Non-stationary Power Signal Research Articles

An optimal extreme learning-based classification method for power quality events using fractional Fourier transform

Mitigation of power quality events (PQEs) needs an accurate, faster, and efficient detection and classification technique for designing the compensating devices as a remedial measure to the problem. This study motivates on this issue to formulate a better technique based on fractional Fourier transform (FRFT) and extreme learning machine (ELM). FRFT is considered for relevant feature extraction due to its characteristics like enhanced order control and capability to provide time, frequency, and intermediate time–frequency depictions for any non-stationary power signals. The possibility of multi-domain feature extraction capability due to its easy order control arrives at a robust feature matrix, which makes the classification more accurate. An optimal ELM-based classifier is designed by tuning its system parameters applying modified teaching–learning-based optimization (MTLBO). This optimal ELM is implemented in this study along with FRFT to formulate the proposed approach denoted as FRFT–MTLBO–ELM. To give a good reason for the enhanced performance of the proposed technique, noisy and hybrid synthetic signals of ten PQEs are generated and tested considering fully all real-time condition cases. At last, a comparative result is presented with other applied signal processing-based approaches and it is found that the proposed FRFT–MTLBO–ELM approach is very effective and comparatively better justifying its real-time implementation in the monitoring systems.

Time‐frequency analysis and classification of power signals using adaptive cuckoo search algorithm

AbstractA new approach to Hilbert energy spectrum and pattern recognition of nonstationary power signals is presented in this paper. In the proposed work, visual localization, detection, and classification of nonstationary power signals are achieved using Hilbert transform (HT)–based adaptive local iterative filter (ALIF). The HT is applied on all the intrinsic mode functions that are obtained from both empirical mode decomposition (EMD) and ALIF to extract instantaneous amplitude and frequency components. The instantaneous Hilbert energy spectrum results in clear visual detection, localization, and classification of the different power signal disturbances. The visual energy spectrum by Hilbert‐Huang Transform (HHT) on ALIF showing a better result than HHT applied on EMD. The feature vectors are extracted from the Hilbert energy spectrum for automatic pattern recognition of various nonstationary signals using a traditional fuzzy C‐means algorithm (FCMA). Finally, the center of the cluster is further optimized using fuzzy C‐means–based adaptive cuckoo search algorithm. The average classification accuracy of the disturbances is 91.25% and 99.25% using fuzzy C‐means and adaptive cuckoo search–based FCMA, respectively.

Detection and classification of islanding and power quality disturbances in microgrid using hybrid signal processing and data mining techniques

This study presents multi-scale morphological gradient filter (MSMGF) and short-time modified Hilbert transform (STMHT) techniques, respectively, to detect and classify multiclass power system disturbances in a distributed generation (DG)-based microgrid environment. The non-stationary power signal samples measured near the target DG's are processed through the proposed MSMGF and STMHT techniques, respectively, and some computations over them generates the target parameter sets. Depending on the complexity of the overlapping in the target attribute values for different disturbance patterns, fuzzy judgment tree structure is incorporated for multiclass event classification, which proves to be robust for most of the classes. In this regard, an extensive simulation on the proposed microgrid models, subjected to a number of multiclass disturbances has been performed in MATLAB/Simulink environment. The faster execution, lower computational burden, superior efficiency as well as better accuracy in multiclass power system disturbance classification by the proposed judgment tree-based MSMGF and STMHT techniques, respectively, as compared to some of the conventional techniques, is significantly illustrated in the performance evaluation section. Further, as illustrated in this section, the real-time capability of the proposed techniques has been verified in the hardware environment, where the results shown are satisfactory.

Advanced signal processing techniques for multiclass disturbance detection and classification in microgrids

This study proposes the application of fuzzy assessment tree (FAT)-based short-time modified Hilbert transform (STMHT) as a new multiclass detection and classification technique, for a distributed generation (DG)-based microgrid. The time varying non-stationary power signal samples extracted near the target DG are initially de-noised by passing through the morphological median filter and then processed through the proposed STMHT technique for disturbance detection. Further based on the overlapping in the target attribute values, an FAT has been incorporated, which significantly classifies the different multiclass disturbances on a standard IEC microgrid model simulated in MATLAB/Simulink environment with highest precision in accuracy.

Parameter estimation of non-stationary power signals using an advanced signal processing technique

In this paper, a new recursive adaptive filter based on a fast Gauss-Newton method has been proposed for the estimation of fundamental and harmonics for time-varying voltage and current signals in an electrical power network. The presented algorithm is based on the minimisation of a weighted forgetting factor-based error cost function by the use of recursive Gauss-Newton method. Further a Hessian matrix approximation is used to produce a fast recursive algorithm, which is immune to random noise, waveform distortion and increases the speed of convergence and accuracy. The algorithm models the typical time-varying signals and damped sinusoids and decaying dc components and the accompanied distortions due to harmonics and random noise in a manner that will be suitable for real-time harmonic estimation, which is a very important requirement in power quality analysis.

Parameter estimation of non-stationary power signals using an advanced signal processing technique

In this paper, a new recursive adaptive filter based on a fast Gauss-Newton method has been proposed for the estimation of fundamental and harmonics for time-varying voltage and current signals in an electrical power network. The presented algorithm is based on the minimisation of a weighted forgetting factor-based error cost function by the use of recursive Gauss-Newton method. Further a Hessian matrix approximation is used to produce a fast recursive algorithm, which is immune to random noise, waveform distortion and increases the speed of convergence and accuracy. The algorithm models the typical time-varying signals and damped sinusoids and decaying dc components and the accompanied distortions due to harmonics and random noise in a manner that will be suitable for real-time harmonic estimation, which is a very important requirement in power quality analysis.

A quadratic polynomial signal model and fuzzy adaptive filter for frequency and parameter estimation of nonstationary power signals

Accurate estimation of amplitude, phase and frequency of a sinusoid in the presence of harmonics/inter harmonics and noise plays an important role in a wide variety of power system applications, like protection, control and state monitoring. With this objective, the paper presents a novel hybrid approach for the accurate estimation of dynamic power system frequency, phasor and in addition to suppressing the effect of harmonics/interharmonics and noise in the voltage and current signals. The algorithm assumes that the current during a fault occurring on a power system consists of a decaying dc component, and time variant fundamental and harmonic phasors. For accurate estimation of fundamental frequency, phasor, decaying dc and ac components in the fault current or voltage signal, the algorithm uses a quadratic polynomial signal model and a fuzzy adaptive ADALINE filter with a modified Gauss–Newton algorithm. Extensive study has been carried out to demonstrate the performance analysis and fast convergence characteristic of the proposed algorithm. The proposed method can also be implemented for accurate estimation of dynamic variations in the amplitude and phase angles of the harmonics and inter harmonics mixed with high noise conditions.

Nonstationary power signal time series data classification using LVQ classifier

A new approach to time-frequency analysis and pattern recognition of non-stationary power signals is proposed in this paper. In this manuscript, visual localization, detection and classification of...

Power signal disturbance identification and classification using a modified frequency slice wavelet transform

This study presents a novel approach to localise, detect and classify non-stationary power signal disturbances using a modified frequency slice wavelet transform (MFSWT). MFSWT is an extension of frequency slice wavelet transform (FSWT), which provides frequency-dependant resolution with additional window parameters for better localisation of the spectral characteristics. An advantage of the MFSWT is attributed to the fact that the modulating sinusoids are fixed with respect to the time axis, whereas a localising scalable modified Gaussian window dilates and translates. Several practical power signals are considered for visual analysis using MFSWT, and the disturbance patterns are appropriately localised with unique signature corresponding to each type. This work also evaluates the detection capability of the proposed methodology and a comparison with earlier FSWT and Hilbert transform to show the superiority of proposed technique in detecting the power quality disturbances. A probabilistic neural network (PNN) based classifier is used for identifying the various disturbance classes. The spread parameter of the Gaussian activation function in PNN is tuned and its effect on the classification at different strengths of noise is studied.

Nonstationary power signal time series data classification using LVQ classifier

A new approach to time–frequency analysis and pattern recognition of non-stationary power signals is proposed in this paper. In this manuscript, visual localization, detection and classification of non-stationary power signals are achieved using wavelet packet decomposition and automatic pattern recognition is carried out through learning vector quantization neural network. The wavelet packet decomposition (WPD) of the non-stationary power signals is carried out to extract the coefficients at multiple level of decomposition. The relevant features for pattern classification are derived from the time-scale information obtained by WPD. The extracted features are used to classify different power quality disturbances by using learning vector quantization neural net. Various non-stationary power signal waveforms are considered to verify the applicability of the proposed technique.

Automatic Classification of Power Quality Events Using Balanced Neural Tree

This paper proposes an empirical-mode decomposition (EMD) and Hilbert transform (HT)-based method for the classification of power quality (PQ) events. Nonstationary power signal disturbance waveforms are considered as the superimposition of various undulating modes, and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMFs). The HT is applied on all the IMFs to extract instantaneous amplitude and frequency components. This time-frequency analysis results in the clear visual detection, localization, and classification of the different power signal disturbances. The required feature vectors are extracted from the time-frequency distribution to perform the classification. A balanced neural tree is constructed to classify the power signal patterns. Finally, the proposed method is compared with an S-transform-based classifier to show the efficacy of the proposed technique in classifying the PQ disturbances.

Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier

This paper proposes fast variants of the discrete S-transform (FDST) algorithm to accurately extract the time localized spectral characteristics of nonstationary signals. Novel frequency partitioning schemes along with band pass filtering are proposed to reduce the computational cost of S-transform significantly. A generalized window function is introduced to improve the energy concentration of the time-frequency (TF) distribution. An application of the proposed algorithms is extended for detection and classification of various nonstationary power quality (PQ) disturbances. The relevant features required for classification were extracted from the time-frequency distribution of the nonstationary power signal patterns. An automated decision tree (DT) construction algorithm was employed to select optimal set of features based on a specified optimality criterion for extraction of the decision rules. The set of decision rules thus obtained were used for identification of the PQ disturbance types. Various single as well as simultaneous power signal disturbances were considered in this paper to prove the efficiency of proposed classification scheme. A comparison of the classification accuracies with techniques proposed earlier, clearly demonstrates the improved performance. The major contributions of this manuscript are new FDST algorithms for fast and accurate time-frequency representation and an efficient classification algorithm for identifying PQ disturbances. The advantages of the classification algorithm are (i) accurate feature derivation from the TF distribution and optimum feature selection by the DT construction algorithm, (ii) robust performance at different signal-to-noise ratios, (iii) simple decision rules for classification, and (iv) recognition of simultaneous PQ events.

Detection and power quality disturbance classification based on wavelet packet decomposition and modified immune optimization algorithm

In this paper, a new approach to time-frequency analysis and pattern recognition of non-stationary power signals is proposed. In this paper, visual localization, detection and classification of non-stationary power signals are achieved using wavelet

Power quality event characterization using support vector machine and optimization using advanced immune algorithm

This paper presents a time-time transform (TT-transform) variant for classification of non-stationary power signal disturbance patterns. The TT-transform variant is derived from the well known S-transform and employs a new window function whose width is inversely proportional to the frequency raised to a constant power with values within 0 and 1. Features are derived from the TT-transform result of the power signal patterns. These features are used for automatic recognition of types of disturbances with the help of kernel based support vector machine (SVM) based clustering. Further, the clustering performance of the TT-SVM based pattern recognizer is improved by a modified immune optimization algorithm. Several test cases are provided to demonstrate the improvement in classification accuracy while resulting in significant reduction of support vectors.

Time frequency analysis and power signal disturbance classification using support vector machine and differential evolution algorithm

The paper proposes a new approach for Time frequency analysis using modified time-time transform TT-transform for recognizing non-stationary power signal disturbance patterns. The TT-transform is derived from the well known S-transform ST and uses a new window function with its width inversely proportional to the frequency raised to a power 'c', varying between 0 and 1. The power disturbance signals after being processed by the TT-transform yields features, which are used for automatic recognition of disturbances; with the help of kernel based support vector machine SVM algorithm. Further to improve the classification performance of the TT-SVM based pattern recognizer, a differential evolution optimization algorithm DEOA is used. Several test cases are provided to prove the significant improvement in recognition, accuracy and drastic reduction of support vectors.

Joint oversampled carrier and time-delay synchronization in digital communications with large excess bandwidth

This paper deals with the joint estimation of the pair dynamical carrier phase/Doppler shift and the time-delay in a digital receiver. We consider a Binary Offset Carrier shaping function as used in satellite positioning, which is a time-limited pulse with a large excess bandwidth, and a Data Aided synchronization scenario, where we have a constant time-delay and a Brownian phase evolution with a linear drift. The proposed study is relative to the use of an oversampled signal model after matched filtering, leading to a colored reception noise and a non-stationary power signal. The contribution of this paper is twofold. First, we derive the Hybrid Cramér–Rao Bound for the joint phase/Doppler estimation problem. Then, we propose a method for the joint time-delay/carrier synchronization, which couples an Extended Kalman Filter and an Expectation-Maximization type algorithm. Our numerical results show the potential gain of using the oversampled signal for carrier synchronization, obtaining better performances than using a classical synchronizer, and good time-delay estimation.

Oversampled phase tracking in digital communications with large excess bandwidth

This paper deals with the on-line carrier phase estimation in a digital receiver. We consider a Brownian phase evolution in a data aided scenario. The proposed study uses an oversampled signal model after matched filtering, leading to a coloured reception noise and a non-stationary power signal. The contribution of this paper is twofold. First, we derive the Bayesian Cramér–Rao bound for this estimation problem. Then, based on a state-space model formulation of the problem, we propose an extended Kalman filter to approach this lower bound for a BOC shaping pulse. Our numerical results illustrate the gain resulting from the use of an oversampled version of the received signal to estimate the phase offset, obtaining better performances than using a classical synchronizer.

Non-stationary power signal classification using local linear radial basis function neural networks

Our work provides an effective feature based method for analyzing both steady state and short duration non-stationary power signal disturbances using a Local Linear Radial Basis Function Neural Network (LLRBFNN). In contrast to the normalized probabilistic neural network (PNN), the proposed LLRBFNN is an excellent approximation network, which performs the classification task with minimal amount of computational power than probabilistic neural network (PNN). The difference of the LLRBFNN with conventional Radial Basis Function Neural Network (RBFNN) is that a local linear model replaces the connection of weights between the hidden layer and output layer of conventional RBFNN. Both normalized time domain and frequency domain features are used for the training purpose. It is noticed that spectral entropy is an effective frequency domain feature for non-stationary power signal classification. Moreover the local linear model provides a robust model for network learning, which is not prone to local linear points. This is supported by the observation that both the LLRBFNN model and the global search optimization techniques like Genetic algorithm provide similar results.

Power Quality Disturbance Classification Using Fuzzy C-Means Algorithm and Adaptive Particle Swarm Optimization

This paper presents a new approach for the visual localization, detection, and classification of various nonstationary power signals using a variety of windowing techniques. Among the various windows used earlier like sine, cosine, tangent, hyperbolic tangent, Gaussian, bi-Gaussian, and complex, the modified Gaussian window is found to provide excellent normalized frequency contours of the power signal disturbances suitable for accurate detection, localization, and classification. Various nonstationary power signals are processed through the generalized S-transform with modified Gaussian window to generate time-frequency contours for extracting relevant features for pattern classification. The extracted features are clustered using fuzzy C-means algorithm, and finally, the algorithm is extended using either particle swarm optimization or genetic algorithm to refine the cluster centers.

Power signal classification using dynamic wavelet network

A new approach to classification of non-stationary power signals based on dynamic wavelet has been considered. This paper proposes a model for non-stationary power signal disturbance classification using dynamic wavelet networks (DWN). A DWN is a combination of two sub-networks consisting of a wavelet layer and adaptive probabilistic network. The DWN has the capability of automatic adjustment of learning cycles for different classes of signals, for minimizing error. DWN models are specifically suitable for application in dynamic environments with time varying non-stationary power signals. The test results showed accurate classification, fast and adaptive learning mechanism, fast processing time and overall model effectiveness in classifying various non-stationary power signals. The classification result of the DWN has been compared with that of the probabilistic neural network (PNN).