PI controller design using ABC algorithm for MPPT of PV system supplying DC motor pump load

Oshaba, Ahmed Said; Ali, E. S.; Elazim, S.M. Abd

doi:10.1007/s00521-015-2067-9

Cited by 65 publications

(18 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Some of them are reviewed here. Oshaba et al (2017), employed a maximal power-points tracking (MPPT) by scheming 2-PI controllers. The initial one was to attain the MPPT system via scrutinizing the voltage together with the current of the PV arrays and also altering the DC/DC converter's duty cycles.…”

Section: Related Workmentioning

confidence: 99%

Designing Proportional Integral Controller for a Solar Powered DC‐DC Converter Using PIPSO Algorithm and Deep Learning approach in Intelligent Bio‐Medical Applications

Rajarajacholan¹,

Moses

Banu

2020

Expert Systems

View full text Add to dashboard Cite

Numerous engineering complexities are simplified using optimization algorithms. In a solar power system, the necessity of the voltage regulator is obvious. To control the regulator existent research works used PI, PID controllers that might have an unwanted transient response. To overcome such drawbacks here, a fresh scheme is proposed for the designing of the adaptive sliding mode (SM) controller of a solar powered LUO converter using optimization algorithms. The PSO ('Particle Swarm Optimization') is proved to expedite the convergence characteristic for many applications. Here, an ameliorated PSO version is developed. This algorithm is termed the Parameter Improved-PSO (PIPSO) algorithm. In this algorithm, the parameters, say, inertia weight, social along with cognitive agents is updated in every generation. The Proportional Integrator (PI) controller is used. The gain of this controller is tuned using the PIPSO. This algorithm's objective function is to lessen ISE ('Integral Squared Error') of the converter's output voltage. This parameter is picked as the objective function of the optimization algorithm. The proposed PIPSO is established to show better outcomes when contrasted to the traditional PSO concerning tuning a collection of parameters. An analysis is also made to evaluate the effect of usage of the solar panel () in the proposed work. K E Y W O R D S integral squared error, parameter improved particle swarm optimization, proportional integral controller, proportional integral controller, solar panel 1 | INTRODUCTION The continuous growth for global demand and environmental concern has lead to the exploration of Renewable Energy (RE) sources. As contrasted to all other 'RE' sources (Mekhilef, et.al, 2011) the photo-voltaic (PV) energy has advantages such as no noise, cleanliness, along with very less maintenance. The PV (Sangwongwanich, 2018) systems are broadly used for low power electrical generation. This paper attempts to use non-RE as the voltage source input. The DC − DC conversion technology has evolved as a major area of research in the power electronics and drives field. DC − DC converters (Chen et.al, 2017), (Forouzesh et.al, 2017), (Riedel et.al, 2017) are nothing but electrical circuits which can transport the energy to a load. In such sorts of converters, the switches can either be diodes or transistors. The voltage value (transferred) relies upon the switches' duty ratios. The DC − DC converters are extensively utilized on industrial applications along with computers hardware circuits. A disparity of the PI-Derivative (PID) control that utilizes just the proportional and integral terms is called PI control. The PI is further widely utilized than the PID.

show abstract

Section: Related Workmentioning

confidence: 99%

Designing Proportional Integral Controller for a Solar Powered DC‐DC Converter Using PIPSO Algorithm and Deep Learning approach in Intelligent Bio‐Medical Applications

Rajarajacholan¹,

Moses

Banu

2020

Expert Systems

View full text Add to dashboard Cite

show abstract

“…Scout bees investigate for novel food resources. Employed and onlooker bees can be seen as an accomplishment of the work of manipulation, whereas scout bees can be seen as an accomplishment of the work of search [14] [15].…”

Section: Pid Controller Design Based On Artificial Bee Colony Algorithmmentioning

confidence: 99%

“…For The optimization program is employed by employing a MATLAB m-file program and connected with the system model program in MATLAB/SIMULINK [19].…”

Section: Pid Controller Design Based On Artificial Bee Colony Algorithmmentioning

confidence: 99%

Optimized PID Controller for a Self-lift Positive Output Luo-Converter

İbrahim¹,

Khather²

2020

Proceedings of the Proceedings of the 1st International Multi-Disciplinary Conference Theme: Sustainable Development and Smart

View full text Add to dashboard Cite

Voltage lift method opens a suitable way to enhance characteristics of DC-DC converters Self-lift can be defined as a typical approach extensively utilized in developing a DC-DC converter for amplifying output voltage. The use of the self-lift approach in the DC-DC conversion technologies result in designing the positive output Luo power converters with a high voltage transfer gains in the mathematical progression, such approach open the way to securely increasing voltage transfer gain that is related to the DC/DC converters as well as enhancing specifications regarding DC-DC converters. These converters could be utilized in the industrial applications as well as the computer peripheral equipment, particularly with regard to the high-output-voltage projects. Self-lift Positive Output Luo Converter in addition to the PID controller, therefore designed in the presented work, has appropriate output voltage regulations, also there aren't undershoots or overshoots and has been very effective. Detailed and full analysis regarding circuit in addition to discrete in addition to optimized PID controllers with the ABC algorithms was conducted. Furthermore, extensive simulations have been conducted and the results have been specified. PID controller platform has been proved with the use of MATLAB/Simulink model.

show abstract

“…In recent years, bio-inspired (BI) methods, such as swarm intelligence algorithms, have gained increased attention as powerful optimization algorithms for solving complex problems and providing optimum solutions. Some soft computing algorithms have been used to adjust the gains of a proportional-integral control for MPPT of PV systems and show a high performance [24]- [26]. The comparison of MPPT methods based on soft computing algorithms for different control variables and DC-DC converters are shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

MPPT Algorithm Based on Artificial Bee Colony for PV System

et al. 2021

View full text Add to dashboard Cite

Energy structures from non-conventional energy source has become highly demanded nowadays. In this way, the maximum power extraction from photovoltaic (PV) systems has attracted the attention, therefore an optimization technique is necessary to improve the performance of solar systems. This paper proposes the use of ABC (artificial bee colony) algorithm for the maximum power point tracking (MPPT) of a PV system using a DC-DC converter. The procedure of the ABC MPPT algorithm is using data values from PV module, the P-V characteristic is identified and the optimal voltage is selected. Then, the MPPT strategy is applied to obtain the voltage reference for the outer PI control loop, which in turn provides the current reference to the predictive digital current programmed control. A real-time and high-speed simulator (PLECS RT Box 1) and a digital signal controller (DSC) are used to implement the hardwarein-the-loop system to obtain the results. The general system does not have a high computational cost and can be implemented in a commercial low-cost DSC (TI 28069M). The proposed MPPT strategy is compared to the conventional perturb and observe method, results show the proposed method archives a much superior performance. INDEX TERMS Maximum power point tracking, Photovoltaic system, Artificial bee colony, Hardware in the loop testing. ABBREVIATION Term Description ABC Artificial bee colony algorithm. ACO Ant colony optimization. ADE Adaptive differential evolution. ANFIS Adaptive neuro-fuzzy inference system. ANNs Artificial neural networks. BA Bat algorithm. BI Bio-inspired methods. COA Coyote optimization algorithm. DSC Digital signal controller. ESC Extremum-seeking control. FL Fuzzy logic algorithm. FPA Flower pollination algorithm. FPGA Field-programmable gate arrays. GA Genetic algorithm. HIL Hardware-in-the-loop. INC Incremental conductance algorithm. MOA Moth-flame optimization algorithm.

show abstract

PI controller design using ABC algorithm for MPPT of PV system supplying DC motor pump load

Cited by 65 publications

References 33 publications

Designing Proportional Integral Controller for a Solar Powered DC‐DC Converter Using PIPSO Algorithm and Deep Learning approach in Intelligent Bio‐Medical Applications

Designing Proportional Integral Controller for a Solar Powered DC‐DC Converter Using PIPSO Algorithm and Deep Learning approach in Intelligent Bio‐Medical Applications

Optimized PID Controller for a Self-lift Positive Output Luo-Converter

MPPT Algorithm Based on Artificial Bee Colony for PV System

Contact Info

Product

Resources

About