Wavelet-Based Intelligent System for Recognition of Power Quality Disturbance Signals

2006 ◽  
pp. 1378-1385 ◽  
Author(s):  
Suriya Kaewarsa ◽  
Kitti Attakitmongcol ◽  
Wichai Krongkitsiri
Keyword(s):  
Power Quality ◽  
Intelligent System ◽  
Power Quality Disturbance

scholarly journals A Novel Scheme of Multiple Identification of Faults in the Transmission Line

2020 ◽  
Vol 9 (9) ◽  
pp. 108-116
Keyword(s):  
Transmission Line ◽  
Power Quality ◽  
Power Supply ◽  
Intelligent System ◽  
Voltage Drop ◽  
Fault Analysis ◽  
Matlab Simulink ◽  
Current Voltage ◽  
Power Quality Disturbance ◽  
Voltage Swell

Recognition and arrangement of current-voltage, power fluctuations are critical functions for the safety of the power system (PS). Many perturbations of power quality (PQ) are unpredictable and ephemeral, and the demand for voltage and current recognition and arrangement is confirmed. By using Fast Fourier transform, expert systems, and neural networks, certain intelligent system technologies dominate fault analysis. As expected, there are five types of issues that include sag and swell, ripple, transient fluctuation, interruption and natural waveform. In this paper, we study the transmission line faults for voltage drop, voltage swell, and transient voltage. Power supply and traffic transmission leakage have been major problems for electricity providers and consumers. Much of the disturbance is non-stationary and intermittent, needing specialized methods and techniques for PQ disturbance research. This article provides a full collection of MATLAB / Simulink models to simulate different energy efficiency disturbances. This paper implements power quality disturbance in the model Matlab/ Simulink. The model provided can be used to simulate various disturbances of energy quality and waveforms for analysis and research into power quality, and to help to develop educational programs and understand the energy quality. This would concentrate on what are PQ problems and current approaches to evaluate and classify such problems.

scholarly journals Detector of power quality disturbance signal with random phase offset

2019 ◽  
Vol 16 (22) ◽  
pp. 20190401-20190401
Author(s):  
Jeonghwa Yoo ◽  
Sangho Choe
Keyword(s):  
Power Quality ◽  
Random Phase ◽  
Phase Offset ◽  
Power Quality Disturbance

scholarly journals High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1238
Author(s):  
Supanat Chamchuen ◽  
Apirat Siritaratiwat ◽  
Pradit Fuangfoo ◽  
Puripong Suthisopapan ◽  
Pirat Khunkitti
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Distribution System ◽  
Classification Accuracy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Electrical Distribution ◽  
Power Quality Disturbance ◽  
Optimal Feature Selection ◽  
Optimal Feature

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

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