Online Auto Selection of Tuning Methods and Auto Tuning PI Controller in FOPDT Real Time Process-pH Neutralization

Akshay, Naregalkar; Subbulekshmi, D.

doi:10.1016/j.egypro.2017.05.235

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…All the component values are calculated with a duty value of 0.5. ANN helps to easily tune the membership function and rule table [16,17]. The inference system of the ANFIS controller matches to a set of fuzzy rulebooks with learning fitness for the optimization of nonlinear functions.…”

Section: Modelling Of the Solar Photovoltaic Systemmentioning

confidence: 99%

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Design and Analysis of ANFIS – Based MPPT Method for Solar Photovoltaic Applications

Revathy

Kirubakaran

Rajeshwaran³

et al. 2022

International Journal of Photoenergy

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The solar photovoltaic energy is becoming popular in the modern-day distribution networks due to the clean energy factor. The photovoltaic modules exhibit a nonlinearity in the output power concerning the environmental conditions. This work suggests an adaptive neuro-fuzzy inference system- (ANFIS-) based maximal power point tracker (MPPT) for the optimization of the solar photovoltaic system (SPVS). The controller modelled is utilized to optimize the output power of a DC-DC converter connected to a 400 W PV array. The entire model is analysed employing MATLAB/SIMULINK using primary features provided by the technical data. The behavior of the controller modelled is tested for various weather conditions and partial shading conditions. The findings show the controller’s tracking speed effectiveness and dynamic response in PSCs.

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Section: Modelling Of the Solar Photovoltaic Systemmentioning

confidence: 99%

“…It has only one node fixed and its output is computed as a total of every incoming signal. The output function of this node is shown in equation (17).…”

Section: Layermentioning

confidence: 99%

Design and Analysis of ANFIS – Based MPPT Method for Solar Photovoltaic Applications

Revathy

Kirubakaran

Rajeshwaran³

et al. 2022

International Journal of Photoenergy

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“…To compare in faulty condition threshold with system operation in nominal condition, there are many approaches and the choice of one technique depends on the following: knowledge of the history of system events, the expert system knowledge, data collected on the system in normal operation conditions, a known model of the system, fault types of the short circuit, open circuit, mismatch of loads, and ground faults by the serious of coding by the data acquired by the control system in the system via MATLAB to indicate which type of defect has occurred on the system [6,7]. Infrared imager for solar panels provides the data and converts it to the CIELAB (it is 3D color space that enables accurate measurement and comparison of all perceivable colors using three color values), and segmentation by processing the panel of different cells, modules, and dust forming increases the temperature of different sizes and temperature ranges [8,9]. The relationship between the different panels' dust density and spectral transmittance is determined.…”

Section: Literature Reviewmentioning

confidence: 99%

Infrared Thermal Images of Solar PV Panels for Fault Identification Using Image Processing Technique

Kirubakaran

Preethi

Arunachalam³

et al. 2022

International Journal of Photoenergy

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Among the renewable forms of energy, solar energy is a convincing, clean energy and acceptable worldwide. Solar PV plants, both ground mounting and the rooftop, are mushrooming thought the world. One of the significant challenges is the fault identification of the solar PV module, since a vast power plant condition monitoring of individual panels is cumbersome. This paper attempts to identify the panel using a thermal imaging system and processes the thermal images using the image processing technique. An ordinary and thermal image has been processed in the image processing tool and proved that thermal images record the hot spots. Similarly, the new and aged solar photovoltaic panels were compared in the image processing technique since any fault in the panel has been recorded as hot spots. The image recorded in the aged panels records hot spots, and performance has been analyzed using conventional metrics. The experimental results have also been verified.

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“…This has allowed systems as complex and delicate as water treatment systems to also choose intelligent control techniques [18]- [20]. In [21], [22], a recent review of state of the art is exposed where they highlight the use of neural networks as an essential technique in water treatment, where one of the critical parameters of the process is the control of pH as stated in [23]- [25].…”

Section: Introductionmentioning

confidence: 99%

Neural network-based pH and coagulation adjustment system in water treatment

Mora

Nieto

Ortega

et al. 2023

IJ-AI

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This document presents a machine learning model development as a tool to improve chemical dosing procedure in ariari regional aqueduct (ARA). The supervised learning model has been addressed starting from the knowledge of data color, turbidity and pH at the water inlet to the aqueduct and the dosing results of type A aluminum sulfate and calcium oxide (lime) obtained through jar tests. The construction of the automatic learning model had a comprehensive implementation and improvement field through continuous system training, which allowed an optimal dosage of Aluminum Sulfate and Lime to generate an outlet pH less than 7.5 and outlet turbidity less than 8 nephelometric turbidity unit (NTU). Those outlet water parameters meet the ministry of social protection criteria in Colombia. Also, a virtual jar test was created to reduce the time required to obtain chemical dosing values to less than a minute. In contrast, a laboratory test takes approximately a half-hour to displays results.

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Online Auto Selection of Tuning Methods and Auto Tuning PI Controller in FOPDT Real Time Process-pH Neutralization

Cited by 11 publications

References 12 publications

Design and Analysis of ANFIS – Based MPPT Method for Solar Photovoltaic Applications

Design and Analysis of ANFIS – Based MPPT Method for Solar Photovoltaic Applications

Infrared Thermal Images of Solar PV Panels for Fault Identification Using Image Processing Technique

Neural network-based pH and coagulation adjustment system in water treatment

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