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https://ptsldigital.ukm.my/jspui/handle/123456789/486783Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Azah Mohmed, Prof. Dr. | - |
| dc.contributor.author | Tamer T.N Khatib | - |
| dc.date.accessioned | 2023-10-11T02:25:32Z | - |
| dc.date.available | 2023-10-11T02:25:32Z | - |
| dc.date.issued | 2013-05-13 | - |
| dc.identifier.other | ukmvital:75169 | - |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/486783 | - |
| dc.description | Photovoltaic (PV) system installation has played an important role worldwide but however, the drawback of PV system is the high capital cost compared with conventional energy sources. To reduce the capital cost, optimization of PV system based on the Malaysian weather profile is conducted. The research methodology is implemented in two phases; prediction of meteorological variables and optimization of PV system. In the first phase, a novel approach is proposed for meteorological variable prediction using the generalized regression neural network (GRNN). The developed GRNN model predicts four meteorological variables, namely, solar energy, ambient temperature, wind speed and relative humidity by using sun shine ratio, day number and location coordinates as the input variables. In the second phase, novel numerical algorithms are proposed for optimizing three types of PV systems, namely, standalone PV systems, hybrid PV/wind systems and hybrid PV/diesel systems. The proposed algorithms aim to optimize the PV array tilt angle, the size of PV array, wind turbine, diesel generator and battery as well as the inverter size. The optimization problem is to minimize the capital and running cost of a PV system subject to the highest achievable reliability. The proposed optimization algorithms use hourly meteorological data, energy models for PV systems, load profile and the concept of loss of load probability to generate an optimum design space for the desired PV systems. To generalize the optimization results, sizing factors which are the ratio of the capacity of the energy source to the daily load demand are defined as CA, CB, CW and CDG for the PV array, battery, wind turbine and diesel generator, respectively. A sizing factor is also defined for the inverter (RS) which is the ratio of PV rated power to inverter's rated power. The developed GRNN and optimization algorithms have been tested and the results showed that the GRNN can accurately predict solar energy, ambient temperature, relative humidity and wind speed with mean absolute percentage errors of 1.3%, 1.3%, 3.2% and 28.9%, respectively. The results also showed that by applying the monthly optimum tilt angle for Malaysia, the collected yield can be increased by 6%. As for the PV system size optimization results, the recommended sizing ratios, CA and CB for a standalone PV system are 2.02 and 0.793 respectively. Meanwhile, the recommended sizing ratios, CA, CDG and CB for a PV/diesel system are 1.12, 0.3, 0.29 respectively. As for a hybrid PV/wind system located in Kuala Terengganu, the recommended sizing ratios, CA, CW and CB are 1.14, 0.72 and 9.55, respectively. In addition, the results of the optimum sizing ratio for inverter, RS for Malaysia is 1.31. A software tool called as PV.MY is also developed with capabilities of predicting solar energy, ambient temperature and wind speed as well as optimizing the PV module tilt angle and the size of PV array, battery, wind turbine, diesel generator and inverter.,PhD | - |
| dc.language.iso | eng | - |
| dc.publisher | UKM, Bangi | - |
| dc.relation | Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina | - |
| dc.rights | UKM | - |
| dc.subject | Solar Energy Prediction Using Numerical Algorithms | - |
| dc.subject | Photovoltaic System Optimization Techniques Using Numerical Algorithms | - |
| dc.subject | Solar Energy Prediction Artificial Neural Networks | - |
| dc.subject | Photovoltaic System Optimization Techniques Artificial Neural Networks | - |
| dc.subject | Photovoltaic power systems | - |
| dc.title | Solar Energy Prediction And Photovoltaic System Optimization Techniques Using Numerical Algorithms And Artificial Neural Networks | - |
| dc.type | Theses | - |
| dc.format.pages | 189 | - |
| dc.identifier.callno | TK1087 .K487 2013 3 | - |
| dc.identifier.barcode | 000526 | - |
| Appears in Collections: | Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_75169+SOURCE1+SOURCE1.0.PDF Restricted Access | 11.03 MB | Adobe PDF |  View/Open |
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