A Three Area Interconnected Power System Network Load Frequency Controller Simulator
Corresponding Author(s) : Ibrahim Olawale Muritala
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol 3, No 4, November 2018
Abstract
This paper presents a MATLAB simulator of a three area interconnected power system of Thermal-Gas-Hydro. Nonlinearities intrinsic in the interconnected power system of communication delay, Generation Rate Constraint and Generation Dead Band were measured. Bat Inspired Algorithm was exploited to select the favourable parameters of the Model Predictive Controller and the Super Conducting Magnetic Energy Storage. Model Predictive Controller was the subordinate controller employed to minimalize the Area Control Error, Super Conducting Magnetic Energy Storage was the energy buffer to balance the load demand and the power generated. Integral Time Absolute Error was the performance metrics employed to minimize the Area Control Error. Parametric dissimilarity was tested on the inter-connected power system to observe the efficacy of the controller. Step load perturbation of was concurrently applied to the three-area inter-connected network, was introduced to the thermal generating unit, was introduced to the gas and hydro generating unit. Value of the tie-line was introduced to examine its effect on the frequency deviation. The results performed better when compared with Model Predictive Controller joined with Super Conducting Magnetic Energy Storage against the Model Predictive Controller without Super Conducting Magnetic Energy Storage in relations to settling time, overshoot and undershoot.
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- K. Jagatheesan, S. Samanta, A. Choudhury, N. Dey, B. Anand, and A. S. Ashour, “Quantum Inspired Evolutionary Algorithm in Load Frequency Control of Multi-area Interconnected Thermal Power System with Non-linearity,” Quantum Comput. An Environ. Intell. Large Scale Real Appl., pp. 389–417, 2018.
- M. Elsisi, M. Soliman, M. A. S. Aboelela, and W. Mansour, “Improving the grid frequency by optimal design of model predictive control with energy storage devices,” Optim. Control Appl. Methods, no. April 2016, pp. 1–18, 2017.
- B. Mohanty, S. Panda, and P. K. Hota, “Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system,” Int. J. Electr. Power Energy Syst., vol. 54, pp. 77–85, 2014.
- V. Jeyalakshmi and P. Subburaj, “PSO-scaled fuzzy logic to load frequency control in hydrothermal power system,” Soft Comput., vol. 20, no. 7, pp. 2577–2594, 2016.
- R. Shankar, S. R. Pradhan, K. Chatterjee, and R. Mandal, “A comprehensive state of the art literature survey on LFC mechanism for power system,” Renew. Sustain. Energy Rev., vol. 76, no. February, pp. 1185–1207, 2017.
- M. Elsisi, M. Soliman, M. A. S. Aboelela, and W. Mansour, “Bat inspired algorithm based optimal design of model predictive load frequency control,” Int. J. Electr. Power Energy Syst., vol. 83, pp. 426–433, 2016.
- M. Elsisi, M. Soliman, M. A. S. Aboelela, and W. Mansour, “Optimal design of model predictive control with superconducting magnetic energy storage for load frequency control of nonlinear hydrothermal power system using bat inspired algorithm,” J. Energy Storage, vol. 12, pp. 311–318, 2017.
- A. Rahman, S. K. Sahu, L. C. Saikia, and P. Dash, “AGC of a Multi-Area Thermal-CCGT System Using Cuckoo Search Optimized Classical Controllers,” pp. 1–6, 2015.
- A. Y. Abdelaziz and E. S. Ali, “Cuckoo Search algorithm based load frequency controller design for nonlinear interconnected power system,” Int. J. Electr. Power Energy Syst., vol. 73, pp. 632–643, 2015.
- H. Shabani, B. Vahidi, and M. Ebrahimpour, “A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems,” ISA Trans., vol. 52, no. 1, pp. 88–95, 2013.
- Rashmita Gochhhayat, “PSO BASED PI CONTROLLER FOR LOAD FREQUENCY CONTROL OF INTERCONNECTED POWER SYSTEMS,” 2014.
- Y. Yoon and Y. Kim, “Charge Scheduling of an Energy Storage System under Time-of-Use Pricing and a Demand Charge,” vol. 2014, 2014.
- X. Yang and A. Hossein Gandomi, “Bat algorithm: a novel approach for global engineering optimization,” Eng. Comput., vol. 29, no. 5, pp. 464–483, 2012.
- H.-P. Halvorsen, “Model Predictive Control in LabVIEW,” pp. 0–21, 2011.
References
K. Jagatheesan, S. Samanta, A. Choudhury, N. Dey, B. Anand, and A. S. Ashour, “Quantum Inspired Evolutionary Algorithm in Load Frequency Control of Multi-area Interconnected Thermal Power System with Non-linearity,” Quantum Comput. An Environ. Intell. Large Scale Real Appl., pp. 389–417, 2018.
M. Elsisi, M. Soliman, M. A. S. Aboelela, and W. Mansour, “Improving the grid frequency by optimal design of model predictive control with energy storage devices,” Optim. Control Appl. Methods, no. April 2016, pp. 1–18, 2017.
B. Mohanty, S. Panda, and P. K. Hota, “Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system,” Int. J. Electr. Power Energy Syst., vol. 54, pp. 77–85, 2014.
V. Jeyalakshmi and P. Subburaj, “PSO-scaled fuzzy logic to load frequency control in hydrothermal power system,” Soft Comput., vol. 20, no. 7, pp. 2577–2594, 2016.
R. Shankar, S. R. Pradhan, K. Chatterjee, and R. Mandal, “A comprehensive state of the art literature survey on LFC mechanism for power system,” Renew. Sustain. Energy Rev., vol. 76, no. February, pp. 1185–1207, 2017.
M. Elsisi, M. Soliman, M. A. S. Aboelela, and W. Mansour, “Bat inspired algorithm based optimal design of model predictive load frequency control,” Int. J. Electr. Power Energy Syst., vol. 83, pp. 426–433, 2016.
M. Elsisi, M. Soliman, M. A. S. Aboelela, and W. Mansour, “Optimal design of model predictive control with superconducting magnetic energy storage for load frequency control of nonlinear hydrothermal power system using bat inspired algorithm,” J. Energy Storage, vol. 12, pp. 311–318, 2017.
A. Rahman, S. K. Sahu, L. C. Saikia, and P. Dash, “AGC of a Multi-Area Thermal-CCGT System Using Cuckoo Search Optimized Classical Controllers,” pp. 1–6, 2015.
A. Y. Abdelaziz and E. S. Ali, “Cuckoo Search algorithm based load frequency controller design for nonlinear interconnected power system,” Int. J. Electr. Power Energy Syst., vol. 73, pp. 632–643, 2015.
H. Shabani, B. Vahidi, and M. Ebrahimpour, “A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems,” ISA Trans., vol. 52, no. 1, pp. 88–95, 2013.
Rashmita Gochhhayat, “PSO BASED PI CONTROLLER FOR LOAD FREQUENCY CONTROL OF INTERCONNECTED POWER SYSTEMS,” 2014.
Y. Yoon and Y. Kim, “Charge Scheduling of an Energy Storage System under Time-of-Use Pricing and a Demand Charge,” vol. 2014, 2014.
X. Yang and A. Hossein Gandomi, “Bat algorithm: a novel approach for global engineering optimization,” Eng. Comput., vol. 29, no. 5, pp. 464–483, 2012.
H.-P. Halvorsen, “Model Predictive Control in LabVIEW,” pp. 0–21, 2011.