Optimization of Load Frequency Control Gain Parameters for Stochastic Microgrid Power System

Murugesan D., Jagatheesan K., Pritesh Shah, Ravi Sekhar

Abstract


Interconnected multi-area microgrids are vital for the future of sustainable and reliable power systems. Effective load frequency control (LFC) is indispensable for ensuring their stable operation. This paper introduces a PID-based LFC system tailored for a stochastic microgrid with diverse power sources, including solar, wind, diesel engine generators, and electrical batteries. The gain parameters of the proposed microgrid PID LFC controller are optimized using genetic algorithms (GA), teaching learning-based optimization (TLBO), and cohort intelligence algorithms. Integral time-multiplied absolute error (ITAE) and integral time-squared error (ITSE) serve as the cost functions for all optimization algorithms. The study evaluated the performance of these optimized microgrid PID LFC configurations under random step load disruptions. Our primary findings reveal that the cohort intelligence-optimized PID LFC controller excels in minimizing computation time (upto 76% and 94% lesser than GA and TLBO respectively) and exhibits superior robust response characteristics. Moreover, the cohort intelligence algorithm requires fewer iterations (upto 66% and 90% lesser than GA and TLBO respectively) and enhances power supply quality within the multi-power microgrid electrical framework, specifically in terms of effective load frequency control.

Keywords


Genetic Algorithm; Load Frequency Control; Teaching Learning based Optimization; Cohort Intelligence; Integral Time Absolute Error

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References


O. I. Elgerd and H. H. Happ, "Electric Energy Systems Theory: An Introduction," in IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-2, no. 2, pp. 296-297, April 1972.

H. Bevrani, Robust Power System Frequency Control, vol. 4, New York: springer, 2014.

A. Kassem, “Neural predictive controller of a two-area load frequency control for interconnected power system,” Ain Shams Engineering Jour- nal, vol. 1, no. 1, pp. 49–58, 2010.

K. Jagatheesan, B. Anand, N. Dey, A. Ashour, and V. Balas, “Load frequency control of hydro-hydro system with fuzzy logic controller considering non-linearity,” in Recent Developments and the New Direction in Soft-Computing Foundations and Applications, pp. 307–318, 2018.

W. Eshetu, P. Sharma, and C. Sharma, “Anfis based load frequency control in an isolated micro grid,” in 2018 IEEE International Conference on Industrial Technology (ICIT), pp. 1165–1170, 2018.

D. Murugesan, K. Jagatheesan, P. Shah, and R. Sekhar, “Fractional order PIλDμ controller for microgrid power system using cohort intelligence optimization,” Results in Control and Optimization, vol. 11, p. 100218, 2023.

E.-H. Dulf, “Simplified fractional order controller design algorithm,” Mathematics, vol. 7, no. 12, p. 1166, 2019.

S. Nangrani and S. Bhat, “Fractional order controller for controlling power system dynamic behavior,” Asian Journal of Control, vol. 20, no. 1, pp. 403–414, 2018.

M. Zheng, G. Zhang, and T. Huang, “Tuning of fractional complex- order direct current motor controller using frequency domain analysis,” Mathematical Methods in the Applied Sciences, vol. 44, no. 4, pp. 3167-3181, 2020.

A. V. Tare, J. A. Jacob, V. A. Vyawahare, and V. N. Pande, “Design of novel optimal complex-order controllers for systems with fractional-order dynamics,” International Journal of Dynamics and Control, vol. 7, no. 1, pp. 355–367, 2019.

M. Shahiri, A. Ranjbar Noei, M. R. Karami, and R. Ghaderi, “Tuning method for fractional complex order controller using standardized k-chart: Application to pemfc control,” Asian Journal of Control, vol. 18, no. 3, pp. 1102–1118, 2016.

B Ross and F. H. Northover, “A use for a derivative of complex order in the fractional calculus,” Indian J. Pure Appl. Math, vol. 9, no. 4 pp. 400-406, 1978.

J. A. Jacob, A. V. Tare, V. A. Vyawahare, and V. N. Pande, “A review of time domain, frequency domain and stability analysis of linear complex- order systems,” in 2016 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), pp. 164–169, 2016.

A. Mohan et al., “Load frequency control of multi-microgrid system considering renewable energy sources using grey wolf optimization,” Journal: Smart Science, no. 3, pp. 198–217, 2019.

U. Rout, R. Sahu, and S. Panda, “Design and analysis of differential evolution algorithm based automatic generation control for interconnected power system,” Ain Shams Engineering Journal, vol. 4, no. 3, pp. 409– 421, 2013.

S. Surendiran and S. Thangavel, “Time varying acceleration coefficients with dominant social component particle swarm optimization for inter- connected power system,” ARPN Journal of Engineering and Applied Sciences, vol. 11, no. 3, pp. 1936–1946, 2016.

D. Murugesan, K. Jagatheesan, and D. Boopathi, “Meta-heuristic strategy planned controller for frequency supervision of integrated thermal plant with renewable source,” in 2021 IEEE 3rd PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS), pp. 1–2, 2021.

K. Naidu, H. Mokhlis, and A. Bakar, “Multiobjective optimization using weighted sum artificial bee colony algorithm for load frequency control,” International Journal of Electrical Power & Energy Systems, vol. 55, pp. 657–667, 2014.

R. Ramjug-Ballgobin and C. Ramlukon, “A hybrid metaheuristic optimization technique for load frequency control,” SN Applied Sciences, vol. 3, no. 5, pp. 1–14, 2021.

H. Shabani, B. Vahidi, and M. Ebrahimpour, “A robust pid controller based on imperialist competitive algorithm for load-frequency control of power systems,” ISA Transactions, vol. 52, no. 1, pp. 88–95, 2013.

F. Daneshfar and H. Bevrani, “Multiobjective design of load frequency control using genetic algorithms,” International Journal of Electrical Power & Energy Systems, vol. 42, no. 1, pp. 257–263, 2012.

S. Ramesh and A. Krishnan, “Modified genetic algorithm based load frequency controller for interconnected power systems,” Int. J. Electr. Power Eng., vol. 3, no. 1, pp. 26–30, 2009.

A. Roy, S. Dutta, and P. Roy, “Load frequency control of interconnected power system using teaching learning based optimization,” International Journal of Energy Optimization and Engineering (IJEOE), vol. 4, no. 1, pp. 102–117, 2015.

D. Murugesan, K. Jagatheesan, A. Kulkarni, P. Shah, and R. Sekhar, “Comparison of biologically inspired algorithm with socio-inspired technique on load frequency control of multi-source single-area power system,” in Applied Genetic Algorithm and Its Variants: Case Studies and New Developments, pp. 185–208, 2023.

R. Rao, V. Savsani, and D. Vakharia, “Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems,” Information Sciences, vol. 183, no. 1, pp. 1–15, 2012.

R. Rao and V. Kalyankar, “Parameter optimization of modern machining processes using teaching–learning based optimization algorithm,” Engineering Applications of Artificial Intelligence, vol. 26, no. 1, pp. 524–531, 2013.

A. Kulkarni, I. Durugkar, and M. Kumar, “Cohort intelligence: a self supervised learning behavior,” in 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1396–1400, 2013.

D. Murugesan, P. Shah, K. Jagatheesan, R. Sekhar, and A. Kulkarni, “Cohort intelligence optimization based controller design of isolated and interconnected thermal power system for automatic generation control,” in 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA), pp. 1–6, 2022.

D. Murugesan, K. Jagatheesan, A. Kulkarni, and P. Shah, “A socio inspired technique in nuclear power plant for load frequency control by using cohort intelligence optimization-based PID controller,” in Renewable Energy Optimization, Planning and Control: Proceedings of ICRTE 2022, pp. 1–12, 2023.

P. Shah and A. Kulkarni, “Application of variations of cohort intelligence in designing fractional PID controller for various systems,” Socio-cultural Inspired Metaheuristics, pp. 175–192, 2019.

R. Sekhar and P. Shah, “Predictive modeling of a flexible robotic arm using cohort intelligence socio-inspired optimization,” pp. 193–198, 2020.

Y. Chen, “Ubiquitous fractional order controls?,” IFAC Proceedings Volumes, vol. 39, no. 11, pp. 481–492, 2006.

Z. Li, L. Liu, S. Dehghan, Y. Chen, and D. Xue, “A review and evaluation of numerical tools for fractional calculus and fractional order controls,” International Journal of Control, vol. 90, no. 6, pp. 1165–1181, 2016.

Y. Chen, I. Petras, and D. Xue, “Fractional order control - A tutorial,” 2009 American Control Conference, pp. 1397-1411, 2009, doi: 10.1109/ACC.2009.5160719.

C. I. Muresan, I. Birs, C. Ionescu, E. H. Dulf, and R. De Keyser, “A Review of Recent Developments in Autotuning Methods for Fractional-Order Controllers,” Fractal and Fractional, vol. 6, no. 1, p. 37, 2022.

I. Petrasˇ, “Practical aspects of tuning and implementation of fractional-order controllers,” in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 54808, pp. 75-84, 2011.

I. Petras, “Tuning and implementation methods for fractional-order controllers,” Fractional Calculus and Applied Analysis, vol. 15, no. 2, pp. 282–303, 2012.

I. Petras, “Practical aspects for implementation of fractional-order con- trollers,” in Proceedings of the 2014 15th International Carpathian Control Conference (ICCC), pp. 428-431, 2014.

I. Petras, “Fractional-order nonlinear controllers: Design and implementation notes,” in 2016 17th International Carpathian Control Conference (ICCC), pp. 579-583, 2016, doi: 10.1109/CarpathianCC.2016.7501163.

I. Petras, “A note on fractional-order non-linear controller: possible neural network approach to design,” in 2016 International Joint Conference on Neural Networks (IJCNN), pp. 603-608, 2016, doi: 10.1109/IJCNN.2016.7727255.

A. Soukkou, M. C. Belhour, and S. Leulmi, “Review, Design, Optimization and Stability Analysis of Fractional-Order PID Controller,” International Journal of Intelligent Systems and Applications, vol. 8, no. 7, pp. 73–96, 2016.

A. Tepljakov et al., “Towards Industrialization of FOPID Controllers: A Survey on Milestones of Fractional-Order Control and Pathways for Future Developments,” IEEE Access, vol. 9, pp. 21016– 21042, 2021.

D. Vale´rio and J. S. da Costa, “Tuning-Rules for Fractional PID Controllers,” IFAC Proceedings Volumes, vol. 39, no. 11, pp. 28– 33, 2006.

B. M. Vinagre, C. A. Monje, A. J. Caldero´n, and J. I. Sua´rez, “Fractional PID Controllers for Industry Application: A Brief Introduction,” Journal of Vibration and Control, vol. 13, no. 9-10, pp. 1419–1429, 2007.

C. Yeroglu and N. Tan, “Note on fractional-order proportional–integral– differential controller design,” IET Control Theory & Applications, vol. 5, no. 17, pp. 1978–1989, 2011.

P. Shah, R. Sekhar, I. Iswanto, and M. Shah, “Complex order PIa+jbDc+jd controller design for a fractional order dc motor system,” Advances in Science Technology and Engineering Systems Journal, vol. 6, pp. 541–551, 03 2021.

R. Sekhar, T. P. Singh, and P. Shah, “ARX/ARMAX modeling and fractional order control of surface roughness in turning nano-composites,” in 2019 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE), pp. 97–102, 2019.

B. H. Abdelfettah, L. Mohamed, and D. Abdelbaki, “Synergy between fractional order control and industry 4.0: a bibliometric analysis,” Procedia Computer Science, vol. 204, pp. 803–810, 2022.

P. Shah, D. Sharma, and R. Sekhar, “Analysis of research trends in fractional controller using latent dirichlet allocation,” Engineering Letters, vol. 29, pp. 109–119, 2021.

P. Shah, R. Sekhar, I. Iswanto, and M. Shah, “Complex order pi a+ jb d c+ jd controller design for a fractional order dc motor system,” Advances in Science Technology and Engineering Systems Journal, vol. 6, pp. 541– 551, 2021.

P. Shah and R. Sekhar, “Predictive modeling and control of clamp load loss in bolted joints based on fractional calculus,” in Advances in Computing and Network Communications, (Singapore), pp. 15–32, 2021.

R. Sekhar, T. Singh, and P. Shah, “Micro and nano particle composite machining: Fractional order control of surface roughness,” In Proceedings of the Third International Conference on Powder, Granule and Bulk Solids: Innovations and Applications PGBSIA, pp. 35-42, 2020.

R. Sekhar, T. Singh, and P. Shah, “System identification of tool chip interface friction while machining CNT-Mg-Al composites,” In AIP conference proceedings, vol. 2317, no. 1, 2021.

R. Bhimte, K. Bhole-Ingale, P. Shah and R. Sekhar, "Precise Position Control of Quanser Servomotor using Fractional Order Fuzzy PID Controller," 2020 IEEE Bombay Section Signature Conference (IBSSC), pp. 58-63, 2020.

R. Sekhar, T. P. Singh, and P. Shah, “Machine learning based predictive modeling and control of surface roughness generation while machining micro boron carbide and carbon nanotube particle reinforced Al-Mg matrix composites,” Particulate Science and Technology, vol. 40, no. 3, pp. 355–372, 2022.

R. Sekhar, T. P. Singh, and P. Shah, “Complex Order PIα+iβDγ+jθ Design for Surface Roughness Control in Machining CNT Al-Mg Hybrid Composites,” Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 299–306, 2020.

P. Shah and S. Agashe, “Design of controller for a higher order system without using model reduction methods,” Progress in Fractional Differentiation and Applications, vol. 3, pp. 289–3004, 2017.

R. Sekhar, T. Singh, and P. Shah, “Micro and nano particle composite machining: Fractional order control of surface roughness,” in Proceedings of the Third International Conference on Powder, Granule and Bulk Solids: Innovations and Applications PGBSIA, pp. 35–42, 2020.

P. Shah, R. Sekhar, and S. Agashe, “Application of fractional PID controller to single and multi-variable non-minimum phase systems,” International Journal of Recent Technology and Engineering, vol. 8, no. 2, pp. 2801–2811, 2019.

P. Shah, D. Sharma, and R. Sekhar, “Analysis of Research Trends in Fractional Controller Using Latent Dirichlet Allocation,” Engineering Letters, vol. 29, no. 1, 2021.

N. Solke, P. Shah, R. Sekhar, and T. Singh, “Machine learning-based predictive modeling and control of lean manufacturing in automotive parts manufacturing industry,” Global Journal of Flexible Systems Management, vol. 23, no. 1, pp. 89–112, 2022.

S. Li, L. Kang, and X.-M. Zhao, “A survey on evolutionary algorithm based hybrid intelligence in bioinformatics,” BioMed research international, vol. 2014, 2014.

J. Liepe, P. Kirk, S. Filippi, T. Toni, C. P. Barnes, and M. P. Stumpf, “A framework for parameter estimation and model selection from experimental data in systems biology using approximate bayesian computation,” Nature protocols, vol. 9, no. 2, pp. 439–456, 2014.

R. Sekhar and P. Shah, “Predictive modeling of a flexible robotic arm using cohort intelligence socio-inspired optimization,” in 2020 1st International Conference on Information Technology, Advanced Mechanical and Electrical Engineering (ICITAMEE), pp. 193–198, 2020.

A. Sohail, “Genetic algorithms in the fields of artificial intelligence and data sciences,” Annals of Data Science, vol. 10, no. 4, pp. 1007–1018, 2023.

M. Gen and L. Lin, “Genetic algorithms and their applications,” in Springer handbook of engineering statistics, pp. 635–674, 2023.

B. Alhijawi and A. Awajan, “Genetic algorithms: Theory, genetic operators, solutions, and applications,” Evolutionary Intelligence, pp. 1–12, 2023.

K. Wu, H. Hu, L. Wang, and Y. Gao, “Parametric optimization of an aperiodic metastructure based on genetic algorithm,” International Journal of Mechanical Sciences, vol. 214, p. 106878, 2022.

D. Murugesan, K. Jagatheesan, A. J. Kulkarni, P. Shah, and R. Sekhar, “Comparison of biologically inspired algorithm with socio-inspired technique on load frequency control of multi-source single-area power system,” in Applied Genetic Algorithm and Its Variants: Case Studies and New Developments, pp. 185–208, 2023.

M. Dastan, S. Shojaee, S. Hamzehei-Javaran, and V. Goodarzimehr, “Hybrid teaching–learning-based optimization for solving engineering and mathematical problems,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 44, no. 9, p. 431, 2022.

K. Y. Go´mez D´ıaz, S. E. De Leo´n Aldaco, J. Aguayo Alquicira, M. Ponce- Silva, and V. H. Olivares Peregrino, “Teaching–learning-based optimization algorithm applied in electronic engineering: A survey,” Electronics, vol. 11, no. 21, p. 3451, 2022.

Y. Xu, Y. Peng, X. Su, Z. Yang, C. Ding, and X. Yang, “Improving teaching–learning-based-optimization algorithm by a distance-fitness learning strategy,” Knowledge-Based Systems, vol. 257, p. 108271, 2022.

R. Kommadath, D. Maharana, C. Sivadurgaprasad, and P. Kotecha, “Parallel computing strategies for sanitized teaching learning based optimization,” Journal of Computational Science, vol. 63, p. 101766, 2022.

M. Arashpour, E. M. Golafshani, R. Parthiban, J. Lamborn, A. Kashani, H. Li, and P. Farzanehfar, “Predicting individual learning performance using machine-learning hybridized with the teaching-learning-based optimization,” Computer Applications in Engineering Education, vol. 31, no. 1, pp. 83–99, 2023.

D. Murugesan, K. Jagatheesan, P. Shah, and R. Sekhar, “Fractional order PIλDμ controller for microgrid power system using cohort intelligence optimization,” Results in Control and Optimization, vol. 11, p. 100218, 2023.

C. Liu, W. Zhang, L. Zeng, B. C. Ooi, J. W. L. Yip, and K. Zheng, “Toward cohort intelligence: A universal cohort representation learning framework for electronic health record analysis,” arXiv preprint arXiv:2304.04468, 2023.

S. Tutun, A. Tosyali, H. Sangrody, M. Khasawneh, M. Johnson, A. Al-bizri, and A. Harfouche, “Artificial intelligence in energy industry: forecasting electricity consumption through cohort intelligence & adaptive neural fuzzy inference system,” Journal of Business Analytics, vol. 6, no. 1, pp. 59–76, 2023.

M. Haghighat et al., “Automated quality assessment of large digitized histology cohorts by artificial intelligence,” Scientific Reports, vol. 12, no. 1, p. 5002, 2022.

M. S. Sapre, A. J. Kulkarni, I. R. Kale, and M. S. Pande, “Application of cohort intelligence algorithm for numerical integration,” in Intelligent Systems and Applications: Select Proceedings of ICISA 2022, pp. 445– 453, 2023.

T. Muther, F. I. Syed, A. K. Dahaghi, and S. Negahban, “Socio-inspired multi-cohort intelligence and teaching-learning-based optimization for hydraulic fracturing parameters design in tight formations,” Journal of Energy Resources Technology, vol. 144, no. 7, p. 073201, 2022.

The MathWorks, Inc., MATLAB Control Toolbox. MATLAB Software, 2014. Version R2014a.




DOI: https://doi.org/10.18196/jrc.v4i5.19772

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