Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification

Kichou, Sofiane; Silvestre, S.; Guglielminotti, Letizia; Mora‐López, Llanos; Muñoz-Cerón, Emilio

doi:10.1016/j.renene.2016.07.002

Cited by 49 publications

(16 citation statements)

References 35 publications

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“…An alternative to these methods is evolutionary optimization algorithms, including GA and minimization methods, which are related to mathematical models and can offer physically meaningful results. Recent publications suggest evolutionary algorithms can very accurately estimate parameters for PV module simulation using different models such as single-diode model [12,13] and Sandia array performance model [14]. On a module level, similar results are achieved using GA optimization to parametrize the single-diode model [15] as well as the two-diode model [16].…”

Section: Machine Learning and Optimization In The Pv Modelling Domainmentioning

confidence: 78%

A Genetic Algorithm Approach as a Self-Learning and Optimization Tool for PV Power Simulation and Digital Twinning

et al. 2020

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A key aspect for achieving a high-accuracy Photovoltaic (PV) power simulation, and reliable digital twins, is a detailed description of the PV system itself. However, such information is not always accurate, complete, or even available. This work presents a novel approach to learn features of unknown PV systems or subsystems using genetic algorithm optimization. Based on measured PV power, this approach learns and optimizes seven PV system parameters: nominal power, tilt and azimuth angles, albedo, irradiance and temperature dependency, and the ratio of nominal module to nominal inverter power (DC/AC ratio). By optimizing these parameters, we create a digital twin that accurately reflects the actual properties and behaviors of the unknown PV systems or subsystems. To develop this approach, on-site measured power, ambient temperature, and satellite-derived irradiance of a PV system located in south-west Germany are used. The approach proposed here achieves a mean bias error of about 10% for nominal power, 3° for azimuth and tilt angles, between 0.01%/C and 0.09%/C for temperature coefficient, and now-casts with an accuracy of around 6%. In summary, we present a new solution to parametrize and simulate PV systems accurately with limited or no previous knowledge of their properties and features.

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Section: Machine Learning and Optimization In The Pv Modelling Domainmentioning

confidence: 78%

A Genetic Algorithm Approach as a Self-Learning and Optimization Tool for PV Power Simulation and Digital Twinning

et al. 2020

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“…Later advanced data analysis compared different algorithms to extract the parameters which contribute to the PV array model output [36]. The numerical Levenberg-Marquardt algorithm and metaheuristics such as Differential Evolution (DE), Genetic (GA), Particle Swarm Optimization (PSO), Ant Bee Colony (ABC) algorithms are used to extract and fit the parameters of two PV models, the five parameters model (5PM) and the Sandia Array Performance model (SAPM).…”

Section: Methodsmentioning

confidence: 99%

Investigation of the Photon to Charge Conversion and Its Implication on Photovoltaic Cell Efficient Operation

2021

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Efficient photon to charge (PTC) transfer is considered to be the cornerstone of technological improvements in the photovoltaic (PV) industry, while it constitutes the most common process in nature. This study aims to investigate the parameters that impact efficient PV-cell photon to charge conversion in two ways: (a) providing a brief research analysis to extract the key features which affect the electrical and optical performance of PV cells’ operation, and (b) investigating the dependance of these characteristics on the photon to charge mechanisms. The former direction focuses on the latest advances regarding the impacts of the microenvironment climate conditions on the PV module and its operational performance, while the latter examines the fundamental determinants of the cell’s efficient operation. The electrical and optical parameters of the bulk PV cells are influenced by both the external microenvironment and the intrinsic photon to charge conversion principles. Light and energy harvesting issues need to be overcome, while nature-inspired interpretation and mimicking of photon to charge and excitation energy transfer are in an infant stage, furthering a better understanding of artificial photosynthesis. A future research orientation is proposed which focuses on scaling up development and making use of the before mentioned challenges.

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“…Photovoltaic is one of the best choices for energy generation [61]. Widespread investigation of Photovoltaic (PV) systems before being installed within the different applications is crucial [62]. Furthermore, PV modelling is needed in many applications.…”

Section: Optimization Techniques For Fault Direction Detection: Direc...mentioning

confidence: 99%

State of the Art on High Voltage Metrology: Past, Present and Future

2022

Journal of Measurement Science and Applications (JMSA)

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High voltage measurements are not only significant for reliable transmission of electricity at high voltage potentials but also play a vital role in different realms and various applications. Their most common applications are in power electronics, medical technology, electromagnetic compatibility, particle accelerators, aluminum production, and electronic ignition systems. High Direct Current (DC) and Alternating Current (AC) measuring devices for high voltage are extensively used in numerous claims. Precise high voltage measurement techniques are crucial to guarantee these measuring devices' performance, assess their standard operating procedures and ensure their measurements' quality. High voltage metrology validates inclusive progresses in science and technology. To comprehensively understand these developments, it is essential to know the high voltage history, its main aspects, and its measuring techniques timeline growth. This state of the art introduces the history and the principles of the high voltage science. Recent measurement techniques and advances in the high voltage metrology are provided. Moreover, future trends in high voltage metrology and precise measurements are discussed.

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Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification

Cited by 49 publications

References 35 publications

A Genetic Algorithm Approach as a Self-Learning and Optimization Tool for PV Power Simulation and Digital Twinning

A Genetic Algorithm Approach as a Self-Learning and Optimization Tool for PV Power Simulation and Digital Twinning

Investigation of the Photon to Charge Conversion and Its Implication on Photovoltaic Cell Efficient Operation

State of the Art on High Voltage Metrology: Past, Present and Future

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