Identifikasi Varietas Unggul Kedelai Berdasarkan Warna Benih Dengan Fuzzy Cluster Means

Telah dilcikukan tugas akhir tentang identifikasi vcirietas unggul kedelai bedasarkan warna dengan Fuzzy Cluster Means (FC'M ). Tugas akhir ini bertujuan untuk menentukan identi fikasi varietas unggul kedelai dengan menggunakan FC`M herdasarkan warna kemudian membandingkaanya dengan hasil pengolahan data melalui jaringan syaraf tiruan (JST). Percobaan ini menggunakan benih kedelai sebagai objek, dari varietas: Anjasmoro, Argomulyo, Argopuro, dan Panderman. Data masukan yang digunakan dalam tugas akhir ini berupa data RGB dari masing-masing varietas dengan variasi warna tempat pengambilan data, yaitu hitarn dan coklat serta jenis lampu yang digunakan, yaitu: pijar dan halogen. Data tersebut diolah dengan menggunakan FCM sehingga diperoleh keluaran berupa pusat klaster dan derajat keanggotaan serta nilai error dengan metode FCM. Hasil yang diperoleh menunjukkan bahwa tiap varietas memenuhi sifat derajat keanggotaan fuzzy, yaitu maksimum pada satu kelas dan minimum untuk kelas yang lain. Untuk tingkat keakuratan, FCM memberikan kebenaran sebesar 80%. Jika dibandingakan dengan JST, FCM kurang akurat namun lebih konsisten dalam memberikan kebenaran. =========================================================================================================================== Final project about identification of soybean varieties based on color by Fuzzy> Cluster Means (FCM ) was done. This final project aims to determine the identification of soybean varieties using FCM based on the color then compared by results of data processing by artificial neural network ( 1ST). This experiment used soybean seeds as objects of varieties: Anjasmoro, Argomulyo, Argopuro, and Panderman. Input data used in this final project in the form of RGB data of each variety with the color variations of data retrieval, which is black and brown and kind of lamps used\ namely: incandescent and halogen. The data is processed by using FCM to produce the output of the cluster centers and membership degrees and the value of an error with FCM method. The resulst of the final project, it is known that each character varieties meet fuzzy membership degrees\ ie at a maximum and minimum class for another class. Tor the level of accuracy, FCM provides the truth as much as H0%. If it compared by JST, FCM is less accurate but more consistent in giving the truth

SIMULASI PENGENDALI SUDUT PITCH BLADE PADA TURBIN ANGIN DENGAN FLOWER POLLINATION ALGORITHM (FPA) UNTUK MENGOPTIMALKAN KONVERSI DAYA LISTRIK

Telah dilakukan penelitian berupa simulasi untuk mengendalikan sudut pitch blade pada turbin angin. Penelitian ini bermaksud untuk mengoptimalkan proses konversi daya listrik. Pengendalian sudut pitch blade pada tubin angin dilakukan dengan PI-Contoller. Nilai konstanta Kp dan Ki dari PI-Controller diperoleh secara optimal dengan algoritma penyerbukan bunga (Flower Pollination Algorithm (FPA)). Simulasi dilakukan dengan variasi kecepatan angin untuk memperoleh sudut pitch blade yang optimal. Analisis yang digunakan adalah analisis kestabilan steady state untuk memperoleh eigen value dan damping ratio. Sedangkan untuk analisis kestabilan transient dilakukan analisis overshoot, settling time, rise time, dan parameter lainnya. Hasil parameter dari simulasi dengan FPA, dibandingkan dengan coba-coba (manual tunning) dan Particle Swarm Optimization (PSO). Semua simulasi dilakukan dengan simulink dan m.file dari software matlab. Berdasarkan hasil simulasi dan analisis, dapat disimpulkan bahwa sistem stabil steady state karena memliliki nilai negatif untuk semua eigen value. Nilai damping ratio juga lebih besar dari 0,05 yang menandakan sistem teredam dengan baik. Pada analisis transient baik untuk frekuensi maupun sudut pitch blade, FPA memberikan respon settling time yang lebih cepat dan overshoot yang lebih rendah jika dibandingkan dengan PSO dan manual tunning. ============================================================ A research of simulation pitch angle blade of wind turbine had been done. The purpose of the research was to optimize the conversion of power. The controller of pitch angle wind turbine was PI-Controller. The value of gain Kp and Ki was obtained optimally by Flower Pollination Algorithm (FPA) The simulation was done by using variation of wind speed to obtain the optimum pitch angle blade. In this simulation, the steady state and trasient stability analysis were applied. Damping ratio and eigen value were obtained to observe the steady state stability. The paramater of transient stability such as: settling time, rise time, and overshoot etc. were obtained by using manual tunning, FPA, and PSO. The parameters result of tunning were compared. All the simulation run in simulink and m.file of matlab. Based on the simulation and analsis result, it could be concluded that the system wes stable steady state. It was shown by all real component of negative eigen value. The damping ratio of the system was more than 0.05 and less than 1 so the system was damped well. In transient analysis in pitch angle blade and rotor frequency, FPA gave the fastest settling time and the lowest overshoot response than manual tunning and Particle Swarm Optimization (PSO)

Optimal Power Flow using Fuzzy-Firefly Algorithm

Development of Metaheuristic Algorithm in engineering problems grows really fast. This algorithm is commonly used in optimization problems. One of the metaheuristic algorithms is called Firefly Algorithm (FA). Firefly Algorithm is a nature-inspired algorithm that is derived from the characteristic of fireflies. Firefly Algorithm can be used to solve optimal power flow (OPF) problem in power system. To get the best performance, firefly algorithm can be combined with fuzzy logic. This research presents the application of hybrid fuzzy logic and firefly algorithm to solve optimal power flow. The simulation is done using the MATLAB environment. The simulations show that by using the fuzzy-firefly algorithm, the power losses, as well as the total cost, can be reduced significantly

Small-disturbance Angle Stability Enhancement using Intelligent Redox Flow Batteries

Small-disturbance angle stability or low-frequency oscillation is one of the important stability in the power system. Although damper windings and power system stabilizer (PSS) have been proved to stabilize and improve small-disturbance angle stability. However, due to increasing demand in the recent years, adding redox flow batteries (RFB) as additional devices is crucial. This paper investigates, the utilization additional devices called RFB to enhance the small-disturbance angle stability in the power system. Furthermore, ant colony optimization (ACO) method is used to tune RFB parameter. To analyze the stability improvement on the power system, single machine infinite bus is used as a test system. Eigenvalue and time domain simulation is used to examine the behavior of the investigated system. From the simulation, it is found that by installing RFB in the system, the small-disturbance angle stability of power system is improved and ACO can be a solution of tune RFB parameter

Smart Frequency Control using Coordinated RFB and TCPS based on Firefly Algorithm

The frequency stability enhancement of a power system is proposed in this paper. To enhance the frequency stability, redox flow batteries (RFB) and the thyristor controlled phase shifter are used. Moreover, to get a better performance, the parameter of RFB and TCSC are optimized by the firefly algorithm (FA). Two area load frequency control plant is used as a test system. Time domain simulation is used to assess the performance of the proposed method (adding RFB and TCPS and optimized using FA). From the simulation results, it is found that by installing RFB and TCSC based on FA in the system, the frequency performance can be maintained above the nadir when perturbation emerges

Improving a two-equation eddy-viscosity turbulence model to predict the aerodynamic performance of thick wind turbine airfoils

Numerical simulations for relatively thick airfoils are carried out in the present studies. An attempt to improve the accuracy of the numerical predictions is done by adjusting the turbulent viscosity of the eddy-viscosity Menter Shear-Stress-Transport (SST) model. The modification involves the addition of a damping factor on the wall-bounded flows incorporating the ratio of the turbulent kinetic energy to its specific dissipation rate for separation detection. The results are compared with available experimental data and CFD simulations using the original Menter SST model. The present model improves the lift polar prediction even though the stall angle is still overestimated. The improvement is caused by the better prediction of separated flow under a strong adverse pressure gradient. The results show that the Reynolds stresses are damped near the wall causing variation of the logarithmic velocity profiles

Low-Frequency Oscillation Mitigation usin an Optimal Coordination of CES and PSS based on BA

Small signal stability represents the reliability of generator for transferring electrical energy to the consumers. The stress of the generator increases proportionally with the increasing number of load demand as well as the uncertainty characteristic of the load demand. This condition makes the small signal stability performance of power system become vulnerable. This problem can be handled using power system stabilizer (PSS) which is installed in the excitation system. However, PSS alone is not enough to deal with the uncertainty of load issue because PSS supplies only an additional signal without providing extra active power to the grid. Hence, utilizing capacitor energy storage (CES) may solve the load demand and uncertainty issues. This paper proposes a coordination between CES and PSS to mitigate oscillatory behavior of the power system. Moreover, bat algorithm is used as an optimization method for designing the coordinated controller between CES and PSS. In order to assess the proposed method, a multi-machine two-area power system is applied as the test system. Eigenvalue, damping ratio, and time domain simulations are performed to examine the significant results of the proposed method. From the simulation, it is found that the present proposal is able to mitigate the oscillatory behavior of the power system by increasing damping performance from 4.9% to 59.9%

Small-disturbance angle stability enhancement using intelligent redox flow batteries

Small-disturbance angle stability or low-frequency oscillation is one of the important stability in the power system. Although damper windings and power system stabilizer (PSS) have been proved to stabilize and improve small-disturbance angle stability. However, due to increasing demand in the recent years, adding redox flow batteries (RFB) as additional devices is crucial. This paper investigates, the utilization additional devices called RFB to enhance the small-disturbance angle stability in the power system. Furthermore, ant colony optimization (ACO) method is used to tune RFB parameter. To analyze the stability improvement on the power system, single machine infinite bus is used as a test system. Eigenvalue and time domain simulation is used to examine the behavior of the investigated system. From the simulation, it is found that by installing RFB in the system, the small-disturbance angle stability of power system is improved and ACO can be a solution of tune RFB parameter

Design Controller Blade Pitch Angle Wind Turbine Using Hybrid Differential Evolution Algorithm-Particle Swarm Optimization

This paper investigates the application of blade pitch angle controller to stabilize the frequency and protection again disaster of wind turbine power system. The blade pitch angle controller optimized by one of the metaheuristic algorithm called Hybrid Differential Evolution Algorithm-Particle Swarm Optimization (HDEAPSO). HDEAPSO is a hybrid algorithm between Differential Evolution Algorithm and Particle Swarm Optimization. To investigate and design the application of blade pitch angle can be done by performing the simulation using MATLAB. Simulation if performed by comparing the stability response of frequency and pitch angle at the wind turbine using conventional controller, optimized by PSO and using Hybrid Differential Evolution Algorithm-Particle Swarm Optimization (HDEAPSO). The simulation result represent that by optimizing blade pitch angle controller using Hybrid Differential Evolution-Particle Swarm Optimization can damp the frequency of wind turbine from −0.01363 pu to −0.1304 pu and reduce settling time from 46.86 second to 33.48 second when there are wind with speed 2 m/s