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

The impact of embedded generation due to harmonic performance

The paper investigates the most suitable location of inverter connection installed into the distribution line feeder. Thus, it can reduce losses, control voltages as well as the protection system. Besides, it is more focused on mitigated a whack-a-mole phenomenon due to its fifth and seventh harmonics. Whack-a-mole phenomenon occurs as an active filter or passive filter installed on the long feeder line. It effect harmonic voltage where increase on some busses and decrease on other busses, especially at point of installation. In this paper, the inverter replaced by directly connected with the current source. Thus, parallels current source with combination of fundamental, third, fifth and seventh harmonic frequencies were tested. From the analysis, the best location of inverter current source should be at least lambda/4 from end bus. Thus, it proves by looking into its harmonic voltage, harmonic current and total harmonics distortion (THD) performanc

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