n Kumar Chanda

A simple and fast approach for allocation and size evaluation of distributed generation

Penetration of distributed generation (DG) units in distribution network has increased rapidly stimulated by reduced network power loss, improved bus voltage profile, and better power quality. Appropriate size and allocation of DG units play a significant role to get beneficial effects. The objective of this study is to demonstrate a simple and fast technique to determine appropriate location and size of DG units. A voltage stability indicator (VSI) is derived which can quantify the voltage stability conditions of buses in distribution network. According to VSI, vulnerable buses of the network are arranged rank-wise to form a priority list for allocation of DG units. To determine the size of DG units, a feed forward artificial neural network is prepared in MATLAB environment (The MathWorks, Inc., Massachusetts, USA). The effectiveness of the proposed methodology has been tested on a 52-bus radial distribution network. After appropriate allocation of DG units, voltage profiles of most of the buses are increased significantly. The results also indicated that the total loss of the distribution network has reduced by nearly 76.39%, and voltage stability conditions of buses are improved considerably. Voltage stability conditions of bus-13, bus-36, and bus-44 are raised by 23.16%, 29.23%, and 37.64% respectively.

Determination of the weakest branch in a radial distribution network using local voltage stability indicator at the proximity of the voltage collapse point

This paper presents a unique method to determine the weakest branch or the overstressed branch in a distribution network. This radial feature of the network has been fully exploited to establish the concept of local voltage stability indicator in terms of reactive power. The effectiveness of the proposed method has been successfully tested in 12 bus radial distribution system and the results are found to be in very good agreement.

An ANN based network reconfiguration approach for voltage stability improvement of distribution network

Recent trend in power sector is to automate distribution system to improve their reliability, efficiency and service quality. To facilitate automation of distribution system, an Artificial Neural Network (ANN) based novel methodology for enhancement of voltage stability by network reconfiguration is presented in this paper. Network reconfiguration is a process which alters the feeder topological structure by changing the open/close status of the sectionalizing (normally closed) and ties switches (normally open) in the system. A new voltage stability index is developed for voltage stability assessment of whole distribution network. In this work, a two stage search of switching option i.e. local search and global search is implemented to achieve desired network configuration. A multilayer ANN model with Error Back Propagation Learning (EBPL) algorithm is simulated for global search to obtained optimal set of candidate switching. The proposed scheme is tested on an 11 kV practical radial distribution system consisting of 52 buses. The experimental results are promising and encouraging. After reconfiguration, better voltage stable condition of the system is attained. Other objectives which are also satisfied are minimization of active and reactive power losses and improvement of voltage profile of most of the buses.

Entropy maximization based segmentation, transmission and Wavelet Fusion of MRI images

A method of progressive transmission of Magnetic Resonance Image with lesions over long distances is proposed. The Magnetic Resonance Images at the transmitter end are segregated on the basis of presence of lesions. Entropy Maximization using Hybrid Particle Swarm Optimization algorithm that incorporates a Wavelet theory based mutation operation is used for segmentation of Magnetic Resonance Images. It applies the Multi-resolution Wavelet theory to overcome the stagnation phenomena of the Particle Swarm Optimization. Thus the segmentation algorithm using Hybrid Particle Swarm Algorithm explores the solution space more effectively for a better solution. Varying percentages of Discrete Cosine Transform coefficients of segmented Magnetic Resonance Images are used for progressive image transmission. For a particular image data, the progressively received images are of different resolutions. At the receivers end the progressively received images of different resolutions are fused using Multi-resolution wavelet analysis to get a visually suitable image for diagnosis. The doctor or a radiologist identifies a particular class of image with lesions and may ask for the entire un-segmented Magnetic Resonance Image dataset of a particular patient for further diagnosis. The proposed system helps to reduce the load on the system by choosing not to transmit the Magnetic Resonance Images without lesions.

MRI segmentation using Entropy maximization and Hybrid Particle Swarm Optimization with Wavelet Mutation

A Hybrid Particle Swarm Optimization algorithm that incorporates a Wavelet theory based mutation operation is used for segmentation of Magnetic Resonance Images. We use Entropy maximization using Hybrid Particle Swarm algorithm with Wavelet based mutation operation to get the region of interest of the Magnetic Resonance Image. It applies the Multi-resolution Wavelet theory to enhance the Particle Swarm Optimization Algorithm in exploring the solution space more effectively for a better solution. Tests on various MRI images with lesions show that lesions are successfully extracted.

Proposed procedure for estimation of maximum permissible load bus voltage of a power system within reactive loading index range

In recent years many authors largely given attention to the effect of active loading index for low voltage distribution system. But this paper presents a novel approach for determining the reactive loading index of a low voltage distribution system at the proximity of voltage collapse point. The proposed procedure of maximum permissible load bus voltage of a power system within reactive loading index range was tested on the simple IEEE 14 bus, IEEE 30 bus, and IEEE 57 bus systems. The effectiveness of the proposed loading index against the load reactive power for different values of load voltages within reactive loading index range are estimated faithfully and significant results are observed to be in very good agreement.

Voltage Stability Index of Radial Distribution Networks by Considering Distributed Generator for Different Types of Loads

The paper presents an approach on voltage stability analysis of distribution networks for loads of different types. A voltage stability index is proposed for identifying the node, which is most sensitive to voltage collapse. It is shown that the node, at which the value of voltage stability index is maximum, is more sensitive to voltage collapse. For the purpose of voltage stability analysis, constant power, constant current, constant impedance and composite load modeling are considered. Distributed generation can be integrated into distribution systems to meet the increasing load demand. It is seen that with the insertion of distributed generator DG, load capability limit of the feeder has increased for all types of loads. By using this voltage stability index, one can measure the level of voltage stability of radial distribution systems and thereby appropriate action may be taken if the index indicates a poor level of stability. The effectiveness of the proposed method is demonstrated through two examples.

An analysis of economic load dispatch with ramp-rate limit constraints using different algorithms

The economic load dispatch (ELD) plays an important role in power system operation and control. Different techniques have been used to solve these problems. Recently, the soft computing techniques have widely used in practical applications. This paper shows successful implementation of four evolutionary algorithms, namely particle swarm optimization (PSO), particle swarm optimization with constriction factor approach (PSOCFA), particle swarm optimization with inertia weight factor approach (PSOIWA) and particle swarm optimization with constriction factor and inertia weight factor approach (PSOCFIWA) algorithms. Here ramp-rate limit constraints are considered to solve this problem. Here three cases have been considered. Four algorithms have been applied for each case. Optimum fuel cost is taken from each algorithm is compared for each cases (Case study I, II and III).

Kruskal's Maximal Spanning Tree Algorithm for Optimizing Distribution Network Topology to Improve Voltage Stability

Abstract—Alteration of power network topology is often required to meet important objectives, such as restoring connectivity, minimizing power losses, maintaining stability, maximizing power transfer capability etc., and is achieved by switching of circuit breakers and other switching devices in the power network. Primary power distribution networks are often interconnected and meshed but should be transformed to radial topology to achieve various operational advantages. Distribution networks also need to be reconfigured after faults to restore power at all the load points. Reconfiguring a power network, however, is a complicated multi-constrained optimization problem, as there may exist many feasible switching combinations in a large power network. This article proposes a novel application of graph theory, supported by Kruskals maximal spanning tree algorithm, to search for the optimal network topology and to optimally convert an interconnected meshed network into a radial system to achieve best operational characteristics, cost, and control. The proposed technique has been demonstrated on a 30-node primary distribution network originally having a mesh topology, and the results indicate significant performance improvement after transformation into optimal radial topology.

Voltage stability margin of distribution networks for composite loads

The paper presents voltage stability margin (VSM) of radial distribution network by considering a unique reactive loading index for composite loads. It is shown that the branch, at which the value of reactive loading index is minimum, is considered to be the weakest branch of the system. Then, the voltage stability margin (VSM) of the feeder is selected heuristically. It is the product of reactive loading indices of all branches of the feeder. The VSM of all feeders can be evaluated. The feeder which has the smallest value of VSM can be considered as the weakest feeder of the system and is at the proximity of voltage collapse. So, for multiple feeders, the voltage stability margin of a system may be considered as the VSM of the feeder which has the smallest value. The effectiveness of the proposed VSM has been successfully tested on a 12.66 kV radial distribution network consisting of 33 nodes and the results are found to be in very good agreement.