Power quality disturbance recognition using hybrid signal processing and machine intelligence techniques (original) (raw)
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IEEE Transactions on Industry Applications, 2015
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.
Turk. J. Elec. Eng. & Comp. Sci., 2013
The classification of power quality (PQ) disturbances to improve the PQ is an important issue in utilities and industrial factories. In this paper, an approach to classify PQ disturbances is presented. First, a signal containing one of the PQ disturbances, like sag, swell, flicker, interruption, transient, or harmonics, is evaluated using the proposed approach. Afterwards, S-transform and TT-transform are applied to the signal and an artificial neural network is used to recognize the disturbance using S-transform and TT-transform data, like the variance and mean values of S-transform and TT-transform matrices. The main features of the proposed approach are the real-time and very fast recognition of the PQ disturbances. Finally, the proposed method’s results are compared with the support vector machine and k-nearest neighbor classification methods to verify the results. The results show the effectiveness of this state-of-the-art approach.
Neural network for Power Quality disturbances recognition and classification using S-transform
Power Quality has become a main problem in the electric power system. The wide use of non-linear loads and other electronic equipment's can causes power disturbances which then lead to poor power quality. Poor power quality is caused by power line disturbances, instability, and short lifetime resulting in failure of End user equipment. To improve the power quality, it is essential to detect, localize and classify power quality disturbances accurately. In this paper, IEEE14 bus system is simulated in Power System Computer Aided Design (PSCAD) software. This paper mainly detects and classify Power quality disturbances such as Voltage sag and Voltage swell. S-transform is used to extract distinguishing features and Artificial Neural Network (ANN) is used as a classifier. Power quality disturbances are localized by S-transform in time and frequency domain.
A Decision Tree and S-transform based approach for power quality disturbances classification
4th International Conference on Power Engineering, Energy and Electrical Drives, 2013
In this paper, it is presented an automated classification based on S-transform as feature extraction tool and Decision Tree as algorithm classifier. The signals generated according to mathematical models, including complex disturbances, have been used to design and test this approach, where noise is added to the signals from 40dB to 20dB. Finally, several disturbances, simple and complex, have been considered to test the implemented system. Evaluation results verifying the accuracy of the proposed method are presented.
Power Quality Detection and Classification Using S-Transform and Rule-Based Decision Tree
International journal of electrical and electronic engineering and telecommunications, 2019
This paper presents a method for detection of Power Quality (PQ) disturbances using Stockwell's transform. Modeling equations are used for PQ disturbance generation using MATLAB program as per IEEE standards. Signals features are extracted from the time-frequency analysis based on Stockwell's transform. A rule-based decision tree are used to classify various PQ disturbances. It can be seen that high efficiency of classification is achieved using S-transform with rule-based decision tree. Several PQ disturbances are addressed with single and combined disturbances. Results demonstrate the accuracy and robustness of the proposed method in detection and recognition of single and combined PQ disturbances under noiseless and noisy conditions. The proposed algorithm also shows good performance in comparison with other reported studies.
Power quality disturbances classification based on S-transform and probabilistic neural network
Neurocomputing, 2012
Classifying power quality (PQ) disturbances is one of the most important issues for power quality control. A novel high-performance classification system based on the S-transform and a probabilistic neural network (PNN) is proposed. The original power quality signals are analysed by the S-transform and processed into a complex matrix named the S-matrix. Eighteen types of time-frequency features are extracted from the S-matrix. Then, after comparing the classification abilities of different feature combinations, a selected subset with 2 features is used as the input vector of the PNN. Finally, the PNN is trained and tested with simulated samples. By reducing the number of features in the PNN's input vector, the new classification system reduces the time required for learning and the computational costs associated with the features and the PNN's memory space. The simulation results show that 8 types of PQ disturbance signals with 2 types of complex disturbances were classified precisely and that the new PNN-based approach more accurately classified PQ disturbances compared to back propagation neural network (BPNN) and radial basis function neural network (RBFNN) approaches.
Power Quality Disturbance Classification
This article presents an artificial neural network (ANN)-based approach for power quality (PQ) disturbance classification. The input features of the ANN are extracted using S-transform. The features obtained from the S-transform are distinct, understandable, and immune to noise. These features after normalization are given to radial basis function (RBF) neural networks. The data required to develop the network are generated by simulating various faults in a test system. The proposed method requires a lesser number of features and less memory space without losing its original property. The simulation results show that the proposed method is effective and can classify the disturbance signals even under a noisy environment.
Recognition of Complex Power Quality Disturbances Using S-Transform Based Ruled Decision Tree
IEEE Access
Deteriorated quality of power leads to problems, such as equipment failure, automatic device resets, data errors, failure of circuit boards, loss of memory, power supply issues, uninterrupted power supply (UPS) systems generate alarm, corruption of software, and heating of wires in distribution network. These problems become more severe when complex (multiple) power quality (PQ) disturbances appear. Hence, this manuscript introduces an algorithm for identification of the complex nature PQ events in which it is supported by Stockwell's transform (ST) and decision tree (DT) using rules. PQ events with complex nature are generated in view of IEEE-1159 standard. Eighteen different types of complex PQ issues are considered and studied which include second, third, and fourth order disturbances. These are obtained by combining the single stage PQ events such as sag & swell in voltage, momentary interruption (MI), spike, flicker, harmonics, notch, impulsive transient (IT), and oscillatory transient (OT). The ST supported frequency contour and proposed plots such as amplitude, summing absolute values, phase and frequency-amplitude obtained by multi-resolution analysis (MRA) of signals are used to identify the complex PQ events. The statistical features such as sum factor, Skewness, amplitude factor, and Kurtosis extracted from these plots are utilized to classify the complex PQ events using rule-based DT. This is established that proposed approach effectively identifies a number of complex nature PQ events with accuracy above 98%. Performance of the proposed method is tested successfully even with noise level of 20 dB signal to noise ratio (SNR). Effectiveness of the proposed algorithm is established by comparing it with the methods reported in literature such as fuzzy c-means clustering (FCM) & adaptive particle swarm optimization (APSO), Wavelet transform (WT) & neural network (NN), spline WT & ST, ST & NN, and ST & fuzzy expert system (FES). Results of simulations are validated by comparing them with real time results computed by Real Time Digital Simulator (RTDS). Different stages for design of complex PQ monitoring device using the proposed approach are also described. It is verified that the proposed approach can effectively be employed for design of the online complex PQ monitoring devices. INDEX TERMS Complex nature PQ event, power quality, ruled decision tree, Stockwell's transform, statistical feature.
This is to certify that Thesis report entitled "Power Quality Disturbance Detection and Classification Using Signal Processing and Soft-computing Techniques." which is submitted by me in partial fulfillment of the requirement for the award of degree M.Tech. In Electrical Engineering to National Institute of Technology, Rourkela comprises only my original work and due acknowledgement has been made in the text to all other material used. Date: 4 ACKNOWLEDGEMENTS Firstly, my deepest thanks to my advisor and thesis Prof. Sanjeeb Mohanty for providing me with the support, valuable technical guidance and financial assistance through the span of the research. I would also like to thank Prof. A K Panda and K B Mohanty for agreeing to be on my defense committee. Their critical reviews are very much acclaimed. It is only the unparalleled love, support and vision of my parents, loved ones and friends that made this works a reality. Thank you one and all and lastly my whole hearted thanks to the Department of Electrical Engineering at the National Institute of Technology, Rourkela for all the resources that helped me in successfully completing my degree requirements. 5 Abstract The quality of electric power and disturbances occurred in power signal has become a major issue among the electric power suppliers and customers. For improving the power quality continuous monitoring of power is needed which is being delivered at customer's sites. Therefore, detection of PQ disturbances, and proper classification of PQD is highly desirable. The detection and classification of the PQD in distribution systems are important tasks for protection of power distributed network. Most of the disturbances are non-stationary and transitory in nature hence it requires advanced tools and techniques for the analysis of PQ disturbances. In this work a hybrid technique is used for characterizing PQ disturbances using wavelet transform and fuzzy logic. A no of PQ events are generated and decomposed using wavelet decomposition algorithm of wavelet transform for accurate detection of disturbances. It is also observed that when the PQ disturbances are contaminated with noise the detection becomes difficult and the feature vectors to be extracted will contain a high percentage of noise which may degrade the classification accuracy. Hence a Wavelet based denoising technique is proposed in this work before feature extraction process. Two very distinct features common to all PQ disturbances like Energy and Total Harmonic Distortion (THD) are extracted using discrete wavelet transform and are fed as inputs to the fuzzy expert system for accurate detection and classification of various PQ disturbances. The fuzzy expert system not only classifies the PQ disturbances but also indicates whether the disturbance is pure or contains harmonics. A neural network based Power Quality Disturbance (PQD) detection system is also modeled implementing Multilayer Feed forward Neural Network (MFNN).
Comparison Of Power Quality Disturbances Classification Based On Neural Network
2015
Power quality disturbances (PQDs) result serious problems in the reliability, safety and economy of power system network. In order to improve electric power quality events, the detection and classification of PQDs must be made type of transient fault. Software analysis of wavelet transform with multiresolution analysis (MRA) algorithm and feed forward neural network (probabilistic and multilayer feed forward neural network )based methodology for automatic classification of eight types of PQ signals (flicker, harmonics, sag, swell, impulse, fluctuation, notch and oscillatory) will be presented. The wavelet family, Db4 is chosen in this system to calculate the values of detailed energy distributions as input features for classification because it can perform well in detecting and localizing various types of PQ disturbances. This technique classifies the types of PQDs problem sevents.The classifiers classify and identify the disturbance type according to the energy distribution. The re...