Aurelian Craciunescu

Identification of Solar Cell Parameters with Firefly Algorithm

Due to the non-linearity, multivariable and multimodal features of current-voltage of solar cell models, the conventional methods are incapable to estimating the parameters of solar cell with high accuracy. Recently, the bio-inspired algorithms have attracted the attention. The firefly algorithm is nature-inspired stochastic optimization algorithm is among the most powerful algorithms in solving modern global optimization for nonlinear and complex system, based on the flashing patterns and behavior of fireflies swarm. In this paper, a firefly algorithm is proposed to extract the parameters of single diode model solar cell from experimental I-V characteristics. The results obtained by firefly algorithm are quite promising and outperform those found by the other methods.

Induction motor's broken bars detection by using Stockwell transform

This paper presents a Stockwell transform-based method for processing starting angular speed data from an induction motor and transforming it into some forms that are useful for fault detection and diagnostics of rotor broken bars. The proposed method generates angular speed frequency-time spectrum that is dependent to the number of rotor broken bars and of the motor load level. The angular speed signal energy, the amplitude of its spectral component with the highest frequency and the amplitude-time diagrams of its spectral components are analyzed as number of broken rotor bars indicators. The results are obtained by simulation in Matlab/Simulink of a faulty induction motor.

The use of ANN to supervise the PV MPPT based on FLC

This paper proposes an intelligent maximum power point tracking (MPPT) strategy based on the combination of fuzzy logic control (FLC) and artificial neural network (ANN) to improve the power output performances of PV system. Firstly, under various weather conditions, ANN is trained to estimate the optimal MPP voltage (Vmpp) of the PV module. Then, this trained ANN is used as a fundamental stage to drive the FLC based MPPT controller. The driven FLC is used to track the MPP by adjusting the duty cycle of the dc-dc converter of the PV system. The performances of the proposed tracking method are simulated and compared with the conventional perturb and observe (P&O) method at actual irradiation and temperature measurements using MATLAB simulation package. As it can be seen from the simulated results, the PV system with the proposed method is more efficient and can provide more energy, with less oscillation and overshoot, as compared with the conventional P&O MPPT method.

Space phasor geometrical loci in polar coordinates as voltage unbalance monitoring tool

Voltage unbalance is a common phenomenon in AC power network. This disturbance can cause on the three-phase induction motors connected to the power supply system serious ill effects such as overheating, drop of efficiency and reduction in output torque. Therefore, operation of an induction motor for any length of time above 5% of voltage-unbalance condition is not recommended and on-line voltage unbalance monitoring is required from economical reasons. In this paper a new method of evaluating the degree of unbalanced voltage in power network is proposed. This new approach uses voltage space phasor geometrical locus in polar coordinates based on a modified “a-b-c” to “d-q” coordinate transformation which gives the possibility to localize the unbalanced phases and to appreciate the severity of the voltage unbalance.

Improving the performance of PV system using genetically-tuned FLC based MPPT

In this paper, an optimization of fuzzy logic controller (FLC) based maximum power point tracking (MPPT) using genetic algorithm (GA) is performed. The optimization process is performed by tuning the FLCs data base (DB) represented by parameters of membership functions (MFs) used. The tuning process is based on an objective function that is defined in terms of the statistic quantity named integral of squared error (ISE). In this paper, the photovoltaic (PV) system including PV module BP SX150S, ideal buck-boost DC-DC converter, MPPT, and resistive load of 6 Ω is used. The simulation performances of the proposed MPPT is evaluated and compared with the pre-tuned FLC based MPPT method using Matlab/Simulink package. The simulation results of the proposed method is quite promising and encouraging in which the PV system is capable of harvesting more solar power compared to pre-tuned FLC MPPT method.

Maximum Power Point Tracking of Photovoltaic Modules: Comparison of Fuzzy Logic and Artificial Network Controllers' Performances

This paper makes a comparison between two control methods for maximum power point tracking (MPPT) of a photovoltaic (PV) system under varying irradiation and temperature conditions: the fuzzy logic and the neural network control. Both techniques have been simulated and analyzed by using Matlab/Simulink software. The power transitions at varying irradiation and temperature conditions have been simulated and the power tracking time realized by the fuzzy logic controller against the neural network controller has been evaluated.

Comparison of Hill-Climbing and Artificial Neural Network Maximum Power Point Tracking Techniques for Photovoltaic Modules

In this paper, two maximum power point tracking (MPPT) algorithms in a photovoltaic electrical energy generation system are analyzed and compared. The Matlab/Simulink is used to establish the model of a photovoltaic system with MPPT function. This system is developed by combining the models of established solar module and DC-DC boost converter with the algorithms of hill climbing (HC) and artificial neural network (ANC), respectively. The system is simulated under different atmospheric conditions and MPPT algorithms. According to the comparisons among the simulation results, it can be concluded that the photovoltaic system with ANN MPPT algorithm is simpler: it does not require knowledge of internal system parameters, needs less calculation, is faster and provides a compact solution for multi-variable problems.

Building integration of 30 kWp photovoltaic system at University Politehnica of Bucharest, Romania

A Building Integrated Photovoltaic Power System of 30 kWp was recently installed at University Politehnica of Bucharest, Romania. It is the first grid-connected and largest PV system of the country. With this opportunity, an innovative non-penetrating system that uses the modules and mounting hardware as ballast was designed and implemented. Personnel with no previous experience with the roof PV systems, putting an innovative mounting system and documentation to the test, performed array installation. In the paper, the PV power system and its installation process are described, special attention being accorded to the complex mounting system. The provided information can be useful to anyone implementing a PV system.

Power flow management in three port converter using PV panel with maximum power point tracker

This paper presents three-port DC-DC converter modeling, its simulation and its control. The three port converter interfaces with the PV panel, bidirectional battery and load which is isolated by a transformer. There are two sources used in the three-port converter and the flow of power in the converter is controlled by switches (MOSFETs) on-off states. To obtain maximum power from the PV panel, a buck-boost converter is added in front of the PV panel and a MPP tracker is used to control the buck-boost converter. The three-port converter circuit is composed of four MOSFETs which are used as switches and a transformer that provides isolation between the input and output sides. Three cases of three-port converter operation are analyzed; in the first case, PV panels feed directly the load; in the second case, the PV panels feed the load and, in same time, charge the battery; in the third case, PV panel together with battery assure the load demand.

Comparative prediction of single and double diode parameters for solar cell models with firefly algorithm

Due to the non-linearity of current-voltage of solar cell model, the conventional methods are incapable to extract the parameters of solar cell with high accuracy. The implicit nonlinear equation describing the single and double diodes solar cell in five and seven parameters is rewritten as optimization problems with constraint functions and it is solved by using a firefly algorithm optimization. The firefly algorithm is a nature-inspired stochastic optimization algorithm, and able to solve modern global optimization for nonlinear and complex system, based on the flashing patterns and behavior of fireflys swarm. Moreover, this paper develops a unique solar cell modelling approach that incorporates search and optimization techniques for the determination of equivalent circuit parameters of RTC France Company mono-crystalline silicon solar cell single and double diodes at 33°C and 1000W/m2 from experimental current-voltage. The statistical errors are used to verify the accuracy of the results. Finally, accuracy of the extracted parameters is verified by comparing the current-voltage curve generated from simulation with those provided by determined experimentally and with different recent algorithms.