Shaik, Abdul Gafoor

This paper presents the experimental investigation of power quality (PQ) disturbances associated with the grid integrated wind energy generator. PQ disturbances associated with the grid synchronization and outage of doubly fed induction generator (DFIG) based wind energy generation system have been investigated in the presence of various types of loads. The voltage signal has been captured at point of common coupling (PCC) using the data acquisition system consisting of power quality analyser, laptop, and WT viewer software. The voltage signal has been analysed using MATLAB software to find the PQ disturbances. It has been observed that the PQ disturbances such as voltage fluctuations, flicker, voltage sag, voltage swell and harmonics are associated with the grid synchronization and outage of DFIG based wind energy conversion system.

This paper presents a novel method for detecting bearing faults using multi-resolution analysis of stator current signals based on Stockwell Transform. The sampled stator current signals are analyzed with the help of Stockwell Transform to extract features namely maximum magnitude and maximum phase angle. The statistics of these features are further utilized to detect and classify the bearing faults in outer race, inner race, cage and balls. The experimental results show that the proposed approach is capable of detecting various bearing faults in induction motor.

This paper presents a Wavelet based transmission line protection algorithm which uses centroid difference for fault detection and support vector regression for the fault location. The sample of three phase currents signals of both the terminals of the line are synchronized and decomposed with Wavelet Transform to obtain the absolute values of approximate coefficients over moving window of a cycle. For the decomposition of current signal dbl mother wavelet is used. Two centroid at each cycle is computed using k-means clustering. The centroid difference is computed at both terminal added to obtain fault index. The fault index is compared with a threshold to detect the faulty phase and to classify the fault. The same approximate coefficients of post fault current transient obtain over half a cycle are used for the estimation of fault location with the help of support vector regression. A large number of case studies involving changes in fault impedance, fault incidence angle and fault location have been conducted to establish the performance of the proposed algorithm.

This paper deals with an algorithm, based on wavelet-Alienation coefficients for protection of wind farm connected distribution network. The proposed algorithm takes the differential current signal of grid node and wind farm coupling node. The Wavelet transform used to extract the approximation coefficients of all the three-phase current signals. Alienation coefficients of each differential phase current obtained over a half cycle is used to identify and categorize the various faults. A modified IEEE13 bus test feeder system used for simulation in MATLAB. Proposed technique tested for detection of all shunt faults at each bus of the distribution system.

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 IMF1 and residue R1. A fault index which is nothing but the absolute mean of R1 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.

This paper presents a wavelet-alienation based protection scheme for multi-terminal transmission system. The proposed algorithm makes use of wavelet transform based approximate coefficients of three-phase voltage and current signals, obtained over a quarter cycle to detect, classify and locate various faults. Detection and classification of faults are achieved with the help of approximate coefficients based alienation coefficients of current signals, measured from all the buses of system. The approximate coefficients of voltage and current signals, obtained over a quarter cycle, are fed to artificial neural network to locate the faults precisely, from respective buses. This algorithm has been tested successfully for all the types of faults with variations in location, inception angle and fault impedance. The proposed protection scheme is generalized for N-terminal system followed by successful testing on three-terminal and five-terminal systems.

Renewable energy (RE) sources when integrated to utility network to form hybrid power system pose challenges in terms of stability and power quality. A method based on Stockwell's transform (S-transform) is presented in this paper for power quality (PQ) assessment and detection of islanding, outage and grid synchronization of renewable energy sources. Voltage signals are decomposed using multi-resolution analysis (MRA) of S-transform and features extracted are realized to assess the power quality disturbances associated with various events. Operational events are detected with the help of features extracted from the S-transform based decomposition of negative sequence component of voltage. A power quality index is proposed to rank the various operational events based on power quality. Proposed algorithm has been tested successfully on IEEE-13 bus test system with necessary modifications to integrate wind and solar PV generators to form the hybrid power system. Simulation results are validated in real time environment with the help of real time digital simulation (RTDS) system.

Wind Energy (WE) is intermittent and uncertain in nature, and its integration with weak AC grid in the presence of inductive loads largely deteriorates the power quality (PQ). In such scenario grid synchronization at higher WE, penetration level becomes a major challenge in the distribution network. This paper presents a method to mitigate PQ issues in weak AC grid with high WE penetration using distribution static compensator (DSTATCOM) controlled by an adaptive linear neural network-based least mean square (ADALINE-LMS) control algorithm. This control algorithm minimizes the current error by continuous tracking of active unit voltage templates and extracting the weight component of the load current to estimate reference current signals. These reference signals and actual weak AC grid current signals are fed to the hysteresis current controller to generate switching signals for DSTATCOM thereby facilitating smooth grid synchronization and enhance WE penetration level. The proposed control algorithm has been successfully implemented in case a weak AC grid with higher WE penetration level in MATLAB environment.

Integration of distributed generation (DG) sources poses new protection challenges in distribution system. The fault current characteristics change due to bidirectional flow of current and unpredictable nature of DG sources. In this work, Stockwell transform based protection scheme has been proposed to detect and classify faults in a distribution network integrated with a solar photo voltaic power plant (SPV). The post fault current transients measured over a moving window at the substation are decomposed using S- Transform to obtain the peak magnitude of second harmonic content which is defined as fault index. This fault index is compared with a threshold (TF) to detect and classify faults. The proposed algorithm has been successfully implemented on IEEE 13 bus system with numerous case studies involving variations of fault incidence angles and fault locations in MATLAB environment. The immunity of the proposed algorithm to switching transients, energization/de-energization of power components and noisy environment has also been established.

This paper presents a wavelet transform based alienation technique for the protection of a radial 5 bus distribution system integrated with four wind type doubly fed Induction generators (DFIG).This technique is used to detect islanding condition and faults, classification of faults and their discrimination. Islanding is simulated at point of common coupling (PCC) and faults are simulated at each DG bus of the network. Daubechies wavelet transform has been used to decompose the current signals to get approximate coefficients. The Alienation coefficients of these approximate decompositions are termed as islanding and fault indexes. These indexes have been compared with predetermined threshold to detect islanding and faults. The same threshold value is utilized to discriminate transients associated with islanding and fault. Alienation coefficients at each bus over a half cycle window clearly detect both islanding and fault. Testing of the proposed algorithm has been carried out for various angles of incidence. Hence, the proposed algorithm is more effective and successful for finding the islanding condition as well as faults in distribution system with distributed generation.