Nidhi Singh Pal

Wind speed and power prediction of prominent wind power potential states in India using GRNN

This paper introduces Generalized Regression Neural Network (GRNN) for long term wind speed prediction of major wind power potential states in India. The performance of proposed GRNN model is evaluated using the publicly available online dataset of National Aeronautics and Space Administration (NASA). Data samples of 26 cities are used for training the generalized regression neural network and remaining 5 cities data samples are used for testing purpose. Air temperature, earth temperature, relative humidity, daily solar radiation, elevation, latitude, heating degree days, cooling degree days, frost days, longitude and atmospheric pressure are used as input variables. Mean square error between measured and forecasted wind speed using training data samples and testing data samples are found to be 0.000042279 and 0.1543. Here it is important to impart that the proposed GRNN model is trained and tested with data samples of different geographical locations in order to make it feasible for wind speed prediction of any other location. Wind power of prominent wind power potential states in India are predicted by a variable pitch and speed control wind turbine G80-2MW.

Installation of DG for optimal demand compensation

The increase in demand, due to rapid industrialization and up gradation in living standard of peoples, has become a serious problem in developing countries. This draws the attention of utility, government and researchers towards the installation of Distributed Generation (DG). Installation of DG has encouraging effects as well as unenthusiastic impacts on the existing power network. These impacts of DG depend on its size and location in the exiting power network. In this paper two stage methodology is used for optimum demand compensation. In first stage only a single capacity DG is considered and in second stage two DGs are considered for compensating the demand. Proposed method is simulated on the IEEE 33 bus standard network and results are presented.

Mitigation of voltage sag/swell and harmonics using self-supported DVR

Unbalanced voltage supply is an important issue in distributed network system. In this paper, we proposed a new scheme to control the self-supported dynamic voltage restorers (DVRs). In this scheme, three phase harmonic filter (double tuned) is used to moderate harmonics, generated by voltage source converter (VSC). Total harmonic distortion (THD) is minimized at load bus. By using a reduced-rating DVR, the reimbursement of the voltage dip, voltage swell, and harmonics is demonstrated. Parks Transformation is used to convert the voltages from rotating vectors to the stationary frame. The load voltage is sustained sinusoidal and in phase by inserting properly required compensation voltage through DVR. Extensive experiments are performed on MATLAB platform to analyze the performance of the proposed scheme.

Reliability Improvement of Distribution System by Optimal Sitting and Sizing of Disperse Generation

Recent studies have publicized that renewable energy resources are the only answer to trim down energy crisis and also able to diminish green house gases. Thats why disperse generation is very significant for sustainable development in power sector. This paper presents an analytical algorithm to find optimal location and a best suitable size of distributed generator (DG) to minimize system losses. Authors also prove that scatter generation is used not only to reduce the gap between demand and supply of power with minimum amount losses, but it can also improve the reliability of distribution system. The results obtained from a case study of 34 bus radial distribution system have proved the efficiency of algorithms for loss minimization and competency of DG to enhance the reliability indices of the conventional distribution system.

Cost-Effective Power Management of Photovoltaic-Fuel Cell Hybrid Power System

Solar energy is the most promising but yet an unreliable source of renewable energy. To overcome continuity and reliability issues in photovoltaic (PV) generation system, solar PV system is integrated with the other forms of energy sources, either conventional or non-conventional. In this paper, a photovoltaic (PV) system is combined with an unconventional source like fuel cell (FC) and a battery system to provide continuous power flow to the utility grid. The system usually focused on the power management of the overall system taking cost factor under consideration. The power management strategies took the high operating cost of the fuel cell under consideration. So, the fuel cell is required when power from PV array and battery is not sufficient for maintaining the constant power flow to the utility. The control operation is carried in such a way that provides effective, reliable, and quality power generation and also minimizes the overall operating cost of the system.

Speed Control of the Sensorless BLDC Motor Drive Through Different Controllers

Nowadays Brushless DC motors (BLDC) are gaining popularity and are also replacing the motor with brushes in numerous applications due to their high efficiency, low maintenance, and effective operation. This paper presents the sensorless speed control of the BLDC drive with the technique of zero-crossing detection of indirect back EMF. Several controllers are employed and compared for acquiring the effective control over the speed. The particular paper demonstrates the performances of sensorless BLDC drive has been evaluated with different controller schemes such as a conventional controller (PI), anti-windup PI, Fuzzy based and the Hybrid (Fuzzy-PI) controller at different load and speed. Their results have been compared in which fuzzy based controller offers a better response in maximum cases. This reduces the cost and complexity without compromising the performance. Fuzzy Logic Controller is used to enhance its robustness and reliability. The effectiveness of the work is demonstrated through simulation done in MATLAB Version (2013) environment and simulation results of the sensorless drive have been analyzed.

Analysis of Energy Storage for Hybrid System Using FLC

In this paper hybrid renewable energy resources (HRES) composed of PV, wind, and batteries as storage units use a fuzzy logic technique to control the energy between load demand and generation. The control technique using a fuzzy logic controller is simulated on MATLAB, which balances the suitable power management between intermittent energy generation by renewable sources and loads.

Optimal Sitting and Sizing of Capacitor Using Iterative Search Method for Enhancement of Reliability of Distribution System

A secure and reliable distribution network is unavoidable prerequisite for socioeconomical development of any nation. Capacitors can be used for improving the voltage profile and reliability, and these can reduce reactive component of current. The aim of this paper is to present a mathematical model to fix an optimum size capacitor at optimum location in a radial distribution system for reactive power compensation. An iterative search technique is developed for loss minimization and voltage improvement of system. Authors also provide the effect of capacitor on the reliability of 34 radial bus systems.

A comparative study between wavelet primarily based ANN and ANFIS algorithm technique to locate fault in a transmission line

In this paper; a comparative study approach is proposed between Feed Forward Neural Network (ANN) and accommodative Adaptive Neuro-Fuzzy Inference System (ANFIS) rule model to find the fault of 3-ph transmission line using MATLAB Toolbox. Post fault current is the key requirement of the technique and its features are extracted using DWT ‘DB4’ as mother wavelet. These features are then used to train the ANN and ANFIS models. The training to testing ratio is maintained at 7∶3 for both the algorithm models. Two test cases on the basis of number of training samples are considered for analysis for each type of four faults viz.; BC; ABC; ACG; AG. ANFIS algorithm proved to be the better algorithm approach as compared; to efficiently estimate the fault location with an extreme standard of accuracy and at minimum requirement of dataset samples.