A new intelligent time-series prediction technique for coherency identification performance enhancement

Authors

  • MOHD AIFAA MOHD ARIFF DEPARTMENT OF ELECTRICAL ENGINEERING, FACULTY OF ELECTRICAL AND ELECTRONIC ENGINEERING, UNIVERSITI TUN HUSSEIN ONN MALAYSIA, 86400 BATU PAHAT, JOHOR. http://orcid.org/0000-0002-6824-3346

DOI:

https://doi.org/10.48129/kjs.v48i4.10383

Keywords:

Time-series prediction, coherency identification, nonlinear autoregressive exogenous, artificial neural network, power system

Abstract

The manuscript proposes a time-series prediction technique to enhance the response time of the coherency identification technique. The proposed methodology utilizes the nonlinear autoregressive exogenous neural network (NARX) algorithm to predict the generator speed deviations following a disturbance in the system. Consequently, the coherency identification technique based on independent component analysis (ICA) is utilized on the predicted system responses. The effectiveness of the proposed approach is demonstrated on of the IEEE 16-generator 68-bus test system model. The result shows that the proposed technique is able to predict $0.2$s ahead following a disturbance in the system accurately. Therefore, the NARX allows a $0.2$s head start for the ICA to determine the real-time coherent generator group in the system. Furthermore, the result shows that the proposed approach is able to identify the coherent group of generators based on the predicted generator speed deviation in all cases considered in this study, accurately. Conclusively, the result implies that the proposed technique is able to speed up the overall coherency identification process in a power system operation.

Author Biography

MOHD AIFAA MOHD ARIFF, DEPARTMENT OF ELECTRICAL ENGINEERING, FACULTY OF ELECTRICAL AND ELECTRONIC ENGINEERING, UNIVERSITI TUN HUSSEIN ONN MALAYSIA, 86400 BATU PAHAT, JOHOR.

He received the B.Eng degree in electrical engineering and the M.Eng degree in electrical-power from Universiti Teknologi Malaysia (UTM) in 2008 and 2010, respectively. Consequently, he obtained the Ph.D. degree at the Department of Electrical and Electronic Engineering, Imperial College London, London, U.K. Currently, he is an academician in Universiti Tun Hussein Onn Malaysia (UTHM). Also, he is Editor of the IEEE Open Access Journal of Power and Energy. His current research interests include of power system dynamics, coherency identification, and adaptive protection in power system.

References

Ak, R., Fink, O., & Zio, E. (2015). Two machine

learning approaches for short-term wind

speed timeseries prediction. IEEE Transactions

on Neural Networks and Learning Systems,

(8):1734–1747.

Ariff, M., & Pal, B.C. (2012). Coherency

identification in interconnected power system—

an independent component analysis approach.

IEEE Transactions on Power Systems,

(2):1747–1755.

Barocio, E., Korba, P., Sattinger, W. & Sevilla,

F.R.S. (2019). Online coherency identification

and stability condition for large

interconnected power systems using an

unsupervised data mining technique. IET

Generation, Transmission & Distribution,

(15):3323–3333.

Biyik, E., & Husein, M. (2018). Damping widearea

oscillations in power systems: a model

predictive control design. Turkish Journal of

Electrical Engineering & Computer Sciences,

(1):467–478.

Chow, J.H. (2013). Power system coherency and

model reduction, Springer. Vol. 84.

Cuicui, J., Weidong, L., Liu, L., Ping, L. & Xian,

W. (2019). A coherency identification method

of active frequency response control based on

support vector clustering for bulk power system.

Energies, 12(16):3155.

Han, M., Zhang, S., Xu, M., Qiu, T. & Wang, N.

(2018). Multivariate chaotic time series online

prediction based on improved kernel recursive

least squares algorithm. IEEE Transactions

on Cybernetics, 49(4):1160-1172.

JanHendrik, M., Nils, B. & Martin, B. (2019). Distribution

system monitoring for smart power

grids with distributed generation using artificial

neural networks. International Journal

of Electrical Power & Energy Systems,

:472–480.

Khalil, A.M. & Iravani, R. (2015). A dynamic

coherency identification method based onfrequency

deviation signals. IEEE Transactions

on Power Systems, 31(3):1779–1787.

Khorramdel, B., Chung, C., Safari, N. and Price, G.

(2018). A fuzzy adaptive probabilistic wind

power prediction framework using diffusion

kernel density estimators. IEEE Transactions

on Power Systems, 33(6):7109–7121.

Koochi, M.H.R., Dehghanian, P., Esmaeili, S.,

Dehghanian, P. & Wang, S. (2018).

A synchrophasor-based decision tree approach

for identification of most coherent generating

units. IECON 2018-44th Annual Conference

of the IEEE Industrial Electronics Society.

Columbia, United States.

Koochi, M.H.R., Esmaeili, S. and Ledwich, G.

(2019). Taxonomy of coherency detection

and coherency-based methods for generators

grouping and power system partitioning.

IET Generation, Transmission & Distribution,

(12):2597–2610.

Lin, Z., Wen, F., Ding, Y. & Xue, Y.

(2017). Data driven coherency identification

for generators based on spectral clustering.

IEEE Transactions on Industrial Informatics,

(3):1275–1285.

Lin, Z., Wen, F., Ding, Y., Xue, Y., Liu, S., &

et al. (2018). WAMS-based coherency detection

for situational awareness in power systems

with renewables. IEEE Transactions on

Power Systems, 33(5):5410-5426.

Liu, J., Vitelli, V., Zio, E. and Seraoui, R. (2015).

A novel dynamic-weighted probabilistic support

vector regression-based ensemble for

prognostics of time series data. IEEE Transactions

on Reliability, 64(4):1203–1213.

Lu, C., Zhang, J., Zhang, X., Zhao, Y.

(2018). Wide-area oscillation identification

and damping control in power systems. Foundations

and Trends in Electric Energy Systems,

(2):133–197.

Ma, F. & Vittal, V. (2012). A hybrid dynamic

equivalent using ANN-based boundary

matching technique. IEEE Transactions

on Power Systems, 27(3):1494–1502.

Martin, K.E. (2015). Synchrophasor measurements

under the IEEE standard c37.118.1-2011 with

amendment c37.118.1a. IEEE Transactions

on Power Delivery, 30(3):1514–1522.

Nayak, C.K. and Nayak, M.R. (2018). Technoeconomic

analysis of a grid-connected pv

and battery energy storage system considering

time of use pricing. Turkish Journal of

Electrical Engineering & Computer Sciences,

(1):318–329.

Nazaripouya, H., Wang, B., Wang, Y., Chu, P.,

Pota, H. & et al. (2016). Univariate time series

prediction of solar power using a hybrid

wavelet-ARMA-NARX prediction method.

Proceedings of 2016 IEEE/PES Transmission

and Distribution Conference and Exposition

(T&D). Latin America, United States.

Ouyang, T., Huang, H., He, Y. & Tang, Z. (2020).

Chaotic wind power time series prediction via

switching data-driven modes. Renewable Energy,

:270-281.

Pal, B. & Chaudhuri, B. (2006). Robust control in

power systems. Springer Science and Business

Media.

Papadopoulos, P.N. & Milanovi´c, J.V. (2016).

Probabilistic framework for transient stability

assessment of power systems with high penetration

of renewable generation. IEEE Transactions

on Power Systems, 32(4):3078-3088.

Pena-Sanchez, Y. & Ringwood, J. (2017). A critical

comparison of AR and ARMA models for

short-term wave forecasting. Proceedings of

the 12th EuropeanWave & Tidal Energy Conference.

Cork, Ireland.

Rezaeian, M.H., Esmaeili, S. and Fadaeinedjad,

R. (2017). Generator coherency and network

partitioning for dynamic equivalencing using

subtractive clustering algorithm. IEEE Systems

Journal, 12(4):3085–3095.

Rinaldi, G., Menon, P.P., Edwards, C. & Ferrara,

A. (2018). Sliding mode based dynamic

state estimation for synchronous generators in

power systems. IEEE control systems letters,

(4):785-790.

Rogers, G. (2012). Power system oscillations.

Springer Science and Business Media.

Saha, M.M., Izykowski, J.J. &R Rosolowski, E.

(2009). Fault location on power networks.

Springer Science and Business Media.

Silva, C., Guimar˜aes, F.G., Sadaei, H.J. and

Coelho, V.N. (2017). A hybrid SARFIMAFTS

model for time series prediction in smart

grids. 2017 IEEE International Conference on

Fuzzy Systems (FUZZ-IEEE). Naples, Italy.

Talari, S., Shafie-Khah,M., Osˆorio, G.J., Aghaei, J.

and Catal˜ao, J.P. (2018). Stochastic modelling

of renewable energy sources from operators’

point-of-view: A survey. Renewable and Sustainable

Energy Reviews, 81:1953–1965.

Thakallapelli, A., Hossain, S.J. & Kamalasadan,

S. (2018). Coherency and online signal selection

based wide area control of wind integrated

power grid. IEEE Transactions on Industry

Applications, 54(4):3712–3722.

Wang, Y., Wang, D. & Tang,Y. (2020). Clustered

hybrid wind power prediction model based on

ARMA, PSO-SVM, and clustering methods.

IEEE Access, 8:17071–17079.

Wang, Z., Wang, W., Liu, C., Wang, B. & Feng, S.

Short-term probabilistic forecasting for

regional wind power using distance-weighted

kernel density estimation. IET Renewable

Power Generation, 12(15):1725-1732.

Wei, J., Kundur, D. & Butler-Purry, K.L. (2014).

A novel bio-inspired technique for rapid real

time generator coherency identification. IEEE

Transactions on Smart Grid, 6(1):178–188.

Wei, Y., Arunagirinathan, P., Arzani, A. and

Venayagamoorthy, G.K. (2019). Situational

awareness of coherency behavior of synchronous

generators in a power system with

utility-scale photovoltaics. Electric Power

Systems Research, 172:38–49.

Zhang, C.Y., Chen, C.P., Gan, M. and Chen,

L. (2015). Predictive deep Boltzmann machine

for multiperiod wind speed forecasting.

IEEE Transactions on Sustainable Energy,

(4):1416–1425.

Zhu, H. & Lu, X. (2016). The prediction of PM2.

value based on ARMA and improved BP

neural network model. Proceedings of International

Conference on Intelligent Networking

and Collaborative Systems (INCoS). Ostrawva,

Czech Republic.

Zina, B., Octavian, C., Ahmed, R., Haritza, C.

and Najiba, M.B., (2018). A nonlinear autoregressive

exogenous (NARX) neural network

model for the prediction of the daily direct solar

radiation. Energies, 11(3):620.

Published

16-08-2021