Artificial Neural Network-Based Voltage Stability Online Monitoring Approach for Distributed Generation Integrated Distribution System
DOI:
https://doi.org/10.13052/dgaej2156-3306.3866Keywords:
Artificial neural network, distributed generation, distribution system, voltage stability index, phasor measurement unit, voltage stability assessmentAbstract
Due to the growth of electric power demand and the intricacy of modern distribution system structure, the voltage stability issue is evolving as a critical problem in distribution grids. Therefore, it is imperative to investigate the corrective measures. In this paper, artificial neural network (ANN) based voltage stability online monitoring approach for distribution systems with distribution generators (DGs) is proposed. The proposed technique employs a local voltage stability index known as the stability index (SI) to identify the weak bus information, which is more effective compared to the conventional load margin techniques. Furthermore, the nonlinear relationship of the distribution grid control status and the resultant SI is mapped using ANN. From the installed distribution-level phasor measurement units (PMUs), the state parameters of buses can be obtained, and the resultant values of SI can be estimated. This approach can significantly enhance the computational speed of SI and evaluate the voltage stability measurement of distribution network in real-time, which assist the operator of the network in order to determine the operational condition and execute actions quickly. The proposed approach is applied on the modified IEEE 33 and IEEE 69-bus system with DGs. It is found that the computation time needed for assessment of voltage stability by CPF method is 16.2500 s and 21.8872 s whilst the computation time needed for the proposed method for the same assessment is 0.0677 s and 0.0749 s respectively for modified IEEE 33 and IEEE 69-bus system. This demonstrates that the proposed method has high accuracy and efficacy.
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References
Chakravorty, M. and D. Das. 2001. Voltage stability analysis of radial distribution networks. Electr. Power Energy Syst. 23(2): 129–135.
Sundarajoo, S. and D.M. Soomro. 2022. Under voltage load shedding and penetration of renewable energy sources in distribution systems: a review. Int. J. Model. Simul. DOI: 10.1080/02286203.2022.2143191.
Ajjarapu, V. and C. Christy. 1992. The continuation power flow: A tool for steady state voltage stability analysis. IEEE Trans. Power Syst. 7(1): 416–423.
Almeida, A.B., E.V. De Lorenci, R.C. Leme, A.C.Z. De Souza, B.I.L. Lopes and K. Lo. 2013. Probabilistic voltage stability assessment considering renewable sources with the help of the PV and QV curves. IET Renew. Power Gener. 7(5): 521–530.
Bharati, A.K. and V. Ajjarapu. 2019. Investigation of relevant distribution system representation with DG for voltage stability margin assessment. IEEE Trans. Power Syst. 35(3): 2072–2081.
Alzaareer, K., M. Saad, D. Asber, S. Lefebvre and L. Lenoir. 2020. Impedance sensitivity-based corrective method for online voltage control in smart distribution grids. Electr. Power Syst. Res. 181: 106188.
Kumar, S., B. Tyagi, V. Kumar and S. Chohan. 2021. PMU-based voltage stability measurement under contingency using ANN. IEEE Trans. Instrum. Meas. 71: 9000111.
Nageswara Rao, A. and P. Vijayapriya. 2021. Salp swarm algorithm and phasor measurement unit based hybrid robust neural network model for online monitoring of voltage stability. Wirel. Networks 27: 843–860.
Li, S., J. Hou, A. Yang and J. Li. 2021. DNN-based distributed voltage stability online monitoring method for large-scale power grids. Front. Energy Res. 9: 625914.
Nageswara Rao, A. and P. Vijayapriya. 2022. A robust machine learning model for monitoring online voltage stability. Int. J. Ambient Energy 43(1): 1021–1035.
Bento, M.E.C. 2022. A method for monitoring the load margin of power systems under load growth variations. Sustain. Energy, Grids Networks 30: 100677.
Bento, M.E.C. 2022. Monitoring of the power system load margin based on a machine learning technique. Electr. Eng. 104: 249–258.
Ashraf, S.M., A. Gupta, D.K. Choudhary and S. Chakrabarti. 2017. Voltage stability monitoring of power systems using reduced network and artificial neural network. Int. J. Electr. Power Energy Syst. 87: 43–51.
Pourbagher, R., S.Y. Derakhshandeh and M.E.H. Golshan. 2022. A novel method for online voltage stability assessment based on PMU measurements and Thevenin equivalent. IET Gener. Transm. Distrib. 16(9): 1780–1794.
England, B.S. and A.T. Alouani. 2020. Real time voltage stability prediction of smart grid areas using smart meters data and improved thevenin estimates. Int. J. Electr. Power Energy Syst. 122: 106189.
Chandra, A. and A.K. Pradhan. 2019. Online voltage stability and load margin assessment using wide area measurements. Int. J. Electr. Power Energy Syst. 108: 392–401.
Kamel, M., A.A. Karrar and A.H. Eltom. 2018. Development and application of a new voltage stability index for on-line monitoring and shedding. IEEE Trans. Power Syst. 33(2): 1231–1241.
Musirin, I. and T.K. Abdul Rahman. 2002. On-line voltage stability based contingency ranking using fast voltage stability index (FVSI). In IEEE/PES Transmission and Distribution Conference and Exhibition. p. 1118–1123.
Pérez-Londoño, S., L.F. Rodríguez and G. Olivar. 2014. A simplified voltage stability index (SVSI). Int. J. Electr. Power Energy Syst. 63: 806–813.
Ratra, S., R. Tiwari and K.R. Niazi. 2018. Voltage stability assessment in power systems using line voltage stability index. Comput. Electr. Eng. 70: 199–211.
Vu, K., M.M. Begovic, D. Novosel and M.M. Saha. 1999. Use of local measurements to estimate voltage-stability margin. IEEE Trans. Power Syst. 14(3): 1029–1035.
Haque, M.H. 2013. A smart instrument for on-line monitoring of static voltage stability index using local measurements. In 2013 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia). p. 1–5.
Li, S., Y. Tan, C. Li, Y. Cao and L. Jiang. 2018. A fast sensitivity-based preventive control selection method for online voltage stability assessment. IEEE Trans. Power Syst. 33(4): 4189–4196.
Yun, Z., X. Cui and K. Ma. 2019. Online thevenin equivalent parameter identification method of large power grids using LU factorization. IEEE Trans. Power Syst. 34(6): 4464–4475.
Goh, H.H., C.W. Tai, Q.S. Chua, S.W. Lee, B.C. Kok, K.C. Goh and K.T.K. Teo. 2014. Dynamic estimation of power system stability in different kalman filter implementations. In Proceedings of the 2014 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference. p. 41–46.
Roy, A., J. Bera and G. Sarkar. 2020. Real time stability analysis with distributed generation in multi-fed RDN using PMU and feed forward technique. Electr. Power Components Syst. 48(12–13): 1221–1233.
Alzaareer, K. and M. Saad. 2018. Real-time voltage stability monitoring in smart distribution grids. In 2018 International Conference on Renewable Energy and Power Engineering (REPE). p. 13–17.
Singh, J. and R. Banerjee. 2019. A study on single and multi-layer perceptron neural network. In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). p. 35–40.
Han, B., M. Li, J. Song, J. Li and J. Faraji. 2021. Optimal design of an on-grid microgrid considering long-term load demand forecasting: A case study. Distrib. Gener. Altern. Energy J. 35(4): 345–362.
Lei, G., C. Xu, J. Chen, H. Zhao and H. Parvaneh. 2021. Performance of a hybrid neural-based framework for alternative electricity price forecasting in the smart grid. Distrib. Gener. Altern. Energy J. 37(3): 405–434.
Long, F., F. Liu, X. Peng, Z. Yu, H. Xu and J. Li. 2021. Power quality disturbance identification and optimization based on machine learning. Distrib. Gener. Altern. Energy J. 37(2): 159–174.
Ayyadevara, V.K. 2018. Artificial Neural Network. In Pro Machine Learning Algorithms. p. 135–165.
Mishra, S., D. Das and S. Paul. 2014. A simple algorithm for distribution system load flow with distributed generation. In International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014). p. 1–5.
Khamis, A., H. Shareef and A. Mohamed. 2015. Islanding detection and load shedding scheme for radial distribution systems integrated with dispersed generations. IET Gener. Transm. Distrib. 9(15): 2261–2275.
Baran, M.E. and F.F. Wu. 1989. Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans. Power Deliv. 4(2): 1401–1407.
Abid, M.S., H.J. Apon, A. Ahmed and K.A. Morshed. 2022. Chaotic slime mould optimization algorithm for optimal load-shedding in distribution system. Ain Shams Eng. J. 13(4): 101659.
Baran, M.E. and F.F. Wu. 1989. Optimal capacitor placement on radial distribution systems. IEEE Trans. Power Deliv. 4(1): 725–734.
Yuvaraj, T., K. Ravi and K.R. Devabalaji. 2017. DSTATCOM allocation in distribution networks considering load variations using bat algorithm. Ain Shams Eng. J. 8(3): 391–403.
