Detection of Anomalies in the Quality of Electricity Supply

From the last two decades, power quality is getting much attention. Proper functioning of the equipment depends upon the quality of power supplied. Every year, demand of electric power goes on increasing and the power system network is expanding and becoming more complex. On account of thrust on clean power supply, use of renewable sources has dramatically increased in grid but it simultaneously causes power quality problems. In this work, power quality disturbance detection in wind farm integrated with grid is presented. For disturbance detection, time-time transform has been employed. The disturbance signal for the detection purpose is generated in MATLAB/Simulink environment by using a Simulink model

Fault Classification in a DG Connected Power System using Artificial Neural Network

Distributed generation is playing an important role in power system to meet the increased load demand. Integration of Distributed Generator (DG) to grid leads to various issues of   protection and control of power system structure.  The effect of the distributed generators to the grid is changes the fault current level, which makes the fault analysis more complex. From the different fault issues occurs in a distributed generator integrated power system, classification of fault remains as one of the most vital issue even after years of in-depth research. This paper emphasis on the classification of faults in DG penetrated power system using Artificial Neural Network (ANN). Because researchers are attempting to detect and diagnose these faults as soon as possible in order to avoid financial losses, this work aims to investigate the sort of fault that happened in the hybrid system. This paper proposed artificial neural network based approaches for fault disturbances in a microgrid made up of wind turbine generators, fuel cells, and diesel generator. The voltage signal is retrieved at the point of common coupling (PCC). The extracted data are used for training and testing purpose.  Artificial neural network technique is utilized for the classification of fault in the simulated model. Furthermore, performance indices (PIs) such as standard deviation and skewness are calculated for reduction of data size and better accuracy. Both the fault and parameters are varied to check the usefulness of the proposed method. Finally, the results are discussed and compared with different DG penetration

Passive Islanding Detection Technique for Integrated Distributed Generation at Zero Power Balanced Islanding

Renewable power generation systems have more advantages in the integrated power system compared to the generation due to fossil fuels because of their advantages like reliability and power quality. One of the important problems due to such renewable distributed generation (DG) system is an unintentional islanding. Islanding is caused if DG supplies power to load after disconnecting from the grid. As per the DG interconnection standards, it is required to detect the islanding within two seconds after islanding with the equipments connected to it. In this paper a new passive islanding detection method is presented for wind DG integrated power system with rate of change of positive sequence voltage (ROCOPSV) and rate of change of positive sequence current (ROCOPSC). The islanding is detected if both the values of ROCOPSV and ROCONSV are more than a predefined threshold value. The test system results carried on MATLAB shows the performance of the proposed method for various islanding and non islanding events with different power imbalances. The results conclude that, this method can detect islanding even at balanced islanding with zero non detection zone (NDZ)

Técnicas para el Análisis Espectral de Armónicos en Sistemas Eléctricos de Potencia

En este artículo se realizó una investigación bibliográfica sobre las técnicas más utilizadas en sistemas eléctricos de distribución e industriales para el análisis espectral de armónicos. Se elaboró una tabla comparativa de las técnicas encontradas de más de 40 artículos científicos de la IEEE de acuerdo a sus ventajas, desventajas y campo de aplicación en sistemas eléctricos de potencia.El resultado de ésta investigación muestra la viabilidad de aplicación de cada una de estas técnicas para el análisis de armónicos según el régimen de operación (Estacionario, No-Estacionario, Transitorio) de la señal analizada en cargas no-lineales

Islanding detection in distribution system embedded with renewable-based distributed generation

Classical view of power system is characterized by a unidirectional power flow from centralized generation to consumers. Power system deregulation gave impetus to a modern view by introducing distributed generations (DGs) into distribution systems, leading to a bi-directional power flow. Several benefits of embedding DGs into distribution systems, such as increased reliability and reduced system losses, can be achieved. However, when a zone of the distribution system remains energized after being disconnected from the grid, DGs become islanded and early detection is needed to avoid several operational issues. In response to this call, a wavelet-based approach that uses the mean voltage index is proposed in this work to detect islanding operation in distribution systems embedding DGs. The proposed approach has been tested in several islanding and non-islanding scenarios using IEEE 13-bus distribution system. The results have shown the effectiveness of the proposed approach compared to other islanding approaches previously published in the literature

Novel method for detection of voltage dips in the grid with distributed generation

U ovoj doktorskoj disertaciji je predstavljena je nova metoda za detekciju propada napona, zasnovana na Rekurentnoj neuronskoj mreži i analizi u harmonijskom domenu. Metoda je namenjena za primenu u savremenim distributivnim mrežama koje sadrže obnovljive izvore, i u skladu sa tim je optimizovana i testirana. Pametna metoda postiže izuzetne rezultate u brzini detekcije, sa prosečnim vremenom detekcije manjim od 1 ms, uz izuzetnu pouzdanost (preko 97%). U doktorskoj disertaciji dokazana je i druga hipoteza, a to je da je moguće predvideti dubinu propada algoritmom zasnovanim na harmonijskoj analizi.In this PhD thesis, a novel method for the detection of voltage dips (sags), based on the Recurrent Neural Network and analysis in the frequency domain, is presented. The method is intended for use in the modern distribution grids that contains renewable sources, and accordingly it is optimized and tested. The smart method achieves exceptional results in detection speed, with an average detection time of less than 1 ms and with high reliability (over 97%). In the PhD thesis, another hypothesis is proved, which claims that is possible to predict the depth of dip with algorithm based on the harmonic analysis

Development of Voltage Controller and Fault Analysis of Self Excited Induction Generator System

Increasing fuel cost and attempt to get pollution free environment, renewable sources of energy such as the wind, solar, micro-hydro, tidal wave, and biomass, etc. have grabbed recently the attention of researchers. Among these available energy resources, the use of wind energy is growing rapidly to generate and supply electricity as grid connected or stand alone mode. To generate electric power from such non-conventional sources, self-excited induction generator (SEIG) is found to be a suitable option for either using in grid connected mode or isolated mode. Selection of SEIG in these areas depends on its advantages such as low cost, less maintenance, and absence of DC excitation. High maintenance and installation costs including transmission losses of conventional power supply to remote or isolated place by means of power grid can be reduced by installing stand-alone wind driven SEIG system at those places. In the year of 1935, self-excitation concept in squirrel cage induction machine with capacitors at their stator terminals was introduced by Basset and Potter. But the problems associated with SEIG are its poor voltage and frequency regulation under load and prime mover speed perturbations which put a limit on the use of SEIG for a long time. By controlling active and reactive power accurately, it is possible to regulate frequency and voltage of SEIG terminal during load and speed perturbations. Various efforts have been put by researchers in developing SEIG voltage and frequency controller but these control schemes demand multiple sensors along with complex electronic circuits.This dissertation presents some studies and development of new voltage controller of the SEIG system for balanced resistive, R − L and induction motor (IM) load that is used in isolated or remote areas. So in this context, an attempt is taken to develop an optimized voltage controller for SEIG using Generalized Impedance Controller (GIC) with a single closed loop. Stable zones of proportional and integral gains for GIC based SEIG system are computed along with parameter evaluation of the GIC based SEIG system. Further, Particle Swarm Optimization(PSO) technique is used to compute the optimal values of proportional and integral gains within the stable zone. The research work on SEIG system is extended to develop a voltage controller for SEIG with minimum number of sensors to make the system less complex and cost effective. Here, a voltage peak computation technique is developed using Hilbert Transform and computational efficient COordinate Rotation DIgital Computer (CORDIC) which requires only one voltage sensor and processed to control SEIG voltage for GIC based SEIG system. This voltage control scheme is implemented on commercially available TMS320F2812 DSP processor and performed laboratory experiment to study the performance of GIC based SEIG system during load switching. The work of this thesis is not confined only to study an optimal and simple voltage controller for SEIG system but also extended to investigate the fault identification methodologies of SEIG system. Here, the features of non-stationary SEIG signal with faults are extracted using Hilbert-Huang Transform (HHT). Further, different classifiers such as MultiLayer Perceptron (MLP) neural network, Probabilistic Neural Network (PNN), Support Vector Machine (SVM), and Least Square Support Vector Machine (LS-SVM) are used to identify faults of SEIG system. In this study, it is observed that LS-SVM among above classifiers provides higher classification accuracy of 99.25%