PID controller design for load frequency control: Past, Present and future challenges

Hote, Yogesh V.; Jain, Shivam

doi:10.1016/j.ifacol.2018.06.162

Cited by 87 publications

(40 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Noise filters systems, there are current opportunities. For example, Hote and Jain [20] recognized that the main challenge in Load Frequency Control is to develop a highly robust PID controller that maintains frequency deviation strictly in specified limits even in the presence of nonlinearities, physical constraints, and uncertain environment. For those reasons the main objective of the present paper is to increase power plants response in their precision and speed for short-term load perturbations and for frequency control using a nonlinear model to asset the performance of the proposed controller.…”

Section: Governormentioning

confidence: 99%

Evaluating Fractional PID Control in a Nonlinear MIMO Model of a Hydroelectric Power Station

et al. 2019

View full text Add to dashboard Cite

In this paper a Fractional PID Control is presented. This control was designed for a hydropower plant with six generation units working in an alternation scheme. The parameters and other features of such a set of hydrogeneration units have been used to perform the respective tuning up. In order to assess the behavior of this controlled system, a model of such nonlinear plant is regulated through a classical PID by classical linearization of its set points, and then a pseudo-derivative part is substituted into a Fractional PID. Both groups of signals contain variations of voltage suggesting some abrupt changes in the supply of electricity fed to the network. Both sets of resulting signals are compared; the simulations show that the Fractional PID has a faster response with respect to those plots obtained from the classical PID used.

show abstract

Section: Governormentioning

confidence: 99%

Evaluating Fractional PID Control in a Nonlinear MIMO Model of a Hydroelectric Power Station

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Therefore, the linear combination of frequency deviation and tie-line deviation forms Area Control Error (ACE), which is also the input of the controller. Since ACE is driven to zero by LFC, the frequency and tie-line deviations are forced to zero in order to keep the system frequency and the tie-line power close to the scheduled value [2], [3].…”

Section: Introductionmentioning

confidence: 99%

An Improved ACO Algorithm Optimized Fuzzy PID Controller for Load Frequency Control in Multi Area Interconnected Power Systems

et al. 2020

View full text Add to dashboard Cite

In this paper, an Improved Ant Colony Optimization (IACO) algorithm optimized fuzzy PID (FPID) controller is proposed for Load Frequency Control of multi area systems. The nonlinear incremental evaporation rate and improvement of pheromone increment updating are proposed in the IACO algorithm to improve the quality of solution. And, a modified objective function using integral time multiply absolute error (ITAE), overshoot, undershoot and settling time with appropriate weight coefficients is proposed to improve the performance of the controller. Initially, a two-area non-reheat thermal system is applied and the FPID controller parameters are optimized by the IACO algorithm with five different objective functions. The modified objective function has better performances than four conventional objective functions. To demonstrate the robustness of the proposed control method, sensitivity analysis is implemented under wide variation of operating conditions and system parameters. Further, the proposed approach is also extended to two-area four-sources hydro thermal power system with/without High Voltage Direct Current (HVDC) link. The superiority of the proposed approach is shown by comparing the results with ZN, GA and hPSO-PS algorithms. The robustness of the proposed method is verified under random load disturbances. Finally, the proposed approach is extended to a two-area power system with governor dead band nonlinearity and results show that the proposed approach can cope with nonlinearity well. The results obtained from all simulations show that the proposed algorithm and modified objective function achieves better performances, such as minimum objective values (ITAE = 0.0255, ITSE = 9.30e-5, ISE = 1.91e-4 and IAE = 0.0273) obtained for the two-area non-reheat thermal system. INDEX TERMS Ant colony optimization (ACO), fuzzy logic controller (FLC), governor dead band (GDB), Load frequency control (LFC).

show abstract

“…Manual-based parameters tuning control method is a common and widely used method [1], [2]. In the field of industrial control, classical control (CC) methods and systems [3], such as PID control, have been applied for a long time.…”

Section: Introductionmentioning

confidence: 99%

“…Problems of existing control parameters online tuning methods are as follows: (1) The control performances are unsatisfied, because more and more practical applications need faster control speed, lower steady-state error and better stability and anti-interference ability. (2) The control system cannot maintain the best performance continuously, because the parameters of the control system cannot be tuned online by the control system itself.…”

Section: Introductionmentioning

confidence: 99%

Multiple Linear Regression Prediction and Wavelet Neural Network Based Intelligent Online Tuning Control Method

Liu

Zhao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Manual-based parameters tuning methods of control system are widely used in many industrial fields. However, better control performances such as faster control speed, more stable, less workload and safer working conditions are expected. Therefore, artificial intelligent based improved classical control (AI-CC) is highly valued in control fields. Main works are as follows: Firstly, wavelet neural network based PID (WNN-PID) method is proposed, BPNN of BPNN based PID method (BPNN-PID) is replaced by WNN. Secondly, multiple linear regression-based wavelet neural network PID (MR-WNN-PID) is proposed, the control parameters are adjusted according to the predicted output of control system. Thirdly, simulations are implemented to compare the performance of the proposed methods and the existing methods. Finally, the stability of the proposed methods is analyzed by theory. Effects are as follows: Firstly, the proposed AI-CC methods have better performances such as faster control speed, smaller overshoot, better ability of antiinterference. Secondly, theoretical analysis of stability of proposed methods is proven. INDEX TERMS Control parameters online tuning; Artificial intelligent control; Auto-tuning control; Wavelet neural network PID, Multiple linear regression-based prediction I.

show abstract

PID controller design for load frequency control: Past, Present and future challenges

Cited by 87 publications

References 34 publications

Evaluating Fractional PID Control in a Nonlinear MIMO Model of a Hydroelectric Power Station

Evaluating Fractional PID Control in a Nonlinear MIMO Model of a Hydroelectric Power Station

An Improved ACO Algorithm Optimized Fuzzy PID Controller for Load Frequency Control in Multi Area Interconnected Power Systems

Multiple Linear Regression Prediction and Wavelet Neural Network Based Intelligent Online Tuning Control Method

Contact Info

Product

Resources

About