Linear Programming for Optimum PID Controller Tuning

Oliveira, Edimar J. de; Honório, Leonardo de Mello; Anzai, Alexandre H.; Soares, Tamara X.

doi:10.4236/am.2014.56084

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…There are a lot of soft computing techniques used to tune the gains and time constant of the PI or PID controller (Stojic et al, 2017). Some examples are simplex algorithm (Gole et al, 2003;Zhao et al, 2007), linear programing based tuning (Oliveira et al, 2014), Modified Genetic Algorithm (Y. P. Wang et al, 2002), offline tuning of discrete-time fractional-order to minimize the cost function (Merrikh-Bayat et al, 2015), Hybrid PID-Artificial Neural Network (ANN) controller to control PWM signal for DC to DC conversion (Muruganandam and Madheswaran, 2013), fuzzy logic algorithm (Sahin and Altas, 2017;Ilyas et al, 2013;Kassem, 2013), State Transition Algorithm (STA) (Saravanakumar et al, 2015), Particle Swarm Optimization (PSO) (Aazim et al, 2017;Rajagopal and Ponnusamy, 2014), etc. With the help of these techniques, it became easy to select parameters intelligently and give optimal results in a very short time compared with the arbitrary or logarithmic searches.…”

Section: Introductionmentioning

confidence: 99%

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Compatibility of objective functions with simplex algorithm for controller tuning of HVDC system

Rasool

Ikram

et al. 2020

Ing. Inv.

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This work aims to tune multiple controllers at the same time for a HVDC system by using a self-generated (SG) simulation-based optimization technique. Online optimization is a powerful tool to improve performance of the system. Proportion integral (PI) controllers of Multi-infeed HVDC systems are optimized by the evaluation of objective functions in time simulation design (TSD). Model based simulation setup is applied for rapid selection of optimal PI control parameters, designed in PSCAD software. A multiple objective function (OF), i.e. Integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE), integral time square error (ITSE), and integral square time error (ISTE), is assembled for testing the compatibility of OFs with nonlinear self-generated simplex algorithm (SS-SA). Improved control parameters are achieved after multiple iterations. All OFs generate optimum responses and their results are compared with each other by their minimized numerical values. Disturbance rejection criteria are also proposed to assess the designed controller performance along with robustness of system. Results are displayed in form of graphs and tables in this paper.

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Section: Introductionmentioning

confidence: 99%

“…Offline tuning methods have also been considered to calculate cost functions by solving the equations (Merrikh-Bayat et al, 2015;M. Wang et al, 2016) or linear models (Oliveira et al, 2014), which is proficient but too complicated.…”

Section: Introductionmentioning

confidence: 99%

Compatibility of objective functions with simplex algorithm for controller tuning of HVDC system

Rasool

Ikram

et al. 2020

Ing. Inv.

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“…Therefore, numerous studies have been used in order to optimize the parameters of these controllers for the LFC of different power systems. Some designers used the conventional methods such as linear programming (LP; Oliveira et al, 2014), interior point algorithm (IP; Topno and Chanana, 2016), and quadratic programming method (QP; Khodabakhshian et al, 2012). However, these methods suffer from stagnation and may be trapped in local minima.…”

Section: Introductionmentioning

confidence: 99%

Load frequency control enhancement of islanded micro-grid considering high wind power penetration using superconducting magnetic energy storage and optimal controller

Mohamed

Mitani

2019

Wind Engineering

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This article proposes a robust load frequency control using a new optimal proportional–integral–derivative controller–based genetic moth swarm algorithm for islanded microgrids considering high wind power penetration. In such microgrids, the replacement of conventional generator units with a large number of renewable energy sources reduces the system inertia, which in turn causes undesirable influence on microgrid frequency stability, leading to weakening of the microgrid. Furthermore, sudden load shedding, load restoring, and short circuits caused large frequency fluctuations which threaten the system security and could lead to complete blackouts as well as damages to the system equipment. In order to solve this challenge, this study proposes a new coordinated optimal load frequency control plus modified control signal to superconducting magnetic energy storage for compensating the microgrid frequency deviation (∆ f). To prove the effectiveness of the proposed coordinated control strategy, an islanded microgrid was tested for the MATLAB/Simulink simulation. The physical constraints of the turbines such as generation rate constraints and speed governor dead band are considered in this study. The results confirmed the effectiveness and robustness of the proposed coordination performance against all scenarios of different load profiles, wind power fluctuation, and system uncertainties in microgrid integrated with high penetration of wind farms. Moreover, the results have been compared with both: the optimal load frequency control with/without the effect of conventional superconducting magnetic energy storage.

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“…These PI controllers have simple control structures but they are unable to withstand nonlinear and complex systems of the SHRES without precise tuning [3]. Authors in [4] utilized the linear programming method for the tuning of PID controller parameters. The authors in [5] highlighted that there is a need to optimize the PI controller's parameters in order to overcome the tedious and time-consuming tuning process through the traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Controllers for Enhancement of Stand-Alone Hybrid System Performance

Kean

Ramasamy

Sukumar

et al. 2018

IJPEDS

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<span lang="EN-US">This paper presents a stand-alone hybrid renewable energy system (SHRES) consisting of solar photovoltaic (PV), wind turbine (WT) and battery energy storage (BES) in an effort reduce the dependence on fossil fuels. The renewable energy sources have individual inverters and the PV inverter of the SHRES is operated using active and reactive power control. The PV inverter have two main control structures which are active power control and reactive power control and each contain a proportional integral (PI) controller. Accurate control of the PV inverter’s active power is essential for PV curtailment applications. Thus, this paper aims to enhance the performance of the SHRES in this work by optimizing the performance of the PV inverter’s active power PI controller parameters through the design of adaptive controllers. Therefore, an adaptive controller and an optimized adaptive controller are proposed in this paper. The performances of the proposed controllers are evaluated by minimizing the objective function which is the integral of the time weighted absolute error (ITAE) criterion and this performance is then compared with a controller that is tuned by the traditional trial and error method. Simulation results showed that the optimized adaptive controller is better as it recorded an error improvement of 42.59%. The dynamic optimized adaptive controller is more adept at handling the fast changes of the SHRES operation.</span>

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Linear Programming for Optimum PID Controller Tuning

Cited by 8 publications

References 16 publications

Compatibility of objective functions with simplex algorithm for controller tuning of HVDC system

Compatibility of objective functions with simplex algorithm for controller tuning of HVDC system

Load frequency control enhancement of islanded micro-grid considering high wind power penetration using superconducting magnetic energy storage and optimal controller

Adaptive Controllers for Enhancement of Stand-Alone Hybrid System Performance

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