Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules

A multiple power quality(MPQ)disturbance has two or more power quality(PQ)disturbances superimposed on a voltage signal.A compact and robust technique is required to identify and classify the MPQ disturbances.This manuscript investigated a hybrid algorithm which is designed using parallel processing of voltage with multiple power quality(MPQ)disturbance using stockwell transform(ST)and hilbert transform(HT).This will reduce the computational time to identify theMPQdisturbances,whichmakes the algorithm fast.A MPQ identification index(IPI)is computed using statistical features extracted from the voltage signal using the ST and HT.IPI has different patterns for various types of MPQ disturbances which effectively identify the MPQ disturbances.A MPQ time location index(IPL)is computed using the features extracted from the voltage signal using ST and HT.IPL effectively identifies the initiation and end of PQ disturbances and thereby locates the MPQ events with respect to time.Classification of MPQ disturbances is performed using decision rules in both the noise-free and noisy environments with a 20 dB noise to signal ratio(SNR).The performance of the proposed hybrid algorithm using ST and HT with rule-based decision tree(RBDT)is better compared to the ST and RBDT techniques in terms of accuracy of classification of MPQ disturbances.MATLAB software is used to perform the study.

Comprehensive Overview of Multi-agent Systems for Controlling Smart Grids

Agents are intelligent entities that act flexibly and autonomously and make wise decisions based on their intelligence and experience.A multi-agent system(MAS)contains multiple,intelligent,and interconnected collaborating agents for solving a problem beyond the ability of a single agent.A smart grid(SG)combines advanced intelligent systems,control techniques,and sensing methods with an existing utility power network.For controlling smart grids,various control systems with different architectures have already been developed.MAS-based control of power system operations has been shown to overcome the limitations of time required for analysis,relaying,and protection;transmission switching;communication protocols;and management of plant control.These systems provide an alternative for fast and accurate power network control.This paper provides a comprehensive overview of MASs used for the control of smart grids.The paper provides a wide-spectrum view of the status of smart grids,MAS-based control techniques and their implementation for the control of smart grids.Use of MASs in the control of various aspects of smart grids-including the management of energy,marketing energy,pricing,scheduling energy,reliability,network security,fault handling capability,communication between agents,SG-electrical vehicles,SG-building energy systems,and soft grids—have been critically reviewed.More than a hundred publications on the topic of MAS-based control of smart grids have been critically examined,classified,and arranged for fast reference.

Recognition of complex and multiple power quality disturbances using wavelet packet-based fast kurtogram and ruled decision tree algorithm

This paper introduces an algorithm based on wavelet packet supported fast kurtogram and decision rules for the identification and classification of complex power quality(PQ)disturbances.Features are extracted from the signals using fast kurtogram,envelope of filtered voltage signal and amplitude spectrum of squared envelop.Proposed algorithm can be implemented for the recognition of the complex PQ disturbances,which include the combination of voltage sag and harmonics,voltage momentary interruption(MI)and oscillatory transient(OT),voltage MI and harmonics,voltage sag and impulsive transient(IT),voltage sag,OT,IT and harmonics.Proposed work has been performed using the MATLAB software.Performance of the algorithm is compared with performance of algorithm supported by discrete wavelet transform(DWT)and fuzzy C-means clustering(FCM).

An Algorithm for the Protection of Distribution Feeders Using the Stockwell and Hilbert Transforms Supported Features

Faults’recognition in the distribution feeders(DFs)is extremely important for improving the reliability of the distribution system.Therefore,this paper proposes a technique to identify the faults on the DF using the Stockwell Transform(ST)dependent variance feature and Hilbert transform(HT)by utilizing current signals.By element to element multiplication of the H-index,we compute using HT aided decompositions of current waveforms and VS-index,and calculate through ST aided decomposition of current waveforms.By utilizing the decision rules,various faults are classified.Different faults studied in this work are line to ground,double line,double line to ground and 3-Φto ground.For high fault impedance,this technique is effectively utilized.Furthermore,variations in the fault incidence angles are also utilized to test the performance of the proposed technique.To perform the proposed algorithm,a IEEE-13 bus system is developed in MATLAB/Simulink software.The algorithm effectively classified the faults with accuracy greater than 98%.The algorithm is also successfully validated on the IEEE-34 bus test system.Furthermore,the algorithm was successfully validated on the practical power system network.It is recognized that the developed method performed better than the discrete Wavelet transform(DWT)and ruled decision tree based protection scheme reported in various literature.