• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1173-1178
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• 2011

The power generated by wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to predict the changing wind power. In this paper, neural network based wind power prediction scheme which uses wind speed and direction is considered. In order to get a better prediction result, compression function which can be applied to the measurement data is introduced. Empirical data obtained from wind farm located in Kunsan is considered to verify the performance of the compression function.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.108-112
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• 2012

A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly wind energy is unlimited in potential. However, due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. It is reported that, compared with physical persistent models, statistical techniques and computational methods are more useful for short-term forecasting of wind power. Among them, support vector regression (SVR) has much attention in the literature. This paper proposes an SVR based wind speed forecasting. To improve the forecasting accuracy, a fuzzy clustering is adopted in the process of SVR modeling. An illustrative example is also given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.

• International Journal of Fluid Machinery and Systems
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• v.10 no.1
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• pp.19-29
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• 2017

An objective of this study is to demonstrate the validity of using a small wind turbine to recover the fluid energy flowing out of an exhaust duct for the generation of power. In these experiments, a butterfly wind turbine of a vertical axis type (D = 0.4 m) is used. The output performance is measured at various locations relative to the exit of a small wind tunnel (W = 0.65 m), representing the performance expected in an exhaust duct flow. Two-dimensional numerical analysis qualitatively agrees with the experimental results for the wind turbine power coefficient and rate of energy recovery. When the turbine is far from the duct exit (more than 2.5 D), an energy recovery rate of approximately 1.3% is obtained.

• Journal of the Korean Society of Marine Environment & Safety
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• v.13 no.1 s.28
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• pp.69-73
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• 2007

As the interest in environmental pollution increases, the use of renewable energy is uprising. Among the renewable energy resources, wind and solar energy have no environmental impact and they can be used as unlimited resources. The two power systems are mutual supplementary systems. So the two systems are usually combined for generation. We have measured several data about wind and solar. These data can be used the foundation data for the construction of wind and solar power generation plants. Also we analyzed the possible optimal solar power system for 10[kW] wind power.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1168-1169
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• 2008

This study suggests a modeling of grid-connected wind turbine generation systems and performs simulation according to increase/decrease of real wind speed. Matlab & Simulink implemented modeling of grid-connected wind turbine generation system. Terminal voltage, grid voltage, and active/reactive power shall be observed following the performance of simulation.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.822-829
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• 2022

According to existing study into the remote fault diagnosis procedure, the current diagnostic approach has an imperfect decision model, which only supports communication in a close distance. An Internet of Things (IoT)-based remote fault diagnostic approach for wind power equipment is created to address this issue and expand the communication distance of fault diagnosis. Specifically, a decision model for active power coordination is built with the mechanical energy storage of power generation equipment with a remote diagnosis mode set by decision tree algorithms. These models help calculate the failure frequency of bearings in power generation equipment, summarize the characteristics of failure types and detect the operation status of wind power equipment through IoT. In addition, they can also generate the point inspection data and evaluate the equipment status. The findings demonstrate that the average communication distances of the designed remote diagnosis method and the other two remote diagnosis methods are 587.46 m, 435.61 m, and 454.32 m, respectively, indicating its application value.

• Journal of Energy Engineering
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• v.11 no.3
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• pp.216-223
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• 2002

Generation facilities of the power system are mainly classified into large-scale concentrated generation and small-scale dispersed generation, but generation planning of the Korea power system has been focusing on the large-scale generation so far. Recently, however, applications of dispersed generation sources including solar cell, fuel cell, wind power, etc. have been rapidly increasing and being strongly promoted, and such generation sources should be comprehensively considered in both planning and operating. Since it is not always possible that the dispersed generation alone meets all the load interconnected to it is especially when a fault occurs, interconnection into the existing utility is desirable and recommended. In relation to wind power generation systems interconnected at the low and extra high voltage levels, this paper performs the simulation and analysis of the system protection and suggests protection coordination plans on various faults which possibly occur.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.1
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• pp.18-26
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• 2014

A microgrid can play a significant role for enlargement of renewable energy sources and emission reduction because it is a network of small, distributed electrical power generators operated as a collective unit. In this paper, an application of optimization method to economical operation of a microgrid is studied. The microgrid to be studied here is composed of distributed generation system(DGS), battery systems and loads. The distributed generation systems include combined heat and power(CHP) and small generators such as diesel generators and the renewable energy generators such as photovoltaic(PV) systems, wind power systems. Both of thermal loads and electrical loads are included here as loads. Also the emissions trading scheme to be applied in near future, the cost of unit start-up and the operational characteristics of battery systems are considered as well as the probabilistic characteristics of the renewable energy generation and load. A mathematical equation for optimal operation of this system is modeled based on the mixed integer programming. It is shown that this optimization methodology can be effectively used for economical operation of a microgrid by the case studies.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.186-192
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• 2014

Recently wind energy penetration into power systems has increased. Wind power, as a renewable energy source, plays a different role in the power system compared to conventional power generation units. As long as only single and small wind power units are installed in the power system, wind power does not influence power system operation and can easily be integrated. However, when wind power penetration reaches a significantly high level and conventional power production units are substituted, the impact of wind power on the power system becomes noticeable and must be handled. The connection of large wind turbines and wind farms to the grid has a large impact on grid stability. The electrical power system becomes more vulnerable to and dependent on wind energy production, and therefore there is an increased concern about the large wind turbines impact on grid stability. In this work, a new type of fuzzy logic controller for the frequency control of wind farms is proposed and its performance is verified using SimWindFarm toolbox which was developed as part of the Aeolus FP7 project.

• Journal of Power Electronics
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• v.9 no.1
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• pp.109-116
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• 2009

This paper addresses the output control of a utility-connected double-fed induction machine (DFIM) for wind power generation systems (WPGS). DFIM has a back-to-back converter to control outputs of DFIM driven by the wind turbine for WPGS. To supply commercially the power of WPGS to the grid without any problems related to power quality, the real and reactive powers (PQ) at the stator side of DFIM are strictly controlled at the required level, which in this paper is realized with the Fuzzy PI controller based on the field orientation control. For the Sinusoidal Pulse Width Modulation (SPWM) converter connected to the rotor side of DFIG to maintain the controllability of PQ at the state side of DFIM, the DC voltage of the DC link capacitor is also controlled at a certain level with the conventional Proportion-Integral (PI) controller of the real power. In addition, the power quality at the grid connected to the rotor side of DFIM through the back-to-back converter is maintained in a certain level with a PI controller of the reactive power. The controllers for the PQ at the stator side of DFIM, the DC link voltage of the back-to-back inverter and the reactive power at the grid connected to the rotor side of DFIM are designed and simulated in the PSIM program, of which the result verifies the performance of the proposed controllers.