Optimization approaches for parameter estimation and maximum power point tracking (MPPT) of photovoltaic systems

Optimization techniques are widely applied in various engineering areas, such as modeling, identi�cation, optimization, prediction, forecasting and control of complex systems. This thesis presents the novel optimization methods that are used to control Photovoltaic (PV) generation systems. PV power systems are electrical power systems energized by PV modules or cells. This thesis starts with the introduction of PV modeling methods, on which our research is based. Parameter estimation is used to extract the parameters of the PV models characterizing the utilized PV devices. To improve e�ciency and accuracy, we proposed sequential Cuckoo Search (CS) and Parallel Particle Swarm Optimization (PPSO) methods to extract the parameters for di�erent PV electrical models. Simulation results show the CS has a faster convergence rate than the traditional Genetic Algorithm (GA), Pattern Search (PS) and Particle Swarm Optimization (PSO) in sequential processing. The PPSO, with an accurate estimation capability, can reduce at least 50% of the elapsed time for an Intel i7 quad-core processor. A major challenge in the utilization of PV generation is posed by its non linear Current-Voltage (I-V ) relations, which result in the unique Maximum Power Point (MPP) varying with di�erent atmospheric conditions. Maximum Power Point Tracking (MPPT) is a technique employed to gain maximum power available from PV devices. It tracks operating voltage corresponding to the MPP and constrains the operating point at the MPP. A novel model-based two-stage MPPT strategy is proposed in this thesis to combine the o�ine maximum power point estimation using the Weightless Swarm Algorithm (WSA) with an online Adaptive Perturb & Observe (APO) method. In addition, an Approximate Single Diode Model (ASDM) is developed for the fast evaluations of the output power. The feasibility of the proposed method is veri�ed in an MPPT system implemented with a Single-Ended Primary-Inductor Converter (SEPIC). Simulation results show the proposed MPPT method is capable of locating the operating point to the MPP under various environmental conditions

Support Vector Machine for Photovoltaic System Efficiency Improvement

Photovoltaic panels are promising source for renewable energy. They serve as a clean source of electricity by converting the radiation coming from the sun to electric energy. However, the amount of energy produced by the photovoltaic panels is dependent on many variables including the irradiation and the ambient temperature, leading to nonlinear characteristics. Finding the optimal operating point in the photovoltaic characteristic curve and operating the photovoltaic panels at that point ensures improved system efficiency. This paper introduces a unique method to improve the efficiency of the photovoltaic panel using Support Vector Machines. The dataset, which is obtained from a real photovoltaic setup in Spain, include temperature, radiation, output current, voltage and power for a period of one year. The results obtained show that the system is capable of accurately driving the photovoltaic panel to produce optimal output power for a given temperature and irradiation levels

European White Book on Real-Time Power Hardware in the Loop Testing : DERlab Report No. R- 005.0

The European White Book on Real-Time-Powerhardware-in-the-Loop testing is intended to serve as a reference document on the future of testing of electrical power equipment, with specifi c focus on the emerging hardware-in-the-loop activities and application thereof within testing facilities and procedures. It will provide an outlook of how this powerful tool can be utilised to support the development, testing and validation of specifi cally DER equipment. It aims to report on international experience gained thus far and provides case studies on developments and specifi c technical issues, such as the hardware/software interface. This white book compliments the already existing series of DERlab European white books, covering topics such as grid-inverters and grid-connected storag

Maximizing solar photovoltaic system efficiency by multivariate linear regression based maximum power point tracking using machine learning

Introduction. In recent times, there has been a growing popularity of photovoltaic (PV) systems, primarily due to their numerous advantages in the field of renewable energy. One crucial and challenging task in PV systems is tracking the maximum power point (MPP), which is essential for enhancing their efficiency. Aim. PV systems face two main challenges. Firstly, they exhibit low efficiency in generating electric power, particularly in situations of low irradiation. Secondly, there is a strong connection between the power output of solar arrays and the constantly changing weather conditions. This interdependence can lead to load mismatch, where the maximum power is not effectively extracted and delivered to the load. This problem is commonly referred to as the maximum power point tracking (MPPT) problem various control methods for MPPT have been suggested to optimize the peak power output and overall generation efficiency of PV systems. Methodology. This article presents a novel approach to maximize the efficiency of solar PV systems by tracking the MPP and dynamic response of the system is investigated. Originality. The technique involves a multivariate linear regression (MLR) machine learning algorithm to predict the MPP for any value of irradiance level and temperature, based on data collected from the solar PV generator specifications. This information is then used to calculate the duty ratio for the boost converter. Results. MATLAB/Simulink simulations and experimental results demonstrate that this approach consistently achieves a mean efficiency of over 96 % in the steady-state operation of the PV system, even under variable irradiance level and temperature. Practical value. The improved efficiency of 96 % of the proposed MLR based MPP in the steady-state operation extracting maximum from PV system, adds more value. The same is evidently proved by the hardware results.Вступ. Останнім часом зростає популярність фотоелектричних (ФЕ) систем, насамперед через їх численні переваги в галузі відновлюваної енергетики. Однією з найважливіших і складних завдань у ФЕ системах є відстеження точки максимальної потужності (MPP), яка необхідна для підвищення їх ефективності. Мета. ФЕ системи стикаються із двома основними проблемами. По-перше, вони демонструють низьку ефективність вироблення електроенергії, особливо в умовах низького випромінювання. По-друге, існує сильний зв’язок між вихідною потужністю сонячних батарей і погодними умовами, що постійно змінюються. Ця взаємозалежність може призвести до невідповідності навантаження, коли максимальна потужність не ефективно відбиратиметься і передаватиметься в навантаження. Цю проблему зазвичай називають проблемою відстеження точки максимальної потужності (MPPT). Для оптимізації пікової вихідної потужності та загальної ефективності генерації ФЕ систем було запропоновано різні методи керування MPPT. Методологія. У цій статті представлено новий підхід до максимізації ефективності сонячних ФЕ систем шляхом відстеження MPP та дослідження динамічної реакції системи. Оригінальність. Цей метод включає алгоритм машинного навчання багатовимірної лінійної регресії (MLR) для прогнозування MPP для будь-якого рівня освітленості і температури на основі даних, зібраних зі специфікацій сонячних ФЕ генераторів. Ця інформація потім використовується для розрахунку коефіцієнта заповнення перетворювача, що підвищує. Результати. Моделювання MATLAB/Simulink та експериментальні результати показують, що цей підхід послідовно забезпечує середню ефективність понад 96 % в режимі роботи ФЕ системи, що встановився, навіть при змінних рівнях освітленості і температурі. Практична цінність. Підвищена ефективність 96 % пропонованого MPP на основі MLR в режимі роботи, що вистачає максимум з ФЕ системи, підвищує цінність. Те саме, очевидно, підтверджують і апаратні результати

Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study

Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG(Lab)(2)) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications

Analytical calculation of photovoltaic systems maximum power point (MPP) based on the operation point

This work proposes a new analytical model to extract the 1-Diode/2-Resistor solar cell/panel equivalent circuit parameters. The methodology is based on a reduced amount of experimentally measured information: short-circuit current, the slope of the I-V curve at that point, the open-circuit voltage, and the current and voltage levels, together with the slope of the I-V curve at the instantaneous operation point. This procedure is specially designed to analyze the performance of autonomous photovoltaic systems, which are most of the primary sources for spacecraft power. Results show good agreement with experimental data. Furthermore, this methodology allows for fast and accurate I-V curve maximum power point (MPP) identification. solar cell; solar panel; photovoltaic array modeling; parameter extractio