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A novel sine augmented scaled sine cosine algorithm for frequency control issues of a hybrid distributed two-area power system

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Abstract

In this research article, a novel approach is proposed by considering the sine augmented scaled sine cosine (SAS-SCA) Algorithm for the load frequency control of multi-area hybrid distributed power system. The said modified algorithm is tested by comparing it with the standard SCA algorithm by considering various standard benchmark test functions. Further, the genuine utilization of the SAS-SCA algorithm is tried by planning a TID controller for frequency regulation of the distributed system which consists of various renewal energy sources. The supremacy of the SAS-SCA-based TID controller is introduced by comparing its outcomes and other modern soft computing techniques, and it is observed that the SAS-SCA-based TID controller is more effective compared to the conventional controller.

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References

  1. Elgerd OI (2006) Electric energy systems theory. Tata McGraw Hill, New Delhi

    Google Scholar 

  2. Hassan KM, Niknam T, Shasadeghi M, Dragicevic T, Blaabjerg F (2017) Load frequency control in microgrids based on a stochastic non-integer controller. IEEE Trans Sustain Energy 9:853–861

    Google Scholar 

  3. Bengiamin NN, Chan WC (1982) Variable structure control of electric power generation. IEEE Trans Power Appar Syst 2:376–380

    Article  Google Scholar 

  4. Alhelouab HH, Hamedani-Golshan ME, Askari-Marnania J (2018) Robust sensor fault detection and isolation scheme for interconnected smart power systems in presence of RER and EVs using unknown input observer. Int J Electr Power Energy Syst 99:682–694

    Article  Google Scholar 

  5. Hasan N (2012) Design and analysis of pole-placement controller for interconnected power systems’. Int J Emerg Technol Adv Eng 2:212–217

    Google Scholar 

  6. Fathy A, Kassem AM (2018) Antlion optimizer-ANFIS load frequency control for multi-interconnected plants comprising photovoltaic and wind turbine. ISA Trans 87:282–296

    Article  Google Scholar 

  7. Tan W, Xu Z (2009) Robust analysis and design of load frequency controller for power systems. Electr Power Syst Res 5:846–853

    Article  Google Scholar 

  8. Tan W, Zhou H (2012) Robust analysis of decentralized load frequency control for multi-area power systems’. Int J Electr Power Energy Syst 43:996–1005

    Article  Google Scholar 

  9. Oysal Y (2005) A comparative study of adaptive load frequency controller designs in a power system with dynamic neural network models. Energy Convers Manag 46:2656–2668

    Article  Google Scholar 

  10. Bing H, Zhou L, Yang F, Xiang Z (2016) Individual pitch controller based on fuzzy logic control for wind turbine load mitigation. IET Renew Power Gener 5:687–693

    Google Scholar 

  11. Alhelou HH, Hamedani-Golshan ME, Zamani R, Forushani EH, Siano P (2018) Challenges and opportunities of load frequency control in conventional, modern and future smart power systems: a comprehensive review. Energies 11:2497

    Article  Google Scholar 

  12. Ali ES, Abd-Elazim SM (2013) BFOA based design of PID controller for two area load frequency control with nonlinearities’. Int J Electr Power Energy Syst 51:224–231

    Article  Google Scholar 

  13. Hassan KM, Niknam T (2015) A new intelligent online fuzzy tuning approach for multi-area load frequency control Self-Adaptive Modified Bat Algorithm. Int J Electr Power Energy Syst 71:254–261

    Article  Google Scholar 

  14. Khadanga RK, Satapathy JK (2015) Time delay approach for PSS and SSSC based coordinated controller design using hybrid PSO–GSA algorithm’. Int J Electr Power Energy Syst 7:262–273

    Article  Google Scholar 

  15. Fatemeh D, Bevrani H (2012) Multiobjective design of load frequency control using genetic algorithms. Int J Electr Power Energy Syst 42:257–263

    Article  Google Scholar 

  16. Almoataz AY, Ali ES (2016) Load frequency controller design via artificial cuckoo search algorithm. Electr Power Compon Syst 44:90–98

    Article  Google Scholar 

  17. Sekhar GC, Sahu RK, Baliarsingh AK, Panda S (2016) Load frequency control of power system under deregulated environment using optimal firefly algorithm. Int J Electr Power Energy Syst 74:195–211

    Article  Google Scholar 

  18. Gorripotu TS, Sahu RK, Panda S (2015) Application of firefly algorithm for AGC under deregulated power system. Comput Intell Data Min 1:677–687

    Google Scholar 

  19. Sahu RK, Panda S, Padhan S (2014) Optimal gravitational search algorithm for automatic generation control of interconnected power systems. Ain Shams Eng J 5:721–733

    Article  Google Scholar 

  20. Khadanga RK, Satapathy JK (2015) A new hybrid GA–GSA algorithm for tuning damping controller parameters for a unified power flow controller’. Int J Electr Power Energy Syst 73:1060–1069

    Article  Google Scholar 

  21. Khadanga RK, Kumar A (2016) Hybrid adaptive ‘gbest’-guided gravitational search and pattern search algorithm for automatic generation control of multi-area power system. IET Gener Transm Distrib 11:3257–3267

    Article  Google Scholar 

  22. Sahu BK, Mohanty PK (2019) Design and implementation of fuzzy-PID controller with derivative filter for AGC of two-area interconnected hybrid power system. Int J Innov Technol Explor Eng 8:4198–4212

    Article  Google Scholar 

  23. Khadanga RK, Kumar A, Panda S (2019) A novel modified whale optimization algorithm for load frequency controller design of a two-area power system composing of PV grid and thermal generator. Neural Comput Appl 13:1–12

    Google Scholar 

  24. Pan I, Das S (2016) Fractional order fuzzy control of hybrid power system with renew-able generation using chaotic PSO. ISA Trans 62:19–29

    Article  Google Scholar 

  25. Lee DL, Wang L (2008) Small-signal stability analysis of an autonomous hybrid renewable energy power generation/energy storage system part I: time-domain simulations. IEEE Trans Energy Convers 23:311–320

    Article  Google Scholar 

  26. Singh K, Amir M, Ahmad F, Khan MA (2020) An integral tilt derivative control strategy for frequency control in multi-microgrid system. IEEE Syst J 15:1477

    Article  Google Scholar 

  27. Hosseinzadeh M, Salmasi FR (2015) Power management of an isolated hybrid AC/DC micro-grid with fuzzy control of battery banks. IET Renew Power Gener 9:484–493

    Article  Google Scholar 

  28. Çam E, Kocaarslan I (2005) Load frequency control in two area power systems using fuzzy logic controller. Energy Convers Manag 46:233–243

    Article  Google Scholar 

  29. Yang J, Zhang S, Xiang Y, Liu J, Han X, Teng F (2020) LSTM auto-encoder based representative scenario generation method for hybrid hydro-PV power system . IET Gener Transm Distrib 14:5935

    Article  Google Scholar 

  30. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Article  Google Scholar 

  31. Kamboj VK, Bath SK, Dhillon JS (2016) Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer’. Neural Comput Appl 27:1301–1316

    Article  Google Scholar 

  32. Attia AF, Sehiemy RAEI, Hasanien HM (2018) Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. Int J Electr Power Energy Syst 99:331–343

    Article  Google Scholar 

  33. Reddy KS, Panwar LK, Panigrahi BK, Kumar R (2018) A new binary variant of sine–cosine algorithm: development and application to solve profit-based unit commitment problem. Arab J Sci Eng 43:4041–4056

    Article  Google Scholar 

  34. Li S, Fang H, Liu X (2018) Parameter optimization of support vector regression based on sine cosine algorithm. Expert Syst Appl 91:63–77

    Article  Google Scholar 

  35. Elaziz MA, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. Expert Syst Appl 90:484–500

    Article  Google Scholar 

  36. Nenavath H, Jatoth RK (2018) Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking. Appl Soft Comput 62:1019–1043

    Article  Google Scholar 

  37. Rizk-Allah RM (2018) Hybridizing sine cosine algorithm with multi-orthogonal search strategy for engineering design problems. J Comput Design Eng 5:249–273

    Article  Google Scholar 

  38. Khadanga RK, Padhy S, Panda S, Kumar A (2018) Design and analysis of tilt integral derivative controller for frequency control in an islanded microgrid: a novel hybrid dragonfly and pattern search algorithm approach’. Arab J Sci Eng 43:3103–3114

    Article  Google Scholar 

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Correspondence to Rajendra Kumar Khadanga.

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Khadanga, R.K., Kumar, A. & Panda, S. A novel sine augmented scaled sine cosine algorithm for frequency control issues of a hybrid distributed two-area power system. Neural Comput & Applic 33, 12791–12804 (2021). https://doi.org/10.1007/s00521-021-05923-w

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  • DOI: https://doi.org/10.1007/s00521-021-05923-w

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