Comparison of queen honey bee colony migration with various MPPTs on photovoltaic system under shaded conditions

Shaded conditions cause a decrease in the performance of photovoltaic (PV) systems. In this situation, the power versus voltage curve shows two maximum power points, namely local (LMPP) and global (GMPP). The main challenge for extracting the maximum power from a PV system during shading conditions is the existence of a false maximum or LMPP along with a true maximum or GMPP. Traditional maximum power point tracking (MPPT) has faced hurdles in overcoming the situation. Therefore, this paper describes the implementation of Queen Honey Bee Migration (or QHBM for short) to track GMPP of PV systems, which called QHBM MPPT. The highlight of this paper is the simulation results of QHBM MPPT on PV systems under various shading conditions. We implemented QHBM MPPT on a boost converter installed on a 1200 Wp PV system. We conducted a simulation using MATLAB® with five scenarios which aim to show the various shadows that PV systems might encounter in reality. The MPPT QHBM is tested repeatedly and then the average value is taken to measure performance in MPP tracking. The average value is used to calculate tracking efficiency, number of iteration or convergence time. We also compared QHBM with other methods, namely incremental conductance (IC) and Particle Swarm Optimization (PSO). The results obtained show that the QHBM and PSO MPPTs outperform the IC MPPT in terms of efficiency, convergence time and the number of iterations. IC MPPTs oscillate under shading conditions since no knowledge of GMPP. Both PSO and QHBM MPPTs know GMPP from scouts or particles, respectively. Therefore, PSO and QHBM MPPTs are better than IC MPPT in various shading case

A comparative study of maximum power point tracking techniques for a photovoltaic grid-connected system

Purpose. In recent years, the photovoltaic systems (PV) become popular due to several advantages among the renewable energy. Tracking maximum power point in PV systems is an important task and represents a challenging issue to increase their efficiency. Many different maximum power point tracking (MPPT) control methods have been proposed to adjust the peak power output and improve the generating efficiency of the PV system connected to the grid. Methods. This paper presents a Beta technique based MPPT controller to effectively track maximum power under all weather conditions. The effectiveness of this algorithm based MPPT is supplemented by a comparative study with incremental conductance (INC), particle swarm optimization (PSO), and fuzzy logic control (FLC). Results Faster MPPT, lower computational burden, and higher efficiency are the key contributions of the Beta based MPPT technique than the other three techniques.Мета. В останні роки фотоелектричні системи набули популярності завдяки низці переваг серед відновлюваних джерел енергії. Відстеження точки максимальної потужності у фотоелектричних системах є важливим завданням і складною проблемою для підвищення їх ефективності. Було запропоновано безліч різних методів керування відстеженням точки максимальної потужності (ВТМП) для регулювання пікової вихідної потужності та підвищення ефективності генерації фотоелектричної системи, підключеної до мережі. Методи. У цій статті представлений контролер ВТМП, заснований на бета-методі, для ефективного відстеження максимальної потужності за будь-яких погодних умов. Ефективність ВТМП на основі цього алгоритму доповнюється порівняльним дослідженням з інкрементною провідністю, оптимізацією рою частинок та нечітким логічним управлінням. Результати. Швидше ВТМП, менші витрати на обчислення та більша ефективність є ключовими перевагами методу ВТМП на основі бета-методу порівняно з трьома іншими методами

Strategy to reduce transient current of inverter-side on an average value model high voltage direct current using adaptive neuro-fuzzy inference system controller

Growing-up of high voltage direct current (HVDC) penetration into modern power systems (PS) makes difficulty on the PS operation. The HVDC produces high and slow transient current (TC) at start-time, especially for higher up-ramp rate (Urr>20 pu/s). Its condition makes the HVDC cannot be linked and synchronized into the PS rapidly. A strategy to reduce the TC is proposed by an adaptive network based fuzzy inference system (ANFIS) control on inverter HVDC-link to cope up this problem. The ANFIS control is tuned with the help of conventional control in various train-data by using offline mode. Response of ANFIS scheme is improved by suppressing TC at the values of 3.75% for the both phases (A and C), and 3.95% for phase B, for the Urr=30 pu/s. While the conventional control achieved at 9.1% for the both phases (A and B), and 9.2% for the phase C. The ANFIS control gives shorter settling time (0.553 s) than the conventional control (0.584 s) for all phases. The proposed control is more effective than the conventional control at all the scenario

Performance Analysis Of Hybrid Ai-Based Technique For Maximum Power Point Tracking In Solar Energy System Applications

Demand is increasing for a system based on renewable energy sources that can be employed to both fulfill rising electricity needs and mitigate climate change. Solar energy is the most prominent renewable energy option. However, only 30%-40% of the solar irradiance or sunlight intensity is converted into electrical energy by the solar panel system, which is low compared to other sources. This is because the solar power system\u27s output curve for power versus voltage has just one Global Maximum Power Point (GMPP) and several local Maximum Power Points (MPPs). For a long time, substantial research in Artificial Intelligence (AI) has been undertaken to build algorithms that can track the MPP more efficiently to acquire the most output from a Photovoltaic (PV) panel system because traditional Maximum Power Point Tracking (MPPT) techniques such as Incremental Conductance (INC) and Perturb and Observe (P&Q) are unable to track the GMPP under varying weather conditions. Literature (K. Y. Yap et al., 2020) has shown that most AIbased MPPT algorithms have a faster convergence time, reduced steady-state oscillation, and higher efficiency but need a lot of processing and are expensive to implement. However, hybrid MPPT has been shown to have a good performance-to-complexity ratio. It incorporates the benefits of traditional and AI-based MPPT methodologies but choosing the appropriate hybrid MPPT techniques is still a challenge since each has advantages and disadvantages. In this research work, we proposed a suitable hybrid AI-based MPPT technique that exhibited the right balance between performance and complexity when utilizing AI in MPPT for solar power system optimization. To achieve this, we looked at the basic concept of maximum power point tracking and compared some AI-based MPPT algorithms for GMPP estimation. After evaluating and comparing these approaches, the most practical and effective ones were chosen, modeled, and simulated in MATLAB Simulink to demonstrate the method\u27s correctness and dependability in estimating GMPP under various solar irradiation and PV cell temperature values. The AI-based MPPT techniques evaluated include Particle Swarm Optimization (PSO) trained Adaptive Neural Fuzzy Inference System (ANFIS) and PSO trained Neural Network (NN) MPPT. We compared these methods with Genetic Algorithm (GA)-trained ANFIS method. Simulation results demonstrated that the investigated technique could track the GMPP of the PV system and has a faster convergence time and more excellent stability. Lastly, we investigated the suitability of Buck, Boost, and Buck-Boost converter topologies for hybrid AI-based MPPT in solar energy systems under varying solar irradiance and temperature conditions. The simulation results provided valuable insights into the efficiency and performance of the different converter topologies in solar energy systems employing hybrid AI-based MPPT techniques. The Boost converter was identified as the optimal topology based on the results, surpassing the Buck and Buck-Boost converters in terms of efficiency and performance. Keywords—Maximum Power Point Tracking (MPPT), Genetic Algorithm, Adaptive Neural-Fuzzy Interference System (ANFIS), Particle Swarm Optimization (PSO

The role of computational intelligence techniques in the advancements of solar photovoltaic systems for sustainable development: a review

The use of computational intelligence (CI) in solar photovoltaic (SPV) systems has been on the rise due to the increasing computational power, advancements in power electronics and the availability of data generation tools. CI techniques have the potential to reduce energy losses, lower energy costs, and facilitate and accelerate the global adoption of solar energy. In this context, this review paper aims to investigate the role of CI techniques in the advancements of SPV systems. The study includes the involvement of CI techniques for parameter identification of solar cells, PV system sizing, maximum power point tracking (MPPT), forecasting, fault detection and diagnosis, inverter control and solar tracking systems. A performance comparison between CI techniques and conventional methods is also carried out to prove the importance of CI in SPV systems. The findings confirmed the superiority of CI techniques over conventional methods for every application studied and it can be concluded that the continuous improvements and involvement of these techniques can revolutionize the SPV industry and significantly increase the adoption of solar energy

Hierarchical Pigeon-Inspired Optimization-based MPPT Method for Photovoltaic Systems under Complex Partial Shading Conditions

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 51907031); 10.13039/501100007914-Brunel University London (Grant Number: Brunel Research Initiative and Enterprise Fund); 10.13039/501100010226-Department of Education of Guangdong Province (Grant Number: 2016KCXTD022)

Power electronics technologies for renewable energy sources

Over the last decades, power grids are facing significant improvements mainly due to the integration of more and more technologies. In particular, renewable energy sources (RES) are contributing to moving from centralized energy production to a new paradigm of distributed energy production. Analyzing in more detail the requirements of the diverse technologies of RES, it is possible to identify a common and key point: power electronics. In fact, power electronics is the key technology to embrace the RES technologies towards controllability and the success of sustainability of power grids. In this context, this book chapter is focused on the analysis of diverse RES technologies from the point of view of power electronics, including the introduction and explanation of the operating principle of the most relevant RES, both in onshore and offshore scenarios. Additionally, are also presented the main topologies of power electronics converters used in the interface of RES.(undefined