• Journal of Electrical Engineering and Technology
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• v.8 no.2
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• pp.197-205
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• 2013

Real time transient stability assessment mainly depends on real-time prediction. Unfortunately, conventional techniques based on offline analysis are too slow and unreliable in complex power systems. Hence, fast and reliable stability prediction methods and simple stability criterions must be developed for real time purposes. In this paper, two new methods for real time determining critical clearing time based on clustering identification are proposed. This article is covering three main sections: (i) clustering generators and recognizing critical group; (ii) replacing the multi-machine system by a two-machine dynamic equivalent and eventually, to a one-machine-infinite-bus system; (iii) presenting a new method to predict post-fault trajectory and two simple algorithms for calculating critical clearing time, respectively established upon two different transient stability criterions. The performance is expected to figure out critical clearing time within 100ms-150ms and with an acceptable accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.736-740
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• 2004

In order to overcome the problems of simulation methods, the power system transient stability assessment method using critical fault clearing time functions has been developed. Using the above method, this paper has developed the new method which can assess accurately and efficiently the effects of control and protection systems on transient stability which is the most important characteristic to assess in power systems. At first, critical fault clearing time functions CCT(W:load) are defined by taking notice of the fact that transient stability is mainly controlled by fault clearing time and load. Next, the method to be enable to assess accurately and efficiently the effects of control and protection systems on transient stability has been newly developed by using the above functions. Finally, it has been applied to the effect assessment in the occurrence of a three-phase fault in a model power system. Results of application have been clarified its effectiveness.

• KIEE International Transactions on Power Engineering
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• v.2A no.4
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• pp.131-135
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• 2002

This paper describes a new ultra-fast contingency screening algorithm for on-line TSA without time simulation. All machines are represented in a classical model and the stability index is defined as the ratio between acceleration power during a fault and deceleration power after clearing the fault. Critical clustering of machines is done based on the stability index, and the power-angle curve of the critical machines is drawn assuming that the angles of the critical machines increase uniformly, while those of the non-critical ones remain constant. Finally, the critical clearing time (CCT) is computed using the power-angle curve. The proposed algorithm is tested on the KEPCO system comprised of 900-bus and 230-machines. The CCT values computed with the screening algorithm are in good agreement with those computed using the detailed model and the SIME method. The computation time for screening about 270 contingencies is 17 seconds with 1.2 GHz PC.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.73-76
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• 2001

This paper presents a new systematic contingency selection, screening and ranking method for on-line transient security assessment. Transient stability of a particular generator is influenced most by fault near it. Fault at the transmission lines adjacent to the generators are selected as contingency. Two screening methods are developed using the sensitivity of modal synchronizing torque coefficient and computing an approximate critical clearing time(CCT) without time simulation. The first method, which considers only synchronizing power, may mislead in some cases since it does not consider the acceleration power. The approximate CCT method, which consider both the acceleration and deceleration power, worked well. Finally the Single Machine Equivalent(SIME) method is implemented using IPLAN of PSS/E for detailed stability analysis.

• Journal of the Korean Solar Energy Society
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• v.23 no.3
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• pp.29-35
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• 2003

Generator for wind power can be either synchronous or asynchronous (induction) types. Induction and synchronous generators behave in a different way when subjected to severe faults. Induction generators does not have an angle stability limit and short circuit in the neighborhood of an Induction generator causes the demagnetization of the machine when the fault is cleared, the voltage raises slowly, while the grid contributes with reactive power to the generator and the magnetic flux recovers. On the other hand in the synchronous generators the recovery of the voltage is immediate, since the excitation of the rotor angle comes from an independent circuit. This paper shows the result of the transient state analysis in the network connected to wind generation system Several case studies have been conducted to determine the effect of the clearing time of a fault on the network stability. It has been found that the critical clearing time can be as low as 61ms in the case of induction generator compared to 370ms in the case of synchronous generator.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.6
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• pp.253-258
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• 2000

This paper presents an application of Kohonen neural networks to assess the dynamic security of power systems. The dynamic security assessment(DSA) is an important factor in power system operation, but conventional techniques have not achieved the desired speed and accuracy. The critical clearing time(CCT) is an attribute which provides significant information about the quality of the post-fault system behaviour. The function of Kohonen networks is a mapping of the pre-fault system conditions into the neurons based on the CCTs. The power flow on each line is used as the input data, and an activated output neuron has information of the CCT of each contingency. The trajectory of the activated neurons during load changes can be used in on-line DSA efficiently. The applicability of the proposed method is demonstrated using a 9-bus example.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.10
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• pp.41-46
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• 2006

Generation of electricity using wind power has received considerable attention worldwide in recent years. In order to investigate the impacts of the integration of wind farm into utilities' network, various studies have been investigated. One such impact is related to the Critical Clearing Time (CCT) of wind power generation system. This paper reports investigation into the factors that influence the transient behavior of the wind power generation system following network fault conditions. It is shown that CCT can be affected by various factors contributed by the network. Such factors include capacity of wind power, power factor, the length of the interfacing line, etc. This investigation is conducted on a simulated grid-connected wind farm using Digsilent Power Factory.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.451-452
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• 2007

In this paper, we simulate power system transient stability using object-oriented programming(OOP). OOP is a more flexible method than procedual programming. There are several advantages in dynamic system simulation using OOP. We also calculate critical fault clearing time using energy functions for detailed models.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.107-114
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• 1999

This paper presents an application of artificial neural networks(ANN) to assess the dynamic security of power systems. The basic role of ANN is to provide assessment of the system's stability based on training samples from off-line analysi. The critical clearing time(CCT) is an attribute which provides significant information about the quality of the post-fault system behaviour. The function of ANN is a mapping of the pre-fault, fault-on, and post-fault system conditions into the CCT's. In previous work, a feed forward neural network is used to learn this mapping by using the generation outputs during the fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault considered. In order to enhance the speed of security assessment, the bus data and line powers are used as the input data of the ANN in thil paper. Test results show that the proposed neural networks have the reasonable accuracy and can be used in on-line security assenssment efficiently.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.535-536
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• 2006

This paper reports investigation into the factors that influence the transient behavior of the wind power generation system following network fault conditions. It is shown that the critical clearing time(CCT) can be affected by various factors contributed by the host network. Such factors include capacity of wind power, power factor, the length of the interfacing line, etc. This investigation is conducted en a simulated grid-connected wind farm using Digsilent Power Factory.