Distribution Network Optimization Based on Genetic Algorithm

This paper presents a new robust optimization technique for distribution network configuration, which can be regarded as a modification of the recently developed genetic algorithm. The multi-objective genetic algorithm has been applied to this problem to optimize the total cost while simultaneously minimize the power loss and maximize the voltage profile. The IEEE 69-bus distribution network is used in the tests, and test results have shown that the algorithm can determine the set of optimal nondominated solutions. It allows the utility to obtain the optimal configuration of the network to achieve the best system with the lowest cost

Characteristic Test of Current Transformer Based EMTP Shoftware

Pemodelan transformator arus merupakan salah satu cara yang praktis untuk mengevaluasi unjuk kerja perlengkapan proteksi. Pada makalah ini disajikan penggunaan perangkat lunak Electromagnetic Transients Program (EMTP), The Output Processor (TOP), dan Mathcad® untuk memodelkan transformator arus (CT) untuk mendapatkan kurva yang menunjukkan karakteristik transformator arus. Disajikan juga pengaruh beban terhadap faktor koreksi rasio dan faktor koreksi sudut fasa transformator arus.Dalam makalah ini transformator arus dimodelkan menggunakan EMTP untuk memvisualkan arus dan tegangan CT. Keluaran dari EMTP ditransfer ke dalam Mathcad melalui perangkat lunak TOP untuk menguji transformator arus terhadap keakuratan dan pengaruh bebannya. Model transformator arus yang dibahas adalah model CT 1200/5 kelas C800. Hasil simulasi menunjukkan bahwa untuk uji karakteristik dengan metode 9 titik memberikan hasil yang terbaik untuk menampilkan karakteristik CT. Hasil simulasi uji eksitasi CT menunjukkan bahwa error rasio maksimum yang terjadi adalah 0,09%. Hasil simulasi ini mengindikasikan bahwa transformator arus ini dapat digunakan dalam aplikasi pengukuran

Estimation of Overhead Transmission Line Fault Distance Using Unsynchronized Two-Terminal Method

This paper presents the estimation of transmission line fault distance using unsynchronized two-terminal method. In operation, high or extra-high overhead voltage transmission lines can be interrupted. The disturbance can come from internal or external interference, which is permanent or temporary. For permanent interference, the network operator must visit the location of the disturbance in order to fix it. Because the transmission line is very long, while it takes quick time to find out the location of the disturbance so that it can be repaired immediately, then a method is needed to find out the location of the disturbance. This research proposes a method for determining the location of faults based on voltage and current data at the time of interference from both ends of the transmission line. The interference voltage and current data need not be synchronized. The use of this data makes this method very simple and easy to use. However, the accuracy of the estimation results can still be relied upon. In this study, a simulation was carried out on a two-end transmission line. The transmission line has a phase disturbance to the ground. The noise resistance applied in the simulation is 0 ohms, 10 ohms, 50 ohms, and 100 ohms. The results showed that the highest estimated error was 0.3%, which indicates that this method has a high degree of accuracy

Model Power System Stabilizer Berbasis Neuro-Fuzzy Adaptif

Low frequency oscillations are detrimental to the goals of maximum power transfer and optimal power system security. A contemporary solution to this problem is the addition of power system stabilizers (PSS) to the automatic voltage regulators on the generators in the power system. For large scale power systems comprising of many interconnected machines, the PSS parameter tuning is a complex exercise due to the presence of several poorly damped modes of oscillation. The problem is further being complicated by continuous variation in power system operating conditions. This research proposes the PSS model based on adaptive neuro-fuzzy for designing robust power system stabilizers for a multi machine system. Simulations were carried out using several fault tests at transmission line on a Two-Area Multimachine Power System. Simulation is done by using Matlab-Simulink software. The result shows that power transfer response using the model is more robust than Delta w PSS, especially for single phase to ground fault

Power Flow Control of Power Systems Using UPFC Based on Adaptive Neuro Fuzzy

Optimization of system capacity electric power transmission systems requires a reliable power flow controller. The power flow controllers must be able to control the level of electrical voltage and active and reactive power flow without reducing the level of stability and security of the transmission system. Latest technology in the control of power flow is a Unified Power Flow Controller (UPFC). The entire transmission line parameters are impedance, voltage, and phase angle can be controlled simultaneously by the UPFC. The method used in the conventional algorithms based UPFC is still firmly with logic. These algorithms have difficulties to electric power transmission systems multimachine very dynamic, i.e. systems that are experiencing rapid changes in the electrical load from time to time. Therefore, in this study was developed based on neuro-fuzzy method is applied to the adaptive UPFC for adaptively controlling the power flow in electric power transmission systems multimachine very dynamic. In this study, three phase fault is applied to the multimachine system. The results are taken to be consideration of PI and neuro-fuzzy controllers. The PI and neuro-fuzzy controllers show nearly same results but there is a low overshoot occurred during the fault in the neuro-fuzzy controllers results. According to results that UPFC improves the system performance under the transient and the normal conditions. However, it can control the power flow in the transmission line, effectively

Modeling of Wind Power Plant with Doubly-Fed Induction Generator

This paper presents the modeling and simulation of wind turbine driven by doubly-fed induction generator which feeds ac power to the distribution network. A stator flux oriented vector control is used for the variable speed doubly-fed induction generator operation. By controlling the generator excitation current the amplitude of the stator EMF is adjusted equal to the amplitude of the grid voltage. To set the generator frequency equal to the grid one, the turbine pitch angle controller accelerates the turbine/generator until it reaches the synchronous speed. The system is modeled and simulated in the Matlab Simulink environment in such a way that it can be suited for modeling of all types of induction generator configurations. The model makes use of rotor reference frame using dynamic vector approach for machine model. The system is also simulated when a fault occurs in 25 kV grid of distribution system. The results of a single line to ground fault and a symmetrical three-phase ground fault is analyzed. The results show that the wind energy conversion system can normally operate in fault conditions

A Neuro-Fuzzy Approach for Vehicle Fuel Consumption Prediction

This paper presents a neuro-fuzzy approach for predicting vehicle fuel consumption. The prediction of fuel consumption of a vehicle has become a strategic issue. This is because it is not only related to the problem of the availability of fuel which is getting thinner but also the problem of the environmental impact caused. In this study, the acquisition of the car parameter data was inputted, namely the number of cylinders, displacement, horsepower, weight, acceleration, and model year. The output variable that will be predicted is fuel consumption in miles per gallon (MPG). 'Weight' and 'Year' are chosen as the two best input variables. Training results and predictions are expressed in the three-dimensional input-output surface graph of the best two-input ANFIS model for MPG prediction. The graph shows a nonlinear and monotonic surface, where MPG is predicted to increase with an increase in 'Weight' and a decrease in 'Year'. The results of the RMSE training were 2.767 and the RMSE examination was 2.996. Based on the results of the study showed that the greater the weight of motor vehicles, the greater the amount of fuel needed to travel the same distance

A Fuzzy Logic Controller Approach for Controlling Heat Exchanger Temperature

This paper presents a fuzzy logic controller approach for controlling heat exchanger temperature. Fuzzy logic controller is an artificial intelligence-based controller. The fuzzy logic controller has been widely used for control applications in the industrial world. One of the tools used in the industrial world that requires accurate control is the heat exchanger. A heat exchanger is a device used to process the mixing of liquids that have different temperatures. In this case, temperature control becomes very important. Fuzzy logic control is applied to the heat exchanger so that the mixed fluid has a constant temperature. Fuzzy logic control models in this study are combined with neural network techniques. The fuzzy logic controller model is simulated in Matlab software. The results showed that the fuzzy logic controller was able to stabilize the temperature of the heat exchanger well