Ahmed Fathy

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 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.

A novel methodology for simulating maximum power point trackers using mine blast optimization and teaching learning based optimization algorithms for partially shaded photovoltaic system

When the photovoltaic (PV) system is fully illuminated, the voltage-power characteristic of the array has one maximum power point (MPP). This point can be tracked by conventional maximum power point tracker algorithms. On the other hand, in partially shaded PV, the voltage--power characteristic has only one global MPP (GMPP) and multiple local MPPs. It is important to operate the PV system at GMPP for achieving optimal operation. This paper presents the application of two novel meta-heuristic optimization algorithms to extract GMPP from the PV system under partial shading conditions: The mine blast algorithm (MBA) and the teaching learning based optimization algorithm (TLBO). A proposed constrained objective function representing the PV array output power is also presented. Different patterns of shadows that strike the PV array surface are studied. The studied patterns are uniform ones, changing from 0% to 375% with steps of 25%, and non-uniform patterns with different locations of GMPP. The obtained resu...

Optimal design of fuzzy PID controller for deregulated LFC of multi-area power system via mine blast algorithm

This paper presents a novel optimal fuzzy proportional–integral–derivative (fuzzy PID) controller for load frequency control (LFC) designed by a proposed approach of mine blast algorithm (MBA) for multi-interconnected areas. The system includes reheat thermal connected power systems with the effect of the governor dead zone and turbine generation rate constraint nonlinearity. The proposed approach is used to determine the optimal parameters of the fuzzy PID controller to minimize the integral time absolute error. The proposed controller is inserted in multi-interconnected power systems which are built in Simulink/MATLAB library; triangular membership function is used for fuzzy PID controller. Additionally, the optimum adjustment of the fuzzy PID controller parameters under contracted scenario for large step demands and disturbances is investigated by MBA. The obtained results are compared to those obtained via antlion optimizer, artificial bee colony, hybrid differential evolution particle swarm optimization and hybrid PSO pattern search algorithm. The obtained results confirmed the superiority of the proposed MBA in designing the fuzzy PID-LFC as it provides less error with best statistical parameters compared to the others.

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.

PI controller parameters design of doubly fed induction generator based on Mine Blast Algorithm

In this paper, a control strategy optimized by Mine Blast Algorithm (MBA) is applied on a variable speed wind generator (VSWG) system based on a doubly fed induction machine. The proposed MBA is applied to evaluate the optimal parameters of the current PI controller such that obtaining rotor current closes to the desired one; this purpose is achieved by minimizing the sum of squared error (SSE) between two currents. A direct closed loop control of stator current is taken place to regulate the reactive power of DFIG. The obtained results confirmed the efficiency of the proposed algorithm for determining the optimal gains of PI controller.