Violeta Holmes

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

Comparing Mamdani Sugeno fuzzy logic and RBF ANN network for PV fault detection

Abstract: This work proposes a new fault detection algorithm for photovoltaic (PV) systems based on artificial neural networks (ANN) and fuzzy logic system interface. There are few instances of machine learning techniques deployed in fault detection algorithms in PV systems, therefore, the main focus of this paper is to create a system capable to detect possible faults in PV systems using radial basis function (RBF) ANN network and both Mamdani, Sugeno fuzzy logic systems interface. The obtained results indicate that the fault detection algorithm can detect and locate accurately different types of faults such as, faulty PV module, two faulty PV modules and partial shading conditions affecting the PV system. In order to achieve high rate of detection accuracy, four various ANN networks have been tested. The maximum detection accuracy is equal to 92.1%. Furthermore, both examined fuzzy logic systems show approximately the same output during the experiments. However, there are slightly difference in developing each type of the fuzzy systems such as the output membership functions and the rules applied for detecting the type of the fault occurring in the PV plant.

Fault detection algorithm for grid-connected photovoltaic plants

Abstract: This paper presents detailed procedure for automatic fault detection and diagnosis of possible faults occurring in a grid-connected photovoltaic (GCPV) plant using statistical methods. The approach has been validated using an experimental data of climate and electrical parameters based on a 1.98 kWp plant installed at the University of Huddersfield, United Kingdom. There are few instances of statistical tools being deployed in the analysis of PV measured data. The main focus of this paper is, therefore, to create a system capable of simulating the theoretical performances of PV systems and to enable statistical analysis of PV measured data. The fault detection algorithm compares the measured and theoretical output power using statistical t-test. In order to determine the location of the fault, the ratio between the measured and theoretical DC power and voltage is monitored. The obtained results indicate that the fault detection algorithm can detect and locate accurately different types of faults. Some of the typical faults are fault in a photovoltaic module, photovoltaic string and faulty maximum power point tracker (MPPT) unit. A virtual instrumentation (VI) LabVIEW software was used in the system development and implementation. This system was used successfully for fault detection on the GCPV plant.

The Blind Men and the Elephant: Towards an Empirical Evaluation Framework for Software Sustainability

Abstract: Software sustainability has been identified as one of the key challenges in the development of scientific and engineering software as we move towards new paradigms of research and computing infrastructures. However, it is suggested that sustainability is not well understood within the software engineering community, which can led to ineffective and inefficient efforts to address the concept or result in its complete omission from the software system. This paper proposes a definition of software sustainability and considers how it can be measured empirically in the design and engineering process of software systems.

Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system

Abstract: This work proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behavior of an existing PV system. For a given set of working conditions, solar irradiance and PV modules' temperature, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation (VI) LabVIEW software. Furthermore, a third order polynomial function is used to generate two detection limits for the VR and PR ratios obtained using VI LabVIEW simulation tool. The high and low detection limits are compared with measured data taken from 1.1 kWp PV system installed at the University of Huddersfield, United Kingdom. Samples lie out of the detection limits are processed by a fuzzy logic classification system which consists of two inputs and one output membership function. In this paper, PV faults corresponds to a short circuited PV module. The obtained results show that the fault detection algorithm can accurately detect different faults occurring in the PV system, where the maximum detection accuracy of before considering the fuzzy logic system is equal to 95.27%. However, the fault detection accuracy is increased up to a minimum value of 98.8% after considering the fuzzy system.

The impact of cracks on photovoltaic power performance

Abstract: This paper demonstrates a statistical analysis approach, which uses T-test and F-test for identifying whether the crack has significant impact on the total amount of power generated by the photovoltaic (PV) modules. Electroluminescence (EL) measurements were performed for scanning possible faults in the examined PV modules. Virtual Instrumentation (VI) LabVIEW software was applied to simulate the theoretical IV and PV curves. The approach classified only 60% of cracks that significantly impacted the total amount of power generated by PV modules.

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