Priyanka Shankya

Bio: Priyanka Shankya is an academic researcher. The author has contributed to research in topics: Artificial bee colony algorithm. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

Artificial Bee Colony Algorithm for Solving Optimal Power Flow Problem

Abstract: This paper proposes an artificial bee colony (ABC) algorithm for solving optimal reactive power flow problem. The proposed ABC can deal with different objectives of the problem such as minimizing the real power losses, improving the voltage profile, and enhancing the voltage stability and properly handle various constraints for reactive power limits of generators and switchable capacitor banks, bus voltage limits, tap changer limits for transformers, and transmission line limits. The proposed approach has been observed and tested on different IEEE bus test system. The performance of ABC to be better in terms of solution superiority and computational time.

Optimal Capacitor Location and Sizing for Reducing the Power Loss on the Power Distribution Systems due to the Dynamic Load of the Electric Buses Charging System using the Artificial Bee Colony Algorithm

Abstract: The modern power distribution system is connected to many loads, affecting the power system reliability and causing more power loss. One of the new loads is the battery charging station for electric vehicles or electric buses. The charging load will have a charge that varies with the operating time of each vehicle. Therefore, this article focuses on optimal capacitor location and sizing for reducing the power loss on the power distribution systems due to the dynamic load of the electric bus system using the artificial bee colony algorithm. The dynamic charging load of 3 types of electric bus systems was applied: Plug-in charging, Pantograph charging, and Battery swapping charging. The simulation results show that the installation of 0.3167 MVar capacitors for Plug-in electric bus systems reduced the power loss by 0.5465 MWh. In comparison, the installation of 0.2893 MVar capacitors for the Pantograph electric bus systems reduces the power loss by 0.5459 MWh. The installation of 0.2922 MVar capacitors for the Battery swapping systems reduces the power loss of 0.5517 MWh. The installation of shunt capacitors can reduce the power distribution system's power loss and the benefit of capacitors installing 1,501.27, 1,443.76, and 1,474.02 USD/year, respectively.

Optimal Power Flow Based on Flower Pollination Algorithm

Abstract: In this study, the Flower Pollination Algorithm (FPA) based on meta-heuristic technique is proposed for optimal power flow (OPF) problems in electrical power systems. An algorithm inspired by the pollination process of flowers is implemented to OPF problem. The objective function of the study is to minimize fuel cost with voltage magnitude and line capacity as a constraint. Control variables are the generation of active power, voltage magnitude at PV and swing bus, settings ratio of tap transformer, and shunt capacitance values under several power system constraints. The FPA was implemented to IEEE 30- bus power system. The results ofthe simulation are then compared with some literatures with the same control variables and constraints. The simulation results show that the proposed FPA method provides accurate solutions for objective functions. In addition, the proposed method proved that the effectiveness and robustness of the results were satisfactory.

Artificial bee colony technique for a study of the influence of impact of metal thickness on the factor of quality-Q in integrated square spiral inductors

Abstract: The goal of this present paper is to investigate the impact of metal thickness on the factor of quality-Q in integrated square spiral inductor, using an efficient application of the Artificial Bee Colony (ABC) algorithm. The inductors were optimized at 2.4 GHz to determinate theirs major geometrical dimensions and theirs number of turns, for uses in radio-frequency integrated circuits (RFICs). The results are validated by simulation using an electromagnetic simulator (ADS Momentum). Using matlab software, the study on the influence of the impact of metal thickness, on the quality of factor-Q of spiral inductors, is shown.

Optimizing Gabor Filter and Local Binary Patterns for multi-texture classification

Abstract: In image classification by texture, it is important to maximize the discrimination between different classes by using an effective descriptor. The objective of this research is a new hybrid approach using state of the art feature extraction methods and improving the classification percentage of optimum filter by combining it with optimized LBP and find low dimensional size of features. The Gabor filter (GF) parameters are processed by the Artificial Bee Colony (ABC) algorithm to select the optimum filter, whereas pertinent features from LBP histogram are obtained using Rough Set Theory (RST) without impacting its classification rate. The classification implemented on texture classes is obtained from the Brodatz database. The results from the proposed approach show an improvement in the classification accuracy and processing time of k-folded crossvalidation Neural Network classifier over the method of LBP with single filter and a reduced processing time of the classifier.