Showing papers by "Violeta Holmes published in 2017"

Simultaneous fault detection algorithm for grid-connected photovoltaic plants

Abstract: In this work, the authors present a new algorithm for detecting faults in grid-connected photovoltaic (GCPV) plant. There are few instances of statistical tools being deployed in the analysis of photovoltaic (PV) measured data. The main focus of this study is, therefore, to outline a PV fault detection algorithm that can diagnose faults on the DC side of the examined GCPV system based on the t-test statistical analysis method. For a given set of operational conditions, solar irradiance and module temperature, a number of attributes such as voltage and power ratio of the PV strings are measured using virtual instrumentation (VI) LabVIEW software. The results obtained indicate that the fault detection algorithm can detect accurately different types of faults such as, faulty PV module, faulty PV String, faulty Bypass diode and faulty maximum power point tracking unit. The proposed PV fault detection algorithm has been validated using 1.98 kWp PV plant installed at the University of Huddersfield, UK.

Effect of micro cracks on photovoltaic output power: case study based on real time long term data measurements

Abstract: This study analyses the impact of micro cracks on photovoltaic (PV) module output power performance and energy production. Electroluminescence imaging technique was used to detect micro cracks affecting PV modules. The experiment was carried out on ten different PV modules installed at the University of Huddersfield, United Kingdom. The examined PV modules which contain micro cracks shows large loss in the output power comparing with the theoretical output power predictions, where the maximum power loss is equal to 80.73%. LabVIEW software was used to simulate the theoretical output power of the examined PV modules under real time long term data measurements.

Multi-layer photovoltaic fault detection algorithm

Abstract: This study proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic (GCPV) system For a given set of working conditions, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation LabVIEW software Furthermore, a third-order polynomial function is used to generate two detection limits (high and low limits) for the VR and PR ratios The high and low detection limits are compared with real-time long-term data measurements from a 11 kWp GCPV system installed at the University of Huddersfield, United Kingdom Furthermore, samples that lie out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs (VR and PR) and one output membership function The obtained results show that the fault detection algorithm accurately detects different faults occurring in the PV system The maximum detection accuracy (DA) of the proposed algorithm before considering the fuzzy logic system is equal to 9527%; however, the fault DA is increased up to a minimum value of 988% after considering the fuzzy logic system

Detecting Defective Bypass Diodes in Photovoltaic Modules using Mamdani Fuzzy Logic System

Abstract: In this paper, the development of fault detection method for PV modules defective bypass diodes is presented. Bypass diodes are nowadays used in PV modules in order to enhance the output power production during partial shading conditions. However, there is lack of scientific research which demonstrates the detection of defective bypass diodes in PV systems. Thus, this paper propose a PV bypass diode fault detection classification based on Mamdani fuzzy logic system, which depends on the analysis of Vdrop, Voc , and Isc obtained from the I-V curve of the examined PV module. The fuzzy logic system depends on three inputs, namely percentage of voltage drop (PVD), percentage of open circuit voltage (POCV), and the percentage of short circuit current (PSCC). The proposed fuzzy system can detect up to 13 different faults associated with defective and non-defective bypass diodes. In addition, the proposed system was evaluated using two different PV modules under various defective bypass conditions. Finally, in order to investigate the variations of the PV module temperature during defective bypass diodes and partial shading conditions, i5 FLIR thermal camera was used.

An Asus_xtion_probased indoor MAPPING using a Raspberry Pi with Turtlebot robot Turtlebot robot

Abstract: The developers of path planning algorithms and localization have significantly improved the usability of the robot those days. By using software such as a Gmapping, the robot will be able to create a map of the surrounding area. This research utilizes the 3D sensor Asus_xtion_ pro to create an indoor map using SLAM and create 3D models for surrounding objects with a Turtlebot robot. In the first case, we used the Turtlebot to generate an indoor map of the robotic lab room using the Gmapping ROS packet. In the second case, we used the robot to create 3D models for the surrounding objects in the room. We used the Raspberry Pi 3 as a replacement of the laptop that was used to control the Turtlebot. The same implementation of the first and second tasks have been repeated to compare the performance. The Raspberry Pi accomplishes the given tasks successfully; however, there is some delay due to the different on the CPU power. Finally, the low cost proposed solution is capable of running ROS based SLAM algorithm and using the point on cloud to create 3D models. In addition, the use of Raspberry Pi allows the robot save considerable amount of power in contrast with the use of a normal laptop.

The impact of cracks on the performance of photovoltaic modules

Abstract: This paper presents a statistical approach for identifying the significant impact of cracks on the output power performance of photovoltaic (PV) modules. Since there are a few statistical analysis of data for investigating the impact of cracks in PV modules in real-time long-term data measurements. Therefore, this paper will demonstrate a statistical approach which uses two statistical techniques: T-test and F-test. Electroluminescence (EL) method is used to scan possible cracks in the examined PV modules. Moreover, virtual instrumentation (VI) LabVIEW software is used to predict the theoretical output power performance of the examined PV modules based on the analysis of I-V and P-V curves. The statistical analysis approach has been validated using 45 polycrystalline PV modules at the University of Huddersfield, UK.

Impact of video streaming quality on bandwidth in humanoid robot NAO connected to the cloud

Abstract: This paper investigates the impact of video streaming quality on bandwidth consumption during the transfer of video data from a humanoid robot `NAO' to computing devices, used to perform face recognition tasks, and to the cloud. It presents the results of profiling the network performance of connecting NAO with an edge controller, and discusses the effect of using different qualities of video streaming on the consumed up-link bandwidth. This study considers the limitation of the up-link bandwidth in the Wi-Fi network. It compares the performances of Wi-Fi and Ethernet connections between the NAO robot and a computer. In addition, it examines the accuracy of the face recognition tasks using various streaming scenarios, such as colored video and black & white video. It investigates real-time video streaming using a wide range of frame rates, and video qualities, and their impact on the bandwidth, and accuracy of face identification. The results of our investigations are used to determine the acceptable video quality, frame rate, buffering and bandwidth that would give optimal results in face recognition using NAO robot, and enable efficient data transfer to the cloud.

Measurement, simulation and optimization of wideband log-periodic antennas

Abstract: Log-periodic antenna is a special antenna type utilized with great success in many broadband applications due to its ability to achieve nearly constant gain over a wide frequency range. Such antennas are extensively used in electromagnetic compatibility measurements, spectrum monitoring and TV reception. In this study, a log-periodic dipole array is measured, simulated, and then optimized in the 470–860 MHz frequency band. Two simulations of the antenna are initially performed in time and frequency domain respectively. The comparison between these simulations is presented to ensure accurate modelling of the antenna. The practically measured net gain is in good agreement with the simulated net gain. The antenna is then optimized to concurrently improve voltage standing wave ration, net gain and front-to-back ratio. The optimization process has been implemented by using various algorithms included in CST Microwave Studio, such as Trusted Region Framework, Nelder Mead Simplex algorithm, Classic Powell and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). The Trusted Region Framework seems to have the best performance in sufficiently optimizing all predefined goals specified for the antenna.

Fault detection algorithm for multiple GCPV array configurations

Abstract: In this paper, a fault detection algorithm for multiple grid-connected photovoltaic (GCPV) array configurations is introduced. For a given set of conditions such as solar irradiance and photovoltaic module temperature, a number of attributes such as power, voltage and current are calculated using a mathematical simulation model. Virtual instrumentation (VI) LabVIEW software is used to monitor the performance of the GCPV system and to simulate the theoretical I-V and P-V curves of the examined system. The fault detection algorithm is evaluated on multiple GCPV array configurations such as series, parallel and series-parallel array configuration. The fault detection algorithm has been validated using 1.98 kWp GCPV system installed at the University of Huddersfield. The results indicates that the algorithm is capable to detect multiple faults in the examined GCPV plant and can therefore be used in large GCPV installations.

VCC: A framework for building containerized reproducible cluster software environments

Abstract: The problem of portability and reproducibility of the software used to conduct computational experiments has recently come to the fore. Container virtualisation has proved to be a powerful tool to achieve portability of a code and it's execution environment, through runtimes such as Docker, LXC, Singularity and others - without the performance cost of traditional Virtual Machines (Chamberlain, Invenshure, and Schommer 2014; Felter et al. 2014). However, scientific software often depends on a system foundation that provides middleware, libraries, and other supporting software in order for the code to execute as intended. Typically, container virtualisation addresses only the portability of the code itself, which does not make it inherently reproducible. For example, a containerized MPI application may offer binary compatibility between different systems, but for execution as intended, it must be run on an existing cluster that provides the correct interfaces for parallel MPI execution. As a greater demand to accomodate a diverse range of disciplines is placed on high performance and cluster resources, the ability to quickly create and teardown reproducible, transitory virtual environments that are tailored for an individual task or experiment will be essential. The Virtual Container Cluster (VCC) is a framework for building containers that achieve this goal, by encapsulating a parallel application along with an execution model, through a set of dependency linked services and built-in process orchestration. This promotes a high degree of portability, and offers easier reproducibility by shipping the application along with the foundation required to execute it - whether that be an MPI cluster, big data processing framework, bioinformatics pipeline, or any other execution model (Higgins, Holmes, and Venters 2017).