DT based intelligent predictor for out of step condition of generator by using PMU data

Aghamohammadi, Mohammad Reza; Abedi, Mohammad

doi:10.1016/j.ijepes.2018.01.001

Cited by 47 publications

(25 citation statements)

References 22 publications

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“…Many authors have targeted the issues related to practical power networks such as Iran's power system area, 34 Taiwan power system, 35 Korean electric power system, 72 Hydro-Québec network, 73 Kansai electric power company, 81 Chilean power system 105 and so forth. New England 39 Bus test system is also a preferred choice of many researchers 32,36,37,54,57,[59][60][61][62][63]85 for implementing the SIPS in different scenarios. Apart from these systems, IEEE 14 bus test system, IEEE 68 bus test system and IEEE 145 bus test system are also favored by some researchers to implement the SIPS.…”

Section: T a B L Ementioning

confidence: 99%

Recent trends in integrity protection of power system: A literature review

Rajalwal

Ghosh

2020

Int Trans Electr Energ Syst

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Background: An increase in the load demand has been forcing the electrical components to work near their allowable limits. The transformation of the power system in terms of technology and geographical stretch has imposed the necessity of employing advanced protection schemes in the system. The system integrity protection scheme (SIPS) has evolved as an effective measure with the gradual sprawl of the conventional grid with local measurements to a smart grid with wide-area measurement system. Aims: SIPS is a rapidly growing field in the power system and thus the study of its performance analysis, implementation methods, and applications is necessary. This paper provides a comprehensive overview of SIPS technology. Materials & Methods: In this paper, SIPS definition and classification are briefly discussed. SIPS normalization schemes that include load shedding, controlled islanding, generation rejection, ou-of-step prediction, relay settings are elaborated. To implement these normalization schemes, various methods such as index-based, optimization-based, WAMS based, EAC based, and ML-based are discussed and compared. Finally, a comparative assessment is framed based on SIPS applied for different power system issues such as transient instability, voltage instability, overloading, line contingency, protection security, etc. Results: It is observed that most of the proposed SIPS are implemented for improving transient stability. The preferred solution for implementing the SIPS by the researchers is load shedding. It is also observed that there is a lack of implementation of SIPS with renewable energy systems.

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Section: T a B L Ementioning

confidence: 99%

Recent trends in integrity protection of power system: A literature review

Rajalwal

Ghosh

2020

Int Trans Electr Energ Syst

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“…When applied online, the model can quickly predict the stability of power system based on measured data. A lot of studies have been done on the application of machine learning algorithms in power systems, but most algorithms are limited to shallow learning, such as support vector machine (SVM) [8]- [10], decision tree (DT) [11], [12], random forest (RF) [13], and K nearest neighbor (KNN) [14] etc. Due to their limited ability in data mining, the generalization ability is limited when dealing with complex problems.…”

Section: Introductionmentioning

confidence: 99%

Anti-Jitter and Refined Power System Transient Stability Assessment Based on Long-Short Term Memory Network

et al. 2020

IEEE Access

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In order to maintain the stable operation of power systems, quick and accurate transient stability assessment (TSA) after the fault clearance is important. Machine learning methods have been widely used in the transient stability analysis of power systems. However, how to make good use of time series data of PMUs and effectively balance the contradiction between rapidity and accuracy brings new challenges to TSA. To address this problem, we propose an anti-jitter dynamic evaluation method based on long-short term memory (LSTM) network. In this model, the trajectory cluster characteristics of generators power angles after fault clearance are taken as inputs, and an improved LSTM is used to learn the nonlinear mapping relationship between the input characteristics and the transient stability. Meanwhile, by the use of sliding time windows and anti-jitter mechanism, a hierarchical real-time prediction framework is constructed to effectively utilize the time series data of PMUs. The case studies on two systems indicate that the proposed method has superior evaluation accuracy and general performance. In addition, the proposed method can effectively evaluate the stability margin or instability degree of samples, which provides reliable reference information for emergency control. INDEX TERMS Deep learning, long-short term memory, recurrent neural network, transient stability assessment, artificial intelligence.

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“…In Abdelaziz, by using the kinetic energy deviation, mechanical input power, and average acceleration during the fault, as input features, a neural network‐based technique is proposed for OOS detection. In Aghamohammadi and Abedi, for OOS prediction, a DT‐based approach consisting of fault detector DT (FDDT), clearance detector DT (CDDT), and instability predictor DT (IPDT) has been proposed. The proposed method in this detects the occurrence and clearance of the fault by FDDT and CDDT technique respectively.…”

Section: Introductionmentioning

confidence: 99%

“…It is seen that generally the algorithms for recognizing OOS condition can be categorized into 2 groups: detection algorithms [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] and prediction algorithms. [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] In Table 1, it is concluded that a number of conventional methods including an adaptive OOS relay, based on the classical equal area criterion using power angle (P-δ), are proposed in which the newly developed technology of synchronized phasor measurements plays an important role. 2 In Taylor et al, 3 the property of the rate of change of apparent impedance is used for proposing an OOS relaying scheme.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent prediction of out-of-step condition on synchronous generators because of transient instability crisis

Zare

Alinejad‐Beromi

Yaghobi

2018

Int Trans Electr Energ Syst

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Summary This paper presents an adaptive scheme for predicting out‐of‐step (OOS) condition of synchronous generator based on the Bayesian technique. The proposed scheme performs as an intelligent OOS method for synchronous generators from which by using training variables, the tripping signals are estimated. For classifying target classes between stable and OOS conditions, a series of measurements are derived under various fault scenarios including topological and operational disturbances. The tripping signals are estimated by using feature selection technique based on the Bayesian technique. In this procedure, the data of input variables and corresponding output target classes are implemented as input‐output pair data for Bayesian training and testing. For this propose, the ability of the OOS protective scheme is examined for a number of unseen samples in working mode. The proposed approach is applied on IEEE 39‐bus test system from which by using trained variables, the tripping signals are estimated online. Furthermore, to evaluate the proposed protective scheme in real‐time environment, a 2‐machine experimental case is used to assess the effectiveness of the proposed scheme. The results show a promising performance of proposed protective scheme for proper estimating of tripping signals.

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DT based intelligent predictor for out of step condition of generator by using PMU data

Cited by 47 publications

References 22 publications

Recent trends in integrity protection of power system: A literature review

Recent trends in integrity protection of power system: A literature review

Anti-Jitter and Refined Power System Transient Stability Assessment Based on Long-Short Term Memory Network

Intelligent prediction of out-of-step condition on synchronous generators because of transient instability crisis

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