Optimal placement of rotor angle transducers for power system stability

Chu, Xiaodong; Liu, Yutian

doi:10.1016/s0378-7796(03)00081-6

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…From here on, the rotor will decelerate and oscillate before settling down at a new steady state operation at the intersection of P m and curve C [8]. Here, the rotor angle experiences damped oscillation according to the machines natural frequency before settling at point h.…”

Section: A Equal Area Criterion Analysismentioning

confidence: 99%

Investigation of power swing phenomenon and verification of critical clearing time through theoretical calculations and simulated studies

Gunasegaran

Tan

Bakar

2014

3rd IET International Conference on Clean Energy and Technology (CEAT) 2014

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Power swing or transient stability commonly pose serious threat to the power system reliability. As such, the equal area criterion is used to identify the critical clearing time. However, the equal area criterion neglected the resistance in the power system and only considered pure reactance in the theoretical calculations. As a result, this paper will consider both the resistance and reactance present in the power system, in order to achieve a realistic transient stability study. A comparison in critical clearing time between the theoretical calculation and realistic simulation were conducted in this paper. It was found that neglecting the resistance in the theoretical assumptions will produce a conservative estimates for the critical clearing time. It was further demonstrated that a realistic power system with resistances can actually withstand a longer critical clearing time before the generator experiences out of step condition. In addition, this paper will also present the co-relation between the active power and rotor angle during a power swing event.

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Section: A Equal Area Criterion Analysismentioning

confidence: 99%

Investigation of power swing phenomenon and verification of critical clearing time through theoretical calculations and simulated studies

Gunasegaran

Tan

Bakar

2014

3rd IET International Conference on Clean Energy and Technology (CEAT) 2014

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“…Typically it is done to reduce computational cost in system reliability studies, in pattern recognition or knowledge discovery studies, and also to obtain valuable guidance in decision making. For instance, generators are grouped based on their slow-coherency performance which gives valuable information for controlled islanding to prevent blackout [7][8][9]. Generators are grouped based on angle gap criteria for fast contingency screening [10].…”

Section: Introductionmentioning

confidence: 99%

Progressive entropy based contingency grouping for deriving decision trees for multiple contingencies

Krishnan

McCalley

2013

International Journal of Electrical Power & Energy Systems

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“…Even if rotor angle measurements from all generators are available, the neural network training would be extremely difficult because of high input dimensionality and a feature selection step must be implemented. Using the optimal placement method proposed in [11], only a part of generators are equipped with rotor angle transducers while system dynamics is represented well, which considers the constraints and dimension reduction simultaneously.…”

Section: A Input Featurementioning

confidence: 99%

“…The system undergoes large power transfer from the west and southwest areas to the central area where load centers are located. Using the optimal placement method of rotor angle transducers proposed in [11], seven generators are selected to install GPS-based rotor angle transducers, which are marked in Fig. 1 with '*'.…”

Section: A System Descriptionmentioning

confidence: 99%

Real-time transient stability prediction using incremental learning algorithm

Chu

Liu

IEEE Power Engineering Society General Meeting, 2004.

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Real-time transient stability prediction is an essential and challenging step of response-based transient stability emergency controls. Machine learning methods including decision trees and artificial neural networks have the potential to be applied to the problem. To counter the inefficiency of common machine learning methods in learning new information, an incremental learning algorithm is employed to train an artificial neural network for real-time transient stability prediction. The resulted learning framework can readily be integrated into on-line dynamic security assessment. The effectiveness of such prediction model is demonstrated by the simulation results of a practical power system. Index Terms-Transient stability prediction, artificial neural network, incremental learning, on-line dynamic security assessment.

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Optimal placement of rotor angle transducers for power system stability

Cited by 10 publications

References 24 publications

Investigation of power swing phenomenon and verification of critical clearing time through theoretical calculations and simulated studies

Investigation of power swing phenomenon and verification of critical clearing time through theoretical calculations and simulated studies

Progressive entropy based contingency grouping for deriving decision trees for multiple contingencies

Real-time transient stability prediction using incremental learning algorithm

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