Mahmood Shaik

Bio: Mahmood Shaik is an academic researcher from Indian Institutes of Technology. The author has contributed to research in topics: Fault (power engineering) & Hilbert–Huang transform. The author has an hindex of 2, co-authored 6 publications receiving 36 citations. Previous affiliations of Mahmood Shaik include Indian Institute of Technology, Jodhpur.

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

Abstract: The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.

An EMD and Decision Tree-Based Protection Algorithm for the Solar PV Integrated Radial Distribution System

Abstract: This article presents a fault identification technique that makes use of the empirical mode decomposition of three-phase current signals of a distribution network. The current signals measured at the substation bus over a moving window are decomposed to obtain the first-level intrinsic mode function and residue. The standard deviation of the first-level residue is calculated as a fault index for each phase and compared with a threshold to detect and categorize the type of fault. A ground fault index based on the average value of the first-level residue of the neutral current is proposed to discriminate the phase–phase and phase–phase–ground faults. The proposed algorithm has been successfully tested on the IEEE 13 bus and 34 bus distribution systems in the presence of the solar photovoltaic power plant by varying the type of fault, fault incidence angle, and fault location in the presence of noise. The selectivity of the proposed algorithm has been established by testing the algorithm with nonfaulty transients such as transformer excitation and deexcitation, feeder energization and deenergization, load switching, capacitor switching, and also those associated with distributed generation (DG) penetration like islanding and tripping of DG. Subsequently, the residue-based features are fed to a decision tree to locate the faults on the distribution system. Thus, the proposed algorithm has been successful in detecting, classifying, and locating faults with DG penetration in the presence of noise within half cycle by utilizing only current signals.

Fast Protection Scheme For Distribution System using Hilbert-Huang Transform

Abstract: In this work, Hilbert-Huang Transform (HHT) is applied for fast detection and location of faults using quarter cycle post fault current information, measured at the secondary of the substation transformer. Empirical Mode Decomposition (EMD) is carried out over a moving window to obtain first level residue, on which Hilbert Transform (HT) is applied in order to obtain the instantaneous attributes. Standard deviations of instantaneous frequency (SDIF), amplitude (SDIA) and average value of residue (AVR) are obtained for computing fault location indices. A tree like logic flow is implemented by comparing these indices with thresholds to identify the zone of fault and subsequently the exact fault location. The proposed algorithm has been implemented for IEEE 13 bus distribution system using MATLAB. Various case studies involving changes in fault type, fault incidence angle, bus location, fault resistance have been carried out in order to establish the proposed algorithm. It has also been established that the protection algorithm is not affected by non faulty transients such as switching of compensating equipment.

An EMD Based Protection Scheme for Distribution System

Abstract: Fast identification and classification of the faults in distribution network is a crucial task to ensure reliable power supply to the end user. This paper presents a protection scheme which makes use of Empirical Mode Decomposition of three phase current signals of a distribution network. The current signals measured at the substation bus over a moving window of one cycle are decomposed to obtain first level intrinsic mode function IMF 1 and residue R 1 . A fault index which is nothing but the absolute mean of R 1 is calculated for each phase and compared with a threshold to detect and categorize the type of fault. The proposed algorithm has been successfully tested on IEEE 13 bus system by varying the type of fault, fault incidence angle and fault location. The selectivity of the proposed scheme has been established by testing the algorithm with non faulty transients such as load switching and capacitor switching.

Near-Optimal PI Controllers of STATCOM for Efficient Hybrid Renewable Power System

Abstract: Connecting different renewable energy sources (RESs) to the electrical grids is presently being urged to fulfill the enormous need for electric power and to decrease traditional sources’ ecological related issues, the so-called hybrid systems. Unfortunately, these hybrid systems suffer from the possible negative environmental impacts of the wind gusts in wind energy conversion systems (WECSs) that may degrade the overall system performance. Additionally, various severe faults may disconnect some RESs from the hybrid system, like three-phase faults. In this paper, the static synchronous compensator (STATCOM) is considered for both improving the performance of a hybrid system, contains WECS and photovoltaics (PVs) against wind gusts and maintaining the continuous operations of RESs during three-phase fault occur at the point of common coupling (PCC) between the RESs and the grid. The STATCOM is stimulated by two PI controllers regulating the reactive power flow between the STATCOM and the hybrid system at PCC and, consequently, regulating the voltage at PCC. A metaheuristic optimizer optimally schedules these two PI controllers based on whale optimization algorithm (WOA). The impartial comparison between the WOA dynamic performance and the particle swarm optimization as another optimization algorithm verifies the efficiency of the WOA for the near-optimal gain scheduling of the PI controller gains.

Empirical Mode Decomposition with Hilbert Transform for power quality assessment

Abstract: The aim of this paper is to develop a method based on combination of Empirical Mode Decomposition (EMD) and Hilbert Transform for assessment of power quality events. A distorted waveform can be conceived as superimposition of various oscillating modes and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMF). Hilbert transform is applied to first three IMF to obtain instantaneous amplitude and phase which are then used for constructing feature vector. The work evaluates the detection capability of the methodology and a comparison with S-Transform is made to show the superiority of the technique in detecting the PQ disturbance like voltage spike and notch. A Probabilistic Neural Network is used as a mapping function for identifying the various disturbance classes. Results show a better classification accuracy of the methodology.

A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

Abstract: Recently, power quality (PQ) issues have drawn considerable attention of the researchers due to the increasing awareness of the customers towards power quality. The PQ issues maintain its pre-eminence because of the significant growth encountered in the smart grid technology, distributed generation, usage of sensitive and power electronic equipments with the integration of renewable energy resources. The IoT and 5G networks technologies have a number of advantages like smart sensor interfacing, remote sensing and monitoring, data transmission at high speed. Due to this, applications of these two are highly adopted in smart grid. The prime focus of the paper is to present an exhaustive survey of detection and classification of power quality disturbances by discussing signal processing techniques and artificial intelligence tools with their respective pros and cons. Further, critical analysis of automatic recognition techniques for the concerned field is posited with the viewpoint of the types of power input signal (synthetic/real/noisy), pre-processing tools, feature selection methods, artificial intelligence techniques and modes of operation (online/offline) as per the reported articles. The present work also elaborates the future scope of the said field for the reader. This paper provides valuable guidelines to the researchers those having interest in the field of PQ analysis and exploring the better methodologies for further improvement. Comprehensive comparisons have been presented with the help of tabular presentations. Although this critical survey cannot be collectively exhaustive, still this survey comprises the most significant works in the concerned paradigm by examining more than 300 research publications.

A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification

Abstract: As a result of the widespread use of power electronic equipment and the increase in consumption, the importance of effective energy policies and the smart grid begins to increase. Nonlinear loads and other loads in electric power systems are considered as the main reason for power quality disturbance. Distortions in signal quality and shape due to power quality disturbance cause a decrease in total efficiency. The proposed hybrid convolutional neural network method consists of a 1D convolutional neural network structure and a 2D convolutional neural network structure. The features acquired by these two convolutional neural network architectures are classified using the fully connected layer, which is traditionally used as the classifier of convolutional neural network architectures. Power signals are processed using a 1D convolutional neural network in their original form. Then these signals are converted into images and processed using a 2D convolutional neural network. Then, feature vectors generated by 1D and 2D convolutional neural networks are combined. Finally, this combined vector is classified by a fully connected layer. The proposed method is well suited to the nature of signal processing. It is a novel approach that covers the steps of an expert examining a signal. The proposed framework is compared with other state-of-the-art power quality disturbance classification methods in the literature. While the proposed method's classification performance is relatively high compared to other methods, the computational complexity is almost the same.