• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.5
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• pp.410-420
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• 2015

This paper provides a comparison of power converter loss and thermal description for voltage source and current source type 5 MW-class medium-voltage topologies of wind turbines. Neutral-point clamped three-level converter is adopted for a voltage source type topology, whereas a two-level converter is employed for current source type topology, considering the popularity in the industry. To match the required voltage level of 4160 V with the same switching device of IGCT as in the voltage source converter, two active switches are connected in series for the case of current source converter. Transient thermal modeling of a four-layer Foster network for heat transfer is done to better estimate the transient junction and case temperature of power semiconductors during various operating conditions in wind turbines. The loss analysis is confirmed through PLECS simulations. Comparison result shows that the VSC-based wind turbine system has higher efficiency than the CSC under the rated operating conditions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1407-1415
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• 2010

In recent years, the smart grid technique for maximizing the energy efficiency of power networks has received a great deal of attentions. In particular, the Demand Response is a core technology differentiated from the present power network under the smart grid paradigm. To minimize the electric cost and maximize users' satisfaction, this paper proposes a unique scheduling algorithm derived by using optimization where the characteristics of various home appliances are taken into account. For this goal, we represent mathematical consumption patterns of the electric loads and propose the optimal scheduling scheme based on the importance factor of each device during one day. In the simulation results, we demonstrate the effectiveness of the proposed algorithm in the viewpoint of the minimal electric costs utilizing real statistical figures.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.759-764
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• 2021

Because sensitive and private information should be exchanged between electric vehicles and a V2G service provider, reliable communication channel is essential to operate Vehicle-to-Grid (V2G) system which considers battery of electric vehicles as a factor of smart grid. The block chain is a platform for cryptocurrency transaction and fully distributed database system running by only equivalent node in the network without help of any central management or 3^rd party. In this paper, the structure and operation method of the blockchain are investigated, and the application of the blockchain for the V2G system was also explained and analyzed.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.1
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• pp.1-10
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• 2015

This paper proposes a control algorithm for permanent magnet synchronous generators with a back-to-back three-level neutral-point clamped voltage source converter in a medium-voltage off-shore wind power system under unbalanced grid conditions. Specifically, the proposed control algorithm compensates for unbalanced grid voltage at the PCC (Point of Common Coupling) in a collector bus of an off-shore wind power system. This control algorithm has been formulated based on symmetrical components in positive and negative synchronous rotating reference frames under generalized unbalanced operating conditions. Instantaneous active and reactive power is described in terms of symmetrical components of measured grid input voltages and currents. Negative sequential component of AC input current is injected into the PCC in the proposed control strategy. The amplitude of negative sequential component is calculated to minimize the negative sequential component of grid voltage under the limitation of current capability in a voltage source converter. The proposed control algorithm enables the provision of balanced voltage at the PCC resulting in the high quality generated power from off-shore wind power systems under unbalanced network conditions.

• ETRI Journal
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• v.45 no.4
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• pp.650-665
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• 2023

A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.566-571
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• 2011

As Plug-in Hybrid Vehicle and Electric Vehicle (PHEV/EV) take a greater share in the personal automobile market, their high penetration levels may bring potential challenges to electric utility especially at the distribution level. Thus, there is a need for the flexible charging management strategy to compromise the benefits of both PHEV/EV owners and power grid side. There are many different management methods that depend on the objective function and the constraints caused by the system. In this paper, the schema and dispatching schedule of centralized PHEV/EV charging spot network are analyzed. Also, we proposed and compared three power allocation strategies for centralized charging spot. The first strategy aims to maximize state of vehicles at plug-out time, the rest methods are equalized allocation and prioritized allocation based on vehicles SoC. The simulation results show that each run of the optimized algorithms can produce the satisfactory solutions to response properly the requirement from PHEV/EV customers.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.7-14
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• 2021

The emergence of the automated smart grid has become an essential device for responding to these problems and is bringing progress toward a smart grid-based society. Smart grid is a new paradigm that enables two-way communication between electricity suppliers and consumers. Smart grids have emerged due to engineers' initiatives to make the power grid more stable, reliable, efficient and safe. Smart grids create opportunities for electricity consumers to play a greater role in electricity use and motivate them to use electricity wisely and efficiently. Therefore, this study focuses on power demand management through machine learning. In relation to demand forecasting using machine learning, various machine learning models are currently introduced and applied, and a systematic approach is required. In particular, the GP learning model has advantages over other learning models in terms of general consumption prediction and data visualization, but is strongly influenced by data independence when it comes to prediction of smart meter data.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.513-522
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• 2016

We propose a mechanism for monitoring load in quality of service (QoS)-enabled wireless networks and show how it can be used for network management as well as for dynamic pricing. Mobile network traffic, especially video, has grown exponentially over the last few years and it is anticipated that this trend will continue into the future. Driving factors include the availability of new affordable, smart devices, such as smart-phones and tablets, together with the expectation of high quality user experience for video as one would obtain at home. Although new technologies such as long term evolution (LTE) are expected to help satisfy this demand, the fact is that several other mechanisms will be needed to manage overload and congestion in the network. Therefore, the efficient management of the expected huge data traffic demands is critical if operators are to maintain acceptable service quality while making a profit. In the current work, we address this issue by first investigating how the network load can be accurately monitored and then we show how this load metric can then be used to provide creative pricing plans. In addition, we describe its applications to features like traffic offloading and user satisfaction tracking.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1527-1534
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• 2016

Online voltage stability monitoring using real-time measurements is one of the most important tasks in a smart grid system to maintain the grid stability. Loading margin is a good indicator for assessing the voltage stability level. This paper presents an Extreme Learning Machine (ELM) approach for estimation of voltage stability level under credible contingencies using real-time measurements from Phasor Measurement Units (PMUs). PMUs enable a much higher data sampling rate and provide synchronized measurements of real-time phasors of voltages and currents. Depth First (DF) algorithm is used for optimally placing the PMUs. To make the ELM approach applicable for a large scale power system problem, Mutual information (MI)-based feature selection is proposed to achieve the dimensionality reduction. MI-based feature selection reduces the number of network input features which reduces the network training time and improves the generalization capability. Voltage magnitudes and phase angles received from PMUs are fed as inputs to the ELM model. IEEE 30-bus test system is considered for demonstrating the effectiveness of the proposed methodology for estimating the voltage stability level under various loading conditions considering single line contingencies. Simulation results validate the suitability of the technique for fast and accurate online voltage stability assessment using PMU data.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.215-216
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• 2014

This paper proposes a control algorithm for permanent magnet synchronous generator with a back-to-back three-level neutral-point clamped voltage source converter in a medium-voltage offshore wind power system under unbalanced grid conditions. The proposed control algorithm particularly compensates for the unbalanced grid voltage at the point of common coupling in a collector bus of offshore wind power system. This control algorithm has been formulated based on the symmetrical components in positive and negative rotating synchronous reference frames under generalized unbalanced operating conditions. Instantaneous active and reactive power are described in terms of symmetrical components of measured grid input voltages and currents. Negative sequential component of ac input current is injected to the point of common coupling in the proposed control strategy. The amplitude of negative sequential component is calculated to minimize the negative sequential component of grid voltage under the limitation of current capability in a voltage source converter. The proposed control algorithm makes it possible to provide a balanced voltage at the point of common coupling resulting in the generated power of high quality from offshore wind power system under unbalanced network conditions.