Mahdi El-Arini

Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control

Abstract In the recent years, the solar energy becomes one of the most important alternative sources of electric energy, so it is important to improve the efficiency and reliability of the photovoltaic (PV) systems. Maximum power point tracking (MPPT) plays an important role in photovoltaic power systems because it maximize the power output from a PV system for a given set of conditions, and therefore maximize their array efficiency. This paper presents a maximum power point tracker (MPPT) using Fuzzy Logic theory for a PV system. The work is focused on the well known Perturb and Observe (P&O) algorithm and is compared to a designed fuzzy logic controller (FLC). The simulation work dealing with MPPT controller; a DC/DC Ćuk converter feeding a load is achieved. The results showed that the proposed Fuzzy Logic MPPT in the PV system is valid.

A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system

One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt.

A New Optimization Approach for Maximizing the Photovoltaic Panel Power Based on Genetic Algorithm and Lagrange Multiplier Algorithm

In recent years, the solar energy has become one of the most important alternative sources of electric energy, so it is important to operate photovoltaic (PV) panel at the optimal point to obtain the possible maximum efficiency. This paper presents a new optimization approach to maximize the electrical power of a PV panel. The technique which is based on objective function represents the output power of the PV panel and constraints, equality and inequality. First the dummy variables that have effect on the output power are classified into two categories: dependent and independent. The proposed approach is a multistage one as the genetic algorithm, GA, is used to obtain the best initial population at optimal solution and this initial population is fed to Lagrange multiplier algorithm (LM), then a comparison between the two algorithms, GA and LM, is performed. The proposed technique is applied to solar radiation measured at Helwan city at latitude 29.87°, Egypt. The results showed that the proposed technique is applicable.

Enhancing the contingency performance of HELENSÄHKÖVERKKO OY 110 KV NETWORK by optimal installation of UPFC based on Genetics Algorithm

Unified Power Flow Controller (UPFC) has a promising effect on controlling the characteristics of the power system operation. During the contingency outage cases, the transmission lines are overloaded and the buses are subjected to voltage violations. Unified Power Flow Controller (UPFC) has both shunt and series controller effect inside its frame. This option gives the UPFC the power to control the voltage profile and the transmission lines flow simultaneously. In this paper, we use the Genetics Algorithm (GA) to find the optimal location and the optimal settings of UPFC to improve the performance of the power system at the contingency of the transmission line outage. The proposed technique will be tested on the IEEE 6-bus system to show the validity of the technique. This procedure is proposed for being applied on the Finnish network, Helsinki HELENSÄHKÖVERKKO OY 110 KV NETWORK. The simulations will be achieved until operating conditions of Year 2020

Optimal UPFC based on Genetics Algorithm to improve the steady-state performance of HELENSÄHKÖVERKKO OY 110 KV NETWORK at increasing the loading pattern

Flexible Alternating Current Transmission Systems (FACTS) devices represent very high efficient tools for controlling the operations and enhancing the performances of the electrical power network. Unified Power Flow Controller (UPFC) is considered as the most powerful member of the FACTS family, where it has both shunt and series controller inside its frame. This option gives to UPFC the power to control the voltage profile and the transmission lines flow simultaneously. In this paper, we use the Genetics Algorithm (GA) to find the optimal location and the optimal settings of UPFC to improve the performance of the power system specially solving the transmission lines overloading during normal operation and configuration at increasing the loading conditions. This procedure is proposed to be applied on Helsinki HELENSÄHKÖVERKKO OY 110 KV NETWORK until the operating conditions of Year 2020. To show the validity of the technique, it will be tested on the IEEE 6-bus system.

Minimization of energy loss using integrated evolutionary approaches

The paper presents a hybrid approach to minimize real energy power losses in the given power-system network to improve system performance and to reduce the overall cost of power transmission. Integration of Genetic Algorithm and hybridized Simulated Annealing with Pattern Search are used and proposed to determine the optimum adjustments to the control variables. Continuous and discrete operating variables such as scheduling of power generations, transformers tap changer settings and voltage control of generating buses as well are considered. The proposed method is applied to 9-bus, IEEE 14-, 30-bus standard systems and New England 39-bus system with different operating scenarios. The numerical test results and simulations with different load patterns and single line outages have been demonstrated and analyzed. Effect of changing the control variables have been studied and investigated as well. The results obtained show the effectiveness, flexibility, and applicability of the proposed approach for power loss minimization with considering overload condition of lines with high accuracy and in acceptable computational time.

An efficient methodology for optimal reconfiguration of electric distribution network considering reliability indices via binary particle swarm gravity search algorithm

One important aspect that should be achieved during the operation of the distribution network is minimizing the total active loss. This objective can be achieved by network reconfiguration in which switching events such as closing tie switches or opening sectionalizing switches are efficiently determined. This paper presents a reliable meta-heuristic algorithm for optimal reconfiguration of the distribution network which is binary particle swarm optimization gravity search algorithm (BPSOGSA). The methodology is applied on four test systems: 16-bus system, 33-bus system, 69-bus system and 119-bus system. Reliability indices, system average interruption frequency, system average interruption duration and energy not supplied, are incorporated to check the validity of the network after reconfiguration process. Comparison with other reported previous methods is performed; the power loss is reduced by 9.3242% in 16-bus system, for 33-bus system; the power loss is reduced by 31.46%. In case of 69-bus system, the power loss is reduced by 56.1761%, while for 119-bus, the power loss is reduced by 33.7216%. Additionally, the performance of the proposed BPSOGSA is the best one compared with the others for all studied cases. The obtained results prove the reliability of the proposed methodology.