Violeta Holmes

Bio: Violeta Holmes is an academic researcher from University of Huddersfield. The author has contributed to research in topics: Photovoltaic system & Fault detection and isolation. The author has an hindex of 18, co-authored 71 publications receiving 1186 citations. Previous affiliations of Violeta Holmes include Durham University.

Preliminary assessment of the solar resource in the United Kingdom

Abstract: Solar radiation resources data are the foundation of knowledge for programs of large-scale deployment of solar energy technologies. This article summarizes the analysis of a new weather stations network in the United Kingdom. The analysis used three years (January 2015–December 2017) of data from 27 weather stations distributed across the country. The data comprises global horizontal irradiance (GHI), diffuse horizontal irradiance, direct normal irradiance and the ambient temperature. Network design, implementation and data quality assurance are described to document the network extent and quality. From all observed datasets, we found that Plymouth (located in southwest England) has the dominant GHI and ambient temperature among all other 26 locations. The least GHI is observed for Aberdeen (located in northeast Scotland) estimated at 77.3 kWh/m2. The least average ambient temperature is equal to 9.1°C; the data were detected by the weather station located in the capital of Scotland (Edinburgh). Although continued measurements are needed to understand the interannual resource variability, the current study should have significant applications for preliminary technology selection, power plant modeling and resource forecasting.

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.

Deployment of AI-based RBF network for photovoltaics fault detection procedure

Abstract: In this paper, a fault detection algorithm for photovoltaic systems based on artificial neural networks (ANN) is proposed. Although, a rich amount of research is available in the field of PV fault detection using ANN, this paper presents a novel methodology based on only two inputs for the training, validating and testing of the Radial Basis Function (RBF) network achieving unprecedented detection accuracy of 98.1%. The proposed methodology goes beyond data normalisation and implements a ‘mapping of inputs’ approach to the data set before exposing it to the network for training. The accuracy of the proposed network is further endorsed through testing of the network in partial shading and overcast conditions.

The Nebuchadnezzar Effect: Dreaming of Sustainable Software through Sustainable Software Architectures

Abstract: Sustainability is emerging as an area of growing interest in the field of software engineering. While there is no agreed definition of the concept it is increasingly being considered as a non-functional requirement; a desired quality of a software system. One of the principal challenges in defining sustainability as a non-functional requirement is how to develop appropriate metrics and measures to demonstrate that the software is sustainable. Software architectures are the foundation of any software system and provide a mechanism for reasoning about quality attributes. However, architectural design is in part a creative process based on the level expertise of the software architect and tacit architectural knowledge, and is often made in an unsystematic and undocumented manner. Given the high dependency of non-functional requirements on their software architecture, this paper proposes that sustainable software architectures are fundamental to the development of technical sustainable software to address architectural drift and erosion, and architectural knowledge vaporization.

An Algorithmic Approach for Signal Measurement Using Symbolic Dynamics of Tent Map

Abstract: The symbolic time series generated by a unimodal chaotic map starting from any initial condition creates a binary sequence that contains information about the initial condition. A binary sequence of a given length generated this way has a one-to-one correspondence with a given range of the input signal. This can be used to construct analogue to digital converters (ADC). However, in actual circuit realizations, component imperfections and ambient noise result in deviations in the map function from the ideal, which, in turn, can cause significant error in signal measurement. In this paper, we propose the ways of circumventing these problems through an algorithmic procedure that takes into account the non-idealities. The most common form of non-ideality—reduction in the height of the map function—alters the partitions that correspond to each symbolic sequence. We show that it is possible to define the partitions correctly if the height of the map function is known. We also propose a method to estimate this height from the symbolic sequence obtained. We demonstrate the efficacy of the proposed algorithm with simulation as well as experiment. With this development, practical ADCs utilizing chaotic dynamics may become reality.

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