A review of community-based solar home system projects in the Philippines

Solar Home Systems (SHS) are easy to deploy in island and in remote communities where grid connection is costly. However, issues related to maintenance of these systems emerge after they are deployed because of the remoteness and inaccessibility of the communities. This study looked into community-based programs in the Philippines and investigated the following: (1) social preparation, (2) role of the community in the project, and (3) sustainability of the program. In this paper, three communities under two government programs offering SHS are presented. These programs are the Solar Power Technology Support (SPOTS) program of the Department of Agrarian Reform (DAR) and the Household Electrification Program (HEP) of the Department of Energy (DOE). A focused group discussion and key informant interviews were conducted in two communities in Bukidnon province and in a community in Kalinga to obtain information from the project beneficiaries and SHS users on the preparation, implementation and maintenance of the projects. The results revealed that emphasis on the economic value of the technology, proper training of the locals on the technical and management aspects of the project, as well as the establishment of a supply chain for replacement parts are crucial factors for the sustainability of the programs

Isolating Defects in Light Beam Induced Current Maps of Solar Cells

The advances in solar technologies has lead higher device conversion efficiency and lower production costs. Ensuring the quality of the solar cells, however, remains a challenge and automation of defect identification in solar cells can potentially make the process efficient. Light Beam Induced Current (LBIC) mapping is a high-resolution imaging technique that allows researchers to see defects in solar cell. LBIC maps show the spatial distribution of the short-circuit current of a solar cell. In this study, surface and sub-surface defects in an LBIC image were identified using watershed segmentation, image dilation and inpainting. The image processing model on the image presented resulted in an Intersection over Union score of 41.33%. The model was able to highlight features on the LBIC map that are potential defects and further investigation can be made on these areas to understand the underlying cause of these defects

A Non-Intrusive Water Consumption Monitoring System

Water is an essential resource for humans as it is used in many activities for both leisure and hygiene. However; the technology available in monitoring water consumption is limited to the traditional flowmeter. Households and small buildings rely only on the end-of-month billing by the water distributor. This study presents a water monitoring system that uses a pressure sensor which is a non-intrusive method of determining water activity. Aside from calculating the volume of water consumed; the system implements fixture recognition using machine learning as its main feature. This provides more information to users allowing them to identify appliances or fixtures that consume a lot of water. Multiple test sites were used with varying pipe networks from building restrooms to houses to see its viability. Results show that the fixture recognition; using preprocessing techniques; improved in performance with accuracy 88 %; precision of 91 %; recall at 88 %; leading to an f1-score of 87 %

Implementation of Home Automation System Using OpenHAB Framework for Heterogeneous IoT Devices

Recent years has seen the growth in home automation industry as it becomes more accessible due to the increasing dependence on smartphones and smart devices. Despite this, problems in interoperability still exists due to the absence of standardized protocol in the application layer which prompts the user to be dependent on multiple applications to access and monitor several smart appliances. In this study, a home automation system was implemented using OpenHAB framework that is focused on the integration of different smart devices and back-end technologies. Since most smart devices have different communication protocols, the focus of this work was on the development a smart home solution framework that is modular and flexible. In addition, this smart home framework utilized RESTful protocol to integrate smart devices such as smart bulb, smart plug, smart TV from various manufacturers and utilized MQTT protocol to integrate sensors based on ESP8266 that monitors the ambient condition of the room. Lastly, the study provides a comparative analysis on the developed smart home framework and the commercially available smart home solution by Samsung known as SmartThings. The study considered the comparison of these two frameworks in categories such as user-friendliness, security, and their degree of compatibility with various smart devices

Hotspots Detection in Photovoltaic Modules Using Infrared Thermography

An increased interest on generating power from renewable sources has led to an increase in solar photovoltaic (PV) system installations worldwide. Power generation of such systems is affected by factors that can be identified early on through efficient monitoring techniques. This study developed a non-invasive technique that can detect localized heating and quantify the area of the hotspots, a potential cause of degradation in photovoltaic systems. This is done by the use of infrared thermography, a well-accepted non-destructive evaluation technique that allows contactless, real-time inspection. In this approach, thermal images or thermograms of an operating PV module were taken using an infrared camera. These thermograms were analyzed by a Hotspot Detection algorithm implemented in MATLAB. Prior to image processing, images were converted to CIE L*a*b color space making k-means clustering implementation computationally efficient. K-means clustering is an iterative technique that segments data into k clusters which was used to isolate hotspots. The devised algorithm detected hotspots in the modules being observed. In addition, average temperature and relative area is provided to quantify the hotspot. Various features and conditions leading to hotspots such as crack, junction box and shading were investigated in this study

THERMAL IMAGING TO DETECT HOTSPOTS IN SOLAR PV MODULES

Defects in solar cells affect the overall output of a photovoltaic (PV) module. When there is a defect in a PV module, that specific defect becomes a load so current concentrates to that part. This creates a hotspot which may lead to severe power reduction. Identification of hotspots is one step to creating countermeasures to improve theefficiency of solar PV arrays. To check for hotspots in the photovoltaic modules, infrared (IR) imaging was utilized as an optical tool. IR is contactless and thus (ahnost) reactionless. Also, it spatially separates radiation source and detector, which means that even very hot or otherwise difficult-to-access objects can be measured. With this, infrared thermography can aid in monitoring the performance of a photovoltaic module years after it has been installed. Likewise, it can provide a better assessment if the solar PV modules are still efficient or should be replaced with new ones. This work used and analyzed the captured IR images of solar PV modules to quantify areas with higher intensitiesand the images were dissected into groups according to its heating intensities through k-means clustering. This allows the classification of hotspots and identification of problem areas in a module.The collected IR images also showed potential causes ofhotspots in solar PV modules

Hotspots Detection in Photovoltaic Modules Using Infrared Thermography

An increased interest on generating power from renewable sources has led to an increase in solar photovoltaic (PV) system installations worldwide. Power generation of such systems is affected by factors that can be identified early on through efficient monitoring techniques. This study developed a non-invasive technique that can detect localized heating and quantify the area of the hotspots, a potential cause of degradation in photovoltaic systems. This is done by the use of infrared thermography, a well-accepted non-destructive evaluation technique that allows contactless, real-time inspection. In this approach, thermal images or thermograms of an operating PV module were taken using an infrared camera. These thermograms were analyzed by a Hotspot Detection algorithm implemented in MATLAB. Prior to image processing, images were converted to CIE L*a*b color space making k-means clustering implementation computationally efficient. K-means clustering is an iterative technique that segments data into k clusters which was used to isolate hotspots. The devised algorithm detected hotspots in the modules being observed. In addition, average temperature and relative area is provided to quantify the hotspot. Various features and conditions leading to hotspots such as crack, junction box and shading were investigated in this study

Effect of Collaborative Learning in Interactive Lecture Demonstrations (ILD) on Student Conceptual Understanding of Motion Graphs

To assess effectively the influence of peer discussion in understandingconcepts, and to evaluate if the conceptual understanding through Interactive Lecture Demonstrations (ILD) and collaborative learning can be translated to actual situations, ten (10) questions on human and carts in motion were presented to 151 university students comprising mostly of science majors but of different year levels. Individual and group predictions were conducted to assess the students’ pre-conceptual understanding of motion graphs. During the ILD, real-time motion graphs were obtained and analysed after each demonstration and an assessment that integrates the ten situations into two scenarios was given to evaluate the conceptual understanding of the students. Collaborative learning produced a positive effect on the prediction scores of the students and the ILD with real-time measurement allowed the students to validate their prediction. However, when the given situations were incorporated to create a scenario, it posted a challenge to the students. The results of this activity identified the area where additional instruction and emphasis is necessary

Identification of Solar PV Array Partial Shading Patterns using Machine Learning

Mismatch losses due to partial shading can limit the energy generation of solar photovoltaic (PV) systems. Isolating the shaded PV modules through electrical reconfiguration can potentially improve the power output of the PV array. To do this, the shaded modules need to be identified before the PV array can be reconfigured to produce the optimum output power. In this study, an algorithm was developed to identify the partial shading pattern of a PV array using machine learning. Measurements from the current sensor integrated into the switching circuit of each module and the solar irradiance from a pyranometer were utilized as input to the machine learning algorithm. The algorithm was trained using the voltage and current readings of an off grid PV system composed of nine 10-W PV modules arranged in a 3 × 3 array in series-parallel configuration. Three machine learning techniques were used, namely SVC, Random Forest, and K-Nearest Neighbors, resulting in 80 %, 86 %, and 66 %, respectively, in terms of accuracy, precision, recall, and f-1 score. Thus, the Random Forest algorithm was found suitable for this type of problem as it can reliably distinguish the shading patterns on the array

A Non-Intrusive Appliance Recognition System

Depleting energy resources and the unstable supply of raw materials call for innovations in the energy industry, such as in energy generation, distribution, and management. Moreover, increasing electricity prices take a toll on consumers that operates within a strict budget. This study in particular focuses on the proper management and utilization of energy from the consumer\u27s perspective. The objective is to develop a non-intrusive appliance recognition system that can identify the appliances that are being used and calculate how much each of these appliances contribute to the total electricity consumption. Installation of the monitoring system, which utilizes a single sensor clamped to the main power line and used to measure the total energy consumption, does not alter the electrical system, thus, non-intrusive. With this system, the homeowner can monitor which appliances are in use and how much energy they consume. Also, this translates to savings for the household when data provided by the system lead to smarter energy-management choices. For this study to be deployable in households, a data-acquisition system to streamline the data-gathering procedure was needed. Also, a machine learning algorithm was trained and implemented to perform the appliance recognition task given input features from the frequency domain of the measured aggregate data from the main power line. Lastly, the system was tested for prediction accuracy and characterized; and then necessary optimizations were implemented