BFOA based design of PID controller for two area Load Frequency Control with nonlinearities

Ali, E. S.; Elazim, S.M. Abd

doi:10.1016/j.ijepes.2013.02.030

Cited by 228 publications

(82 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Comparison of frequency responses on the basis of settlingtime, over -shoot and under-shoot as given in the tabular form in Table 2 -Table 3 and the tie IJEECS ISSN: 2502-4752 27 -line comparison is given in Table 4. It is found that HSA tuned PID is better than controllers in [5,[27][28][29][30]. [27] 13.0 1.65 × 10 −4 −6.32 × 10 −4 Conv-PID [28] 27.50 1.05 × 10 −4 −9.70 × 10 −4 PSO-PID [5] 27.10 4.4 × 10 −4 −1.7 × 10 −4 BFOA-PID [29] 16.20 2.8 × 10 −5 −5.4 × 10 −4 Conv-PID [30] 28.55 3.00 × 10 −4 −5.5 × 10 −4 …”

Section: Performance Comparisonmentioning

confidence: 99%

Optimal Design of PID Controller for Load Frequency Control using Harmony Search Algorithm

Sambariya

Shrangi

2017

IJEECS

View full text Add to dashboard Cite

This paper deals with soft computing technique, used for tuning PID controller. Controller tuned with a harmony search algorithm is used for controlling the frequency and Tie-line power responses of a non reheat two area power system.Step load perturbation has been given in both areas simultaneously. The dynamic results obtained by the proposed controller are compared with PID controller of recent published paper. The performance of the controllers is simulated using MATLAB/Simulation software. The results of tuned PID are compared with conventional controller on the basis of settling-time, peak over-shoot and peak under-shoot. Proposed PID gives better results than the conventional controller. The comparative results also tabulated as a comparative performance.

show abstract

Section: Performance Comparisonmentioning

confidence: 99%

Optimal Design of PID Controller for Load Frequency Control using Harmony Search Algorithm

Sambariya

Shrangi

2017

IJEECS

View full text Add to dashboard Cite

show abstract

“…Step 6: Substitute (20) in (12) and repeat the algorithmic steps 2-6 over the specified number of chemotactic movements.…”

Section: Bacterial Foraging Particle Swarm Optimization Algorithm (Bfmentioning

confidence: 99%

“…The BFOA mimics the group foraging strategy of Escherichia Coli (E.Coli) bacteria in multioptimal function optimization [11]. It has been demonstrated as a powerful evolutionary computational technique, compared to other intelligent approaches in many optimization problems [12,13]. However, the BFOA approach experienced many difficulties which will be briefly discussed in section 3.3 of this paper.…”

Section: Introductionmentioning

confidence: 99%

Hybrid BFPSO Approach for Effective Tuning of PID Controller for Load Frequency Control Application in an Interconnected Power System

Anbarasi¹,

Muralidharan²

2017

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-Penetration of renewable energy sources makes the modern interconnected power systems to have more intelligence and flexibility in the control. Hence, it is essential to maintain the system frequency and tie-line power exchange at nominal values using Load Frequency Control (LFC) for efficient, economic and reliable operation of power systems. In this paper, intelligent tuning of the Proportional Integral Derivative (PID) controller for LFC in an interconnected power system is considered as a main objective. The chosen problem is formulated as an optimization problem and the optimal gain parameters of PID controllers are computed with three innovative swarm intelligent algorithms named Particle Swarm Optimization (PSO), Bacterial Foraging Optimization Algorithm (BFOA) and hybrid Bacterial Foraging Particle Swarm Optimization (BFPSO) and a comparative study is made between them. A new objective function designed with necessary time domain specifications using weighted sum approach is also offered in this report and compared with conventional objective functions. All the simulation results clearly reveal that, the hybrid BFPSO tuned PID controller with proposed objective function has better control performances over other optimization methodologies.

show abstract

“…Ali et al [26] have suggested BFOA to adjust the parameters of PID controller for nonlinear LFC problem. To express the suggested method, a two area power system was regarded.…”

Section: Literature Reviewmentioning

confidence: 99%

A Neural Network-Bacterial Foraging Algorithm to Control the Load Frequency of Power System

Palanisamy¹,

Mahadevan²

2014

ijeei

View full text Add to dashboard Cite

Abstract:In this paper, a neural network (NN)-bacterial foraging algorithm (BFA) is proposed to control the load frequency of the power system. Traditionally, the tumbling decision element of BFA is defined by the stochastic values. But, these values are changed as per the variations of chemotactic step size so that the convergence time is increased. Here, NN is used to ensure the distribution of stochastic value with respect to the chemotactic step size and thus the performance of BFA is enhanced. The feed forward NN is used here with back propagation training algorithm. Using the proposed controller, the controller error, load changes, and speed changer position are tuned and the stability of the interconnected power system is improved. The proposed tuning controller is implemented in MATLAB/Simulink platform and the load frequency control responses are evaluated. The performances of proposed controller are compared with those of the PID controller and the BFA-PID controller.

show abstract

BFOA based design of PID controller for two area Load Frequency Control with nonlinearities

Cited by 228 publications

References 16 publications

Optimal Design of PID Controller for Load Frequency Control using Harmony Search Algorithm

Optimal Design of PID Controller for Load Frequency Control using Harmony Search Algorithm

Hybrid BFPSO Approach for Effective Tuning of PID Controller for Load Frequency Control Application in an Interconnected Power System

A Neural Network-Bacterial Foraging Algorithm to Control the Load Frequency of Power System

Contact Info

Product

Resources

About