Erees Queen B. Macabebe

Blend composition study of poly(3,3‴-didodecylquaterthiophene)/[6,6]-phenyl C61 butyric acid methyl ester solution processed organic solar cells

Photovoltaic devices made from blends of poly(3,3‴-didodecylquaterthiophene) (PQT-12) and [6,6]-phenyl C61 butyric acid methyl ester have been fabricated and characterized. By varying the polymer loading in the blend, an optimal power conversion efficiency (PCE) of 0.70% has been achieved for a blend consisting of 15 wt % PQT-12, which is an order of magnitude higher than the PCE for a 50 wt % blend. The apparent reason for the large difference is the fact that blends with higher PQT-12 loading are transport limited, with much larger hole-to-electron mobility ratios.

Effects of shading on current, voltage and power output of total cross-tied photovoltaic array configuration

The Philippines, being a tropical country, has a high photovoltaic (PV) energy generation potential that can help meet demand due to impending power supply shortage in the coming years. One factor that limits solar PV generation is nonuniform illumination or partial shading. Partial shading causes voltage and current mismatch which affect the performance of PV arrays. Partially shaded PV systems cannot operate at maximum efficiency because of shadows cast by the surrounding structures, foliage and cloud cover. In this study, the researchers observed the effects of partial shading on the voltage, current and power output of the total cross-tied (TCT) array configuration using 16 shading patterns. Results show that the TCT configuration has the ability to balance out the effects of uneven irradiance on the PV modules. This results in higher power output. However, this effect optimizes the power output most if shaded modules complete columns. Such cases minimize current mismatch among the rows in the array.

Real-time energy monitoring system for grid-tied Photovoltaic installations

A real-time energy monitoring system for grid-tied Photovoltaic (PV) installations was developed and deployed in a household located at Barangka, Marikina. The system consists of a BeagleBone Black which serves as the device to processes the measured data from the two power analyzers and the DC sensor. It also features a Mobile and Web-based monitor and analytics platform for end user utilization, and provides a forecasting algorithm for the users projected monthly bill. One of the receiving ends is a server that stores the data and hosts the web application which aggregates, analyzes and presents the general power generation and grid consumption to the user. The other receiving end, an Android application, displays the real-time graph of the generated solar energy and the total energy consumed by the load, in this case, a residential load.

Inferring Appliance Energy Usage from Smart Meters using Fully Convolutional Encoder Decoder Networks

Energy management presents one of the principal sustainability challenges within urban centers given that they account for 75% of the energy consumption worldwide. In the context of a smart city framework, the use of intelligent urban systems provides a key opportunity in addressing the energy sustainability issue as an informatics problem where the goal is to deliver energy usage feedback to the users as a means of enabling behavioral change towards energy sustainability. In this paper we present a method to provide appliance energy usage feedback from smart meters using energy disaggregation. We put energy disaggregation in the context of a source separation and signal reconstruction problem in which we train a fully convolutional encoder decoder network to separate appliance energy usage from aggregate whole house electricity consumption data. The results show that the proposed fully convolutional encoder decoder model can achieve competitive accuracy compared with several state-of-the-art methods.

Image segmentation using K-means color quantization and density-based spatial clustering of applications with noise (DBSCAN) for hotspot detection in photovoltaic modules

The increasing demand for the use of solar energy as an alternative source of energy to generate electricity has multiplied the need for more photovoltaic (PV) arrays. With the growth of the PV manufacturing industry, automation for defect detection is seen as a great potential in ensuring the quality of these PV modules. Hotspot formation due to defects is detrimental to the performance of PV devices. Thus this research aims to detect and isolate hotspot areas in PV modules by applying two machine learning techniques, namely K-means color quantization for pre-processing, and density-based spatial clustering of applications with noise (DBSCAN) for processing, in the images captured by an infrared camera. In the preprocessing, K-means clustering algorithm produced a quantized color image represented by the contours while in the processing or clustering part, DBSCAN resulted in the segmentation of the image, isolating the hotspot. Further investigation of the PV module through visual inspection found a crack in one of the solar cell where the hotspot occurred.

Cost-effective LBIC system for solar cell characterization

Defects in solar cells such as localized shunts greatly reduce the efficiency of the device by diverting current away from the output. Laser beam induced current (LBIC) technique is a non-destructive characterization tool to identify the spatial distribution of defects by measuring the generated current of the cells. This technique determines the defects by scanning a laser beam onto the cell while measuring the generated current as a function of position. This contribution presents the development of a cost-effective LBIC system using three wavelengths: 650 nm, 532 nm, and 450nm. LBIC resolution is optimized by varying the spot size of the light source and the step size of the machine. LBIC maps generated from different laser wavelengths show variations in image quality and details.