Statistical fault detection in photovoltaic systems

Faults in photovoltaic (PV) systems, which can result in energy loss, system shutdown or even serious safety breaches, are often difficult to avoid. Fault detection in such systems is imperative to improve their reliability, productivity, safety and efficiency. Here, an innovative model-based fault-detection approach for early detection of shading of PV modules and faults on the direct current (DC) side of PV systems is proposed. This approach combines the flexibility, and simplicity of a one-diode model with the extended capacity of an exponentially weighted moving average (EWMA) control chart to detect incipient changes in a PV system. The one-diode model, which is easily calibrated due to its limited calibration parameters, is used to predict the healthy PV array’s maximum power coordinates of current, voltage and power using measured temperatures and irradiances. Residuals, which capture the difference between the measurements and the predictions of the one-diode model, are generated and used as fault indicators. Then, the EWMA monitoring chart is applied on the uncorrelated residuals obtained from the one-diode model to detect and identify the type of fault. Actual data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria, are used to assess the performance of the proposed approach. Results show that the proposed approach successfully monitors the DC side of PV systems and detects temporary shading.Peer ReviewedPostprint (author's final draft

Comparative study of various methods for extraction of multi quantum wells Schottky diode parameters

In this work, forward current voltage characteristics for multi-quantum wells Al0.33Ga0.67As Schottky diode were measured at temperature ranges from 100 to 300 K. The main parameters of this Schottky diode, such as the ideality factor, barrier height, series resistance and saturation current, have been extracted using both analytical and heuristics methods. Differential evolution (DE), particle swarm optimization (PSO) and artificial bee colony (ABC) have been chosen as candidate heuristics algorithms, while Cheung technic was selected as analytical extraction method. The obtained results show clearly the high performance of DE algorithms in terms of parameters accuracy, convergence speed and robustness

The Efficiency of Best-so-far ABC Algorithm Versus Analytical Methods for Schottky Diode Parameters Extraction

In this work, we investigate the forward current-voltage characteristics of p-type Ti/Au/Al0.29Ga0.71As Schottky diode, which were measured over a range of temperatures from 260 to 400 K. The experimental current-voltage characteristics are used to extract the Schottky diode main electrical parameters such as ideality factor, saturation current, height barrier, and series resistance. The parameters extraction has been reached using the best-so-far Artificial Bee Colony (ABC) algorithm. ABC is an optimization technique, which is used in finding the best solution from all feasible solutions. The efficiency of this method has been assessed using current-voltage characteristics obtained over wide range of temperatures. The accuracy of the best-so-far ABC algorithm is compared to two well-known analytical methods called Cheung and standard. Extracted diode parameters from different methods show that best results are obtained by the suggested method

A Novel Parameter Identification Approach for C–V–T Characteristics of Multi-Quantum Wells Schottky Diode Using Ant Lion Optimizer

We report the capacitance-voltage (C–V) characteristics of multi quantum wells Schottky diode. This diode is based on Aluminum gallium arsenide, which is highly promising wide band gap semiconductor for applications in high power electronic and optoelectronic devices. The elaboration process and the characterization phase have been carried out at Nottingham University. The C–V characteristics have been measured at different temperature ranging from 20 to 400 K. The barrier height and effective density were than extracted from 1/(C/A)2 plot using heuristic algorithm which called ALO (Ant Lion Optimizer). The accuracy of the extraction method is verified through the gotten results

Artificial bee colony algorithm: a novel strategy for optical constants and thin film thickness extraction using only optical transmittance spectra for photovoltaic applications

An effective approach to determine thin film thickness (d) and optical constants (n, k, α) from transmittance spectrum with interference fringes is proposed. The developed strategy is based on applying the artificial bee colony (ABC) algorithm and Cauchy dispersion model. The accuracy test of this method has been assessed by using simulated and real tests. Simulated test is used to check the ability of ABC algorithm to determine the parameters of simulated transmittance spectra. Real test deals with the investigation of the determination approach on experimental measured transmittance spectra. Those spectra were measured from six elaborated samples of amorphous hydrogenated silicon (a-Si:H) thin films with different thicknesses, which will be used as an eco-friendly layer for solar cell applications. The obtained results noticeably show the high effectiveness of the developed strategy to accurately determine the thin film thickness and optical constants

Investigation of structural and electrical properties of ITO thin films and correlation to optical parameters extracted using novel method based on PSO algorithm

Thermally annealed DC sputtered indium tin oxide (ITO) thin films were investigated for improvement in properties. The structural and optoelectronic characteristics of as-grown and air annealed films were studied and correlated to the film deposition time. Raman spectroscopy analysis showed low crystalline quality films for as-grown films and were significantly improved after annealing. X-ray diffraction analysis confirmed the crystallinity of samples with (222)preferential orientation. The 30-min ITO films showed a peak at (400). The films optical study shows an increased transmittance (in the transparency region) with decreasing deposition time, yielding a high transparency of 90% for the 5- and 15-min ITO films annealed at 400°C. The films thickness and optical constants were determined from optical transmission only without interference fringe using a novel method based on particle swarm optimization (PSO) algorithm. The absorption coefficient and calculated refractive index decreased with increasing deposition time and their valuereduced further after annealing treatment. The 30-min ITO films showed a comparable low resistivity of 4 9 10-3 X cm before and after annealing as determined by Hall effect measurements. This observation confirms their non-sensitivity to the oxygen post-contamination that resulted from (400) orientation. A shift of the absorption edge towards shorter wavelengths accompanied with an increase in the optical bandgap before and after annealing with decreasing thickness were observed. We have demonstrated that the optical parameters such as the optical gap depend mainly on the electrical parameters such as the carrier concentration

Investigation of the effects of thermal annealing on the structural, morphological and optical properties of nanostructured Mn doped ZnO thin films

The control of the optical properties of ZnO nanostructured thin films by using different dopant elements paves the way for the development of potential materials for photonic and optoelectronic applications. In this work manganese (Mn) doped ZnO thin films were fabricated by rapid thermal evaporation method on a glass substrate having the same Mn content level of ~10% and annealed at different temperatures. XRD analysis showed that the annealed layers have hexagonal wurtzite structure, however, the unannealed layers showed only Zn peaks without any preferential direction. The elemental analysis of the films has been investigated by XPS, which revealed the presence of Mn and oxygen atoms for all layers. In addition, it was observed by FIB-SEM that the morphology of thin films changed with the annealing temperature. For an anneal at 500 °C nanoneedles appeared. Raman spectroscopy showed E1 (TO) mode in the sample annealed at 500 °C which was attributed with the formation of nanoneedles structures. The optical transmission of the annealed films was in the range of 75–77% and the optical bandgap varied from 3.97 to 3.72 eV. These variations are related to the structural and morphological changes of the thin films with annealing temperature

A statistical based approach for fault detection and diagnosis in a photovoltaic system

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper reports a development of a statistical approach for fault detection and diagnosis in a PV system. Specifically, the overarching goal of this work is to early detect and identify faults on the DC side of a PV system (e.g., short-circuit faults; open-circuit faults; and partial shading faults). Towards this end, we apply exponentially-weighted moving average (EWMA) control chart on the residuals obtained from the one-diode model. Such a choice is motivated by the greater sensitivity of EWMA chart to incipient faults and its low-computational cost making it easy to implement in real time. Practical data from a 3.2 KWp photovoltaic plant located within an Algerian research center is used to validate the proposed approach. Results show clearly the efficiency of the developed method in monitoring PV system status.Peer ReviewedPostprint (author's final draft

An enhanced machine learning based approach for failures detection and diagnosis of PV systems

In this paper, a novel procedure for fault detection and diagnosis in the direct current (DC) side of PV system, based on probabilistic neural network (PNN) classifier, is proposed. The suggested procedure consists of four main stages: (i) PV module parameters extraction, (ii) PV array simulation and experimental validation (iii) elaboration of a relevant database of both healthy and faulty operations, and (iv) network construction, training and testing. In the first stage, the unknown electrical parameters of the one diode model (ODM) are accurately identified using the best-so-far ABC algorithm. Then, based on these parameters the PV array is simulated and experimentally validated by using a PSIM™/Matlab™ co-simulation. Finally, efficient fault detection and diagnosis procedure based on PNN classifier is implemented. Four operating cases were tested in a grid connected PV system of 9.54 kWp: Healthy system, three modules short-circuited in one string, ten modules short-circuited in one string, and a string disconnected from the array. Moreover, the PNN method was compared, under real operating conditions, with the feed forward back-propagation Artificial Neural Network (ANN) classifiers method, for noiseless and noisy data to evaluate the suggested method’s accuracy and test its aptitude to support noisy data. The obtained results have demonstrated the high efficiency of the proposed method to detect and diagnose DC side anomalies for both noiseless and noisy data cases.Peer Reviewe

Ефективність поки що найкращого алгоритму ABC порівняно з аналітичними методами для вилучення параметрів діода Шоткі

У роботі досліджені прямі вольт-амперні характеристики діода Шотткі p-типу Ti/Au/Al0.29Ga0.71As, які вимірювались в діапазоні температур від 260 до 400 K. Експериментальні вольт-амперні характеристики використовуються для вилучення основних електричних параметрів діода Шотткі, таких як коефіцієнт ідеальності, струм насичення, висота бар'єру та послідовний опір. Вилучення параметрів було реалізовано за допомогою поки що найкращого алгоритму штучної "бджолиної колонії" (ABC). ABC – це технологія оптимізації, яка використовується для пошуку найкращого розв'язку з усіх можливих розв'язків. Ефективність цього методу була оцінена за допомогою вольт-амперних характеристик, отриманих в широкому діапазоні температур. Точність поки що найкращого алгоритму ABC порівнюється з двома відомими аналітичними методами, такими як метод Чонга і стандартний метод. Вилучені з різних методів параметри діода показують, що найкращі результати отримані запропонованим методом.In this work, we investigate the forward current-voltage characteristics of p-type Ti/Au/Al0.29Ga0.71As Schottky diode, which were measured over a range of temperatures from 260 to 400 K. The experimental current-voltage characteristics are used to extract the Schottky diode main electrical parameters such as ideality factor, saturation current, height barrier, and series resistance. The parameters extraction has been reached using the best-so-far Artificial Bee Colony (ABC) algorithm. ABC is an optimization technique, which is used in finding the best solution from all feasible solutions. The efficiency of this method has been assessed using current-voltage characteristics obtained over wide range of temperatures. The accuracy of the best-so-far ABC algorithm is compared to two well-known analytical methods called Cheung and standard. Extracted diode parameters from different methods show that best results are obtained by the suggested method