21 research outputs found
Module temperature modelling for free-standing photovoltaic system in equatorial climate / Hedzlin Zainuddin
Get PDFThe final generation of energy is always one of the important issues in any assessment protocol in determining the performance of a photovoltaic (PV) system. This energy generation depends on several key factors, which may be linked and formulated in a mathematical model that addresses the operating PV module temperature (MT). Whilst much effort have been put into modelling these relationships, very few studies have been conducted in this aspect under equatorial rainforest and fully humid climate region, vis-a-vis Malaysia. Moreover, none of these models developed in Malaysia are for free-standing (FS) systems. This has large impact on solar farms, as an accurate model is critical, in view of the proliferation of such farms in Malaysia. This study presents the development of MT models for FS PV system in Malaysia via simple linear regression (SLR), multiple linear regression (MLR) and multi layer feedforward neural network (MLFFNN) techniques. These techniques address two specific issues; quantification of relative contribution of predictors to MT model such as: solar irradiance (SI), ambient temperature (AT), relative humidity (RH) and wind speed (WS); and the modelling performance of the simple linear, multiple linear and MLFFNN models. The modelling performance was analysed using root mean square error (RMSE), coefficient of determination (R2) and mean absolute percentage error (MAPE). This study was done in four segments which are: field testing; mathematical modelling; statistical analysis; and artificial neural network (ANN). The field testing was conducted at a grid connected PV system in Shah Alam, Malaysia. The mathematical and statistical segments are done to establish simple linear and multiple linear models. These models are developed using SLR and MLR techniques. Finally, the ANN segment is done to establish a nonlinear model via MLFFNN technique. The modelling performance of the models developed is then compared with other published models. In this work, a new and novel data filtration technique was developed and a new threshold value of SI was established. The technique is called thermal equilibrium point (TEP) and the threshold value is 40 Wm’2. With respect to the quantification of the relative contribution of the predictors towards MT, it was found that SI = 53.8 %, AT = 37.2 %, RH = 4.9 % and WS = 4.1 % respectively. In addition, the MLFFNN model perform better than the multiple linear model by 12.0 % and 5.3 % in terms of RMSE and R2 respectively; but the multiple linear model perform better than MLFFNN model by 1.0 % in terms of MAPE. This shows that MLFFNN MT model is the best model in terms of modelling performance. In conclusion, this study has succeeded in quantifying the relative contribution of solar irradiance (SI), ambient temperature (AT), relative humidity (RH) and wind speed (WS) towards MT; establishing a new data filtration technique; identifying a new threshold value; and developing an accurate MT model for FS PV system in Malaysia
Ambient factors influencing Photovoltaic module temperature in Klang valley
No full textThe study is conducted to evaluate the significance of solar irradiance, ambient temperature and relative humidity as predictors and to quantify the relative contribution of these ambient parameters as predictors for photovoltaic module temperature model. The module temperature model was developed from experimental data of mono-crystalline and poly-crystalline PV modules retrofitted on metal roof in Klang Valley. The model was developed and analyzed using Multiple Linear Regressions (MLR) and Principle Component Analysis (PCA) Techniques. Solar irradiance, ambient temperature and relative humidity have been proven to be the significant predictors for module temperature. For poly-crystalline PV module, the relative contribution of solar irradiance, ambient temperature and relative humidity are 64.28 %, 17.45 % and 12.64 % respectively. For mono-crystalline PV module, the relative contribution of solar irradiance, ambient temperature and relative humidity are 66.12 %, 17.46 % and 12.48 % respectively. Thus, there is no significant difference in terms of relative contribution of these ambient parameters towards photovoltaic module temperature between poly-crystalline and mono-crystalline PV module technologies
Ambient factors influencing photovoltaic module temperature in Klang Valley / Hedzlin Zainuddin ... [et al.].
Get PDFThe study is conducted to evaluate the significance of solar irradiance, ambient temperature and relative humidity as predictors and to quantify the relative contribution of these ambient parameters as predictors for photovoltaic module temperature model. The module temperature model was developed from experimental data of mono-crystalline and poly-crystalline PV modules retrofitted on metal roof in Klang Valley. The model was developed and analyzed using Multiple Linear Regressions (MLR) and Principle Component Analysis (PCA) Techniques. Solar irradiance, ambient temperature and relative humidity have been proven to be the significant predictors for module temperature. For poly-crystalline PV module, the relative contribution of solar irradiance, ambient temperature and relative humidity are 64.28 %, 17.45 % and 12.64 % respectively. For mono-crystalline PV module, the relative contribution of solar irradiance, ambient temperature and relative humidity are 66.12 %, 17.46 % and 12.48 % respectively. Thus, there is no significant difference in terms of relative contribution of these ambient parameters towards photovoltaic module temperature between poly-crystalline and mono-crystalline PV module technologies
Solarpv-revb: sustainable energy storage technology for rural electrification using second-life batteries
No full textSolarPV-REVB (Retired Electric Vehicle Battery) is an innovative energy storage technology that utilizes retired electric vehicle batteries (REVBs) to power stand-alone photovoltaic (SAPV) systems. This approach addresses three significant issues, which are SAPV system reliability, cost of energy storage and environmental sustainability. SolarPV-REVB assures optimal system performance by integrating second-life battery in SAPV system design which balances system reliability and cost-effectiveness. The aim of the project is to repurpose electric vehicle batteries as energy storage and to enhance the SAPV system’s reliability and cost. This approach positively impacts both the socioeconomic and environmental landscapes by minimizing battery waste, lowering energy costs, and boosting off-grid communities' access to sustainable electricity. SolarPV-REVB encourages the circular economy, renewable energy usage, and technology innovation. Its reliability, cost competitiveness, and environmental benefits offer major commercialization opportunities, making it an appealing solution for remote energy applications. This system solves global energy storage concerns by developing a sustainable approach and significantly provides solutions for renewable energy system
Fault detection using acceptance ratio analysis on polycrystalline grid-connected photovoltaics syste
Get PDFAround the world, electricity generation from PV photovoltaic systems is increasing, achieving 10-20% PV system efficiency. However, PV systems degrade due to the technology and the operating conditions and become worse in tropical climate countries. Hence, degradation is one of the key performance indicators for the reliability assessment of a PV system. This paper presents the acceptance ratio (AR) analysis grid-connected photovoltaic (GCPV) located on the campus of the Universiti Teknologi MARA, Shah Alam, Malaysia as the key performance indicators. A comparative analysis of the actual and predicted AC Power and AR of the polycrystalline GCPV system is carried out over monitoring of a one-year period. MATLAB software is chosen to simulate the output power using actual data. Malaysian Standard MS2692:2020 has noted that the AR value must ≥ 0.9 to classify as accepted in testing and commissioning tests and AR < 0.9 has been indicated as a non-accepted GCPV system. The results of acceptance ratio (AR), yield (Y), specific yield (SY), and performance ratio (PR) show that almost half of the AR’s data results show below 0.9 with the performance ratio of PV systems was less than 75%, indicating that the systems needed to be completely replaced
Cuckoo search for determining Artificial Neural Network training parameters in modeling operating photovoltaic module temperature
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Modeling of Operating Photovoltaic Module Temperature Using Hybrid Cuckoo and Artificial Neural Network
No full textMounting configuration factor for building integrated photovoltaic and retrofitted grid-connected photovoltaic system / Noor Farhana Yusoff ... [et al.].
Get PDFThis paper presents the findings of a study to determine the effects of immediate built environment (mounting configuration) on output generated by photovoltaic (PV) module for building integrated photovoltaic (BIPV) and retrofitted PV systems under Malaysian climate. All the systems under study are grid-connected PV (GCPV) system. Eight GCPV systems used in this study; four for BIPV systems and the other four for retrofitted systems. Data for PV module temperature, ambient temperature, plane-of-array solar irradiance and AC power were logged at 5-min interval for all systems. The operating temperature were analysed as the temperature differential with respect to the ambient temperature. The mounting factor was established for both mounting configuration type
A hybrid artificial neural network for grid-connected photovoltaic system output prediction
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