Optimal Power Flow using Ant Colony Search Algorithm to Evaluate Load Curtailment Incorporating Voltage Stability Margin Criterion

This paper proposes a method to compute load curtailment evaluation using ACSA based optimal power flow incorporating voltage stability margin criterion. . In a deregulated environment, congestion alleviation could mean load curtailment in certain situations.  The utilities would definitely prefer to curtail a load as lower as possible during a viability crisis situation. A criterion based on the voltage stability indicator is them incorporated as an additional constraint into the optimal power flow using ACSA algorithm and it is evaluated in a WSCC 9-bus test system.DOI:http://dx.doi.org/10.11591/ijece.v3i5.2738  

A Single Phase Multistring Multi level Inverter Topology for Distributed Energy Resources using Space Vector Modulation

Abstract-The objective of this paper is to study a seven level multi string inverter topology for DERs based DC/AC conversion system using SVM. In the micro grid system, the distributed energy resources (DER) based single-phase inverter is usually adopted. In order to reduce conversion losses in the system, the key is to save costs and size by removing any kind of transformer as well as reducing the power devices

Multilevel SVM and AI based Transformer Fault Diagnosis using the DGA Data

The Dissolved Gas Analysis (DGA) is utilized as a test for the detection of incipient prob-lems in transformers, and condition monitoring of transformers using software-based diagnosis tools has become crucial. This research uses dissolved gas analysis as an intel-ligent fault classification of a transformer. The Multilayer SVM technique is used to de-termine the classification of faults and the name of the gas. The learned classifier in the multilayer SVM is trained with the training samples and can classify the state as normal or fault state, which contains six fault categories. In this paper, polynomial and Gaussi-an functions are utilized to assess the effectiveness of SVM diagnosis. The results demonstrate that the combination ratios and graphical representation technique is more suitable as a gas signature, and that the SVM with the Gaussian function outperforms the other kernel functions in diagnosis accuracy

Dolphin Echolocation Algorithm for Solving Optimal Reactive Power Dispatch Problem

This paper proposes Dolphin echolocation Algorithm (DEA) for solving the multi-objective reactive power dispatch problem. Echolocation is the genetic sonar used by dolphins and more than a few kinds of other animals for direction-finding and hunting in different environments. This aptitude of dolphins is mimicked in this paper to develop a new process for solving optimal reactive power dispatch problem. A detailed study has shown that meta-heuristic algorithms have certain overriding rules. These rules will facilitate to get enhanced results. Dolphin echolocation algorithm takes reward of these rules and outperforms many active optimization methods. The new approach DEA leads to outstanding results with little computational efforts. In order to evaluate the efficiency of the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other specified algorithms. Simulation results show that DEA is superior to other algorithms in tumbling the real power loss and enhancing the voltage stability.

Generation of a near infra-red guide star catalog for thirty-meter telescope observations

The requirements for the production of a near Infra-Red Guide Star Catalog (IRGSC) for Thirty Meter Telescope (TMT) observations are identified and presented. A methodology to compute the expected J band magnitude of stellar sources from their optical (g, r, i) magnitudes is developed. The computed and observed J magnitudes of sources in three test fields are compared and the methodology developed is found to be satisfactory for the magnitude range, JVega = 16–22 mag. From this analysis, we found that for the production of final TMT IRGSC (with a limiting magnitude of JVega = 22 mag), we need g, r, i bands optical data which go up to iAB ~ 23 mag. Fine tuning of the methodology developed, such as using Spectral Energy Distribution (SED) template fitting for optimal classification of stars in the fainter end, incorporating spectral libraries in the model, to reduce the scatter, and modification of the existing colour–temperature relation to increase the source density are planned for the subsequent phase of this work

ATTRACTIVE AND REPULSIVE PARTICLE SWARM OPTIMIZATION, HYBRID ARTIFICIAL BEE ALGORITHM FOR SOLVING REACTIVE POWER OPTIMIZATION PROBLEM

Reactive Power Optimization is a complex combinatorial optimization problem involving non-linear function having multiple local minima, non-linear and discontinuous constrains. This paper presents Attractive and repulsive Particle Swarm Optimization (ARPSO) and Hybrid Artificial Bee Colony (HABC) applied for reactive optimization problem. It is one of the recent additions to the class of swarm intelligence based algorithms that mimics the foraging behaviour of honey bees. Artificial bee colony (ABC) consists of three groups of bees namely employed, onlooker and scout bees. In ABC, the food locations represent the potential candidate solution. In the present study an attempt is made to generate the population of food sources (Colony Size) adaptively and the variant is named as A-ABC. A-ABC is further enhanced to improve convergence speed and exploitation capability, by employing the concept of elitism, which guides the bees towards the best food source. This enhanced variant is called E-ABC. ARPSO and HABC are applied to Reactive Power Optimization problem and are evaluated on standard IEEE 30Bus System. The results show that ABC prevents premature convergence to high degree but still keeps a rapid convergence. It gives best solution when compared to Attractive and repulsive Particle Swarm Optimization (ARPSO) and Particle Swarm Optimization (PSO)