Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system

Sahu, Binod Kumar; Pati, Swagat; Mohanty, Pradeep Kumar; Panda, Sidhartha

doi:10.1016/j.asoc.2014.11.027

Cited by 224 publications

(109 citation statements)

References 25 publications

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“…In short span of time, this algorithm became popular in research community. A lot of optimization problems have been solved by using TLBO algorithm and provides better results in comparison to existing algorithm [29][30][31][32]. As far as our knowledge is concerned, the TLBO algorithm is not applied in the field of WSNs.…”

Section: Teaching Learning Based Optimization (Tlbo) Algorithmmentioning

confidence: 99%

A Teaching Learning Based Optimization Algorithm for Cluster Head Selection in Wireless Sensor Networks

Yadav¹,

Kumar

2017

IJFGCN

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Section: Teaching Learning Based Optimization (Tlbo) Algorithmmentioning

confidence: 99%

A Teaching Learning Based Optimization Algorithm for Cluster Head Selection in Wireless Sensor Networks

Yadav¹,

Kumar

2017

IJFGCN

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“…In the teacher phase, all the students learn from the teacher, whereas in the learner phase, students learn through the interaction between other students. More detailed description of original TLBO could refer to the paper [12][13][14][15][16][17][18]. In this section, more attention would be focused on the teaching-learning-based optimization with dynamic group strategy (DGS-TLBO).…”

Section: Teaching-learning-based Optimization With Dynamic Group Stramentioning

confidence: 99%

Tuning of a PID Controller using Modified Dynamic Group based TLBO Algorithm

Luo¹,

Guo²,

Zhao³

et al. 2017

IJCA

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This paper presents a new version of Teaching LearningBased Optimization (TLBO) algorithm to find the optimal parameters of Proportional Integral Derivative (PID) controller. The proposed algorithm is an altered version of dynamic group strategy TLBO (DGS-TLBO) and is named as modified dynamic group based TLBO (MDG-TLBO) algorithm. The proposed algorithm is tested on 12 benchmark functions to verify its efficiency over other procedures. The results show that the MDG-TLBO algorithm offers better solution quality and has better convergence rate. Finally, the proposed algorithm is tested on a three-tank liquid-level control system for the optimization of PID gains. The simulation result indicate that the proposed algorithm is an effective method in tuning of PID controllers to obtain better performance measures the error values and the time domain specifications.

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“…The fuzzy-PID control algorithm is a promising control method for complex nonlinear problems [1,16,21]. Sahu et al [15] proposed a hybrid firefly algorithm (FA) and pattern search (PS) optimized fuzzy-PID controller for load frequency control (LFC) of multi-area power systems.…”

Section: Introductionmentioning

confidence: 99%

“…Sahu et al [15] proposed a hybrid firefly algorithm (FA) and pattern search (PS) optimized fuzzy-PID controller for load frequency control (LFC) of multi-area power systems. Sahu et al [16] examined a teaching-learning based optimization algorithm-based fuzzy-PID controller for automatic generation control of a multi-area power system. The fuzzy-PID control algorithm finds the relationship between three parameters (Kp, Ki, and Kd), error (e), and the rate of change in the error (∆e) based on fuzzy math theory, the fuzzy language variable, and fuzzy logic reasoning [6,19].…”

Section: Introductionmentioning

confidence: 99%

Motion analysis and fuzzy-PID control algorithm designing for the pitch angle of an underwater glider

Liu¹,

Su²,

L³

et al. 2017

J. Math. Computer Sci.

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Underwater gliders are used for deep-water gliding to observe large areas with minimal energy consumption. The pitch angle of the underwater glider is an important control parameter. This study involved designing a fuzzy-PID controller for the pitch angle of an underwater glider based on hydrodynamics analysis. The formula of pitch angle is obtained and a system identification method was used to identify the transfer function based on the time-domain equation and initial experimental data. The fuzzy-PID control algorithm was used to design the controller. Lake and sea trials indicated that the minimum overshoot reached 0% and the settling time was about 34s when the change of the angle was 15 • . The minimum steady-state error was 0.8 • . These advantages could reduce the consumption of energy and improve the accuracy of gliding trajectory. Therefore, this control algorithm should be applied to control the pitch of the gliders.

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Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system

Cited by 224 publications

References 25 publications

A Teaching Learning Based Optimization Algorithm for Cluster Head Selection in Wireless Sensor Networks

A Teaching Learning Based Optimization Algorithm for Cluster Head Selection in Wireless Sensor Networks

Tuning of a PID Controller using Modified Dynamic Group based TLBO Algorithm

Motion analysis and fuzzy-PID control algorithm designing for the pitch angle of an underwater glider

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