Wind Turbine Emulation Based on the Accurate Control of Servomotors

The objective of this work is the creation of a novel Wind Turbine Emulation (WTE) technique to support the needs of a new generation of multi-bladed vertical-axis wind turbine (VAWT) designs aimed at the urban environment. The scheme, presented in this thesis, uses Computational Fluid Dynamics (CFD) data, from the analysis of such devices, as the basis of its operation. CFD methods are used for the analysis of wind turbine performance but CFD models do not incorporate physical system inertial response or provide a physical test bed. WTE must, therefore, continue to play a role in the support of wind turbine design, research and development. Ongoing work, on enhancing wind turbine designs for use in the urban environment, is leading towards the use of complex, drag-based, multi-bladed, vertical-axis devices to deal with the problems inherent in the urban situation. Current WTE systems are found to be incapable of modelling the complex torque output of these devices adequately, since they are based on the use of a steady-state model modulated by an approximating analytical function. The WTE technique developed in this thesis uses CFD profiles mapped to a two-dimensional array to generate torque coefficient values in real time. Initially a standard inertia-compensation scheme, based on an acceleration observer, is used, but testing shows that this method is inadequate due to the requirement for a low-pass filter in the feedback path. To achieve the performance necessary to accurately model the output from a multi-bladed VAWT, a novel inertia-compensation scheme is designed and implemented. The improved technique eliminates the filter induced performance degradation by dynamically manipulating z-plane pole positions based on real-time observation of system stiffness and incorporating an adaptive `lag-lead' pre-filter at the input to the torque compensation control loop. System behaviour is approximated by an s-plane model and pole manipulation is achieved by dynamic modulation of the wind turbine (WT) moment of inertia quantity used in the feedback path. Tests show that the novel inertia compensation scheme meets the requirements for accurate emulation of VAWT performance. Mean torque and power output tests confirm that, for all profiles used, the output accurately reflects those predicted by the original CFD analyses. Time and frequency domain analyses of generator load current signals confirm that the technique facilitates the analysis of generator output signals on a WTE test bed for the development of fault diagnosis and Condition Based Maintenance (CBM) strategies

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

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

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

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

Output Power Enhancement for Hot Spotted Polycrystalline Photovoltaic Solar Cells

Hot spotting is a reliability problem in photovoltaic (PV) panels where a mismatched cell heats up significantly and degrades PV panel output power performance. High PV cell temperature due to hot spotting can damage the cell encapsulate and lead to second breakdown, where both cause permanent damage to the PV panel. Therefore, the development of two hot spot mitigation techniques are proposed using a simple and reliable method. PV hot spots in the examined PV system was inspected using FLIR i5 thermal imaging camera. Multiple experiments have been tested during various environmental conditions, where the PV module I-V curve was evaluated in each observed test to analyze the output power performance before and after the activation of the proposed hot spot mitigation techniques. One PV module affected by hot spot was tested. The output power during high irradiance levels is increased by approximate to 1.26 W after the activation of the first hot spot mitigation technique. However, the second mitigation technique guarantee an increase in the power up to 3.97 W. Additional test has been examined during partial shading condition. Both proposed techniques ensure a decrease in the shaded PV cell temperature, thus an increase in the PV output power

Parallel fault detection algorithm for grid-connected photovoltaic plants

In this work, we 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 PV measured data. The main focus of this paper is, therefore, to outline a parallel fault detection algorithm that can diagnose faults on the DC-side and AC-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's 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 parallel fault detection algorithm can detect and locate accurately different types of faults such as, faulty PV module, faulty PV String, Faulty Bypass diode, Faulty Maximum power point tracking (MPPT) unit and Faulty DC/AC inverter unit. The parallel fault detection algorithm has been validated using an experimental data climate, with electrical parameters based on a 1.98 and 0.52 kWp PV systems installed at the University of Huddersfield, United Kingdom

Fault detection algorithm for multiple GCPV array configurations

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

The impact of cracks on the performance of photovoltaic modules

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

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

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

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

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

PV output power enhancement using two mitigation techniques for hot spots and partially shaded solar cells

Hot spotting is a reliability problem in photovoltaic (PV) panels where a mismatched cell heats up significantly and degrades PV panel output power performance. High PV cell temperature due to hot spotting can damage the cell encapsulate and lead to second breakdown, where both cause permanent damage to the PV panel. Therefore, the design and development of two hot spot mitigation techniques are proposed using a simple, costless and reliable method. The hot spots in the examined PV system was carried out using FLIER i5 thermal imaging camera. Several experiments have been examined during various environmental conditions, where the PV module I-V curve was evaluated in each observed test to analyze the output power performance before and after the activation of the proposed hot spot mitigation techniques. One PV module affected by hot spot was tested. The output power during high irradiance levels is increased by approximate to 1.25 W after the activation of the first hot spot mitigation technique. However, the second mitigation technique guarantee an increase of the power equals to 3.96 W. Additional test has been examined during partial shading condition. Both proposed techniques ensure a decrease in the shaded PV cell temperature, thus an increase in the output measured power