In this paper, a Fuzzy proportional–integral–derivative (Fuzzy PID) controller design is presented to improve the automatic voltage regulator (AVR) transient characteristics and increase the robustness of the AVR. Fuzzy PID controller parameters are determined by a genetic algorithm (GA)-based optimization method using a novel multi-objective function. The multi-objective function, which is important for tuning the controller parameters, obtains the optimal solution using the Integrated Time multiplied Absolute Error (ITAE) criterion and the peak value of the output response. The proposed method is tested on two AVR models with different parameters and compared with studies in the literature. It is observed that the proposed method improves the AVR transient response properties and is also robust to parameter changes.
AkbıyıkBEksin GüzelkayaİM, et al. (2005) Evaluation of the performance of various fuzzy PID controller structures on benchmark systems. In: ELECO 2005, 4th international conference on electrical and electronics engineering, Bursa, Turkey, https://www.emo.org.tr/ekler/a638ebf561da3b2_ek.pdf
2.
Al GiziAJMustafaMAl-geelaniNA, et al. (2015) Sugeno fuzzy PID tuning, by genetic-neutral for AVR in electrical power generation. Applied Soft Computing28: 226–236.
3.
AngKHChomgG (2006) PID control system analysis and design. IEEE Control Systems Magazine26: 32–41.
4.
AthertonD (2009) Control Engineering. London: Bookboon.
5.
AyasMSSahinE (2021) FOPID controller with fractional filter for an automatic voltage regulator. Computers & Electrical Engineering90: 106895.
6.
BagisAKonarM (2016) Comparison of Sugeno and Mamdani fuzzy models optimized by artificial bee colony algorithm for nonlinear system modelling. Transactions of the Institute of Measurement and Control38(5): 579–592.
7.
BagisASenberberH (2017) ABC algorithm based PID controller design for higher order oscillatory systems. Elektronika Ir Elektrotechnika23(6): 3–9.
8.
BingulZKarahanO (2018) A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system. Journal of the Franklin Institute355(13): 5534–5559.
9.
CastilloOLizárragaESoriaJ, et al. (2015) New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system. Information Sciences294: 203–215.
10.
ChatterjeeSMukherjeeV (2016) PID controller for automatic voltage regulator using teaching–learning based optimization technique. International Journal of Electrical Power & Energy Systems77: 418–429.
11.
ChenJZhanYGuoY, et al. (2018) Fuzzy adaptive PI decoupling control for gas supply system of proton exchange membrane fuel cell. In: 21st international conference on electrical machines and systems (ICEMS), Jeju, South Korea, 7–10 October, pp. 1145–1150. New York: IEEE.
12.
ConkerCBaltaciogluMK (2020) Fuzzy self-adaptive PID control technique for driving HHO dry cell systems. International Journal of Hydrogen Energy45(49): 26059–26069.
13.
DevarajDSelvabalaB (2009) Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional–integral–derivative controller in automatic voltage regulator system. IET Generation, Transmission & Distribution3(7): 641–649.
14.
EdalatiLSedighAKShooredeliMA, et al. (2018) Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints. Mechanical Systems and Signal Processing100: 311–329.
15.
EkinciSHekimoğluB (2019) Improved kidney-inspired algorithm approach for tuning of PID controller in AVR system. IEEE Access7: 39935–39947.
16.
ElsisiMSolimanM (2021) Optimal design of robust resilient automatic voltage regulators. ISA Transactions108: 257–268.
17.
FaroukNBingqiT (2012) Application of self-tuning fuzzy PID controller on the AVR system. In: IEEE international conference on mechatronics and automation, Chengdu, China, 5–8 August, pp. 2510–2514. New York: IEEE.
18.
GaingZ-L (2004) A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Transactions on Energy Conversion19(2): 384–391.
19.
GozdeHTaplamaciogluMC (2011) Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system. Journal of the Franklin Institute348(8): 1927–1946.
20.
HasanienHM (2012) Design optimization of PID controller in automatic voltage regulator system using Taguchi combined genetic algorithm method. IEEE Systems Journal7(4): 825–831.
21.
HekimoğluB (2019) Sine-cosine algorithm-based optimization for automatic voltage regulator system. Transactions of the Institute of Measurement and Control41(6): 1761–1771.
22.
KangSRongYChouW (2020) Antidisturbance control for AUV trajectory tracking based on fuzzy adaptive extended state Observer. Sensors20(24): 7084.
23.
KimDH (2011) Hybrid GA–BF based intelligent PID controller tuning for AVR system. Applied Soft Computing11(1): 11–22.
24.
KrohlingRAReyJP (2001) Design of optimal disturbance rejection PID controllers using genetic algorithms. IEEE Transactions on Evolutionary Computation5(1): 78–82.
25.
KundurPBaluNJLaubyMG (1994) Power System Stability and Control. New York: McGraw-Hill.
26.
LiCTongSWangW (2011) Fuzzy adaptive high-gain-based observer backstepping control for SISO nonlinear systems. Information Sciences181(11): 2405–2421.
27.
LinSWangG (2017) Fuzzy PID control algorithm based on PSO and application in BLDC motor. IOP Conference Series: Earth and Environmental Science69: 012186.
28.
MicevMĆalasanMOlivaD (2021) Design and robustness analysis of an Automatic Voltage Regulator system controller by using Equilibrium Optimizer algorithm. Computers & Electrical Engineering89: 106930.
29.
MokeddemDMirjaliliS (2020) Improved whale optimization algorithm applied to design PID plus second-order derivative controller for automatic voltage regulator system. Journal of the Chinese Institute of Engineers43(6): 541–552.
30.
MosaadAMAttiaMAAbdelazizAY (2018) Comparative performance analysis of AVR controllers using modern optimization techniques. Electric Power Components and Systems46(19–20): 2117–2130.
31.
MosaadAMAttiaMAAbdelazizAY (2019) Whale optimization algorithm to tune PID and PIDA controllers on AVR system. Ain Shams Engineering Journal10(4): 755–767.
32.
MukherjeeVGhoshalS (2007) Intelligent particle swarm optimized fuzzy PID controller for AVR system. Electric Power Systems Research77(12): 1689–1698.
33.
OonsivilaiAPao-La-OrP (2008) Application of adaptive tabu search for optimum PID controller tuning AVR system. WSEAS Transactions on Power Systems3(6): 495–506.
34.
PrecupR-EPreitlS (2003) Development of fuzzy controllers with non-homogeneous dynamics for integral-type plants. Electrical Engineering85(3): 155–168.
35.
PriyambadaSSahuBKMohantyPK (2015) Fuzzy-PID controller optimized TLBO approach on automatic voltage regulator. In: International conference on energy, power and environment: Towards sustainable growth (ICEPE), Shillong, India, 12–13 June, pp. 1–6. New York: IEEE.
36.
RawatSJhaBPandaMK (2018) Operation and control of a hybrid isolated power system with type-2 fuzzy PID controller. Iranian Journal of Science and Technology, Transactions of Electrical Engineering42(4): 403–417.
37.
SahibMAAhmedBS (2016) A new multiobjective performance criterion used in PID tuning optimization algorithms. Journal of Advanced Research7(1): 125–134.
38.
SalmanMSenoNI (2010) A comparison of Mamdani and Sugeno inference systems for a satellite image classification. Anbar Journal of Engineering Sciences: 296–306, https://www.iasj.net/iasj/download/f6064b1a8b7c6e9e
39.
SikanderAThakurP (2020) A new control design strategy for automatic voltage regulator in power system. ISA Transactions100: 235–243.
40.
SikanderAThakurPBansalR, et al. (2018) A novel technique to design cuckoo search based FOPID controller for AVR in power systems. Computers & Electrical Engineering70: 261–274.
41.
TakagiTSugenoM (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics15: 116–132.
42.
TangYCuiMHuaC, et al. (2012) Optimum design of fractional order PIλDμ controller for AVR system using chaotic ant swarm. Expert Systems with Applications39(8): 6887–6896.
43.
TsukamotoY (1993) An approach to fuzzy reasoning method. In: DuboisDRonaldHPYagerR (eds) Readings in Fuzzy Sets for Intelligent Systems. San Mateo, CA: Morgan Kaufmann, pp. 523–529.
44.
VenkatakrishnanGRengarajRBrathindaraS, et al. (2006) Differential evolution algorithm for an optimal tuning of proportional integral derivative controller for automatic voltage regulator. ARPN Journal of Engineering and Applied Sciences12(12): 3751–3756.
45.
VivekanandanSSaravananGKamalakannanP, et al. (2015) Chaotic differential evolution algorithm based PID controller for automatic voltage regulator system. International Journal of Scientific and Research Publications5(6): 431–436.
46.
ZhangD-LYing-GanTXin-PingG (2014) Optimum design of fractional order PID controller for an AVR system using an improved artificial bee colony algorithm. Acta Automatica Sinica40(5): 973–979.
47.
ZhuHLiLZhaoY, et al. (2009) CAS algorithm-based optimum design of PID controller in AVR system. Chaos, Solitons & Fractals42(2): 792–800.
