A systematic approach for dynamic security assessment and the corresponding preventive control scheme based on decision trees

Liu, Chengxi; Sun, Kening; Rather, Zakir Hussain; Chen, Zhe; Bak, Claus Leth; Thøgersen, Paul; Lund, Per

doi:10.1109/pesgm.2014.6938856

Cited by 29 publications

(35 citation statements)

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“…The application of the DT to power system security assessment proves the adaptability and performance of the DT to security assessment. However, the performance can be further improved using an ensemble classifier by using many DT models, known as the random forest introduced by Leo Breiman .…”

Section: History Of Decision Tree and Random Forestmentioning

confidence: 89%

“…The application of the DT is found in many fields of science, technology, and management, whereas its introduction to the power system was brought in by Wehenkel et al [23]. The application of the DT to power system security assessment [9][10][11][12][13][14][15] proves the adaptability and performance of the DT to security assessment. However, the performance can be further improved using an ensemble classifier by using many DT models, known as the random forest introduced by Leo Breiman [24].…”

Section: History Of Decision Tree and Random Forestmentioning

confidence: 99%

“…For the security assessment, the literature revealed the application of data mining technique known as the decision trees (DT) [9][10][11][12][13][14][15], proving its importance for the application to power system static security assessment. However, the performance of the classifier model can be improved using an ensemble model, which uses many DT models.…”

Section: Introductionmentioning

confidence: 99%

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Classification and assessment of power system static security using decision tree and random forest classifiers

Sekhar

Mohanty

2015

Int J Numerical Modelling

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Summary The power system static security classification and assessment is essential in order to identify the post‐contingency problems and take corrective measures and to protect the system from blackout. In this paper, application of two data mining classifiers have been proposed for the security classification and assessment of a multiclass security problem. To design the security problem, contingency analysis is carried out under N‐1 line outage, and static severity index (SSI) is computed, which is a function of the line overload and the voltage deviation using Newton–Raphson load flow method, considering the variable load and generating conditions. Corresponding to the computed values of SSI, the voltage, phase angle, Mega Volt Ampere line flow and so on, a 1 × 7 pattern vector is generated. The generated pattern vectors are used to design a multiclass security problem. The designed security pattern vectors are given as inputs to the decision tree (DT) and random forest (RF) model in order to classify the security status of the power system. The proposed classifiers are investigated on an IEEE 30‐bus test system. The classification accuracy of the DT and the RF are compared with state‐of‐the‐art classifier models, namely, multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM).The simulation results clearly indicate that the proposed DT and RF classifiers are more efficient, reliable, and out performs MLP, RBF, and SVM classifiers for the assessment of the security status of the power system. Hence, DT and RF classifiers are found to be suitable for online implementation. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: History Of Decision Tree and Random Forestmentioning

confidence: 89%

Section: History Of Decision Tree and Random Forestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification and assessment of power system static security using decision tree and random forest classifiers

Sekhar

Mohanty

2015

Int J Numerical Modelling

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“…The database not only contains the measurement data which are the predictor values, but also the target values, namely, secure (1) and insecure (0). The latter is assigned according to the following criteria: a) Transient Stability: The system is termed transient stable for a given contingency if the transient stability index (TSI) [30] of the system is lower than 10% [31]. Mathematically, TSI can be expressed as, = 360− 360+ * 100%…”

Section: B Simulation Setupmentioning

confidence: 99%

Application of Machine Learning for Online Dynamic Security Assessment in Presence of System Variability and Additive Instrumentation Errors

Nath

Biswas

Pal

2019

2019 North American Power Symposium (NAPS)

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Large-scale blackouts that have occurred in the past few decades have necessitated the need to do extensive research in the field of grid security assessment. With the aid of synchrophasor technology, which uses phasor measurement unit (PMU) data, dynamic security assessment (DSA) can be performed online. However, existing applications of DSA are challenged by variability in system conditions and unaccounted for measurement errors. To overcome these challenges, this research develops a DSA scheme to provide security prediction in real-time for load profiles of different seasons in presence of realistic errors in the PMU measurements. The major contributions of this paper are: (1) develop a DSA scheme based on PMU data, (2) consider seasonal load profiles, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA with and without erroneous measurements. The performance of this approach is tested on the IEEE-118 bus system. Comparative analysis of the accuracies of the ML algorithms under different operating scenarios highlights the importance of considering realistic errors and variability in system conditions while creating a DSA scheme.

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“…It deals with static security analysis after a contingency. Decision tree (DT)-based systematic approach has been used in [4] for cooperative online power system dynamic security assessment (DSA) and preventive control. Generation schedule is changed according to the DT output to prevent power system insecurity.…”

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confidence: 99%

Enhanced Protection Security Using the System Integrity Protection Scheme (SIPS)

Kundu

Pradhan

2016

IEEE Trans. Power Delivery

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Events such as faults, loss of load or generation may cause unwanted relay operations which initiate cascade tripping leading to system collapse. Security of power system protection is a major concern for utilities. A System Integrated Protection Scheme (SIPS) based approach is proposed for improving security of protection operations. Identification of vulnerable points which may maloperate during a stressed condition and initiation of preventive strategy are proposed in this paper. Synchronized measurements obtained from strategic locations in the system are used for this purpose. It includes two algorithms; the first of which changes the operating characteristic of relays in accordance with system condition. The second one, identifies nodes in the system where load reduction can reduce the risk of maloperation. The proposed method is tested for New England 39 bus system and found to be accurate.

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A systematic approach for dynamic security assessment and the corresponding preventive control scheme based on decision trees

Cited by 29 publications

References 0 publications

Classification and assessment of power system static security using decision tree and random forest classifiers

Classification and assessment of power system static security using decision tree and random forest classifiers

Application of Machine Learning for Online Dynamic Security Assessment in Presence of System Variability and Additive Instrumentation Errors

Enhanced Protection Security Using the System Integrity Protection Scheme (SIPS)

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