Design of the PID Controller for Hydro-turbines Based on Optimization Algorithms

Perng, Jau-Woei; Kuo, Y.C.; Lu, Kuan-Chung

doi:10.1007/s12555-019-0254-7

Cited by 23 publications

(11 citation statements)

References 14 publications

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“…We analyse equation (39) and find that the product of (G T 1 • e 1 (k)) T and G T 1 • e 1 (k) is positive. erefore, we can conclude…”

Section: Neural Network Supervision Control Designmentioning

confidence: 99%

“…Tuning PID parameters are used to optimize system performance according to actual conditions. As PID parameter tuning technology becomes more and more mature, some interested scholars use PID as a controller to study system stability and tracking issues [39][40][41][42]. In order to adapt PID to more situations, some scholars have launched the control research of fuzzy PID [43,44] and fractional PID [45,46].…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control

Gao

et al. 2021

Discrete Dynamics in Nature and Society

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This paper presents several control methods and realizes the stable tracking for the inverted pendulum system. Based on the advantages of RBF and traditional PID, a novel PID controller based on the RBF neural network supervision control method (PID-RBF) is proposed. This method realizes the adaptive adjustment of the stable tracking signal of the system. Furthermore, an improved PID controller based on RBF neural network supervision control strategy (IPID-RBF) is presented. This control strategy adopts the supervision control method of feed-forward and feedback. The response speed of the system is further improved, and the overshoot of the tracking signal is further reduced. The tracking control simulation of the inverted pendulum system under three different signals is given to illustrate the effectiveness of the proposed method.

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“…We analyse equation (39) and find that the product of (G T 1 • e 1 (k)) T and G T 1 • e 1 (k) is positive. erefore, we can conclude…”

Section: Neural Network Supervision Control Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control

Gao

et al. 2021

Discrete Dynamics in Nature and Society

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“…It should be noted that machine-learning mechanisms are being utilized effectively in optimization and detection tasks, as observed in [ 8 – 16 ]. Such approaches can also be applied for the convergence analysis along with the regulation methods [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid soft computing optimization framework for dynamic economic dispatch problem of complex non-convex contiguous constrained machines

et al. 2022

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The reformations of the electrical power sector have resulted in very dynamic and competitive market that has changed many elements of the power industry. Excessive demand of energy, depleting the fossil fuel reserves of planet and releasing the toxic air pollutant, has been causing harm to earth habitats. In this new situation, insufficiency of energy supplies, rising power generating costs, high capital cost of renewable energy equipment, environmental concerns of wind power turbines, and ever-increasing demand for electrical energy need efficient economic dispatch. The objective function in practical economic dispatch (ED) problem is nonlinear and non-convex, with restricted equality and inequality constraints, and traditional optimization methods are incapable of resolving such non-convex problems. Over the recent decade, meta-heuristic optimization approaches have acquired enormous reputation for obtaining a solution strategy for such types of ED issues. In this paper, a novel soft computing optimization technique is proposed for solving the dynamic economic dispatch problem (DEDP) of complex non-convex machines with several constraints. Our premeditated framework employs the genetic algorithm (GA) as an initial optimizer and sequential quadratic programming (SQP) for the fine tuning of the pre-optimized run of GA. The simulation analysis of GA-SQP performs well by acquiring less computational cost and finite time of execution, while providing optimal generation of powers according to the targeted power demand and load, whereas subject to valve point loading effect (VPLE) and multiple fueling option (MFO) constraints. The adequacy of the presented strategy concerning accuracy, convergence as well as reliability is verified by employing it on ten benchmark case studies, including non-convex IEEE bus system at the same time also considering VPLE of thermal power plants. The potency of designed optimization seems more robust with fast convergence rate while evaluating the hard bounded DEDP. Our suggested hybrid method GA-SQP converges to achieve the best optimal solution in a confined environment in a limited number of simulations. The simulation results demonstrate applicability and adequacy of the given hybrid schemes over conventional methods.

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“…The role of control theories and Lyapunov analysis (us Saqib et al, 2018;Tang and Li, 2019;Zhao et al, 2019;Gao and Liu, 2020;Santos et al, 2020;Yao et al, 2020) can be useful in attaining optimization issues. The stability analysis, as observed in Dev and Sarkar (2019), Perng et al (2020), Souza et al (2020), Xie et al (2020), andYin et al (2020), can be applied to attain coordination in generation systems.…”

Section: Introductionmentioning

confidence: 99%

Optimality Condition and Distributed Optimization for Economic Dispatch Using a Novel Weighted Incremental Cost Consensus Approach

et al. 2022

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This research report investigates a novel optimization approach for the economic dispatch problem (EDP) based on the weighted sum of generators’ costs under supply-demand balance. Unlike conventional approaches, we present a distributed optimization approach that ensures optimality using weighted incremental cost (IC) consensus and sign-consensus error convergence. We can apply the optimization of a weighted sum of generators’ costs to address several constraints, such as capacity and environmental constraints, in addition to the supply-demand balance. The proposed distributed weighted incremental cost consensus approach has been applied to the IEEE-30 bus and IEEE-118 bus systems over a communication topology. The results indicate the efficacy of weights to address generation constraints and the convergence of weighted ICs under supply-demand balance.

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Design of the PID Controller for Hydro-turbines Based on Optimization Algorithms

Cited by 23 publications

References 14 publications

Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control

Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control

A novel hybrid soft computing optimization framework for dynamic economic dispatch problem of complex non-convex contiguous constrained machines

Optimality Condition and Distributed Optimization for Economic Dispatch Using a Novel Weighted Incremental Cost Consensus Approach

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