A cascade-type hierarchical fuzzy system with additional defuzzification of layers for the automatic power quality diagnosis

2020

The issue of power quality (PQ) has become more prominent over the last few decades as the demand of clean and high quality power is increasing around the globe. The effect of power quality disturbances on the equipment most of the time is very destructive, usually generates disruptions, which consecutively affects the other load connected to the power systems. The main purpose of this article is to present a comprehensive review of various power quality analysis techniques such as heuristic optimization, signal processing, machine learning, neural networks, artificial intelligence, and hardware implementation, so that a brief overview will be presented to the researcher and power engineers working in the field of power quality. Additionally, a comparative analysis is also reported on various methods based on several criteria including timing and accuracy, use of industrial and non-industrial data set, noisy and noiseless conditions, and finally single and multiple power quality events for analysis. More than 200 research publications are included for analysis and listed in reference so that it will be easy for the researcher in the domain of power quality to explore the possibility of further improvement in this field.

“…[148] In ref. [149], authors presented a cascade-type hierarchical fuzzy system for classification of PQ events. In ref.…”

Section: T T Transformmentioning

confidence: 99%

Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

2020

“…The concept of PCA [17] is also explored for steady state analysis of power quality events but the scenario of analysis is synthetic which will not cover the uncertainties which occur in real-time systems. In [18] cascade-type hierarchical fuzzy analysis is reported for analysis of power quality events with the help of commercial instrument which collects the data of disturbances, therefore this technique have dependence on external device and error also occur while extraction of time instants of disturbances so this drawback makes this technique inefficient in real-time application. Another method of research and implementation of a USB Interfaced real-time power quality disturbance classification [19] is proposed but this method differentiates between single and complex power quality disturbances by feature difference and classification of spike is considered as single event not in combination with other, in actual scenario this is impractical, which ignore correlations between single events.…”

Section: Previous Workmentioning

confidence: 99%

Dual Tree Complex Wavelet Transform with Multiobjective Optimization Algorithm for Real‐Time Power Quality Events Classification

2020

This article proposes an application of Dual Tree Complex Wavelet Transform (DTCWT) with non-dominated sorting genetic algorithm III (NSGA III) based multi-objective optimization and directed acyclic graph support vector machine (DAG-SVM) for recognition and classification of power quality events. The proposed method first employs DTCWT for extraction of features then NSGA III algorithm for an effective optimization of extracted features. After that, DAG-SVM based classifier are used to predict the classes of PQ disturbances. The NSGA III algorithm generates optimalsolutions based on multi objective optimization and then fitness function is generated with the help of Pareto front to obtain unique features set from power signals. The NSGA III not only optimizes selected features with DTCWT but also reduces the computational time in comparison with the traditional NSGA II. The obtained unique feature vectors are used for training of Directed Acyclic Graph-SVM to classify the power quality disturbances. The short event detection, lesser computational timing, superior classification accuracy, and high anti-noise performance are the main advantages of the proposed method.

“…In ref. [18] cascade‐type hierarchical fuzzy analysis is reported for analysis of power quality events with the help of commercial instrument which collects the data of disturbances, therefore this technique is dependent on external device and error also occurs while extraction of time instants of disturbances, so this drawback makes this technique inefficient in real time application. Another method of research and implementation of a USB interfaced real‐time power quality disturbance classification [ 19 ] is proposed but this method differentiates between single and complex power quality disturbances by feature difference and classification of spike is considered as single event not in combination with other, in actual scenario this is impractical, which ignore correlations between single events.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Power Quality Analysis: Adaptive FEM Algorithm with Sparse Signal Decomposition

2020

An adaptive approach of discretized triangular mesh based on finite element method hybrid with type‐2 fuzzy is utilized for detection and classification of power quality events. The concept of sparse signal decomposition is utilized with extraction of disturbances from power signals. An adaptive finite element method is then applied with the concept of stiffness matrix which extracts some relevant features of power quality events that act as input to type‐2 fuzzy classifiers. Finally performance comparison is carried out, the results of which show that the proposed technique has potential to analyze disturbances with high accuracy and minimum computational overhead.