A hybrid approach for security evaluation and preventive control of power systems

Niazi, K. R.; Arora, C. M.; Surana, S.L.

doi:10.1109/pecon.2003.1437442

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…It is important to notice that the ML techniques should only use the static variables available from ACOPF. It would not be practical if the algorithm required pre-fault dynamic (state variables) or post-fault variables as inputs since they impose huge pre-processing calculations [16][17][18][19].…”

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Security Assessment of the Electric Power System

Meghdadi

Tack

Liebman

2019

2019 9th International Conference on Power and Energy Systems (ICPES)

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The transition to a new low emission energy future results in a changing mix of generation and load types due to significant growth in renewable energy penetration and reduction in system inertia due to the exit of ageing fossil fuel power plants. This increases technical challenges for electrical grid planning and operation. This study introduces a new decomposition approach to account for the system security for short term planning using conventional machine learning tools. The immediate value of this work is that it provides extendable and computationally efficient guidelines for using supervised learning tools to assess first swing transient stability status. To provide an unbiased evaluation of the final model fit on the training dataset, the proposed approach was examined on a previously unseen test set. It distinguished stable and unstable cases in the test set accurately, with only 0.57% error, and showed a high precision in predicting the time of instability, with 6.8% error and mean absolute error as small as 0.0145.

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Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Security Assessment of the Electric Power System

Meghdadi

Tack

Liebman

2019

2019 9th International Conference on Power and Energy Systems (ICPES)

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“…The secure and insecure regions under the contingency, and , can be represented by the unions of corresponding patterns: (17) where and denote the th secure and th insecure patterns, respectively.…”

Section: Pattern-based Dynamic Secure/insecure Regionsmentioning

confidence: 99%

“…In [16], load shedding strategies are inferred from inverse reading of the DT. In [17], a hybrid method using NN and DT is designed for preventive generation rescheduling. In [18], the dynamic secure boundaries are approximated by a linear combination of DT rules, which are then applied for generation rescheduling and load shedding.…”

Section: Introductionmentioning

confidence: 99%

Preventive Dynamic Security Control of Power Systems Based on Pattern Discovery Technique

Zhang

Guan

et al. 2012

IEEE Trans. Power Syst.

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This paper presents a statistical learning-based method for preventive dynamic security control of power systems. Critical operating variables regarding system dynamic security are first selected via a distance-based feature estimation process. An unsupervised learning procedure called pattern discovery (PD) is then performed in the space of the critical variables to extract the subtle structure knowledge called patterns. The patterns are geometrically non-overlapped hyper-rectangles, representing the system dynamic secure/insecure regions and can be explicitly presented to provide decision support for real-time security monitoring and situational awareness. By formulating the secure patterns into a standard optimal power flow (OPF) model, the preventive control against dynamic insecurities can be efficiently and transparently attained. The proposed method is validated on the New England 39-bus system considering both single-and multi-contingency conditions.

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“…The obtained thresholds not only build a fast prediction model, but also formulate the assessment plans and in turn infer the preventive control rules. This superiority makes DTs very popular for designing static, dynamic security and also transient stability of power systems [9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Comparison of CART- and MLP-based power system transient stability preventive control

Lin

2013

International Journal of Electrical Power & Energy Systems

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A hybrid approach for security evaluation and preventive control of power systems

Cited by 4 publications

References 10 publications

Data-Driven Security Assessment of the Electric Power System

Data-Driven Security Assessment of the Electric Power System

Preventive Dynamic Security Control of Power Systems Based on Pattern Discovery Technique

Comparison of CART- and MLP-based power system transient stability preventive control

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