Machine Learning for the Assessment of Transient Stability of Power Systems

Rao, T Subha Mastan; Anuradha, T; Kaur, Upinder; Rajan, M. Sundar; Patnala, Tulasi Radhika; Kumar, V Raja

doi:10.1109/iciccs53718.2022.9788363

Cited by 6 publications

(9 citation statements)

References 19 publications

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“…ST FV law can be distinguished: ML algorithms and classical algorithms for transient analysis in EPS [43][44][45][46][47].…”

Section: Methodsmentioning

confidence: 99%

“…Table 1 provides an analysis of the methods considered for synthesizing the ST FV law. [31] 1993 ANN µ min , µ inf , T FV [32] 2002 MAS µ min , µ inf , T FV [33] 1997 LST µ min [34] 1995 RBFNN µ min , µ inf , T FV [35] 1993 MP µ min [36] 2018 TEF µ min [37] 2007 FL µ min [38] 2021 EAC µ min , T FV Among the studies reviewed, two groups of methods used to synthesize the optimal ST FV law can be distinguished: ML algorithms and classical algorithms for transient analysis in EPS [43][44][45][46][47].…”

mentioning

confidence: 99%

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Methodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine

Senyuk,

Beryozkina,

Safaraliev

et al. 2024

Mathematics

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This study presents the results of the development and testing of a methodology for selecting parameters of the characteristics of fast valving in a steam turbine for emergency power system management to maintain dynamic stability based on machine learning algorithms. Modern power systems have reduced inertia and increased stochasticity due to the active integration of renewable energy sources. As a result, there is an increased likelihood of incorrect operation in traditional emergency automation devices, developed on the principles of deterministic analysis of transient processes. To date, it is possible to increase the adaptability and accuracy of emergency power system management through the application of machine learning algorithms. In this work, fast valving in a steam turbine was chosen as the considered device of emergency automation. To form the data sample, the IEEE39 mathematical model was used, for which benchmark laws of change in the position of the cutoff valve during the fast valving of a steam turbine were selected. The considered machine learning algorithms for classifying the law of change in the position of the steam turbine’s cutoff valve, k-nearest neighbors, support vector machine, decision tree, random forest, and extreme gradient boosting were used. The results show that the highest accuracy corresponds to extreme gradient boosting. For the selected eXtreme Gradient Boosting algorithm, the classification accuracy on the training set was 98.17%, and on the test set it was 97.14%. The work also proposes a methodology for forming synthetic data for the use of machine learning algorithms for emergency management of power systems and suggests directions for further research.

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“…ST FV law can be distinguished: ML algorithms and classical algorithms for transient analysis in EPS [43][44][45][46][47].…”

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Methodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine

Senyuk,

Beryozkina,

Safaraliev

et al. 2024

Mathematics

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“…References [29,30] predict power system transient stability by incorporating power system dynamics into the data collection process through machine learning models. Active power may be transported between the rectifier station and the inverter station; however, reactive power does not go through the converter [31,32].…”

Section: The Model Of a Rotating Coordinate System Dc-ac Convertermentioning

confidence: 99%

“…The stability control strategy in (29) indicates the relationships among AC CPLs power P, storage discharging power P b , micro power source power P G , outer voltage loop control parameters, k p(d) of the outer voltage loop control parameter of the DC/DC converter, and k ip(d) of the inner current loop control parameters of the DC/AC converter. To guarantee large signal stability, the derived control strategy provides important constraints on control parameters k p(d) and k ip(d) in the discharging state, and AC CPLs are also considered.…”

Section: Stability Control Strategy When Energy Storage System In Dis...mentioning

confidence: 99%

“…Furthermore, appropriate controls for energy storage systems could compensate dynamic characteristics of AC CPLs and enhance system stability [27,28]. References [29,30] predict power system transient stability by incorporating power system dynamics into the data collection process through machine learning models. Although energy storage control techniques and characteristics have gained a lot of attention, few studies have derived quantitative design guidelines for energy storage systems from the aspect of stability improvement.…”

Section: Introductionmentioning

confidence: 99%

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Stability Control Strategies for Bidirectional Energy Storage Converters Considering AC Constant Power Loads

et al. 2023

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In islanded AC microgrids, negative impedance characteristics of AC constant power loads (AC CPLs) easily introduce large signal instability to the system, while energy storage systems sometimes compensate for the dynamic characteristics of AC CPLs, and increase the system stability. Although energy storage control techniques and characteristics have gained a lot of attention, few studies have derived quantitative design guidelines for energy storage systems from the aspect of stability improvement. In order to fill this gap, this paper proposes stability control strategies for bidirectional energy storage converters considering the characteristics of AC CPLs to guarantee large signal stability of islanded AC microgrids. The presented control techniques create quantitative limits for the DC bus voltage loop control parameters of the energy storage DC/DC converter and the integral control loop control parameter of the energy storage DC/AC converter, and also interpret the positive stability influence of energy storage systems and the negative stability influence of AC CPLs. The structure of the paper is as follows. Firstly, DQ coordinate transformation is adopted, and AC microgrid nonlinear models with the energy storage system in charging and discharging states are constructed. Then, large signal models are constructed depending on mixed potential theory. Stability control strategies for bidirectional energy storage converters are obtained, and AC CPLs power, storage system equivalent resistor, and micro power source power are all taken into account. Finally, based on simulation and experimental results, it is obvious that regulating the control parameters of the energy storage converter significantly increases the large signal stability of islanded AC microgrids without extra equipment. The method is very simple and easy to implement.

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Application of Machine Learning Algorithms for Enhanced Smart Grid Control and Management

Khounsari,

Fazeli

2024

2024 International Conference on Energy and Electrical Engineering (EEE)

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Machine Learning for the Assessment of Transient Stability of Power Systems

Cited by 6 publications

References 19 publications

Methodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine

Methodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine

Stability Control Strategies for Bidirectional Energy Storage Converters Considering AC Constant Power Loads

Application of Machine Learning Algorithms for Enhanced Smart Grid Control and Management

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