Placement and Quantitating of FACTS Devices in a Power System Including the Wind Unit to Enhance System Parameters

One of the main concerns of network operators is the enhancement of system parameters; accordingly, a set of different means to this end are posed. However, the use of renewable energies such as the wind could increase the importance of the debate over sustainability and conditions of power system parameters. In this study, the condition of said parameters is examined by placing FACTS (Flexible Alternating Current Transmission System) devices in a 24-bus power system including a wind farm. Research data entailing information on the wind and the amount of consumption load per year are classified by using the K-means classification algorithm; then, the objective function is obtained according to the parameters intended for optimization. This function is optimized by using the Honey-bee mating optimization (HBMO) algorithm followed by obtaining the suitable place and amount for FACTS devices. The results showed that the examined parameters are optimized when using FACTS devices.

Implementasi Honey Bee Mating Optimization Pada Vehicle Routing Problem With Time Windows Dalam Perencanaan Jalur Wisata Malang

Abstrak Vahicle Routing Problem adalah suatu masalah pencaian jalur yang akan dilalui dengan tujuan mencari rute yang paling cepat atau pendek. Vahicle Routing Problem with Time Windows (VRPTW) yang merupakan sebutan bagi VRP dengan kendala tambahan berupa adanya time windows pada masing-masing pelanggan yang dalam hal ini berupa destinasi wisata. Dalam penelitian ini diterapkan Honey Bee Mating Optimization (HBMO) dalam menyelesaikan VRPTW. HBMO sendiri terinspirasi oleh perilaku koloni lebah ketika bereproduksi. Algoritma tersebut bertujuan untuk mengevaluasi pencarian individu atau solusi terbaik. Tujuan dari penelitian ini adalah bagaimana mengimplementasikan Honey Bee Mating Optimization dalam menyelesaikan VRPTW pada perencanaan jalur wisata di Malang. Sehingga dapat meminimumkan waktu dan jarak tempuh perjalanan. Berdasarkan hasil pengujian, parameter yang optimal untuk optimasi VRPTW menggunakan HBMO pada kasus perencannan jalur wisata Malang yaitu dengan menggunakan 800 generasi, populasi lebah jantan sebesar 300, batas kapasitas spermatheca sejumlah 100, nilai mutation ratio (Pm) dan royal jelly masing-masing bernilai 0.5.Abstract Vahicle Routing Problem is a problem of finding the best route that will be passed with the purpose to finding the fastest or shortest route. Vahicle Routing Problem with Time Windows (VRPTW) is a part of VRP with additional obstacles in the form of time windows in each customer. In this research, Honey Bee Mating Optimization (HBMO) was applied to completing VRPTW. HBMO itself was inspired by the behavior of bee colonies when reproducing. The purpose of this algorithm is to evaluate the best individual or the best solutions. The purpose of this research is how to implement Honey Bee Mating Optimization to completing VRPTW in Malang tourism route planning. So that it can minimize travel time and distance. Based on the results of the testing, the optimal parameters for VRPTW optimization using HBMO in Malang tourism route planning case are using 800 generations, the male bee population is 300, the capacity limit of spermatheca is 100, the mutation ratio (Pm) and royal jelly are respectively 0.5.

Penjadwalan Kompetisi Sepak Bola Liga PSN Ngada Menggunakan Algoritma Honey Bee Mating Optimization

Soccer scheduling is one of the key factors so that the competition can be done well or not. Poor scheduling will affect the condition of the team that will compete, because a poor schedule will prevent the team from preparing their team well for the next match. For this research, we will discuss a system that can determine scheduling and also discuss issues that must be resolved in the big phase 10 PSN Ngada competition that uses a competition system. In this study, we will use the marriage honey bee optimization algorithm, this algorithm was chosen because it is easy to use and can produce quite good scheduling. This algorithm allows the formation of scheduling that avoids predetermined boundaries of combinations that have been formed, which have passed the process of mating flights, crossover, mutations and queen selection. This cycle will continue to run until it reaches the specified and choose scheduling with the best fitness value.

Advanced Honey Bee Coupling Intensification Techniques Combined with Neural Networks for Proficient Facts Connotation

Flock Aptitude Is A Communal Performance Of Societal Classifications Like Individuals Like Ant Cluster Escalation, Fish Training, Birds Assembling, Bee Cluster Optimization And Particle Crowd Escalation. In This Work, A Mixture Crowd Intelligence Based Performance For Statistics Classification Is Suggested Using Honey Bee Mating Optimization Algorithm With Neural Network (HBMO-NN). Honey Bee Reproducing Procedure May Be Restrained As A Distinctive Group-Founded Attitude To Intensification, In Which The Exploration Procedure Is Stimulated By The Progression Of Factual Sweetie-Bee Marital And Imitator The Iterative Coupling Progression Of Honey Bees And Schemes To Excellent Qualified Drones For Copulating Progression Through The Aptness Functions Enrichment For Mixture Of Greatest Weights For Secreted Layers Of NN Classifiers. Advanced HBMO (AHBMO-NN) Procedure Is Nowadays Realistic To Categorize The Information Efficiently Through Teaching The Neural System. The Arrangement Precision Of AHBMO-NN Is Associated With Several Other Procedures. In This Work, Promoted Honey-Bee Coupling Optimization Process (AHBMONN) Is Offered And Verified By Few Benchmark Instances. A Developed Way Of Honey Bee Mating Optimization Performance Is Joined With Neural Network Which Increases Exactitude And Decrease Time Interruption In Complication Of Numerous Factual World Datasets.