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- Abadi, Imam, et al.
(författare)
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Design of Adaptive Neuro-Fuzzy Inference Control Based One-Axis Solar Tracker on Battery Charging System
- 2020
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Ingår i: E3S Web of Conferences. - : EDP Sciences. - 2267-1242. ; , s. 1-15
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Konferensbidrag (refereegranskat)abstract
- The photovoltaic (PV) panel can produce electrical energy that is very environmentally friendly and easy to use. The use of PV panels is suitable for supplying peak loads or at night using batteries as energy storage. However, the battery needs to manage for control, and the battery can last long. The solution to battery management problems is through research about the battery charging system. The DC-DC converter used is the Single Ended Primary Inductance Converter (SEPIC) type. Voltage Control of the battery charging using Adaptive Neuro-Fuzzy Inference System (ANFIS). In the simulation of bright conditions, ANFIS controls can track the charging point set point and obtain a voltage response with a rise time of 0.0028 s, a maximum overshoot of 0.027 %, a peak time of 0.008 s, and a settling time of 0.0193 s. When charging a solar tracker, PV battery gets a 0.25 % increase compared to a fixed PV panel. PV solar tracker can follow the direction of the sun's position. The irradiation value and maximum temperature affect the input voltage and input current that enters the converter.
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| 2. |
- Abadi, Imam, et al.
(författare)
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Performance Study of Maximum Power Point Tracking (MPPT) Based on Type-2 Fuzzy Logic Controller on Active Dual Axis Solar Tracker
- 2020
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Ingår i: E3S Web of Conferences. - : EDP Sciences. - 2267-1242. ; , s. 1-16
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Konferensbidrag (refereegranskat)abstract
- World energy consumption increases with time, so that occur an energy imbalance. Many breakthroughs have developed to utilize renewable energy. The photovoltaic system is one of the easy-to-use renewable energies. The power conversion from PV fixed is still low, so the PV system is designed using the active dual-axis solar tracker. The PV tracker position can be adjusted to change the sun position to get maximum efficiency. The active dual-axis solar tracker system is integrated with the maximum power point tracking (MPPT) algorithm to keep PV operating at a maximum power point even though input variations change. The active dual-axis solar tracker system integrated with the maximum power point tracking (MPPT) algorithm to keep PV operating at a maximum power point even though input variations change. Tracking test simulation had done by comparing the output power of a fixed PV system with the active dual-axis solar tracker. Type-2 fuzzy logic based MPPT successfully increased the average output power by 10.48 % with the highest increase of 17.48 % obtained at 15:00 West Indonesia Time (GMT+7). The difference in power from a fixed PV system with the active dual-axis solar tracker of 36.08 W is from the output power worth 206.3 to 242.4 W.
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| 3. |
- Purba, Washington, et al.
(författare)
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Potentials of Gas Emission Reduction (GHG) by the Glass Sheet Industry through Energy Conservation
- 2021
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Ingår i: 1<sup>st</sup> International Conference on Bioenergy and Environmentally Sustainable Agriculture Technology (ICoN BEAT 2019). - : EDP Sciences.
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Konferensbidrag (refereegranskat)abstract
- Sheet Glass Industry is one industry that uses 75 % natural gas energy and 25 % electricity. Using the Intergovernmental Panel on Climate Change, IPCC-2006 emission calculation method, the average greenhouses gas (GHG) emissions obtained from the calcination process obtained 112 211 t CO2 yr(-1) per plant and an average emission factor (EFkl) of 0.18 CO2 t(-1) yr(-1) of pull. With the technology of converting heat into electrical energy, residual combustion as flue gases has the potential to be used to produce electrical energy. Referring to the analysis and calculation; one of factories has potential to generate 0.8 MW to 3 MW electric energy. It's efficiency of 10 % to 40 % so that it can be calculated as a component of GHG emission reductions whose value is 4.6 t CO2 yr(-1) to 18.7 t CO2 yr(-1) per plant. With this reduction, each of the GHG emission and emission factors per plant dropped to 93 442 t CO2 yr(-1) and 0.16 CO2 t-pull(-1)
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