By focusing on the accuracy limitations of the naive Bayesian classifier in the transient stability assessment of power systems, a tree augmented naive Bayesian (TAN) classifier is adopted for the power system transient stability assessment. The adaptive Boosting (AdaBoost) algorithm is used in the TAN classifier to form an AdaBoost-based tree augmented naive Bayesian (ATAN) classifier for further classification performance improvement. To construct the ATAN classifier, eight attributes that reasonably reflect the transient stability or transient instability of a power system are selected as inputs of the proposed classifier. In addition, the class-attribute interdependence maximization (CAIM) algorithm is used to discretize the attributes. Then, the operating mechanism of the power system is used to obtain the dependencies between the attributes, and the parameters of the ATAN classifier are learned according to the Bayes’ theorem and the criterion of maximizing a posterior estimation. Four evaluation indicators of the ATAN classifier are used, that is, the value of Kappa, the area under the receiver operating characteristic curve (AUC), F1 score, and the average evaluation indicator. Lastly, experiments are implemented on the IEEE 3-generator 9-bus system and IEEE 10-generator 39-bus system. The simulation results show that the ATAN classifier can significantly improve the classification performance of the transient stability assessment of the power system.
KundurPPaserbaJAjjarapuV, et al. Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions. IEEE Trans Power Syst2004; 19(3): 1387–1401.
2.
LvJ. Transient stability assessment in large-scale power systems based on the sparse single index model. Elect Power Syst Res2020; 184: 106291.
3.
AlinezhadBKaregarHK. Out-of-step protection based on equal area criterion. IEEE Trans Power Syst2016; 32(2): 968–977.
4.
XiongLZhuoFWangF, et al. Static synchronous generator model: a new perspective to investigate dynamic characteristics and stability issues of grid-tied PWM inverter. IEEE Trans Power Electron2016; 31(9): 6264–6280.
5.
MaJ. Power system wide-area stability analysis and control. Beijing: John Wiley & Sons, 2018.
6.
FarantatosEHuangRCokkinidesGJ, et al. A predictive generator out-of-step protection and transient stability monitoring scheme enabled by a distributed dynamic state estimator. IEEE Trans Power Deliv2016; 31(4): 1826–1835.
7.
RajapakseADGomezFNanayakkaraK, et al. Rotor angle instability prediction using post-disturbance voltage trajectories. IEEE Trans Power Syst2010; 25(2): 947–956.
8.
PalenskyPVan Der MeerAALopezCD, et al. Cosimulation of intelligent power systems: fundamentals, software architecture, numerics, and coupling. IEEE Ind Electron Mag2017; 11(1): 34–50.
9.
PicoHNAliprantisDCLinX. Transient stability assessment of power systems with uncertain renewable generation. In: Proceedings of the 10th bulk power systems dynamics and control symposium, Espinho, Portugal, 27 August–1 September 2017.
10.
RenCXuYZhangY. Post-disturbance transient stability assessment of power systems towards optimal accuracy-speed tradeoff. Prot Control Mod Power Syst2018; 3(1): 1–10.
11.
HuangDChenQMaS, et al. Wide-area measurement—based model-free approach for online power system transient stability assessment. Energies2018; 11(4): 958.
12.
LiMJiangPWuW, et al. Artificial neural network classifier of transient stability based on time-domain simulation. In: IEEE advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi’an, China, 25–27 May 2018, pp.685–689.
13.
HuWLuZWuS, et al. Real-time transient stability assessment in power system based on improved SVM. J Mod Power Syst Clean Energy2019; 7(1): 26–37.
14.
YuJJQHillDJLamAYS, et al. Intelligent time-adaptive transient stability assessment system. IEEE Trans Power Syst2018; 33(1): 1049–1058.
15.
YangY. Test based safety-critical software reliability estimation using Bayesian method and flow network structure. Proc IMechE, Part O: J Risk and Reliability2019; 233(5): 847–856.
16.
JiangHWangHHuW, et al. Fast incremental SVDD learning algorithm with the Gaussian kernel. Proc AAAI Conf Artif Intell2019; 33: 3991–3998.
17.
GuoJLiZJinJ. System reliability assessment with multilevel information using the Bayesian melding method. Reliab Eng Syst Saf2018; 170: 146–158.
18.
FoulliaronJBouillautLAkninP, et al. A dynamic Bayesian network approach for prognosis computations on discrete state systems. Proc IMechE, Part O: J Risk and Reliability2017; 231(5): 516–533.
19.
MuyeenSMHasanienHMAl-DurraA. Transient stability enhancement of wind farms connected to a multi-machine power system by using an adaptive ANN-controlled SMES. Energy Convers Manag2014; 78: 412–420.
20.
SiddiquiSAVermaKNiaziKR, et al. Real-time monitoring of post-fault scenario for determining generator coherency and transient stability through ANN. IEEE Trans Ind Appl2018; 54(1): 685–692.
21.
ZhengLHuWZhouY, et al. Deep belief network based nonlinear representation learning for transient stability assessment. In: 2017 IEEE power & energy society general meeting, Chicago, IL, 16–20 July 2017, pp.1–5.
22.
SoniBPSaxenaAGuptaV, et al. Transient stability-oriented assessment and application of preventive control action for power system. J Eng2019; 2019(18): 5345–5350.
23.
ChenMLiuQChenS, et al. XGBoost-based algorithm interpretation and application on post-fault transient stability status prediction of power system. IEEE Access2019; 7: 13149–13158.
24.
WuSZhengLHuW, et al. Improved deep belief network and model interpretation method for power system transient stability assessment. J Mod Power Syst Clean Energy2020; 8(1): 27–37.
25.
SunKLikhateSVittalV, et al. An online dynamic security assessment scheme using phasor measurements and decision trees. IEEE Trans Power Syst2007; 22(4): 1935–1943.
26.
RovnyakSKretsingerSThorpJ, et al. Decision trees for real-time transient stability prediction. IEEE Trans Power Syst1994; 9(3): 1417–1426.
27.
GomezFRRajapakseADAnnakkageUD, et al. Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements. IEEE Trans Power Syst2011; 26(3): 1474–1483.
28.
HuangXShiLSuykensJA. Support vector machine classifier with pinball loss. IEEE Trans Pattern Anal Mach Intell2014; 36(5): 984–997.
29.
MosaviABAmiriAHosseiniH. A learning framework for size and type independent transient stability prediction of power system using twin convolutional support vector machine. IEEE Access2018; 6: 69937–69947.
30.
TangJSuiH. Power system transient stability assessment based on PCA and support vector machine. In: 2018 International conference on Mechanical, Electrical, Electron. Engineering & Science (MEEES 2018), Chongqing, China, 26–27 May 2018, pp.361–365.
31.
TanBYangJPanX, et al. Representational learning approach for power system transient stability assessment based on convolutional neural network. J Eng2017; 2017(13): 1847–1850.
32.
ShiZYaoWZengL, et al. Convolutional neural network-based power system transient stability assessment and instability mode prediction. Appl Energy2020; 263: 114586.
33.
YanRGengGJiangQ, et al. Fast transient stability batch assessment using cascaded convolutional neural networks. IEEE Trans Power Syst2019; 34(4): 2802–2813.
34.
ZhangHMarshDWR. Generic Bayesian network models for making maintenance decisions from available data and expert knowledge. Proc IMechE, Part O: J Risk and Reliability2018; 232(5): 505–523.
35.
RoperoRFRenooijSvan der GaagLC. Discretizing environmental data for learning Bayesian-network classifiers. Ecol Model2018; 368: 391–403.
XiaoLDongYDongY. An improved combination approach based on Adaboost algorithm for wind speed time series forecasting. Energy Convers Manag2018; 160: 273–288.
39.
LeeWJunCHLeeJS. Instance categorization by support vector machines to adjust weights in AdaBoost for imbalanced data classification. Inf Sci2017; 381: 92–103.
40.
DownsTTangA. Boosting the tree augmented naive Bayes classifier. In: International conference on intelligent data engineering and automated learning, Exeter, UK, 25–27 August 2004, pp.708–713.
41.
RuedaJLErlichI. Probabilistic framework for risk analysis of power system small-signal stability. Proc IMechE, Part O: J Risk and Reliability2012; 226(1): 118–133.
42.
MansourYChangAYTambyJ, et al. Large scale dynamic security screening and ranking using neural networks. IEEE Trans Power Syst1997; 12(2): 954–960.
43.
DavidsonJFouadAA. Application of artificial neural networks in power system security and vulnerability assessment. IEEE Trans Power Syst1994; 9(1): 525–532.
44.
KurganLACiosKJ. CAIM discretization algorithm. IEEE transactions on Knowledge and Data Engineering. 2004 Mar 3; 16(2): 145–153.
HandDJAnagnostopoulosC. When is the area under the receiver operating characteristic curve an appropriate measure of classifier performance?Pattern Recognit Lett2013; 34(5): 492–495.
47.
LiptonZCElkanCNaryanaswamyB. Optimal thresholding of classifiers to maximize F1 measure. In: Joint European conference on machine learning and knowledge discovery in databases, Nancy, France, 15–19 September 2014, pp.225–239.
48.
XuTBirchfieldABShetyeKS, et al. Creation of synthetic electric grid models for transient stability studies. In: The 10th bulk power systems dynamics and control symposium – IREP, Espinho, Portugal, 27 August–1 September 2017, pp.1–6.
