Improved dynamic reconfiguration strategy for power maximization of TCT interconnected PV arrays under partial shading conditions

In photovoltaic (PV) systems, partial shading is a major issue that may cause power losses, hot spots, and PV modules damage. Thus, PV array dynamic reconfiguration approaches based on irradiance equalization (IEq) between rows have been proposed to alleviate the shading effect thereby improving PV power production. However, the existing IEq-based reconfiguration techniques focus only on the minimization of row current error, without taking into consideration the voltage effect, which in turn, may result in power losses. In this regard, an improved reconfiguration strategy is proposed in the present paper to maximize the power production of a TCT interconnected PV array operating under partial shading conditions. The proposed strategy aims to achieve a PV array reconfiguration that mitigates the droop voltage issue by considering irradiance levels in both rows and columns. An in-depth investigation of a typical PV module and TCT module is provided, demonstrating that there are cases where the partial shading does not affect the PV module current but the operating voltage. In addition, an analysis highlighting the limitations of the IEq technique regarding the droop voltage issue is presented. Furthermore, mathematical development is established for deriving the objective function of the proposed strategy. The efficiency of the proposed reconfiguration strategy is assessed through experimental tests carried out on a 20 MWp PV station in Ain El-Melh, Algeria. The obtained results reveal that the proposed method overcomes the weaknesses of the existing IEq strategy and ensures power production higher than the TCT and IEq configurations by 17.25% and 19.34%, respectively

Leveraging the capture effect for indoor localization.

This Msc. Thesis builds upon a work-in progress called collocal which researched room-level indoor-localization. Localization is the action of estimating the position of a certain entity on a map. For example tracking the postion of furniture and other inventory in a building over a long period of time. To localize these entities other static nodes are employed that know their own location. These static nodes, called anchors, beacon their coordinates periodically. Mobile nodes are the entities that try to estimate their position based on these beacon messages. In conventional systems this beaconing is done asynchronously. System designers employ complex schemes to prevent anchor nodes from beaconing at the same time to avoid interference. These systems consume lots of energy at the mobile node, because they have to keep their radio on for a longer time. A deployment of such a localization system is expected to last for a long term period without replacing batteries of the used device. Counterintuitive to what asynchronous system designers do, in collocal the anchor nodes beacon at the same time on purpose. Collocal reduces the energy consumption considerably by shortening the listening interval at the mobile node. Collocal however suffers from two major drawbacks: a dead zone area (an area where you can not be localized) between anchor nodes and a very high bit-error rate. In this Msc. thesis the two problems are solved with the use of orthogonal codes. I was able to improve the battery lifetime of the mobile node from 3 months in the asynchronous case to 2 years using a localization period of 1 second. The proposed method is evaluated against five state-of-the-art localization algorithms.Embedded SystemsEmbedded SoftwareElectrical Engineering, Mathematics and Computer Scienc

Application of neural networks and genetic algorithms for sizing of photovoltaic systems

In this paper, an artificial neural network-based genetic algorithm (ANN-GA) model was developed for generating the sizing curve of stand-alone photovoltaic (SAPV) systems. Firstly, a numerical method is used for generating the sizing curves for different loss of load probability (LLP) corresponding to 40 sites located in Algeria. The inputs of ANN-GA are the geographical coordinates (Lat, Lon and Alt) and the LLP while the output is the sizing curve represented by CA=f(CS). Subsequently, the proposed ANN-GA model has been trained by using a set of 36 sites, whereas data for 4 sites which are not included in the training dataset have been used for testing the ANN-GA model. The results obtained are compared and tested with those of the numerical method. In addition, two new regression models have been developed and compared with the conventional regression models. The results show that, the proposed exponential regression model with three coefficients presents more accurate results than the conventional regression models. A new ANN has been used for predicting the sizing coefficients for the best regression model. These coefficients can be used for developing the sizing curve in different locations in Algeria. The results obtained showed that the coefficient of multiple determination (R2) is 0.9998, which can be considered as very promisin

Smart Management of Virtualized Network Service Chains in 5G Infrastructure

International audienceFuture 5G infrastructures promise to deliver un- precedented Quality of Services (QoS) guarantees through ultra- low latency and high data rate specifications that lead to handle many critical applications and services. Network Function Virtualization (NFV) is the paradigm that enables the implementation of network functions and capabilities as software components executed in virtual entities provisioned in general-purpose hardware. This paradigm plays a pivotal role to achieve management flexibility of 5G services and reduce infrastructures’ investment and operational costs. Recent advances in artificial intelligence (AI) and the large amounts of data collected on orchestration platforms offer new perspectives. Many recent publications advocate the use of AI in the orchestration of virtualized networks and many algorithms are proposed. Though, today small amount of literature works implement or test these concepts in a real platform that considers network service chains data. In this work, we present a framework for Service Function Chains’ (SFCs) profiling and management through Machine- learning approaches. We propose an extended network orchestration platform based on Openstack, Kubernetes (K8s), and Open Source Mano (OSM) orchestrator. The benefits of this architecture are shown by an algorithm that realizes proactive auto-scaling procedure for service chains. It considers features’ importance per service type while achieving a trade-off between services’ stringent QoS requirements and the cost of resources usage

Smart Management of Virtualized Network Service Chains in 5G Infrastructure

International audienceFuture 5G infrastructures promise to deliver un- precedented Quality of Services (QoS) guarantees through ultra- low latency and high data rate specifications that lead to handle many critical applications and services. Network Function Virtualization (NFV) is the paradigm that enables the implementation of network functions and capabilities as software components executed in virtual entities provisioned in general-purpose hardware. This paradigm plays a pivotal role to achieve management flexibility of 5G services and reduce infrastructures’ investment and operational costs. Recent advances in artificial intelligence (AI) and the large amounts of data collected on orchestration platforms offer new perspectives. Many recent publications advocate the use of AI in the orchestration of virtualized networks and many algorithms are proposed. Though, today small amount of literature works implement or test these concepts in a real platform that considers network service chains data. In this work, we present a framework for Service Function Chains’ (SFCs) profiling and management through Machine- learning approaches. We propose an extended network orchestration platform based on Openstack, Kubernetes (K8s), and Open Source Mano (OSM) orchestrator. The benefits of this architecture are shown by an algorithm that realizes proactive auto-scaling procedure for service chains. It considers features’ importance per service type while achieving a trade-off between services’ stringent QoS requirements and the cost of resources usage

SwiftTV - Bringing 4th generation P2P to SmartTV

Since their invention, TV's have become one of the most popular media devices and can be found in almost every livingroom in the world. For a long time, the functionality of the TV stayed the same: the ability to view television programs at certain fixed times of the day. Recently there has been development in the television market adding computing power and internet connectability to televisions. These new features open a whole new world of possibilities. The goal of this project was to create an application that runs on a Samsung SmartTV and uses the libswift peer-to-peer engine to download, upload and stream files. To create an application for a Samsung SmartTV a software development kit has been provided which allows programmers to create apps using JavaScript, HTML, CSS and Flash. This software development kit was used to create the front-end of our application. The front-end consists of an internal media player to handle streaming content and media playback found on an external USB device.Parallel and Distributed Systems GroupSoftware TechnologyElectrical Engineering, Mathematics and Computer Scienc

Power Transformers Differential Protection Using the p-q Power Theory

Part 11: EnergyInternational audienceThis paper describes the application of the p-q power theory to the differential protection of power transformers. The information provided by the harmonic content of the differential active and reactive power components is used to detect winding insulation failures and to distinguish them from magnetizing inrush current transients. A variety of test cases is presented in the paper, demonstrating the effectiveness of the protection strategy