Probabilistic Framework for Online Identification of Dynamic Behavior of Power Systems With Renewable Generation

Papadopoulos, Panagiotis; Guo, Tingyan; Milanović, Jovica V.

doi:10.1109/tpwrs.2017.2688446

Cited by 41 publications

(42 citation statements)

References 37 publications

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“…Once the coefficients are solved, the non-parametric additive model in (14) can be used for fast prediction of transient stability margin.…”

Section: Predictor Trainingmentioning

confidence: 99%

“…The proposed scheme is also applied to a practical 756-bus transmission system in China [14]. The 500 kV backbone network is demonstrated in Fig.…”

Section: Application In a Practical Systemmentioning

confidence: 99%

“…Some promising results of response-based TSA by data mining have been reported in [10][11][12][13][14][15][16][17][18][19][20][21][22]. Decision trees (DTs) have the superiority of transparency [11] and do not rely on the backpropagation (BP) training process [12] in comparison with neural networks (NNs).…”

Section: Introductionmentioning

confidence: 99%

“…To reduce the input dimension without compromising the predictor's accuracy, a novel DT-based TSA scheme is proposed by introducing the characteristic ellipsoid (CELL) theory to extract the key features from limited PMU measurements in [13]. Another DT-based framework is proposed in [14] to predict the unstable generator grouping pattern in power systems with renewable generation. On the basis of stability prediction, the response-based one-shot control scheme is also developed by using PMU measurements and DTs in [15].…”

Section: Introductionmentioning

confidence: 99%

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Non‐parametric statistics‐based predictor enabling online transient stability assessment

Liu

et al. 2018

IET Generation, Transmission & Distribution

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Online transient stability assessment (TSA) is of great necessity for fast awareness of transient instability caused by fault contingencies. In this paper, a non-parametric statistics based scheme is proposed for response-based online TSA. A critical clearing time-based stability margin index is defined as the predictive output and 14 kinds of severity indicators are proposed as input features for the TSA predictor. With no prior knowledge of the correlation structure, the non-parametric additive model is used as the basis of the predictor. To screen out the weakly correlated indicators and reduce the dimensionality of the input space, two-stage feature selection is fulfilled by non-parametric independence screening and group Lasso penalised regression successively. The predictor is then learnt by least-squares regression in the reduced multi-feature space. With phasor measurement unit measurements at generator buses, severity indicators can be computed in the real-time and fast evaluation of post-fault stability margin can be made by the offline-trained predictor. The effectiveness of the proposed non-parametric statistics based scheme is demonstrated in a modified New England 39-bus system and a practical 756-bus transmission system in China.

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“…Once the coefficients are solved, the non-parametric additive model in (14) can be used for fast prediction of transient stability margin.…”

Section: Predictor Trainingmentioning

confidence: 99%

“…The proposed scheme is also applied to a practical 756-bus transmission system in China [14]. The 500 kV backbone network is demonstrated in Fig.…”

Section: Application In a Practical Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Non‐parametric statistics‐based predictor enabling online transient stability assessment

Liu

et al. 2018

IET Generation, Transmission & Distribution

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show abstract

“…In consequence, predictive techniques capable of assessing whether a network disturbance will evolve into a stable or unstable condition in advance by means of processing PMU information have been the focus of numerous research works in the past years, e.g. [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Angular Threshold Criteria for Transient Instability Identification in Uncertain Power Systems

Morales

Milanović

Papadopoulos

2019

2019 IEEE Milan PowerTech

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This paper addresses the problem of finding a threshold (critical value of rotor angle) for the identification of transient instability by the sole use of the information on rotor angles. Several threshold values for rotor angles have been used in the past without a clear agreement on a standard value. In this paper a probabilistic approach to the problem is proposed, so that for any network a threshold can be defined based on set accuracy, while at the same time minimizing the instability identification time. Results demonstrate that highly accurate robust rotor angle thresholds can be found, even for highly uncertain power networks with a high penetration of Renewable Energy Sources (RES).

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Online transient stability margin prediction of power systems with wind farms using ensemble regression trees

Mi¹,

Wang

Gao

et al. 2021

Int Trans Electr Energ Syst

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Summary A new method for online evaluation of the transient stability of wind farms incorporated system based on random forest regression is proposed in this paper. The data before contingency was employed as the inputs instead of the post fault features. The critical clearing time is employed as the transient stability boundary, which determines how stable the system is after the given contingency. The mapping function between the pre‐contingency conditions and the corresponding critical clearing time is modeled as ensemble regression trees model, which consists of lots of base learner. Through the bootstrap method and the random selection of variables in the training process, the problem of dimensionality disaster can be avoided naturally without the need to specifically select features. The out‐of‐bag error generated during the bootstrap process is used for parameter selection and variable importance measures. Case study on the New England 39‐bus system incorporated wind farms and IEEE 118‐bus system shows that the proposed method has a strong prediction accuracy and generalization ability.

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Probabilistic Framework for Online Identification of Dynamic Behavior of Power Systems With Renewable Generation

Cited by 41 publications

References 37 publications

Non‐parametric statistics‐based predictor enabling online transient stability assessment

Non‐parametric statistics‐based predictor enabling online transient stability assessment

Analysis of Angular Threshold Criteria for Transient Instability Identification in Uncertain Power Systems

Online transient stability margin prediction of power systems with wind farms using ensemble regression trees

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