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The performance analysis of photovoltaic (PV) plants plays an important role in making investment decisions, creating more efficient designs, and in the operation and maintenance decisions in new plants. In addition, performance analysis provides a way to identify real-time operational problems and allows comparisons among different PV plants. Performance of PV plants can be indexed with many parameters, but service providers are giving energy guarantees according to their performance ratio (PR). There are many factors that affect PR, such as irradiation, temperature, wind speed, humidity and soiling. But, the most important factors affecting PR are irradiation and temperature. Hence, both the irradiation and the temperature must be measured correctly. Solar irradiation is usually measured with a pyranometer or reference cell. However, there are differences between the obtained data from these two sensors, which affects the PR significantly. In this study, the PR of an MW PV plant installed in Turkey was investigated. Two types of irradiation sensors (pyranometer and reference cells) and PVGIS program were used. The data were collected every 5 min during 1 year. The data passed through quality control and filtered process and some improper values were removed from the row data set. In addition, new data were written instead of the missing data. PR was calculated by using conventional and corrected method. As a result, the average annual PR is determined to be 81.05%, 79.04% and 78.96% and the corrected PR is determined to be 83.69%, 82.48% and 80.87% using the reference cell, pyranometer and PVGIS program, respectively. © 2019, The Korean Institute of Electrical Engineers.

This article investigates the competence of ensemble learning techniques in solar irradiance prediction. It was seen from the literature survey, an ensemble tree model, random forests is studied more frequently as ensemble models. However, ensemble of support vector regression (SVR) and artificial neural networks (ANN) is also possible. So, this study is the first detailed evaluation of ensemble models in solar irradiance estimation domain. Boosting and bagging ensembles of SVR, ANN and decision tree (DT), are developed to estimate solar irradiance in hourly basis in five cities in Turkey. First frequently used base models (SVR, ANN, and DT) are created and tested with the use of 5 years meteorological data. Then boosting and bagging ensembles of the base models are developed and tested with the same data. The base models are compared with their ensemble counterparts in terms of average coefficient of determination (R2) and root mean squared error (RMSE). The comparative results show that boosting and bagging ensemble models improve SVR, ANN, and DT in terms of RMSE between 4.6 and 14.6% in average. The results show empirically that ensemble models improve prediction accuracies of various base regression models and it can be applied to other machine learning models used in solar irradiance prediction. © 2019, King Fahd University of Petroleum & Minerals.

Residential buildings need demand of energy for both heat and electricity. However, it’s not always possible to meet this need by using individual panels due to the limited roof area. In addition, performing electrical and thermal energy production by using separate panels causes loss of performance and efficiency. The hybrid photovoltaic thermal (PV/T) collectors could be used in order to meet this necessity into the same collector. This paper investigates the performance and economic analysis of a PV/T collector for a building application in Turkey climatic conditions. For this purpose, the Matlab/Simulink model of the PV/T collector was prepared. The electrical and thermal performance of the PV/T collector has been investigated by changing various parameters on this model. In addition, market survey was conducted for economic analysis. 11 different input variables such as average daily irradiation, electrical and thermal efficiency, price of electricity and heating, operation and management cost, capital cost, debt to equity ratio, interest rate, discount rate and inflation rate are used to calculate the economic evaluation parameters such as net present value (NPV), levelized cost of energy (LCOE) and payback period (PBP). The results show that, the mean value of LCOE, NPV and PBP are 0.0467 €/kWh, 7905.3 € and 6 years respectively for the project size at 8.96 m2 which is consist of 7 panels in the 25 years life cycle. Also, the average electrical and thermal efficiencies are defined as 13.4% and 69.3% respectively during.

Renewable energy systems such as photovoltaic (PV) and wind energy systems are widely designed grid connected or autonomous. This is a problem especially in small powerful system due to the restriction on the inverter markets. Inverters which are utilised in these kinds of energy systems operate on grid or off grid. In this study, a novel power management strategy has been developed by designing a wind-PV hybrid system to operate both as an autonomous system and as a grid-connected system. The inverter used in this study has been designed to operate both on-grid and off-grid. Due to the continuous demand for energy, gel batteries are used in the hybrid system. The designed Power Management Unit performs measurement from various points in the system and in accordance with this measurement; it provides an effective energy transfer to batteries, loads and grid. The designed control unit provided the opportunity to work more efficiently up to 10% rate. © The Institution of Engineering and Technology 2016.

Since the harmful effects of climate warming on our planet were first observed, the use of renewable energy resources has been significantly increasing. Among the potential renewable energy sources, photovoltaic (PV) system installations keep continuously increasing world-wide due to its economic and environmental contributions. Despite its significant benefits, the inherent variability of PV power generation due to meteorological parameters can cause power management/planning problems. Thus, forecasting of PV output data (directly or indirectly) in an accurate manner is a critical task to provide stability, reliability, and optimisation of the grid systems. In considering the literature reviewed, there are various research items utilizing PV output power forecasting. In this study, a systematic literature review based on the search of primary studies (published between 2010 and 2020), which forecast PV power generation using machine learning and deep learning methods, is reported. The studies are evaluated based on the PV material used, their approaches, generated outputs, data set used, and the performance evaluation methods. As a result, gaps and improvable points in the existing literature are revealed, and suggestions which include novelties are offered for future works.

Bu çalışmada, fotovoltaik/termal(PV/T) kolektörlerin elektriksel ve termal verim analizi yapılmıştır. Buamaçla, sıcak su karşılama ve elektrik enerjisi üretimi için uygun olan SıvıTip Düz PV/T Kolektör sistemi tercih edilmiştir. Bu tip sistemlerin maksimumverimle çalışabilmesi için sızdırmazlık faktörü, ışınım, giriş sıcaklığı, ortamsıcaklığı, emici plaka parametreleri (tüp aralığı, boru çapı, kanatçıkkalınlığı vb.), emici plakadaki akışkanın termal iletkenliği gibi çeşitliparametreler göz önünde bulundurulmalıdır. PV/T kolektörlerin termalverimliliği, giriş sıcaklığı ile ortam sıcaklığı (Ti-Ta) arasındaki sıcaklıkfarkının kolektör yüzeyine düşen global güneş radyasyonuna (G) oranından önemliölçüde etkilenmektedir. (Ti-Ta)/G oranının artması termal verimin düşmesinesebep olmaktadır. Ayrıca, termal verime etki eden diğer faktörler sızdırmazlıkfaktörü (s) ve farklı kanatçık oranı (d/w) değerleridir. d/w oranının artmasıkolektör alanının artması ve PV modül sıcaklığının düşmesine sebep olduğundanelektriksel verim artışı da sağlamaktadır. PV/T sistemlerdeki önemli sorun, tümbu parametrelerin göz önünde bulundurularak optimum verim elde edilebilmesidir.Bu çalışmada, PV/T kolektörünMATLAB/Simulink modeli, matematiksel eşitliklerinden faydalanılarakhazırlanmıştır. Bu model üzerinde, tasarım parametreleri ile bunların termal veelektriksel verime etkileri incelenmiştir. Yapılan çalışma ile PV/T kolektördenelde edilen maksimum termal verim %64,5, elektriksel verim %13,5 ve toplamverim %78 olarak tespit edilmiştir.&nbsp, In this study, electrical and thermal efficiencyanalyses of photovoltaic/thermal (PV/T) collectors were performed. For thispurpose, a liquid type flat PV/T collector system which is suitable for hotwater supply and electrical power production is used. Various parameters suchas sealing factor, radiation, inlet temperature, ambient temperature, absorbentplate parameters (tube spacing, pipe diameter, flap thickness, etc.) and thethermal conductivity of the fluid in the absorbent plate must be taken into consideration for such systems to operate withmaximum efficiency. The thermal efficiency of the PV/T collectors issignificantly influenced by the ratio of the temperature difference between theinlet temperature and the ambient temperature (Ti-Ta) to the global solar radiation(G) falling on the collector surface. (Ti-Ta) / G ratio increase decreases thethermal efficiency. In addition, other factors affecting thermal efficiency arethe sealing factor (s) and the different flap ratio (d/w) values. The increaseof the d/w ratio also increases the collector area and decreases the PV moduletemperature, thus increasing the electrical efficiency. The major problem inPV/T systems is that optimum efficiency can be achieved by considering allthese parameters.In this study, aMATLAB/Simulink model of PV/T collector was prepared by using mathematicalequations. With the help of this model, the design parameters and their effectsto thermal and electrical efficiency were investigated. The maximum thermalefficiency, electrical efficiency and total efficiency of the PV/T collectorwere determined as 64.5%, 13.5% and 78%, respectively.

The load-flow study is a numerical analysis of the flow of electric power in an interconnected system in power engineering. Simplified notation such as the one-line diagram and per-unit system is used for power-flow study and focuses on various features of AC power parameters, such as voltages, voltage angles, real power and reactive power. It analyses the power systems in normal steady-state operation. So, the load flow studies play a major role in analyzing of the power systems. Generally, power system buses are categorized into three classes named load bus, power grid bus and slack bus or swing bus. In fact, slack buses in the power system are chosen among PV buses and also voltage value and phase angle of slack buses must be set 1 and 0, respectively. In this study, analysis of power flow was performed on six bus bar system using Gauss Seidel method. The study analyzed the change of effects that come from choosing different slack buses to determine the critical stress points, voltage stability, in the multi-bus systems. At the beginning of the each power flow analysis, different busses which have generator were determined as the slack bus and the analysis was performed without increasing the load. The effects of changes in critical points were examined at the end of the all possible analysis. © 2017 IEEE.

The design criteria of PV cell, DC/DC converter and grid connected DC/AC inverter which belong to the photovoltaic system are determined and Simulink models are established in this study. The control of DC/DC converter and DC/AC inverter is ensured by using fuzzy logic and PI control. The results of fuzzy logic and PI control were compared. It was seen in the result of simulation that fuzzy logic control is more efficient than PI control, output current of inverter has sinusoidal waveform and it follows the waveform of line voltage with same phase angle. © 2016 IEEE.