Equilibrium optimization algorithm for automatic generation control of interconnected power systems

• Autorzy: Agwa Ahmed Mahmoud

Identyfikatory

DOI

10.15199/48.2020.09.30

Warianty tytułu

PL

Algorytm optymalizacyjny do automatycznego sterowania częstotliwości

• Języki publikacji: EN

Abstrakty

EN

In the electric grid, when the loads increase, the frequency decreases and vice versa. Therefore a controller is utilized for maintaining the frequency within its boundaries via balancing the generation and the loads which is called automatic generation control (AGC) or load frequency control. While utilizing proportional‒integral–derivative (PID) controller for AGC of interlinked power systems, then tuning its gains can be addressed as a nonlinear optimization issue. The objective function is intended to minimize the integral‒time‒absolute‒errors of frequencies and tie‒line power with subjection to group of PID controller gains constraints. In this article, an innovative equilibrium optimization algorithm (EOA) is proposed to tune gains of the required PID controller. Subsequently, a successive controller composed of PI and PD controllers are innovatively employed rather than the PID controller. The proposed approaches (EOA–PID) and (EOA–PI–PD) are applied to the two‒region power systems when the load demand is changed in one and in two regions to legalize their efficacy. To validate the results of EOA–PID and EOA–PI–PD, they are compared with other approaches results. It is found that the EOA performs perfectly and owns a fine potency to tune controller gains with smaller errors than other methods while favoring the results of the EOA‒PI‒PD over EOA‒PID.

PL

W artykule opisano system AGC sterujący częstotliwością przy zmiennym obciążeniu wykorzystujący sterownik PID. Opisano nowy algorytm wykorzystujący sterowniki PI i PD. Porównano różne metody sterowania częstotliwością.

Słowa kluczowe

EN

automatic generation control; load frequency control; PID control

PL

kontrola generacji automatyczna; kontrola częstotliwości obciążenia; kontrola PID; optymalizator równowagi

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2020
• Tom: R. 96, nr 9
• Strony: 143--148
• Opis fizyczny: Bibliogr. 39 poz. rys., tab.

Twórcy

autor

Agwa Ahmed Mahmoud

• Electrical Engineering Department, Faculty of Engineering, Northern Border University, Arar 1321, Saudi Arabia
• Electrical Engineering Department, Faculty of Engineering, AlAzhar University, Cairo 11651, Egypt

Bibliografia

• [1] Heshmati M, Noroozian R, Jalilzadeh S, Shayeghi H., Optimal design of CDM controller to frequency control of a realistic power system equipped with storage devices using grasshopper optimization algorithm, ISA Trans, 97 (2020), 202- 215.
• [2] Egido I., Fernandez-Bernal F., Rouco L., Saboya I., Operation of Rapid Start Units in an AGC Area, Przegląd Elektrotechniczny, 88 (2012), No. 1a,141-145.
• [3] Duman S., Yorukeren N., Automatic generation control of the two area non-reheat thermal power system using gravitational search algorithm, Przegląd Elektrotechniczny, 88 (2012), No. 10a, 254-259.
• [4] Rasolomampionona D., Analysis of interaction between LFC and TCPS in power system, Przegląd Elektrotechniczny, 85 (2009), No. 10, 42-49.
• [5] Chen G., Li Z., Zhang Z., Li S., An Improved ACO algorithm optimized fuzzy PID controller for load frequency control in multi area interconnected power systems, IEEE Access, 8 (2020), 6429-6447.
• [6] Kler D., Kumar V., Rana K.P.S., Optimal integral minus proportional derivative controller design by evolutionary algorithm for thermal-renewable energy-hybrid power systems, IET Renew Power Gener, 13 (2019), 2000-2012.
• [7] Jagatheesan K., Anand B., Samanta S., Dey N., Ashour A.S., Balas V.E., Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm, IEEE/CAA J Autom Sin, 6 (2019), 503-515.
• [8] Debbarma S., Chandra S.L., Sinha N., Solution to automatic generation control problem using firefly algorithm optimized I?Dµ controller, ISA Trans, 53 (2014), 358-366.
• [9] Nahas N., Abouheaf M., Sharaf A., Gueaieb W., A selfadjusting adaptive AVR-LFC scheme for synchronous generators, IEEE Trans Power Syst, 34 (2019), 5073-5075.
• [10] Guha D., Roy P.K., Banerjee S., A maiden application of salp swarm algorithm optimized cascade tilt-integral-derivative controller for load frequency control of power systems, IET Gener Transm Distrib, 13 (2018), 1110.
• [11] Çelik E., Improved stochastic fractal search algorithm and modified cost function for automatic generation control of interconnected electric power systems, Eng Appl Artif Intell, 88 (2020), 103407.
• [12] Abd-Elazim S.M., Ali E.S., Load frequency controller design via bat algorithm for nonlinear interconnected power system, Int J Electr Power Energy Syst, 77 (2016), 166-177.
• [13] Mishra S., Gupta S., Yadav A., Design and application of controller based on sine-cosine algorithm for load frequency control of power system, 18th Int. Conf. Intell. Syst. Des. Appl. (ISDA 2018), 941 (2018), 301-311.
• [14] Nosrati K., Mansouri H.R., Saboori H., Fractional-order PID controller design of frequency deviation in a hybrid renewable energy generation and storage system, CIRED - Open Access Proc. J., (2017) 2017, 1148-1152.
• [15] Singh S.P., Prakash T., Singh V.P., Babu M.G., Analytic hierarchy process based automatic generation control of multiarea interconnected power system using Jaya algorithm, Eng Appl Artif Intell, 60 (2017), 35-44.
• [16] Mohamed T.H., Abubakr H., Alamin M.A.M., Hassan A.M., Modified WCA-based adaptive control approach using balloon effect: electrical systems applications. IEEE Access, 8 (2020), 60877–60889.
• [17] Latif A., Das D.C., Ranjan S., Barik A.K., Comparative performance evaluation of WCA-optimised non-integer controller employed with WPG–DSPG–PHEV based isolated two-area interconnected microgrid system, IET Renew Power Gener, 13 (2019), 725-736.
• [18] Guha D., Roy P.K., Banerjee S., Multi-verse optimisation: a novel method for solution of load frequency control problem in power system. IET Gener Transm Distrib, 11 (2017), 3601- 3611.
• [19] Raju M., Saikia L.C., Sinha N., Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller, Int J Electr Power Energy Syst, 80 (2016), 52-63.
• [20] Cai L., He Z., Hu H., A new load frequency control method of multi-area power system via the viewpoints of Port-Hamiltonian system and cascade system, IEEE Trans Power Syst, 32 (2017), 1689-1700.
• [21] Jagatheesan K., Anand B., Samanta S., Dey N., Santhi V., Ashour A.S., Balas V.E., Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity, Neural Comput Appl, 28 (2017), 475-488.
• [22] Mercader P., Astrom K.J., Banos A., Hagglund T., Robust PID design based on QFT and convex-concave optimization, IEEE Trans Control Syst Technol, 25 (2017), 441-452.
• [23] Sahu R.K., Panda S., Biswal A., Sekhar G.T.C., Design and analysis of tilt integral derivative controller with filter for load frequency control of multi-area interconnected power systems, ISA Trans, 61 (2016), 251-264.
• [24] El-Fergany A.A., El-Hameed M.A., Efficient frequency controllers for autonomous two-area hybrid microgrid system using social-spider optimiser, IET Gener Transm Distrib, 11 (2017), 637-648.
• [25] Abdelaziz A.Y., Ali E.S., Cuckoo search algorithm based load frequency controller design for nonlinear interconnected power system, Int J Electr Power Energy Syst, 73 (2015), 632-643.
• [26] Guha D., Roy P.K., Banerjee S., Load frequency control of interconnected power system using grey wolf optimization, Swarm Evol Comput, 27 (2016), 97-115.
• [27] Sheela A., Meenakumari R., Load frequency control in power systems using genetic algorithm, Proc. Int. Conf. Control. Commun. Power Eng. 2010, (2010), 190-192.
• [28] Panda S., Yegireddy N.K., Automatic generation control of multi-area power system using multi-objective non-dominated sorting genetic algorithm-II, Int J Electr Power Energy Syst, 13 (2013), 54-63.
• [29] Shirvani M., Abdollahi M., Memaripour A., Behzadipour E., Multi-area Load Frequency Control using IP controller tuned by Tabu Search, Przegląd Elektrotechniczny, 88 (2012), No. 8, 233-243.
• [30] Khooban M.H., Niknam T., Blaabjerg F., Davari P., Dragicevic T., A robust adaptive load frequency control for micro-grids, ISA Trans, 65 (2016), 220-229.
• [31] Shabani H., Vahidi B., Ebrahimpour M., A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems, ISA Trans, 52 (2013), 88-95.
• [32] Ghoshal S.P., Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control. Electr Power Syst Res, 72 (2004), 203-212.
• [33] Mallesham G., Mishra S., Jha A.N., Ziegler-Nichols based controller parameters tuning for load frequency control in a microgrid, Int. Conf. Energy Autom. Signal, (2011), 335-342.
• [34] Hasanien H.M., El-Fergany A.A., Symbiotic organisms search algorithm for automatic generation control of interconnected power systems including wind farms, IET Gener Transm Distrib, 11 (2017), 1692-1700.
• [35] Ali E.S., Abd-Elazim S.M., Bacteria foraging optimization algorithm based load frequency controller for interconnected power system, Int J Electr Power Energy Syst, 33 (2011), 633- 638.
• [36] Faramarzi A., Heidarinejad M., Stephens B., Mirjalili S., Equilibrium optimizer: a novel optimization algorithm, Knowledge-Based Syst, 191 (2020), 105190.
• [37] Abdel-Basset M., Chang V., Mohamed R., A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems, Neural Comput Appl, (2020).
• [38] Menesy A.S., Sultan H.M., Kamel S., Extracting model parameters of proton exchange membrane fuel cell using equilibrium optimizer algorithm, Int. Youth Conf. Radio Electron. Electr. Power Eng., (2020), 1-7.
• [39] Gao Z.M., Zhao J., Li S.R., The binary equilibrium optimization algorithm with sigmoid transfer functions, 12th Int. Conf. Mach. Learn. Comput., (2020), 193-197.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).