2005년 대한 생화학·분자생물학회 하계학술대회를 다녀와서

Doubly fed induction generators (DFIGs), often organized in wind parks, are the most important generators used for variable-speed wind energy generation. This paper reviews the control systems for the operation of DFIGs and brushless DFIGs in wind energy applications. Control systems for stand-alone operation, connection to balanced or unbalanced grids, sensorless control, and frequency support from DFIGs and low-voltage ride-through issues are discussed.

Overview of Control Systems for the Operation of DFIGs in Wind Energy Applications

Energy sustainability and environmental preservation have become worldwide concerns with the many manifestations of climate change and the continually increasing demand for energy. As cities and nations become more technologically advanced, electricity consumption rises to levels that may no longer be manageable if left unattended. The Smart Grid offers an answer to the shift to more sustainable technologies such as distributed generation and microgrids. A general public awareness and adequate attention from potential researchers and policy makers is crucial. This paper presents an overview of the Smart Grid with its general features, functionalities and characteristics. It presents the Smart Grid fundamental and related technologies and have identified the research activities, challenges and issues. It demonstrates how these technologies have shaped the modern electricity grid and continued to evolve and strengthen its role in the better alignment of energy demand and supply. Smart Grid implementation and practices in various locations are also unveiled. Concrete energy policies facilitate Smart Grid initiatives across the nations. Interestingly, Smart Grid practices in different regions barely indicate competition but rather an unbordered community of similar aspirations and shared lessons.

A review of the development of Smart Grid technologies

Offshore wind farm (OWF) is an emerging technology in the wind energy conversion system. These wind resources are abundant, stronger, and are more consistent in terms of their availability than land-based wind resources. As a matter of fact significantly higher energy production is achieved due to larger wind turbine ratings and stronger wind profiles. This paper highlights the present scenario and challenges in development of offshore wind power. The challenges and opportunities that exist in the development stages of an offshore wind farm project, from exploration to erection and installation of wind turbines, construction of platforms and laying of sea cables, up to maintenance and de-commissioning, involving important technical aspects are addressed. An application of high voltage direct current (HVDC) transmission for integration of large scale offshore wind farm with onshore grid is attractive as compared to high voltage alternating current (HVAC) transmission system. To make the offshore wind farm feasible, reliable and secure, the different aspects in its planning, design and operation are also reviewed in this paper.

Off-shore wind farm development: Present status and challenges

Feature extraction for fault signals is critical and difficult in all kinds of fault detection schemes. A novel simple and effective method of faulty feeder detection in resonant grounding distribution systems based on the continuous wavelet transform (CWT) and convolutional neural network (CNN) is presented in this paper. The time-frequency gray scale images are acquired by applying the CWT to the collected transient zero-sequence current signals of the faulty feeder and sound feeders. The features of the gray scale image will be extracted adaptively by the CNN, which is trained by a large number of gray scale images under various kinds of fault conditions and factors. The features extraction and the faulty feeder detection can be implemented by the trained CNN simultaneously. As a comparison, two faulty feeder detection methods based on artificial feature extraction and traditional machine learning are introduced. A practical resonant grounding distribution system is simulated in power systems computer aided design/electromagnetic transients including DC, the effectiveness and performance of the proposed faulty feeder detection method is compared and verified under different fault circumstances.

Deep-Learning-Based Earth Fault Detection Using Continuous Wavelet Transform and Convolutional Neural Network in Resonant Grounding Distribution Systems

Double circuit transmission lines are frequently subjected to a variety of technical problems from the perspective of protection engineering. These problems are mainly due to the mutual coupling effects between adjacent circuits of the line. In this paper, a new fault location approach for double circuit transmission lines is introduced. It depends only on the data extracted from one end of the line. This practically facilitates implementing and developing this approach, as it needs no information from the other end. The approach is based on modifying the apparent impedance method using modal transformation. Depending on modal transformation, the coupled equations of the transmission line are converted into decoupled ones. This greatly eliminates the mutual effects resulting in an accurate estimation for the fault distance in a straightforward manner. Also the effects of prefault currents, charging currents, and the unknown fault resistance on the estimation accuracy are compensated. The proposed approach was tested via digital simulation using ATP-EMTP in conjunction with MATLAB. Applied test results corroborate the superior performance of the proposed approach.

A practical fault location approach for double circuit transmission lines using single end data

In this paper, the development and implementation of a new fault diagnosis scheme for generator winding protection using artificial neural networks (ANN) is introduced. The proposed scheme performs internal fault detection, fault type classifications and faulted phases identification. This scheme is characterized with higher sensitivity and stability boundaries as compared with the differential relay. Effect of the presence of nonsynchronous frequencies on the scheme performance is examined. Effect of different values of ground resistance on ground fault detection sensitivity is outlined. The scheme hardware is implemented based on a digital signal processing (DSP) board interfaced with a multi input/output (MIO) board. Test results of the proposed scheme corroborate the scheme stability and sensitivity.

Development and implementation of an ANN-based fault diagnosis scheme for generator winding protection

In Nordic countries, distribution networks are unearthed or compensated. Earth faults, particularly in compensated networks, provide fault currents that are low compared to the load currents. Identification of the faulty feeder is therefore difficult. A preliminary description of discrete wavelet transform (DWT)-Bayesian selectivity technique was introduced in reference 1 to identify the faulty feeder. It was dependent on a conditional probabilistic method applied to transient features extracted by using the DWT. However, a practical setting for this technique has not yet been presented. Furthermore, its sensitivity is limited to 1.5-k fault resistances, and is further reduced to 170 when considering current transformer and network noise. In this paper, the ratio between the absolute sums of the DWT detail level from each feeder is used as an input to the conditional probability approach, providing an enhanced selectivity decision. This input contributes to discriminating the faulty feeder during high resistance faults. The relay setting is introduced as a function of the number of feeders and their characteristic impedances, as the proposed algorithm is dependent on the discharge's initial transients. The performance is evaluated taking into account current-transformer and network noises. A digital implementation is experimentally verified by using two digital signal processing boards.

Bayesian Selectivity Technique for Earth Fault Protection in Medium-Voltage Networks

Summary form only given as follows. In this paper, an artificial neural network (ANN) based internal fault detector algorithm for generator protection is proposed. The detector uniquely responds to the winding earth and phase faults with remarkably high sensitivity. Discrimination of the fault type is provided via three trained ANNs having a six dimensional input vector. This input vector is obtained from the difference and average of the currents entering and leaving the generator windings. Training cases for the ANNs are generated via a simulation study of the generator internal faults using Electromagnetic Transient Program (EMTP). A genetic algorithm is employed to reduce training time. The proposed ANN algorithm is compared with a conventional differential algorithm. It is found to be superior regarding sensitivity and stability.

Tamer A. Kawady

Papers

A practical fault location approach for double circuit transmission lines using single end data

Development and implementation of an ANN-based fault diagnosis scheme for generator winding protection

Bayesian Selectivity Technique for Earth Fault Protection in Medium-Voltage Networks

ANN-based novel fault detector for generator windings protection

Arcing fault identification using combined Gabor Transform-neural network for transmission lines