SIZING OPTIMIZATION OF STANDALONE PHOTOVOLTAIC SYSTEM FOR RESIDENTIAL LIGHTING

This study presents a sizing methodology to optimize photovoltaic array and battery storage in a standalone photovoltaic (PV) system with lighting load. Sizing using the deterministic method was carried out for initial design. Stochastic method by mean of Loss of Power Supply Probability (LPSP) calculation was developed using daily weather data input. The weather data consists of ambient temperature, relative humidity and solar radiation. An estimation method of hourly solar radiation data is also developed in this study to complete any missing solar radiation data in the dataset. Then, complete time-series dataset can be used for LPSP calculation and system simulation. Size optimization was carried out using a graphical method. System size was optimized based on desired system performance (indicated by LPSP value) and minimum cost that consist of Capital Cost and Life-cycle cost. In addition, Excess energy value is used as over-design indicator. A comparison of sizing optimization methods was carried out, and it was found that the design space approach giving flexibility in the selection of PV panel and battery capacity compared to other methods. The sizing result of this method is a configuration of 500Wp PV array and 400Ah battery. Economic analyses, including cost of the energy and payback period for selected configuration are studied. A simulation model was developed using TRNSYS 16 to test the sizing results. Experimental data was used to validate the simulation results. Simulation validation shows that result accuracy has improved. The results indicated that Root Mean Square Error (RMSE) between simulated and measured battery voltage has decreased by 50% compared to previous work. The validated simulation model then was used to test and analyse the selected PV system configuration

SIZING OPTIMIZATION OF STANDALONE PHOTOVOLTAIC SYSTEM FOR RESIDENTIAL LIGHTING

This study presents a sizing methodology to optimize photovoltaic array and battery storage in a standalone photovoltaic (PV) system with lighting load. Sizing using the deterministic method was carried out for initial design. Stochastic method by mean of Loss of Power Supply Probability (LPSP) calculation was developed using daily weather data input. The weather data consists of ambient temperature, relative humidity and solar radiation. An estimation method of hourly solar radiation data is also developed in this study to complete any missing solar radiation data in the dataset. Then, complete time-series dataset can be used for LPSP calculation and system simulation. Size optimization was carried out using a graphical method. System size was optimized based on desired system performance (indicated by LPSP value) and minimum cost that consist of Capital Cost and Life-cycle cost. In addition, Excess energy value is used as over-design indicator. A comparison of sizing optimization methods was carried out, and it was found that the design space approach giving flexibility in the selection of PV panel and battery capacity compared to other methods. The sizing result of this method is a configuration of 500Wp PV array and 400Ah battery. Economic analyses, including cost of the energy and payback period for selected configuration are studied. A simulation model was developed using TRNSYS 16 to test the sizing results. Experimental data was used to validate the simulation results. Simulation validation shows that result accuracy has improved. The results indicated that Root Mean Square Error (RMSE) between simulated and measured battery voltage has decreased by 50% compared to previous work. The validated simulation model then was used to test and analyse the selected PV system configuration

Rancang Bangun Fermentor Yogurt dengan Sistem Kontrol Logika Fuzzy Menggunakan Mikrokontroler ATMega32

Yogurt is milk fermented product that becomes popular recently. In yogurt processing, fermenter is the main device. Lactobacillus sp. and Streptococcus sp. are two probiotic bacteria species that are common to be used in yogurt fermentation process. Both bacteria grow well in a specific range of temperature between 40-45 C, so temperature control in fermenter operational becomes one of the important things to ensure speed and quality of fermentation process. Fermentation process is a process with high degree of uncertainty and categorized as non-linear time invariant system. Thus, classical control system method is difficult to be implemented. To overcome this issue, intelligent control system can be implemented to yogurt's fermenter temperature control. One of intelligent control system method that can be implemented is fuzzy logic-based control system. In this study, fuzzy control system has been designed and implemented for fermenter temperature control. Control system algorithm is integrated in ATMega16 (for On-Off logic control) and ATMega32 (for Fuzzy Logic control) microcontrollers. Experimental results of fermenter control system shows that temperature profile of fermenter with fuzzy logic control system is more stable by settling time around an hour and 15 minutes and error average of -0.36 oC. Fermentation process for 16 hours with fuzzy logic controller produce yogurt with pH value of 3.66, total number of Lactobacillus sp. is 4.85 x 10 cfu/mL and Streptococcus sp. is 1.34 x 106 cfu/mL

Rancang Bangun Fermentor Yogurt dengan Sistem Kontrol Logika Fuzzy Menggunakan Mikrokontroler ATMega32

Yogurt is milk fermented product that becomes popular recently. In yogurt processing, fermenter is the main device. Lactobacillus sp. and Streptococcus sp. are two probiotic bacteria species that are common to be used in yogurt fermentation process. Both bacteria grow well in a specific range of temperature between 40-45 C, so temperature control in fermenter operational becomes one of the important things to ensure speed and quality of fermentation process. Fermentation process is a process with high degree of uncertainty and categorized as non-linear time invariant system. Thus, classical control system method is difficult to be implemented. To overcome this issue, intelligent control system can be implemented to yogurt’s fermenter temperature control. One of intelligent control system method that can be implemented is fuzzy logic-based control system. In this study, fuzzy control system has been designed and implemented for fermenter temperature control. Control system algorithm is integrated in ATMega16 (for On-Off logic control) and ATMega32 (for Fuzzy Logic control) microcontrollers. Experimental results of fermenter control system shows that temperature profile of fermenter with fuzzy logic control system is more stable by settling time around an hour and 15 minutes and error average of -0.36 oC. Fermentation process for 16 hours with fuzzy logic controller produce yogurt with pH value of 3.66, total number of Lactobacillus sp. is 4.85 x 10 cfu/mL and Streptococcus sp. is 1.34 x 106 cfu/mL.ABSTRAKYogurt merupakan produk olahan susu terfermentasi yang akhir-akhir ini mulai banyak disukai oleh masyarakat. Pada pengolahan susu menjadi yogurt, fermentor digunakan sebagai alat utama. Lactobacillus sp. dan Streptococcus sp. merupakan dua spesies bakteri yang biasa digunakan dalam proses fermentasi yogurt. Kedua jenis bakteri ini tumbuh dengan baik pada suhu yang spesifik yaitu antara 40–45 C, sehingga pengendalian suhu pada operasi fermentor merupakan hal yang penting agar proses fermentasi dapat berjalan secara cepat dan baik. Proses fermentasi merupakan proses yang memiliki tingkat ketidakpastian yang tinggi dan merupakan sistem non-linear time variant, sehingga desain sistem kontrol klasik akan sulit untuk diterapkan. Untuk mengatasi hal ini sistem kontrol cerdas dapat untuk diimplementasikan pada pengendalian suhu fermentor yogurt. Salah satu dari metode sistem kontrol cerdas yang dapat digunakan adalah sistem kontrol dengan logika fuzzy. Pada penelitian ini telah dilakukan rancang bangun sistem pengendalian suhu berbasis algoritma fuzzy pada fermentor yogurt. Algoritma sistem kendali diintegrasikan dalam mikrokontroler ATMega16 (untuk logika ON-OFF) dan ATMega32 (untuk logika fuzzy). Hasil uji sistem pengendalian suhu fermentor menunjukkan bahwa dengan menggunakan algoritma fuzzy sistem pengendalian lebih stabil dengan settling time selama 1 jam 20 menit dan rata-rata error sebesar -0,36 oC. Proses fermentasi selama 16 jam menggunakan fermentor dengan kontroler fuzzy menghasilkan yogurt dengan pH sebesar 3,66, jumlah mikroba Lactobacillus sp. sebanyak 4,85 x 108cfu/mL, dan Streptococcus sp. sebanyak 1,34 x 10 6 cfu/mL

Classification of water stress in cultured Sunagoke moss using deep learning

Water stress greatly determines plant yield as it affects plant metabolism, photosynthesis rate, chlorophyll content index, number of leaves, physiological, biochemical compound, and vegetative growth. The research aimed to detect and classify water stress of cultured Sunagoke moss into several categories i.e. dry, semi-dry, wet, and soak by using a low-cost commercial visible light camera combined with a deep learning model. Cultured Sunagoke moss is a commercial product which has the potential use as rooftop-greening and wall-greening material. This research compared the performance of four convolutional neural network models, such as SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The best convolutional neural network model according to the training and validation result was ResNet50 with RMSProp optimizer, 30 epoch, and 128 mini-batch size; this also gained an accuracy rate at 87.50%. However, the best result of the convolutional neural network model on data testing using confusion matrices on different data sample was ResNet50 with Adam optimizer, 30 epoch, 128 mini-batch size, and average testing accuracy of 94.15%. It can be concluded that based on the overall results, convolutional neural network model seems promising as a smart irrigation system that real-time, non-destructive, rapid, and precise method when controlling water stress of plants

Prediction of Physico-Chemical Characteristics in Batu Tangerine 55 Based on Reflectance-Fluorescence Computer Vision

Oranges (Citrus sp.) are one of the most abundant agricultural commodities in Indonesia. One of the popular local citruses is Batu Tangerine 55. Harvesting tangerines begins 252 days after the flowers bloom. Conventionally, we still determine the level of maturity by observing the color, shape, and hardness. The results of manual grouping tend to be subjective and less accurate. Destructive testing could be carried out and provide objective results; however, it would require sampling and damaging the fruits. Computer vision could be used to evaluate the maturity level of the fruit non-destructively. Dual imaging computer vision, i.e., reflectance-fluorescence mode, could be used to enhance the accuracy of the prediction. This study aims to develop a classification model and predict the physico-chemical characteristics of Batu Tangerine 55. Destructive testing is still being carried out to determine the value of TPT, the degree of acidity, and the firmness of the fruit. Non-destructive testing was carried out to obtain reflectance and fluorescence images. Once we obtain the destructive and non-destructive data, we will incorporate them into the classification and prediction models. The machine learning method for maturity classification uses three models, namely KNN, SVM, and Random Forest. The best results on the reflectance data (RGB) SVM model resulted in an accuracy of 1 for training data and 0.97 for testing data. The maturity parameter prediction method uses the PLS method. The best results for the predicted Brix/Acidity ratio R2 parameter are 0.81 and RMSE 3.4

POLA SUSTAINABLE LIVELIHOOD PADA KEGIATAN PENDAMPINGAN MASYARAKAT: KEGIATAN PENGABDIAN MASYARAKAT DI KABUPATEN SITUBONDO

POLA SUSTAINABLE LIVELIHOOD PADA KEGIATAN PENDAMPINGAN MASYARAKAT: KEGIATAN PENGABDIAN MASYARAKAT DI KABUPATEN SITUBOND

Experimental and Simulation Study of Small Scale PV Powered Photobioreactor for Nannochloropsis oculata Cultivation

Microalgae has become promising third generation source of biodiesel material recently. One of the methods that is used for microalgae cultivation is by using photobioreactor. The advantages in using photobioreactor to cultivate microalgae are the easiness to control optimum growth parameters and to protect culture from external contamination. However, to run a photobioreactor, considerable amount of energy is required in the form of electricity. To achieve zero emission production, renewable energy system such as PV system can be used to run the photobioreactor. In this research a PV system has been used to supply energy of a small scale photobioreactor for Nannochloropsis oculata cultivation. The experimental setup were operated for one cycle cultivation that takes about seven days long. 500 Wp PV panel and 400 Ah battery capacity of 12V PV system were used. It is shown that the system can supply the required energy during the cultivation time. Yet, the SoC of battery shown decreasing level during operation. It is revealed that the system that is used for experiment need to be redesign and simulation using TRNSYS is used to test the recommended system setup

External Defects and Soil Deposits Identification on Potato Tubers using 2CCD Camera and Principal Component Images

Precise recognition of potato external defects and the ability to identify defects and non-defect areas are in demand. Common scab represents a significant issue that requires detection, yet identifying the extent of common scab infection remains challenging when using a standard RGB camera. In this research, a 2CCD camera system that could obtain a set of RGB and near-infrared images, which could enhance defect detection, has been used. Image segmentation strategies based on a single principal component image and the principal component pseudo-colored image have been proposed to identify external potato defects while excluding soil deposits on the potato surface, often recognized as defects by the normal color machine vision system. Performance metrics calculation results show relatively good results, with segmentation true accuracy around 64% for both methods. Principal component pseudo-colored images were able to discriminate defects area and soil deposits in a single image. The methods presented in this paper could be used as the basis to develop further classification and grading algorithms