scholarly journals Power Quality Assessment of Perturbed Power System Implementing Fuzzy Logic and Discrete Wavelet Transform

2019 ◽  
Vol 9 (1) ◽  
pp. 4711-4717
Keyword(s):  
Wavelet Transform ◽  
Power System ◽  
Discrete Wavelet Transform ◽  
Power Quality ◽  
Quality Index ◽  
Harmonic Distortion ◽  
Electric Power System ◽  
Total Harmonic Distortion ◽  
Discrete Wavelet ◽  
Non Linear

A novel power quality index (PQI) is determined in this paper which helps in determining the power quality under non-sinusoidal condition. Power quality monitoring is important due to exponential growth of non linear loads in electric power system. As non linear loads creates the distortion level in distribution system so it is highly necessary to measure power quality index. The innovative power quality index has been found out by considering three component such as Representative quality factor(RQPF), Detailed pollution factor(DPF), Total harmonic Distortion(THD). When total harmonic distortion of voltage(THDV) and Total harmonic distortion of current(THDI ) amalgamation occur then THD has been formed using fuzzy inference system. An experimental model has been developed to verify the PQI under different cases by measuring voltage and current both on the source side and utility side . The measured voltage and current are reformulated as wavelet function using discrete wavelet transform (DWT) to calculate referred power quality factors . This new power quality index has been validated through hardware model to justify its importance under different non-sinusoidal conditions.

Power System Disturbance Recognition Using Wavelet and S-Transform Techniques

2004 ◽  
Vol 1 (2) ◽  
Author(s):  
Jaya Bharata Reddy ◽  
Dusmanta Kumar Mohanta ◽  
B.M. Karan
Keyword(s):  
Wavelet Transform ◽  
Power System ◽  
Power Quality ◽  
A Priori ◽  
Electrical Power ◽  
Quality Analysis ◽  
Discrete Wavelet ◽  
Electrical Power System ◽  
Window Width ◽  
S Transform

Power quality issues have been a source of major concern in recent years due to extensive use of power electronic devices and non-linear loads in electrical power system and consequently sensitive detection and accurate classification of power disturbances have become very much necessary. To monitor electrical power quality disturbances, short time discrete Fourier transform (STFT) is most often used. But for non- stationary signals, the STFT does not track the signal dynamics properly due to the limitations of a fixed window width chosen a priori. This paper presents a new approach for power quality analysis using a modified wavelet transform, known as S–transform and the analysis of several power quality problems using both S–transform as well as discrete wavelet transform validates the superiority of S–transform.

scholarly journals Power Quality Disturbances Classification using Discrete Wavelet Transform and Support Vector Machine

2018 ◽  
Vol 7 (4.35) ◽  
pp. 939
Author(s):  
Tiagrajah V. Janahiraman ◽  
Muhammad Hazwan Harun
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Power Quality ◽  
Service Providers ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Statistical Parameters ◽  
Power Utility ◽  
Power Quality Disturbances

Power utility providers and power industry service providers face a significant challenge in identifying the type of Power Quality Disturbances (PQD) automatically. This paper discusses a method to classify PQD using signal decomposition, statistical analysis and machine learning. Firstly, Discrete Wavelet Transform (DWT) is applied on the generated PQD signals to decompose the signal to obtain its representation in time and frequency domain. Secondly, first and second order statistical parameters are computed on the selected sub-band of DWT. These parameters are used as features vector for the machine learning based classifier. Our database consists of 2400 generated signals of PQD, which were divided into train and test set. Another set of noise corrupted signal database was generated to evaluate the capability of the system. SVM using quadratic kernel was selected as the classifier of the Power Quality Disturbances feature vector. Comparisons were also made with other types of classifiers and other types of mother wavelet filter functions. The results show that the combination of DWT and SVM managed to classify Power Quality Disturbances with high accuracy and has a strong resistance towards noise.  

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