Paper Title, Authors, Abstract (Issue 1, Volume 6, 2012)

Pages

Using Clonal Selection Algorithm to Optimize the Control Parameter for Microgrid System
H. Piarehzadeh, A. Khanjanzadeh

Abstract: This paper presents a controller which used for operation of Microgrid. Microgrid has two operation modes. First mode is grid connected and second mode is Islanding. During grid connected mode, DGs operates in PQ control mode. It means that DGs inject constant active and reactive power. In this mode the voltage and frequency of Microgrid are controlled with main grid. During Islanded mode, the Microgrid must be able to supply the loads and controlled the voltage and frequency of Microgrid in permissible range. The proposed controller has power and current control for regulating voltage and frequency and droop controller for power sharing between the parallel DG systems. In this research the control parameters are optimized by Clonal Selection Algorithm (CSA).
 

Neural Network for Modeling Solar Panel
Moufdi Hadjab, Smail Berrah, Hamza Abid

Abstract: In this paper, we present the results of the characterization and modeling of the electrical current-voltage and power-voltage of the photovoltaic (PV) panel BP 3160W, using a new approach based on artificial intelligence. We analyze the electrical parameters of solar cells and electrical parameters of the optimal PV panel (current, voltage and power) according to changes in weather (temperature, irradiation...) by the simulation programs carried out in MATLAB. These simulation results were compared with experimental data to be validated.
 

Artificial Intelligence Techniques to Evaluate Transformer Switching Overvoltages
Iman Sadeghkhani, Arezoo Mortazavian, Nima Haratian, Abbas Ketabi, Rene Feuillet

Abstract: One of the major concerns, especially at the beginning of a system restoration, is related to temporary overvoltages. Such an overvoltage might damage some equipment and delay power system restoration. In this paper an Artificial Neural Network (ANN)-based approach is used to evaluate switching overvoltages during transformer energization. In proposed methodology, Levenberg-Marquardt method is used to train the multilayer perceptron. The developed ANN is trained with the extensive simulated results, and tested for typical cases. Then the new algorithms are presented and demonstrated for a partial of 39- bus New England test system. The simulated results show that the proposed technique can estimate the peak values and duration of switching overvoltages with good accuracy.
 

Estimating Global Solar Energy Using Multilayer Perception Artificial Neural Network
Tamer Khatib, Azah Mohamed, M. Mahmoud, K. Sopian

Abstract: This paper presents a global solar energy estimation method using artificial neural networks (ANNs). The clearness index is used to calculate global solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number and sunshine ratio; the output is the clearness index. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92%, 1.46% and 7.96%.
 

EMTP Modeling of Cogeneration System for Transient Stability Analysis
Wei-Neng Chang, Chia-Han Hsu

Abstract: This paper focuses on the EMTP modeling of a cogeneration system for transient stability analysis. First, the structure and operation mode of the cogeneration system are introduced. Then, use of the electromagnetic transient program (EMTP) constructs the overall cogeneration system. The effects of unbalanced faults on the transient stability of the cogeneration system are observed with the modeled system. Several fault types, including 3-line-to-ground (3LG) fault, double line-to-ground (2LG) fault, line-to-line fault (2LF), and single line-to-ground (SLG) fault are assigned respectively for transient stability simulations. Finally, use of critical clearing time (CCT) curves evaluate the transient stability of the cogeneration system to different types of short-circuit faults. The simulation results are listed and evaluated.
 

Real Finnish Network Reliability and Performance Enhancing by Optimal UPFC
Ahmed M. Othman, Mahdi M. El-Arini, Matti Lehtonen

Abstract: UPFC has an efficient feature to control some of the network parameters as the active power flow, reactive power flow, and voltage magnitude at the UPFC installation buses. Using UPFC, the performance of the power system during various operating conditions can be improved. In this paper, a Genetic Algorithm (GA) is used to find the optimal location and the optimal UPFC parameters setting to enhance the transmission lines overloading issue keeping the voltage profile inside the enhanced limits. Additionally the outage cost of the power system is used to verify the impact of the optimized UPFC on the reliability. The application of this procedure is proposed on Helsinki HELENSÄHKÖVERKKO 110 KV NETWORK, which is a real Finnish world 110-kV sub transmission network with operating conditions of the present year until the year 2020. To show the validity of the technique, it will be tested first on the IEEE 6-bus system. The impact of UPFC installation in Helen network on the reliability in terms of outage costs will be discussed.
 

The Fuel Temperature Influence on Gas Emissions when it is Used Diesel-Palm Oil Mixtures as Fuel
Charalampos Arapatsakos, Dimitrios Christoforidis, Styliani Gkavaki

Abstract: One of the main global problems is air pollution. The main sources of air pollution are transportation and fuel combustion. Therefore, there is a big need to produce appropriate fuels, which will have minimal environmental and social degradation. The main aim of this work is to examine the effect of fuel temperature in gas emissions that is used in a four-stroke diesel engine. The fuel temperatures that have been used are 20oC, 30oC, 40oC, 50oC, 60oC, 70oC and 80oC. The engine was functioned on 1000rpm, 1500rpm, 2000rpm, 2500rpm, 2000rpm, 1500rpm, 1000rpm(pyramid of rpm) when it has been used different mixtures of diesel-palm oil as fuel. For those fuel temperatures the gas emissions of carbon monoxide (CO), hydrocarbons (HC), nitrogen monoxide (NO) and smoke have been examined.