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MiladGheydi ¹, Reza Effatnejad ², Parviz Ramezanpour ²

Affiliations
1 Department of Electrical Engineering, Islamic Azad University, Science and Research Branch, Alborz, IR
2 Department of Electrical Engineering, Islamic Azad University, Karaj branch, Alborz, IR

Abstract

This paper examines the possibility of hybrid plants with a variable consumption in necessary conditions. The use of an adaptive controller includes fuzzy controller with increasing the high-reliability of system as an appropriate option. In his study inputs are voltage conditions and power; output variables include the operation of fuel cell and renewable energies. In this investigation, the objective function is the possibility and the ability of working in low and full load conditions along with the changes in renewable energies conditions. These controllers help to use a controller for hybrid systems. Consequently, communication of this situation by a central controller would be possible. Changes in wind speed and also the inherent changes in photovoltaic system are created during the simulation process. The system should be able to comply with the existing abnormal terms in addition to providing the electricity to the load. In a way that it can provide the required power through the commands which are transmitted from system controllers to AC and DC subsystems and eventually the system reach to a stable condition in a short period of time. In this case it is so important that the essential parameters of the network remain stable. This stability will be illustrated through the stabilization of the line voltage in 1 p.u and also restoring the frequency to its pervious condition. The active power control and voltage controller transmit the commands to the other controlling part which is an interface between two AC and DC systems in order to control the network values. A PID controller was used for controlling parameters in active power regulator. These parameters are designed with fuzzy logic algorithm.

Keywords

Fuel Cell, Fuzzy Logic, PV, Renewable Energy, Uncertainty, Wind Turbine

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Meysam Rahmati ¹, Reza Effatnejad ², Amin Safari ³

Affiliations
1 Department of Electrical Engineering, Islamic Azad University, Ahar branch, Ahar, IR
2 Department of Electrical Engineering, Islamic Azad University-Karaj branch, Karaj, IR
3 3Department of Electrical Engineering, Faculty of Engineering, Azarbaijan Shahid Madani University, Tabriz, IR

Abstract

The Optimal Power Flow (OPF) problem has been widely used in power system operation and planning for determining electricity prices and amount of emission. This paper presents a Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithm to solve the highly constrained multi-objective OPF involving conflicting objectives, considering fuel cost and emission level functions. The proposed technique has been carried out on IEEE 30-bus test system. The results demonstrate the capability of the proposed CLPSO approach to generate well-distributed Pareto optimal non-dominated solutions of multi-objective OPF problem. The results show that the approaches developed are feasible and efficient.

Keywords

Comprehensive Learning, Multi-objective Optimization, Particle Swarm Optimization, Optimal Power Flow

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Sajad Bagheri ¹, Reza Effatnejad ², Abolfazl Salami ³

Affiliations
1 Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Arak, IR
2 Electrical Engineering Department, Karaj Branch, Islamic Azad University, Karaj, IR
3 Faculty of Electrical Engineering, Arak Technology University, Arak, IR

Abstract

Frequency response analysis method is used for certain evaluating and monitoring of the winding displacement and deformation of transformer core to detect incipient faults. Frequency response analysis is based on the graphical analysis and comparison of the current measured transfer function with a base current. In this paper we use compound of haar wavelet transform and intelligent Simulated Anealing Algorithms (SA) and finally we compare the achieved parameters with Genetic Algorithm (GA).

Keywords

Frequency Response Analysis, Haar Wavelet Transform, Genetic Algorithm, Simulated Annealing, Transformers Winding Parameter Identification, Traveling Wave Model

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Fazlollah Rouhi ¹, Reza Effatnejad ²

Affiliations
1 Department of Electrical Engineering, Alborz branch-University of Science and Research, Islamic Azad University, IR
2 Electrical Engineering Department, Karaj branch, Islamic Azad University, IR

Abstract

Unit commitment is a key and important function in power system operation. The goal of this function is to supply the load demand economically. In order to achieve this goal, unit commitment function determines the condition of the units to be on or off and the amount of generation for generating units of power system for a period of time.

The extended form of this operational function, i.e. Security constrained unit commitment, which is the objective of this paper, considers the security constraints, for example transmission lines overload, beside the other constraints in determining the condition of the generating units to be on or off.

In this paper, a hybrid method is proposed for dynamic programming, genetic algorithm and particle swarm optimization. In order to solve the unit commitment of power systems considering system security constraints. The condition of the units to be on or off and economic dispatch are determined and solved by considering system security constraints through previously mentioned combination method.

Keywords

Combinatorial Solution Strategy, Dynamic Programming (DP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), SCUC Security Constrained Unit Commitment.

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Hamed Aliyari ¹, Reza Effatnejad ², Mehdi Izadi ¹, Seyed Hossein Hosseinian ³, Masoud Shirkhani ¹

Affiliations
1 Electrical and Biomedical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IR
2 Engineering Department, Science and Research, Alborz Branch, Islamic Azad University, Karaj, IR
3 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, IR

Abstract

The reactive power distribution affects the security and the economic performance of the power system chiefly. Although the reactive power generation is in does not cost anything in the operation phase, but it affects the whole cost by the system losses. The mentioned problem is a non-linear optimization topic with the combination of continuous and noncontinuous variables. This paper proposes a new innovative method to reach the increase in the power system deviation and transmission loss. In this study, it is tried to reach the most optimum multiple goals by based on multi objective Genetic Algorithm with Pareto optimal non-dominated solutions of OPF problem. In this study had employed the limit accept block until for put variable to the allowable limits. Simulation result shows the effect of proposes algorithm in reducing transmission loss and voltage deviation. A MNSGA-II is presented in this paper to solve the multi objective problems. The main goal is to determine the optimal combination of power outputs for all generating units that optimize the total power loss and voltage deviations while satisfying load demand (active and reactive) and operating constraints. According to results, the ability to jump out of the local optima enhanced and thus the high precision are achieved.

Keywords

NSGA-II, Optimization, Power Loss, Reactive Power, Voltage Deviation.

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