Transient stability assessment via decision trees and multivariate adaptive regression splines

Rahmatian, M.; Chen, Yu Christine; Palizban, Atefeh; Moshref, A.; Dunford, W.G.

doi:10.1016/j.epsr.2016.09.030

Cited by 45 publications

(47 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among these factors are the variable number of wind power generators in service and the degree of randomness of the active power output of wind power generators. A detailed comparison with other options with data mining based algorithms or other forms of decision trees, e.g., like algorithms presented in Kamwa et al (2010) and Rahmatian et al (2017), is a topic for future research after this paper. Future work should also be performed to provide a comparative assessment of using other optimization tools and by using real-time data (which can be generated by using a digital twin of a real system built in a real-time digital simulator).…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, the focus of this paper provides insight into how optimization can improve to achieve an optimal performance of a selected computational intelligence method when challenged to provide confident transient stability assessment in systems with very high penetration of power electronic interfaced generation. A detailed comparison with other options with data mining based algorithms or other forms of decision trees, e.g., like algorithms presented in Kamwa et al (2010) and Rahmatian et al (2017), is a topic for future research after this paper. Particularly, a group of variables related to transient stability are selected as key variables candidates to build DTs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MVMO-Based Identification of Key Input Variables and Design of Decision Trees for Transient Stability Assessment in Power Systems With High Penetration Levels of Wind Power

et al. 2020

View full text Add to dashboard Cite

Unlike synchronous generators, wind turbines cannot directly respond to large disturbances, which may cause transient instability, due to their power electronic-based interface and maximum power control strategy. To effectively monitor the influence of wind turbines, this paper proposes an approach that combines decision trees (DTs), and a newly developed variant of the Mean-Variance Mapping Optimization (MVMO) algorithm, to simultaneously tackle the problem of selecting the key variables that properly reflect the transient stability performance of a system dominated by wind power, and designing the DTs for reliable online assessment of transient stability. The notion of key variables refers to the set of variables that are closely related to the modified power system transient stability performance as a consequence of the replacement of conventional power plants by wind generators. The selection of key variables is formulated as a non-linear optimization problem with weight factors as decision variables and is tackled by MVMO. A weight factor is assigned to each key variable candidate, and its value is considered to reflect the degree of influence of the key variable candidate on the splitting property and estimation accuracy of the DTs. The samples of the key variable candidates and the initialized weight factors are used to build the first group of DTs. Then, MVMO iteratively evolves the weight factors according to its special mapping function with minimizing DTs' estimation error. According to the final list of optimized weight factors, system operators can select a reduced set of variables with the largest weight factors as key variables, depending on the resulting accuracy of the DTs. Meanwhile, DTs built by using key variables are considered as the optimal performance trees for transient stability estimation. In this way, the selection of key variables and the development of DTs are made jointly and automatically, without the interference of the users of the DTs. Test results Wang et al. MVMO Based Transient Stability Assessment on the modified IEEE 9 bus system and a synthetic model of a real power system show that the proposed method can correctly identify the set of key variables related to wind turbine dynamics, as well as its ability to provide a reliable estimation of the transient stability margin.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MVMO-Based Identification of Key Input Variables and Design of Decision Trees for Transient Stability Assessment in Power Systems With High Penetration Levels of Wind Power

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The real-time principle requires that the selected features can fully reflect the running state of power system after a fault occurs. Based on the existing researches [11][12][13][14][15][16][17][18][19][25][26][27], 32 features following the above principles are chosen to form the initial feature set after a great deal of time domain simulations, where t 0 , t 1 and t 2 represent the fault-free time, fault-occurring time and fault-clearing time respectively. The details are shown in Table 1.…”

Section: Construction Principles Of the Initial Input Features For Trmentioning

confidence: 99%

“…Moreover, sample and pattern recognition based machine learning methods provide researchers another feasible path. Owing to the advantages of fast real-time response and high precision, many machine learning-based methods are proposed to solve TSA problems [11][12][13][14][15][16][17][18][19][20], such as probabilistic neural networks [11], core vector machine [12], decision trees [13], extreme learning machine [14], etc.…”

Section: Introductionmentioning

confidence: 99%

Feature Reduction for Power System Transient Stability Assessment Based on Neighborhood Rough Set and Discernibility Matrix

Xiao

Wang

2018

Energies

View full text Add to dashboard Cite

In machine learning-based transient stability assessment (TSA) problems, the characteristics of the selected features have a significant impact on the performance of classifiers. Due to the high dimensionality of TSA problems, redundancies usually exist in the original feature space, which will deteriorate the performance of classification. To effectively eliminate redundancies and obtain the optimal feature set, a new feature reduction method based on neighborhood rough set and discernibility matrix is proposed in this paper. First, 32 features are selected to structure the initial feature set based on system principle. An evaluation index based on neighborhood rough set theory is used to characterize the separability of classification problems in the specified feature space. By constructing the discernibility matrix of input features, a feature selection strategy is designed to find the optimal feature set. Finally, comparative experiments based on the proposed feature reduction method and several common feature reduction techniques used in TSA are applied to the New England 39 bus system and Australian simplified 14 generators system. The experimental results illustrate the effectiveness of the proposed feature reduction method.

show abstract

“…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%

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

Liu

et al. 2018

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

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.

show abstract

Transient stability assessment via decision trees and multivariate adaptive regression splines

Cited by 45 publications

References 23 publications

MVMO-Based Identification of Key Input Variables and Design of Decision Trees for Transient Stability Assessment in Power Systems With High Penetration Levels of Wind Power

MVMO-Based Identification of Key Input Variables and Design of Decision Trees for Transient Stability Assessment in Power Systems With High Penetration Levels of Wind Power

Feature Reduction for Power System Transient Stability Assessment Based on Neighborhood Rough Set and Discernibility Matrix

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

Contact Info

Product

Resources

About