Analysis of spatial fixed PV arrays configurations to maximize energy harvesting in BIPV applications

This paper presents a new approach for efficient utilization of building integrated photovoltaic (BIPV) systems under partial shading conditions in urban areas. The aim of this study is to find out the best electrical configuration by analyzing annual energy generation of the same BIPV system, in terms of nominal power, without changing physical locations of the PV modules in the PV arrays. For this purpose, the spatial structure of the PV system including the PV modules and the surrounding obstacles is taken into account on the basis of virtual reality environment. In this study, chimneys which are located on the residential roof-top area are considered to create the effect of shading over the PV array. The locations of PV modules are kept stationary, which is the main point of this paper, while comparing the performances of the configurations with the same surrounding obstacles that causes partial shading conditions. The same spatial structure with twelve distinct PV array configurations is considered. The same settling conditions on the roof-top area allow fair comparisons between PV array configurations. The payback time analysis is also performed with considering local and global maximum power points (MPPs) of PV arrays by comparing the annual energy yield of the different configurationsPeer ReviewedPostprint (author’s final draft

Fault detection and automatic supervision methodology for PV systems

In this work, we show a new methodology for automatic supervision and fault detection of PV Systems, based mainly on the analysis of the power losses. This methodology includes parameter extraction techniques to calculate main PV system parameters from monitoring data [1-2], taking into account the real irradiance and module temperature, allowing simulation and evaluation of the PV system behaviour in real time.Postprint (published version

Battery Internal Fault Monitoring Based on Anomaly Detection Algorithm

Battery internal faults are one of the major factors causing safety concern, performance degradation, and cost increases. To extend the lifetime of the battery and bring more security in the system, internal fault detection of solar battery is proposed in this paper using an unsupervised machine learning algorithm based on anomaly detection method. The advantages of adopting such a method consist of using unlabeled data that meet the battery case in the difficulty of obtaining the fault data. In contrast, healthy data can easily be obtained from the battery and therefore allows building the anomaly detection algorithm. The effectiveness of the proposed method is validated using a simulation platform of a stand-alone photovoltaic system developed in Matlab/Simulink that takes as system input a real profile of irradiance and temperature captured from the Centre de Development des Energies renewable (CDER), Algeria. The test results in real-time data show the ability of the proposed approach to detect the fault occurrence in the battery

EXODuS: Exploratory OLAP over Document Stores

OLAP has been extensively used for a couple of decades as a data analysis approach to support decision making on enterprise structured data. Now, with the wide diffusion of NoSQL databases holding semi-structured data, there is a growing need for enabling OLAP on document stores as well, to allow non-expert users to get new insights and make better decisions. Unfortunately, due to their schemaless nature, document stores are hardly accessible via direct OLAP querying. In this paper we propose EXODuS, an interactive, schema-on-read approach to enable OLAP querying of document stores in the context of self-service BI and exploratory OLAP. To discover multidimensional hierarchies in document stores we adopt a data-driven approach based on the mining of approximate functional dependencies; to ensure good performances, we incrementally build local portions of hierarchies for the levels involved in the current user query. Users execute an analysis session by expressing well-formed multidimensional queries related by OLAP operations; these queries are then translated into the native query language of MongoDB, one of the most popular document-based DBMS. An experimental evaluation on real-world datasets shows the efficiency of our approach and its compatibility with a real-time setting

New algorithm for energy dispatch scheduling of grid-connected solar photovoltaic system with battery storage system

Purpose. In last decade the problem of energy management system (EMS) for electric network has received special attention from academic researchers and electricity companies. In this paper, a new algorithm for EMS of a photovoltaic (PV) grid connected system, combined to an storage system is proposed for reducing the character of intermittence of PVs power which infect the stability of electric grid. In simulation model, the PV system and the energy storage system are connected to the same DC bus, whereas EMS controls the power flow from the PV generator to the grid based on the predetermined level of PV power. In the case where the PV power is less than the predefined threshold, energy is stored in the batteries banc which will be employed in the peak energy demand (PED) times. Otherwise, it continues to feed the principal grid. The novelty of the proposed work lies in a new algorithm (smart algorithm) able to determine the most suitable (optimal) hours to switching between battery, Solar PVs, and principal grid based on historical consumption data and also determine the optimal amount of storage energy that be injected during the peak demand. Methods. The solution of the problem was implemented in the Matlab R2010a Platform and the simulation conducted on Laptop with a 2.5 GHz processor and 4 GB RAM. Results. Simulation results show that the proposed model schedules the time ON/OFF of the switch in the most optimal way, resulting in absolute control of power electric path, i.e. precise adaptation at the peak without compromising consumers comfort. In addition, other useful results can be directly obtained from the developed scheme. Thus, the results confirm the superiority of the proposed strategy compared to other improved techniques.Мета. В останнє десятиліття проблемі системи енергоменеджменту (СЕМ) для електричної мережі приділялася особлива увага з боку науковців та електроенергетичних компаній. У цій роботі пропонується новий алгоритм для СЕМ фотоелектричної (ФЕ) системи, підключеної до мережі, об'єднаної з системою накопичення енергії для зменшення характеру переривчастості потужності ФЕ системи, що впливає на стабільність електричної мережі. У розрахунковій моделі ФЕ система та система накопичення енергії підключені до однієї і тієї ж шини постійного струму, тоді як СЕМ керує потоком потужності від ФЕ генератора до мережі на основі заздалегідь визначеного рівня потужності ФЕ. У тому випадку, коли потужність ФЕ менше заздалегідь визначеного порогу, енергія накопичується в батареях акумуляторів, що буде використано в часи пікового попиту на енергію. В іншому випадку ФЕ продовжує живити основну мережу. Новизна запропонованої роботи полягає в новому алгоритмі (розумному алгоритмі), здатному визначити найбільш підходящі (оптимальні) години для перемикання між акумулятором, сонячними ФЕ та основною мережею на основі даних про історію споживання, а також визначити оптимальну величину енергії накопичення, що вводиться під час пікового попиту. Методи. Розв‘язання задачі було реалізовано на платформі Matlab R2010a, а моделювання проведено на ноутбуці з процесором 2,5 ГГц та 4 ГБ оперативної пам'яті. Результати. Результати моделювання показують, що запропонована модель найоптимальніше планує час увімкнення/вимкнення вимикача, що призводить до абсолютного контролю потужності шляху електроенергії, тобто точної адаптації на піку без шкоди для комфорту споживачів. Крім того, з розробленої схеми можна безпосередньо отримати інші корисні результати. Таким чином, результати підтверджують перевагу запропонованої стратегії порівняно з іншими вдосконаленими методами

Evaluation of the performance and degradation of crystalline silicon-based photovoltaic modules in the Saharan environment

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The aim of this paper is to present three years of an evaluation of the performance and degradation rate of three different crystalline silicon-based photovoltaic (PV) modules in the Saharan environment. The PV modules are: mc-Si (multi-crystalline), c_Si (mono-crystalline, back contacted) and HiT (heterojunction with intrinsic thin-layer); they are installed in Saida which is located at the proximity of Algeria’s Sahara. Two methods were used to calculate the degradation rate; the effective peak power of the PV modules and the temperature corrected performance ratio. It was found that the HIT technology performs worse than the other technologies with the highest degradation rate, ranging from -1.53%/year to -1.92%/year. The mc_Si PV and c_Si PV module technologies present a lower degradation rate than the HIT technology in the range of -0.74%/year to -0.83%/year and -0.58%/year to -0.79%/year respectively.Peer ReviewedPostprint (author's final draft

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

H∞ based control of a DC/DC buck converter feeding a constant power load in uncertain DC microgrid system

DC microgrids are gaining more and more popularity and are becoming a more viable alternative to AC microgrids (MGs) due to their advantages in terms of simpler power converter stages, flexible control algorithms and the absence of synchronization and reactive power. However, DC-MGs are prone to instability issues associated with the presence of nonlinear loads such as constant power loads (CPL) known by their incremental negative impedance (INI), which may lead to voltage collapse of the main DC Bus. In this paper, -based controller of a source side buck converter is designed to avoid the instability issues caused by the load-side converter acting as a CPL. Besides, the proposed controller allows a perfect rejection of all perturbations that may arise from parameter variations, input voltage and CPL current fluctuations. The design process of H-based controller is based on the Golver Doyle Optimization Algorithm (GDOA), which requires an augmented system extracted from the small-signal model of the DC/DC converter including the mathematical model of parameter variations and overall external perturbations. The​ based controller involves the use of weight functions in order to get the desired performances. The proposed controller is easy to implement and lead to reducing the implementation cost and avoid the use of current measurement that may have some disadvantages. The derived controller is validated by simulation performed in Psim software and experimental setup

Improved power computation method for droop‐controlled single‐phase VSIs in standalone microgrid considering non‐linear loads

Computation of active and reactive powers is a crucial step in droop-controlled single-phase voltage source inverters (VSIs) in standalone microgrid since the performance and stability of the power-sharing strategy are strongly influenced by its speed and accuracy, especially in the case of non-linear loads. Here, an improved performance of power-sharing among single-phase droop-controlled VSIs in an islanded microgrid, considering DC component and nonlinear loads is presented. To achieve this goal, an enhanced power-sharing control scheme including a Multiple Enhanced Second-Order Generalized Integrator Frequency-Locked Loop (MESOGI-FLL) for power calculation is proposed. As a result, the proposed power computation technique provides high rejection capability of DC component and current harmonics, hence, perfect estimation of the fundamental component of the inverter output current and its 90◦ phase-shifted component. This strategy makes the power calculation method-based control scheme immune to disturbance effects of the DC component and the high current harmonics. Detailed analysis, mathematical modelling of MESOGI, as well as a comparison with recent methods, are also provided. Simulation and experimental tests were carried out and the obtained results have shown the effectiveness and robustness of the proposed power-sharing controller even under nonlinear load operating conditions