135 research outputs found
Analysis load forecasting of power system using fuzzy logic and artificial neural network
Load forecasting is a vital element in the energy management of function and execution purpose throughout the energy power system. Power systems problems are complicated to solve because power systems are huge complex graphically widely distributed and are influenced by many unexpected events. This paper presents the analysis of load forecasting using fuzzy logic (FL), artificial neural network (ANN) and ANFIS. These techniques are utilized for both short term and long-term load forecasting. ANN and ANFIS are used to improve the results obtained through the FL. It also studied the effects of humidity, temperature and previous load on Load Forecasting. The simulation is done by the Simulink environment of MATLAB software
Dynamic Voltage Restorer Application for Power Quality Improvement in Electrical Distribution System: An Overview
Dynamic Voltage Restorer (DVR) is a custom power device that is used to improve voltage
disturbances in electrical distribution system. The components of the DVR consist of voltage source
inverter (VSI), injection transformers, passive filters and energy storage. The main function of the
DVR is used to inject three phase voltage in series and in synchronism with the grid voltages in order
to compensate voltage disturbances. The Development of (DVR) has been proposed by many
researchers. This paper presents a review of the researches on the DVR application for power quality
Improvement in electrical distribution network. The types of DVR control strategies and its
configuration has been discussed and may assist the researchers in this area to develop and proposed
their new idea in order to build the prototype and controller
Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model 1
Abstract: An irregular activity on electric power distribution feeder, which does not draw adequate fault current to be detected by general protective devices, is called as High impedance fault (HIF). This paper presents the algorithm for HIF detection based on the amplitude of third and fifth harmonics of current, voltage and power. This paper proposes an intelligent algorithm using the Takagi SugenoKang (TSK) fuzzy modeling approach based on subtractive clustering to detect the high impedance fault. The Fast Fourier Transformation (FFT) is used to extract the feature of the faulted signals and other power system events. The effect of capacitor bank switching, non-linear load current, no-load line switching and other normal event on distribution feeder harmonics is discussed. The HIF and other operation event data were obtained by simulation of a 13.8 kV distribution feeder using PSCAD. It is evident from the outcomes that the proposed algorithm can effectively differentiate the HIFs from other events in power distribution feeder
Performance of Modification of a Three Phase Dynamic Voltage Restorer (DVR) for Voltage Quality Improvement in Electrical Distribution System
There is growing concern over power quality of ac supply systems. Power quality can be defined as the ability of utilities to provide electric power without interruption. Various power quality problems can be categorized as voltage sags, swells, harmonics, transients and unbalance are considered are the most common power quality problems in electrical distribution systems (Elandy etl., 2006). These types of disturbances can cause fails in the equipments, raising the possibility of an energy interruption.Voltage swells can be defined as a short duration increase in rms of main source with an increase in voltage ranging from 1.1 p.u up to 1.8 p.u. of nominal voltage source. There are various solutions to these problem
Using Probabilistic Neural Network for Classification High Impedance Faults on Power Distribution Feeders
An intelligent approach probabilistic Neural Network (PNN) combined with advanced signalprocessing
techniques such as Discrete Wavelet Transform (DWT) is presented for detection High impedance
faults (HIFs) on power distribution networks. HIFs detection is usually very difficult using the common over
current devices, both frequency and time data are needed to get the exact information to classify and detect no
fault from HIF. In this proposed method, DWT is used to extract features of the no fault and HIF signals.
The features extracted using DWT which comprises the energy, standard deviation, mean, root mean square
and mean of energy of detail and approximate coefficients of the voltage, current and power signals are utilized
to train and test the PNN for a precise classification of no fault from HIFs. The proposed method shows that
it is more convenient for HIF detection in distribution systems with ample varying in operating cases
Triplen Harmonics Mitigation 3 Phase Four-Wire Electrical Distribution System Using Wye- Zig-Zag Transformers
This paper studies an application of wye- zigzag transformes for reducing harmonics in the neutral conductors of a three
phase 415/240V distribution system. Triplen harmonic currents add up in the neutral conductor of the distribution system
feeding the non linear loads such as personal computers and electronic office machines with switch mode power supplies.
The zigzag transformer is installed between the distribution panel and the high harmonics producing loads. This research
makes use of a star-zigzag grounded transformer
Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model
An irregular activity on electric power distribution feeder, which does not draw adequate
fault current to be detected by general protective devices, is called as High impedance fault (HIF). This
paper presents the algorithm for HIF detection based on the amplitude of third and fifth harmonics of
current, voltage and power. This paper proposes an intelligent algorithm using the Takagi Sugeno-
Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect the high impedance
fault. The Fast Fourier Transformation (FFT) is used to extract the feature of the faulted signals and
other power system events. The effect of capacitor bank switching, non-linear load current, no-load
line switching and other normal event on distribution feeder harmonics is discussed. The HIF and other
operation event data were obtained by simulation of a 13.8 kV distribution feeder using PSCAD. It is
evident from the outcomes that the proposed algorithm can effectively differentiate the HIFs from
other events in power distribution feeder
Detection of high impedance faul on power distribution system using probabilistic neural network
High impedance fault (HIF) is abnormal event
currents on electric power distribution feeder which does
not draw sufficient fault current to be detected by
conventional protective devices. The waveforms of normal
and HIF current signals on electric power distribution
feeders are investigated and analysis the characteristic of
HIF. The purpose of this study is to use a new feature which
indicates HIF faults. Fast Fourier Transformation (FFT) is
used to extract the feature of the fault signal and other
power system events, odd harmonics frequency components
of the phase currents are analyzed. The effect of capacitor
banks and other events on distribution feeder harmonics is
discussed. The features extracted are using to train and test the probabilistic neural network (PNN) which is used as the classifier to detect HIF from other normal event in power distribution system
DETECTION OF HIGH IMPEDANCE FAULT USING A PROBABILISTIC NEURAL-NETWORK CLASSFIER
In this paper, a simple and efficient method for detection high impedance fault (HIF) on power distribution systems using an intelligent approach the probabilistic neural network (PNN) combined with wavelet transform technique is proposed. A high impedance fault has impedance enough high so that conventional overcurrent devices, like overcurrent relays and fuses, cannot detect it. While low impedance faults, which include comparatively large fault currents are easily detected by conventional overcurrent devices. Both frequency and time data are needed to get the exact information to classify and detect no fault from HIF. In the proposed method, DWT is used to extract feature of the no fault and HIF signals. The features extracted which comprise the energy of detail and approximate coefficients of the voltage, current and power signals calculated at a chosen level frequency are utilized to train and test the probabilistic neural network (PNN) for a precise classification of no fault from HIFs
Voltage stability assessment of power system network using QV and PV modal analysis
The analysis of voltage instability in electric power system is very crucial in order to maintain the equilibrium of the power system. This paper presents the analysis of voltage instability of electric power system by using reactive power-voltage (QV) and real power-voltage (PV) modal analysis. This research focuses on the voltage instability analysis by implementing the QV and PV modal analysis for mesh-type power system. IEEE 14-bus system has been chosen as the power system. Both QV and PV modal analysis will be run by using MATLAB application software
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