Online Critical Unit Detection and Power System Security Control: An Instance-Level Feature Importance Analysis Approach

Rapid and accurate detection of critical units is crucial for the security control of power systems, ensuring reliable and continuous operation. Inspired by the advantages of data-driven techniques, this paper proposes an integrated deep learning framework of dynamic security assessment, critical unit detection, and security control. In the proposed framework, a black-box deep learning model is utilized to evaluate the dynamic security of power systems. Then, the predictions of the model for specific operating conditions are interpreted by instance-level feature importance analysis. Furthermore, the critical units are detected by reasonable local interpretation, and the security control scheme is extracted with a sequential adjustment strategy according to the results of interpretation. The numerical simulations on the CEPRI36 benchmark system and the IEEE 118-bus system verified that our proposed framework is fast and accurate for specific operating conditions and, thereby, is a viable approach for online security control of power systems.

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Real-time dynamic security assessment of power systems with large amount of wind power using Case-Based Reasoning methodology

2012 IEEE Power and Energy Society General Meeting ◽

10.1109/pesgm.2012.6345033 ◽

2012 ◽

Cited By ~ 2

Author(s):

R. Tiako ◽

D. Jayaweera ◽

S. Islam

Keyword(s):

Power Systems ◽

Real Time ◽

Wind Power ◽

Case Based Reasoning ◽

Security Assessment ◽

Time Dynamic ◽

Dynamic Security ◽

Dynamic Security Assessment ◽

Case Based

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Combining Strategies to Compute the Loadability Margin in Dynamic Security Assessment of Power Systems

10.1109/pesgm46819.2021.9638115 ◽

2021 ◽

Author(s):

Murilo E. C. Bento ◽

Daniela A. G. Ferreira ◽

Ahda P. Grilo-Pavani ◽

Rodrigo A. Ramos

Keyword(s):

Power Systems ◽

Security Assessment ◽

Dynamic Security ◽

Dynamic Security Assessment

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5-year Recurrence Prediction After Hepatocellular Carcinoma Resection Using Deep Learning and Cox Regression Models: A Large Prospective Study

10.21203/rs.3.rs-1062761/v1 ◽

2021 ◽

Author(s):

Hon-Yi Shi ◽

King-The Lee ◽

Chong-Chi Chiu ◽

Jhi-Joung Wang ◽

Ding-Ping Sun ◽

...

Keyword(s):

Neural Network ◽

Hepatocellular Carcinoma ◽

Deep Learning ◽

Surgical Resection ◽

Scale Score ◽

Cox Regression ◽

Recurrence Prediction ◽

Feature Importance ◽

Importance Analysis ◽

Hcc Recurrence

Abstract BackgroundRisk of hepatocellular carcinoma (HCC) recurrence after surgical resection is unknown. Therefore, the aim of this study was 5-year recurrence prediction after HCC resection using deep learning and Cox regression models.MethodsThis study recruited 520 HCC patients who had undergone surgical resection at three medical centers in southern Taiwan between April, 2011, and December, 2015. Two popular deep learning algorithms: a deep neural network (DNN) model and a recurrent neural network (RNN) model and a Cox proportional hazard (CPH) regression model were designed to solve both classification problems and regression problems in predicting HCC recurrence. A feature importance analysis was also performed to identify confounding factors in the prediction of HCC recurrence in patients who had undergone resection.ResultsAll performance indices for the DNN model were significantly higher than those for the RNN model and the traditional CPH model (p<0.001). The most important confounding factor in 5-year recurrence after HCC resection was surgeon volume followed by, in order of importance, hospital volume, preoperative Beck Depression Scale score, preoperative Beck Anxiety Scale score, co-residence with family, tumor stage, and tumor size. ConclusionsThe DNN model is useful for early baseline prediction of 5-year recurrence after HCC resection. Its prediction accuracy can be improved by further training with temporal data collected from treated patients. The feature importance analysis performed in this study to investigate model interpretability provided important insights into the potential use of deep learning models for predicting recurrence after HCC resection and for identifying predictors of recurrence.

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