Deep learning based total transfer capability calculation model

Yan, Jiongcheng; Li, Changgang; Liu, Yutian

doi:10.1109/powercon.2018.8601615

Cited by 15 publications

(16 citation statements)

References 11 publications

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“…Method 3: In [10], the TTC estimation model is established using the Deep Network (DN) supervised method with Neural Network non-linear regression approach.…”

Section: F Comparison With Other Methodsmentioning

confidence: 99%

“…(3) Learn the relationship between the TTC and the pivotal features using a supervised learning approach. Deep Belief Network (DBN) and Deep Network (DN) are recently used to learn the precise relationship between the TTC and pivotal features [9], [10]. However, the real power system is massive and non-linear regression learning approaches cost a lot of time.…”

Section: Introductionmentioning

confidence: 99%

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Online Dynamic Total Transfer Capability Estimation Using Cotraining-Style Semi-Supervised Regression

et al. 2020

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With the increasing integration of wind power, the operating condition of the power system varies more rapidly. As the total transfer capability (TTC) of the transmission interface changes with the operating condition, the offline TTC estimation has become less suitable for online security control. In this paper, an efficient online dynamic TTC estimation method using semi-supervised learning approach is proposed. First, considering the high-order uncertainties of wind and load, a sample database of expected operating conditions with or without corresponding TTCs is generated. Then, the pivotal features which greatly correlate with the TTC are selected. Finally, the relationship between the TTC and pivotal features is learned, using the cotraining-style semi-supervised regression algorithm (COREG), thus the dynamic TTC estimation model is established. With real-time data inputting the model, the TTC can be estimated. The proposed method is validated on Gansu Province Power Grid in China, and the results and accuracy and efficiency comparison with other typical existing methods indicate that, the proposed method can provide accurate TTC estimation, and because of the high efficiency of the semi-supervised learning approach, the whole process of model establishment and TTC estimation can be refreshed every 15 minutes. Therefore, the proposed method of online dynamic TTC estimation is suitable for online security control.

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“…Method 3: In [10], the TTC estimation model is established using the Deep Network (DN) supervised method with Neural Network non-linear regression approach.…”

Section: F Comparison With Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online Dynamic Total Transfer Capability Estimation Using Cotraining-Style Semi-Supervised Regression

et al. 2020

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“…Also, the minimum-redundancy and maximum-relevance (mRMR) applied on features related to power and angle for large-scale power systems TSA have been considered in [5,6]. In Reference [7], the fast correlation-based filter method (FCBF) to eliminate irrelevant features is applied for the total transfer capability calculation considering static security, static voltage stability, and transient stability. Also, measuring correlations between variables via partial mutual information (PMI) and Pearson correlation coefficient (PCC) has been considered for selecting key features on TSA in [8], and 2) filter-wrapper approach; In Reference [9], the optimal features called global trajectory clusters feature subset (GTCFS) are selected from the large observations of rotor angle and voltage magnitude swing curves by the filter-wrapper approach in the form of the Relief-Support vector machine (SVM) scenario for TSP.…”

Section: Introductionmentioning

confidence: 99%

Extracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction

Mosavi

2021

IEEE Access

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Real-time transient stability assessment (TSA) of power systems based on mining system dynamic response has been widely considered by scholars. In this regard, extracting the most discriminative transient features (MDTFs) to achieve high-performance transient stability prediction (TSP) should be regarded as a fundamental issue in the transient learning strategy. In fact, MDTFs extraction is raised to make a trade-off between paradoxically intertwined indices, namely the accuracy and processing time of TSP. To this end, we offer a bi-mode hybrid feature selection scheme called BMHFSS for extracting MDTFs in high dimensional transient multivariate time series (TMTS). First, we used the TMTS, which are effective features on TSA. Next, the trajectory-based filter-wrapper mode (TFWM) is applied on TMTS to surmount the curse of dimensionality in two phases. In the filter phase, statistical and intrinsic characteristics of the TMTS in the form of agglomerative hierarchical clustering (AHC) are measured, and relevant TMTS (RTMTS) is selected according to obtained weight. In the wrapper phase, the RTMTS is entered into the trihedral kernel-based approach, including both fuzzy imperialist competitive algorithm (FICA) and incremental wrapper subset selection (IWSS) to find the intersected most RTMTS (IMRTMTS). As a complementary step, the filter-wrapper scenario in point-based mode (PFWM) is conducted for selecting MDTFs per time series in IMRTMTS. Finally, the aggregated MDTFs (AMDTFs) are tested to verify their efficacy for TSP based on cross-validation. The results show that the proposed framework has prediction accuracy greater than 98 % and a processing time of 52.94 milliseconds for TSA. INDEX TERMSFuzzy imperialist competitive algorithm (FICA), Most discriminative transient features (MDTFs), Support vector machine (SVM), Transient stability assessment (TSA).

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“…The min-redundancy and max-relevance (mRMR) FSS applied on the set of inter-complementary dynamic stability features for TSA [19]. In Reference [20], the FCBF as feature pre-screening has been considered for the total transfer capability calculation model regarding transient stability constraint.…”

Section: Introductionmentioning

confidence: 99%

Extracting Discriminative Features in Reactive Power Variations by 1- Persistence Parallel Fragmented Hybrid Feature Selection Scheme for Transient Stability Prediction

Mosavi¹

2021

IJIES

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The corrective action for power systems over transient space requires the transient stability prediction (TSP) in an accurate and timely manner. To this end, applying data mining techniques to achieve high performance on TSP is an inevitable approach. One of the influential factors on TSP performance is extracting the most relevant features (MRFs) of transient data. In this paper, first, we constructed the transient dataset in the form of reactive power-based two-variate time series (RP2vTS). Next, for exploiting the optimal feature subset, the MRFs of RP2vTS are obtained by the 1-persistence parallel fragmented hybrid feature selection scheme (PFHFS) based on filter and wrapper methods. Finally, to evaluate the efficacy of the proposed framework, experimental comparisons on inter-connected New England and New York systems (NETS-NYPS) were applied using the support vector machine (SVM) classifier. The results showed that the proposed framework by selecting MRFs of RP2vTS offers high-performance capacity on TSP.

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Deep learning based total transfer capability calculation model

Cited by 15 publications

References 11 publications

Online Dynamic Total Transfer Capability Estimation Using Cotraining-Style Semi-Supervised Regression

Online Dynamic Total Transfer Capability Estimation Using Cotraining-Style Semi-Supervised Regression

Extracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction

Extracting Discriminative Features in Reactive Power Variations by 1- Persistence Parallel Fragmented Hybrid Feature Selection Scheme for Transient Stability Prediction

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