Mehmet Cebeci

Investigation of pollution flashover on high voltage insulators using artificial neural network

High voltage insulators form an essential part of the high voltage electric power transmission systems. Any failure in the satisfactory performance of high voltage insulators will result in considerable loss of capital, as there are numerous industries that depend upon the availability of an uninterrupted power supply. The importance of the research on insulator pollution has been increased considerably with the rise of the voltage of transmission lines. In order to determine the flashover behavior of polluted high voltage insulators and to identify to physical mechanisms that govern this phenomenon, the researchers have been brought to establish a modeling. Artificial neural networks (ANN) have been used by various researches for modeling and predictions in the field of energy engineering systems. In this study, model of VC=f (H,D,L,?,n,d) based on ANN which compute flashover voltage of the insulators were performed. This model consider height (H), diameter (D), total leakage length (L), surface conductivity (?) and number of shed (d) of an insulator and number of chain (n) on the insulator.

Monitoring a grid connected PV power generation system with labview

Generating power with solar cells has been developed rapidly in recent years and in parallel of photovoltaic (PV) systems becoming widespread in power generation systems in all around the world, there have been some requirements about integration of PV power generation systems into the traditional generation systems. One of these requirements will be monitoring PV generation systems and also adopting PV power generation systems to the present power system is another significant subject. In this study, a monitoring system was introduced for developed PV power generation system in the laboratory with Labview by using its useful and effective tools. 1.2 kWp grid connected PV system was used as a PV power generation system and NI USB-6221 data acquisition (DAQ) card and developed electrical measurement circuits were used for monitoring the PV system. Parameters and parameter changes at current, voltage and generated power in PV system acquired from the system and these parameters were monitored in real time thanks to Labview DAQ card. Output voltage, output current and power generated from the PV system were also monitored with developed Labview software. All the experimental results obtained from the PV power generation system were explained detailed in the paper. Proposed real time monitoring system is robust and reliable for grid connected PV power generating systems and it can be adapted easily to other PV generation systems.

A hybrid tracking method for scaled and oriented objects in crowded scenes

Abstract Traditional kernel based means shift assumes constancy of the object scale and orientation during the course of tracking and uses a symmetric/asymmetric kernel, such as a circle or an ellipse for target representation. In a tracking scenario, it is not uncommon to observe objects with complex shapes whose scale and orientation constantly change due to the camera and object motions. In this paper, we propose a multi object tracking method which tracks the complete object regions, adapts to changing scale and orientation, and assigns consistent labels to each object throughout real world video sequences. Our approach has five major components: (1) dynamic background subtraction, (2) level sets, (3) mean shift convergence, (4) object identification, and (5) occlusion handling. The experimental results show that the proposed method is superior to the traditional mean shift tracking in the following aspects: (1) it provides consistent multi objects tracking instead of single object throughout the video, (2) it is not affected by the scale and orientation changes of the tracked objects, (3) its computational complexity is much less than traditional mean shift due to using level set method instead of probability density.

A novel labview based anti islanding detection method for grid connected PV systems

Renewable energy sources (RES) have been come into prominence all over the world and solar energy is the most developed energy sources in RES. Photovoltaic (PV) systems have been used with related to solar energy is one of the most preferred systems thanks to their improved technology and user-friendly applications. Grid connected PV systems also have been developed all over the world and as long as they have been become widespread there have been some drawbacks about connecting them to the electrical grid. One of the drawbacks of connecting PV systems to the grid and maybe the most important one is unintentional islanding condition. Islanding can be dangerous for utility workers and damage utility equipment so anti islanding is a crucial subject for grid connected PV systems. There have been many anti islanding detection methods in the literature but most of them have some boundaries related with local load and PV inverter. In this application study, a novel Labview based islanding detection method for grid connected PV systems was proposed. For this aim, 1.2 kWp grid connected PV system installed in laboratory at Fırat University Electrical and Electronics Engineering Department was used and a real time controller was developed with Labview and its useful tools. Proposed method is not inverter resident so it can be easily adapted to the grid connected PV systems. The results obtained from the proposed real time anti islanding detection method shows that it is reliable, robust and independent from load and inverter. Non detection zone (NDZ) is almost zero and it has a significant short response time according to IEEE 929-2000. In this study, all the experimental results were explained in detail and compared with simulation results developed by Matlab Simulink All obtained results correspond with both simulation and experimental results. Proposed method is effective and applicable to all grid connected PV systems independently from load and PV inverter and it presents a realistic solution to islanding.

A Communication Based Islanding Detection Method for Photovoltaic Distributed Generation Systems

PV based distributed generation (DG) systems must have some electrical connection standards while they connected to an electrical grid. One of these electrical conditions and the most important one is unplanned islanding condition. Islanding is a very dangerous condition because it could damage the PV system and related electrical systems and also working people have been at risk in islanding condition. In this application study, a new communication based islanding detection method was introduced for grid tied PV systems. A real time controller was developed with Labview for detecting islanding condition. Developed method is a hybrid method which uses the effective ways of communication based and passive methods. The results obtained from the proposed real time islanding detection method show that proposed method is reliable, robust, and independent from load and inverter. Nondetection zone (NDZ) is almost zero and islanding detection time is approximately 1-2 cycles indicated in experimental results so this time has a significant short response time according to IEEE 929-2000 standard. The proposed method is effective and presents a realistic solution to islanding so it could be implemented easily to grid tied PV systems and could be used in real system applications.

A novel approach to power flow analysis for grid connected micro grid

This paper proposes a neural network based power flow analysis method that applied on a grid connected and ring-shaped micro grid. As the use of micro grids increasing rapidly, it becomes necessary to analyze them for different operating and loading conditions as large power systems. At the outset, a MG is designed and simulated under MATLAB / Simulink platform. Normal operation data collected and stored. Then, different loading scenarios performed, operational data collected and stored to use for proposed method. Intelligent systems are used to process these data and also for training. After training a fully different scenario is created and the effectiveness of the proposed method is verified through simulation study. Also a fault condition analysis is performed for validity.

Lagrange multipliers for the intermediate metallic regions in the computation of the potential distribution of insulator chains by FEM

It is very important to predict the behaviour of the high voltage insulators at the operating conditions from the point of improving of the design and practical selection of the insulator type. Therefore, it is necessary to compute the potential and electric field distributions and to determine the critical regions forced much more by the electric field. In case the insulator surface is clean or the pollution film at the surface is dry, a little capacitive current flows and the solution of the problem results in a capacitive potential distribution. In the numerical computational methods of potential distribution of insulator chains, it requires to insert into the resulting equation system as boundary conditions of the intermediate metallic zones that connect two units that have the same potential at every point. In this work, the potential distribution of clean insulator chains have been computed using the Finite Element Method (FEM). The metallic zones between the units have been taken into account as equipotential surfaces by Lagrange Multipliers. The computed results were compared with experimental results obtained by using the Conducting Paper Method and good agreement was observed.

Real time power flow monitoring in a PLL inverter based PV distributed generation system

Solar cell technology has been developed rapidly in recent years, and in parallel of the photovoltaic (PV) systems becoming widespread in power generation systems, in all around the world, there have been some requirements about the integration of PV distributed generation (DG) systems into the traditional generation systems. One of these requirements will be monitored PV based DG systems and also adopting PV based DG systems to the present power system is another significant subject. In this study, an experimental monitoring system was introduced for grid connected PV power generation systems by using Labview and its useful and effective tools. The simulation of the proposed grid connected PV system test-bed is created by using Simulink where each part of the system is modelled separately. A phase locked loop (PLL) tracks the grid and inverter voltages in order to match the outputs at the same intervals in the simulation. In the laboratory, 1 kWp installed power grid connected PV array was set up, and a PLL based inverter was used as a power conditioning unit. A data acquisition (DAQ) card and developed electrical measurement circuits were used for monitoring the real time power flow in the developed PV system. Parameters and parameter changes at current, voltage and generated power in PV system acquired from the system, and these parameters were monitored in real time thanks to the Labview DAQ card. Output voltage, output current and power generated from the PV system were also monitored with developing Labview software. All the experimental results obtained from the PV power generation system were explained detailed in the paper. Proposed real time monitoring system is robust and reliable for grid connected PV based DG systems, and it can be adapted easily to other PV power generation systems.

Real-time kernel based object tracking using mean shift

In this paper, we improve the real-time object tracking algorithm of Yang [1] which uses a symmetric similarity function between spatially smoothed kernel-density estimates of the model and the target distributions. This spatial smoothed process applied on the centre points of the probability density functions increases not only computational complexity but also noise sensitivity. After reducing background information and using a new simple similarity function between the model and the target distributions, the proposed algorithm successfully coped with camera motion, partial occlusions and clutter with lower computational complexity. For tracking object, the mean shift algorithm is used iteratively. The simplicity of the new similarity function leads to an efficient and robust nonparametric tracking algorithm. The mathematical results obtained about the performance of the proposed algorithm on several image sequences are represented comparatively in a tabular format.

Development of a Renewable Energy Based DC Excitation System in a Micro Hydro Power Plant

MHPPs (micro hydro power plants) have become prominent in hydropower plants as a solution to provide the energy demands of the grid. In this study, a new hybrid renewable energy based DC excitation system for synchronous generator in the developed MHPP system is introduced. Proposed hybrid DC excitation system consists of solar & h ydrogen energy based power generating systems. Hybrid renewable energy based system is used for the excitation of the synchronous generator in the MHPP test system. The renewables are used as a secondary energy source to provide the excitation current to a synchronous generator that generates energy in MHPP. A PV (photovoltaic) array is used as the main source of excitation, and a FC (fuel cell) stack is used for DC excitation in the lack of sunshine. In the experimental setup, an electrical control card is developed, and a microcontroller is used to perform the proposed excitation system. All experimental results obtained from 5 kW rated power MHHP test system. Experimental results show that, the proposed method provides the continuous excitation current, and the operation of the synchronous generator is uninterrupted. The proposed method is also practical and easily implemented for MHPP systems.