposed technique can be integrated into smart grid devices such as smart meter in order to perform a real-time disturbance monitoring. The continuous monitoring of power quality in smart meters will give helpful insight for quality power transmission and management.

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Online power quality disturbance detection by support vector machine in smart meter

Computational time and hardware resource are key issues in hardware implementation of any signal-processing algorithm. This paper presents the design and implementation of a polyphase-decomposition-based new architecture of wavelet filter for power system harmonics estimation using discrete wavelet packet transform (DWPT). Usually, DWPT provides coefficients as the output; however, the proposed architecture also includes provision for providing root mean square values directly. The proposed method reduces computational requirements and save memory resources. Xilinx system generator, a higher abstraction level tool, has been used to simulate and implement the proposed scheme on the Xilinx Artix-7 field-programming gate array AC701 board. Performance of the proposed architecture has been validated and compared through hardware cosimulation with variety of synthetic and experimental signals.

Hardware Implementation of Polyphase-Decomposition-Based Wavelet Filters for Power System Harmonics Estimation

The effects of power quality (PQ) disturbances on power utilities and power customers are increasing day by day with widespread use of power electronics devices. These disturbances should be detected rapid and accurate. Wavelet transform (WT) has become a popular method to detect, classify and analyze various types of PQ disturbances in power systems. In this study, the fundamentals of WT, its distinctions from Fourier transform and the results of an extensive literature review of published studies are presented. The results of the literature review reveal that the detection of PQ disturbances using WT is a very powerful method and has been used in PQ mitigation devices effectively (that is, Custom Power, Flexible AC Transmission System, Flexible,Reliable and Intelligent Energy Delivery System).

	 

	Key words: Wavelet transform, power quality disturbances, literature review, feature extraction.

/pdf/fundamentals-and-literature-review-of-wavelet-transform-in-2owmuhoeia.pdf

Fundamentals and literature review of wavelet transform in power quality issues

This paper proposes an optimal design of two-channel quadrature mirror filter (QMF) bank using optimized polyphase component enforcing very efficient global swarm optimization techniques cuckoo search (CS) and modified cuckoo search (MCS) based fractional derivative constraints. The objective function is structured in frequency domain, as sum of energy of errors in passband and stopband. This objective function is optimized using a well recognized true least square method called Lagrange multiplier method enforcing CS and MCS based fractional derivative constraints and perfect reconstruction condition of two-channel QMF bank as a constraint. Fractional derivative constraints are optimized by employing CS and MCS algorithms taking peak reconstruction error (PRE) of two-channel QMF bank as an objective function. The proposed method is evaluated by several performance attributes of two-channel QMF bank which promulgate the excellence of proposed method. Comparative results clearly show the superiority of proposed method over recently developed techniques.

Design of quadrature mirror filter bank using polyphase components based on optimal fractional derivative constraints

Several window functions are currently applied to improve the performance of the discrete Fourier transform (DFT) harmonic detection method. These window functions exhibit poor accuracy in measuring the harmonic contents of a signal with high-order and weak-amplitude components when the power frequency fluctuates within a small range. In this paper, a minimum side-lobe optimization window function that is aimed at overcoming the abovementioned issue is proposed. Moreover, an improved DFT harmonic detection algorithm based on the six-term minimum side-lobe optimization window and four-spectrum-line interpolation method is proposed. In this context, the minimum side-lobe optimization window is obtained by optimizing the conventional cosine window function according to the optimization rules, and the characteristics of the new proposed window are provided to analyze its performance. Then, the proposed optimization window function is employed to improve the DFT harmonic detection algorithm based on the six-term minimum side-lobe optimization window and four-spectrum-line interpolation method. The proposed technique is used to detect harmonics of an electricity gird in which the six-term minimum side-lobe optimization window is utilized to eliminate the influence of spectrum leakage caused by nonsynchronous sampling of signal processing. The four-spectrum-line interpolation method is employed to eliminate or mitigate the fence effect caused by the inherent measurement error of the DFT method. Simulation experiments in two complex conditions and an experiment test are carried out to validate the improved performance of the proposed window. Results reveal that the six-term minimum side-lode optimization window has the smallest peak side lobe when compared with existing windows, which can effectively reduce the interaction influence of spectrum leakage, improve the measurement accuracy of the DFT harmonic detection method, and meet the standard requirement of harmonic measurement in complex situations.

https://www.mdpi.com/1996-1073/12/13/2619/pdf

A Minimum Side-Lobe Optimization Window Function and Its Application in Harmonic Detection of an Electricity Gird

In this letter, the use of near perfect reconstruction filter banks on the harmonic analysis of power system waveforms is investigated. For this aim, wavelet packet transform for the rms measurement is implemented. Besides commonly used wavelet filters, the influence of several of Johnston's filters is examined. Experimental tests are performed under different conditions in case of stationary and nonstationary signals. The obtained results show that Johnston filters outperform the commonly used wavelet filters for harmonic analysis.

Measurement of Harmonics in Power Systems Using Near Perfect Reconstruction Filter Banks

The wavelet transform has been successfully used in the area of power quality analysis. There are many published papers with methods for power quality disturbance classification or harmonics measurement, which use wavelet transform. However, the properties of the wavelet transform can drastically vary from the choice of the wavelet. In this paper we analyze the influence of the choice of the wavelet to the accuracy of the power quality classification method and to high frequency harmonics measurements. Additionally to the well known wavelet filters we introduce near perfect reconstruction filter banks. The simulation results indicate that these filter banks are a good choice for classification of power quality disturbances, especially in the presence of noise and for high frequency harmonics measurements.

Near Perfect Reconstruction Filter Banks for Power Quality Analysis

The wavelet transform has been successfully used in the area of power quality analysis. There are many published papers with methods for power quality disturbances classification or harmonic measurement, which use wavelet transform. However, the properties of the wavelet transform can drastically vary from the choice of the wavelet. In this paper we analyze the influence of choice of the wavelet to the accuracy of the PQ classification methods. We test two classification methods with wavelets of frequently used families and support sizes. Also, we introduce near perfect reconstruction filter banks. The simulation results indicate that the Johnston's filters are a good choice, especially in the presence of noise.

Application of near perfect reconstruction filter banks in power quality disturbances classification methods

In this paper the influence of different wavelet filters on the harmonic analysis of power system waveforms is investigated. For this aim, wavelet packet transform-based algorithm for rms measurement is implemented. Besides commonly used Daubechies filters, Vaidyanathan with 24 coefficients and Coiflets the influence of several Johnston's filters with different characteristics is examined. Experimental tests are performed under different conditions, in case of stationary signals with different harmonics components and in case of non-stationary harmonics distortion. Experimental results show that Johnston's filters outperform the commonly used wavelet filters for harmonic analysis.

Ljubica Koleva

Papers

Measurement of Harmonics in Power Systems Using Near Perfect Reconstruction Filter Banks

Near Perfect Reconstruction Filter Banks for Power Quality Analysis

Application of near perfect reconstruction filter banks in power quality disturbances classification methods

Measurement of power systems harmonics using QMF filter banks