Power Quality Disturbance Signals Research Articles

Fast Methods for Power Quality Analysis

AbstractIn this paper, the lifting based second generation Wavelet Transform (SGWT) is implemented along with the widely used discrete Wavelet Transform (DWT) for the detection and localization of ten different types of power quality (PQ) disturbance signals. The SGWT provides the time domain interpretation which is an opposition to the frequency domain analysis of DWT. Further, the selected features are extracted from the detail coefficient of the variants of WT and given as inputs to the classifiers in order to characterize the signals. Moreover, a comparative assessment of the PQ signal carried out with different classifiers such as Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) have been presented along with aforementioned detection techniques. The RF is an ensemble decision tree and used for the classification of large number of data set. Hence, various single as well as combined power quality disturbance signals have been simulated in noisy and noise free environment in order to demonstrate the efficiency of the proposed techniques. Moreover, in order to represent in realistic environment, these proposed techniques are tested with both the single phase and tree phase signals captured from different transmission panels. Further, to aid this PQ disturbance detection, different types of real time fault signals are characterized with these aforementioned approaches.

Recognition of power quality disturbances using S-transform based ruled decision tree and fuzzy C-means clustering classifiers

A method based on Stockwell's transform and Fuzzy C-means (FCM) clustering initialized by decision tree has been proposed in this paper for detection and classification of power quality (PQ) disturbances. Performance of this method is compared with S-transform based ruled decision tree. PQ disturbances are simulated in conformity with standard IEEE-1159 using MATLAB software. Different statistical features of PQ disturbance signals are obtained using Stockwell's transform based multi-resolution analysis of signals. These features are given as input to the proposed techniques such as rule-based decision tree and FCM clustering initialized by ruled decision tree for classification of various PQ disturbances. The PQ disturbances investigated in this study include voltage swell, voltage sag, interruption, notch, harmonics, spike, flicker, impulsive transient and oscillatory transient. It has been observed that the efficiency of classification based on ruled decision tree deteriorates in the presence of noise. However, the classification based on Fuzzy C-means clustering initialized by decision tree gives results with high accuracy even in the noisy environment. Validity of simulation results has been verified through comparisons with results in real time obtained using the Real Time Digital Simulator (RTDS) in hardware synchronization mode. The proposed algorithm is established effectively by results of high accuracy to detect and classify various electrical power quality disturbances.

A Research on Test Platform of Energy-Saving and Loss-Reducing Experiment for Distribution Network

Based on the study of quantitative energy consumption reduction model, a test platform was established to test and verify the theoretical method. In the experiment, a power supply device with different power quality disturbances is required. This paper proposes a series multi-objective VQDG which can generate typical voltage disturbance, such as flicker, sag or swell, harmonics, unbalance and their superimposition applied to testing load. In the application, the cascade H-bridges inverter is seriesly connected between the gird source and the testing load. The device has two advantages: the output disturbance voltage level is low and the power absorbed by load is mostly provided by grid. Compared with those devices with high power rating, the size of the capacitor of VQDG will be decreased remarkably. The device is designed and physical tests are performed to demonstrate the variety of functions. Therefore, it can provide the power quality disturbance signal for the simulation experiment platform of energy saving and loss reduction of distribution network.

Analysis of Power Quality Signals Using an Adaptive Time-Frequency Distribution

Spikes frequently occur in power quality (PQ) disturbance signals due to various causes such as switching of the inductive loads and the energization of the capacitor bank. Such signals are difficult to analyze using existing time-frequency (TF) methods as these signals have two orthogonal directions in a TF plane. To address this issue, this paper proposes an adaptive TF distribution (TFD) for the analysis of PQ signals. In the proposed adaptive method, the smoothing kernel’s direction is locally adapted based on the direction of energy in the joint TF domain, and hence an improved TF resolution can be obtained. Furthermore, the performance of the proposed adaptive technique in analyzing electrical PQ is thoroughly studied for both synthetic and real world electrical power signals with the help of extensive simulations. The simulation results (specially for empirical data) indicate that the adaptive TFD method achieves high energy concentration in the TF domain for signals composed of tones and spikes. Moreover, the local adaptation of the smoothing kernel in the adaptive TFD enables the extraction of TF signature of spikes from TF images, which further helps in measuring the energy of spikes in a given signal. This new measure can be used to both detect the spikes as well as to quantify the extent of distortion caused by the spikes in a given signal.

Symmetrical Components-Based Modified Technique for Power-Quality Disturbances Detection and Classification

This paper presents a modified approach based on symmetrical components in time domain for the detection and classification of various power-quality (PQ) disturbances. Single-phase PQ disturbance signal along with the two other ideal phases generated using phase-locked loop (PLL) is processed to find out the symmetrical components of all phases in time domain. Triggering points for PQ disturbances have been detected from the negative-sequence component of disturbance phase and have been easily classified from the signatures of the waveforms using the summation of the positive- and negative-sequence components. PQ disturbances, such as voltage sag, swell, interruption, harmonics, flicker, transients, notches, and spike present in a distribution system, have been investigated and are easily localized and segmented with this technique. Being a time-domain technique, it can be adopted for the online monitoring and assessment of the PQ disturbances. Simulated and real-time results are presented for a number of PQ disturbances to show the effectiveness of the proposed method for the PQ disturbances’ detection and classification.

Research on the transient power quality disturbance detection based on HHT

Real time detection of transient power quality disturbance signal is an effective way to analyze and eliminate the fault of power grid and improve power quality. The traditional time frequency analysis method has some limitations and large errors in the detection of transient power quality disturbances. In order to improve the accuracy of real-time detection and analysis of power system signal which is interfered by complex disturbance signal, the power quality multi-disturbance signal is detected by the Hilbert-Huang transform method. And the simulation results are verified by MATLAB, and the simulation results show that the method is effective and accurate.

Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest

Power quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the original input feature vector of RF classifier to recognize 15 kinds of PQ signals with six kinds of complex disturbance. During the RF training process, the classification ability of different features is quantified by EnI. Secondly, without considering the features with zero EnI, the optimal perturbation feature subset is obtained by applying the sequential forward search (SFS) method which considers the classification accuracy and feature dimension. Then, the reconstructed RF classifier is applied to identify disturbances. According to the simulation results, the classification accuracy is higher than that of other classifiers, and the feature selection effect of the new approach is better than SFS and sequential backward search (SBS) without EnI. With the same feature subset, the new method can maintain a classification accuracy above 99.7% under the condition of 30 dB or above, and the accuracy under 20 dB is 96.8%.

Modified S transform and ELM algorithms and their applications in power quality analysis

Modified S transform (MST) and Extreme Learning Machine (ELM) algorithms are developed and are applied to power quality (PQ) analysis. Two adjustable parameters are introduced in MST to control the Gaussian window width, free from the limitation of time–frequency resolution in the standard S-transform (ST) with an uncontrollable window. Compared with ST, MST provides more convenient means for achieving desired time–frequency resolution for various PQ disturbances signals. In order to optimize the adjustable parameters, three optimization indexes are introduced to make the optimization process more adaptively. Based on the time–frequency matrix of MST, four disturbance features are enough to construct the feature vector, solving the problem of the statistical feature redundancy. Compared with the algorithms such as Back Propagation Neural Network (BPNN) and the Support Vector Machine (SVM), ELM has the advantages of simple structure, fast training speed and high precision, more suitable for engineering application. The simulation experiments show that the MST-ELM algorithms, could provide higher classification accuracy, better anti-noise property, less computational cost and independent of training set.

Hybrid Methods for Fast Detection and Characterization of Power Quality Disturbances

In this paper, recently developed variants of wavelet transform, namely the maximum overlapping discrete wavelet transform and the second-generation wavelet transform, are used for detection of ten types of the power quality (PQ) disturbance signals. Further, the features of PQ signal disturbances are extracted using these wavelet transforms. Those extracted features are then used to classify various PQ disturbances. Random forest (RF) classifier is presented in this paper. The RF is constructed with multiple trees for classification of large number of classes simultaneously. In order to represent realistic situation, the proposed technique is tested with noisy data.

Simultaneous denoising and compression of power system disturbances using sparse representation on overcomplete hybrid dictionaries

This study introduces a novel unified framework for simultaneous denoising and compression of electric power system disturbance signals using sparse signal decomposition and reconstruction on overcomplete hybrid dictionary (OHD) matrix. In the proposed method, the power quality signal is first decomposed into deterministic sinusoidal components and non-deterministic components using the OHD matrix, including discrete impulse dictionary ( I ), cosine dictionary ( C ), sine dictionary ( S ) and the l1-norm optimisation algorithm. Then, the hard-thresholding, uniform threshold dead-zone quantisation, modified index coding and Huffman coding techniques are used for compression of significant detail signal samples and approximation coefficients. To justify the selection of OHD matrix, four compression methods are implemented using the decomposition techniques based on the dictionaries Ψ = [ I C S ] and Ψ = [ I C ], the wavelet transform (WT) and the discrete cosine transform (DCT). The performance of each method is tested and validated using a wide variety of typical power quality disturbance (PQD) signals taken from the IEEE-1159-PQE and GIM–PQE databases and generated using the Microgrid model. The results show that the method with dictionary Ψ = [ I C S ] is capable of effectively compressing the PQD signals as well as suppressing the noise components in the signals.

A Novel Power Quality Disturbances Detection and Classification Method

Power quality disturbance detection and identification is the prerequisite and basis for the power quality management and control. This paper presents a new power quality disturbance detection and classification method. Firstly, the time-time transform is applied to power quality disturbance signal analysis. According to spectrum analysis results of the diagonal elements of time-time transform matrix, a preliminary judge about whether the disturbance signal contains harmonics and inter harmonic was given. For disturbances with non-harmonics, based on time-time transform modulus matrix diagonal sequence, the beginning and ending time of the disturbance is located, and the disturbance amplitude is calculated. For the disturbances which contain harmonics, time-time transform is perform twice to get the row mean value curve and the column mean value curve, which are required by disturbance time location and amplitude measurement. Finally, disturbance classification had realized by using rule tree. Simulation results reveal that this method is very robust and adaptable, which can identify transient power quality disturbance with minor magnitude under noisy environment, and the recognition rate is satisfactory.

Application of Slantlet Transform Based Support Vector Machine for Power Quality Detection and Classification

Concern towards power quality (PQ) has increased immensely due to the growing usage of high technology devices which are very sensitive towards voltage and current variations and the de-regulation of the electricity market. The impact of these voltage and current variations can lead to devices malfunction and production stoppages which lead to huge financial loss for the production company. The deregulation of electricity markets has made the industry become more competitive and distributed. Thus, a higher demand on reliability and quality of services will be required by the end customers. To ensure the power supply is at the highest quality, an automatic system for detection and localization of PQ activities in power system network is required. This paper proposed to use Slantlet Transform (SLT) with Support Vector Machine (SVM) to detect and localize several PQ disturbance, i.e. voltage sag, voltage swell, oscillatory-transient, odd-harmonics, interruption, voltage sag plus odd-harmonics, voltage swell plus odd-harmonics, voltage sag plus transient and pure sinewave signal were studied. The analysis on PQ disturbances signals was performed in two steps, which are extraction of feature disturbance and classification of the dis- turbance based on its type. To take on the characteristics of PQ signals, feature vector was constructed from the statistical value of the SLT signal coefficient and wavelets entropy at different nodes. The feature vectors of the PQ disturbances are then applied to SVM for the classification process. The result shows that the proposed method can detect and localize different type of single and multiple power quality signals. Finally, sensitivity of the proposed algorithm under noisy condition is investigated in this paper.

Improved Threshold Function for Power Quality Disturbance Signal Denoising

Mutation point feature of power quality (PQ) disturbance signals are very conducive to the wavelet-based detection and localization of PQ events, but the PQ signals are often disturbed by noise. In order to suppress noise and keep mutation points, an improved threshold function was proposed. According to the fact that the wavelet coefficients of signal and noise distributed on different scale, the threshold σj2lnk amended by to calculate threshold value for each scale adaptively. (k is the number of wavelet coefficients at level j). In simulation, four type of PQ signals and three degrading degrees are testing; meanwhile, four existing algorithm with wavelet shrinkage are performed for comparison. Results reveal that the proposed scheme not only suppresses noise of PQ signal well but also keeps the mutation points nicely.

Power Quality Disturbances Signal De-noising Based on Hard and Soft Threshold

Power Quality Disturbances Signal De-noising Based on Hard and Soft Threshold

Detection and Localization of Power Quality Disturbances Based on Complex Wavelet

电能质量扰动信号的检测与定位为分析扰动产生的原因提供依据。本文提出了一种基于复小波的电能质量扰动检测与定位方法，该方法利用离散复小波变换，提取扰动信号的复小波系数的幅值和相位信息，再利用幅值和相位的复合信息实现对5种暂态电能质量扰动信号的快速准确定位。针对噪声条件下的扰动特征不明显时的定位，提出了一种基于信号预处理的辅助定位方法。仿真表明，该方法能在噪声条件下实现对电能质量扰动信号的快速准确定位。 Detection and localization of power quality disturbance signals provide a basis to analyze the causes of the disturbance. This paper presents a complex wavelet-based power quality disturbance detection and localization. First of all, the amplitude and phase of complex wavelet coefficients are extracted by discrete complex wavelet transform, and then, the composite information of amplitude and phase is used to achieving fast and accurate positioning of 5 kinds of transient power quality disturbance signals. Under the condition of noise and disturbance characteristics is not obvious, this paper proposes an auxiliary positioning method based on signal preprocessing. Simulation shows, under the condition of noise, the method can achieve fast and exact positioning of power quality disturbance signals.

De-noising of Power Quality Disturbance Detection Based on Ensemble Empirical Mode Decomposition Threshold Algorithm

Actual power quality signal which is often affected by noise pollution impacts the analysis results of the disturbance signal. In this paper, EEMD (Ensemble Empirical Mode Decomposition) -based threshold de-noising method is proposed for power quality signal with different SNR ( S ignal-to- N oise R atio ). A s a comparison, we use other four thresholds, namely, the heuristic threshold, the self-adaptive threshold, the fixed threshold and the minimax threshold to filter the noises from power quality signal. Through the analysis and comparison of three characteristics of the signal pre-and-post de-noised, including waveforms, SNR and MSE ( M ean S quare E rror), furthermore the instantaneous attribute of corresponding time by HHT (Hilbert Huang T ransform). Simulation results show that EEMD threshold de-noising method can make the wavefor m clos e to the actual value . T he SNR is high er and the MSE is small er c ompared wit h other four thresholds . T he instantaneous attribute can reflect the actual disturbance signal more exactly. The o ptimal threshold EEMD -based algorithm is proposed for power quality disturbance signal de-noising. Meanwhile, EEMD threshold de-noising method with adaptivity is suitable for composite disturbance signal de-noising.

Detection and Localization Method of Power Quality Disturbance Based on Improved TT-Transform

Towards the problem of power quality disturbance detecting and localization in the power system, this paper proposed a new method based on improved TT-transform. Amplitude’s mutation in maximum element sequence of the TT- module matrix’s row is detected to locate beginning and ending time of power quality disturbances. This method can not only detect single power quality disturbance, but also detect composite disturbance accurately. The simulation results show that the method proposed can accurately detect the common power quality disturbance signal.

A new fast discrete S-transform and decision tree for the classification and monitoring of power quality disturbance waveforms

SUMMARY In this paper, a new fast discrete S-transform (ST)-based time-frequency signal analyzer has been proposed for the detection, classification, and monitoring of power quality (PQ) disturbances varying in an electric power system. The proposed algorithm is based on the generalized Fourier algorithm that is used to obtain the time-localized spectral characteristics of the time-varying voltage and current signals belonging to PQ events. The fast ST algorithm is realized with different types of frequency scaling, band pass filtering, and interpolation techniques based on Heisenberg's uncertainty principle resulting in a reduced computation cost. In the conventional ST, the window width decreases at higher frequencies with a reduction in frequency resolution and conversely at low frequencies with wider windows. Therefore, the time-varying PQ disturbance signal is down sampled at low frequencies and cropped at high frequencies resulting in the evaluation of a fewer samples. From the time–frequency matrix output, important features are extracted and used with a binary decision tree for an accurate classification of single and simultaneous PQ events. Further, a unified approach is presented to track the time-varying PQ disturbance waveforms like voltage sag, swell, harmonics, and oscillatory transients and produce estimation of their amplitudes and phase angles. Copyright © 2013 John Wiley & Sons, Ltd.

A new fast discrete S-transform for power quality disturbance monitoring

AbstractIn this paper a new fast discrete S-transform (ST) has been proposed for the estimation of power quality (PQ) disturbance signal parameters in an electric power system. The proliferation of non-stationary non-linear loads and various PQ disturbances such as voltage dips, swells, transients, interruptions, voltage flicker, and spikes, etc., necessitate high PQ level in micro and smart grids. The proposed algorithm is based on the generalised Fourier algorithm that combines down sampling and signal cropping to produce a fast discrete ST. The technique is based on Heisenberg’s uncertainty principle, which limits the time-frequency resolution of ST and employs a trade-off between the time resolution and frequency resolution. In ST the window width decreases at higher frequencies with a reduction in frequency resolution and conversely at low frequencies with wider windows. Hence the signal can be down sampled at low frequencies and cropped at high frequencies to result in fewer samples to be evaluated....

A New Fast Discrete S-Transform for Power Quality Disturbance Monitoring

In this paper a new fast discrete S-transform (ST) has been proposed for the estimation of power quality (PQ) disturbance signal parameters in an electric power system. The proliferation of non-stationary non-linear loads and various PQ disturbances such as voltage dips, swells, transients, interruptions, voltage flicker, and spikes, etc., necessitate high PQ level in micro and smart grids. The proposed algorithm is based on the generalised Fourier algorithm that combines down sampling and signal cropping to produce a fast discrete ST. The technique is based on Heisenberg’s uncertainty principle, which limits the time-frequency resolution of ST and employs a trade-off between the time resolution and frequency resolution. In ST the window width decreases at higher frequencies with a reduction in frequency resolution and conversely at low frequencies with wider windows. Hence the signal can be down sampled at low frequencies and cropped at high frequencies to result in fewer samples to be evaluated. The algorithm models the typical time-varying signal and the accompanied distortions due to harmonics and random noise in a manner that will be suitable for real-time PQ indices estimation. Various PQ disturbance waveforms like voltage sag, swell, harmonics and interharmonics, and oscillatory transients are considered for tracking and estimation of PQ indices.