Design and Investigation of Solar PV Fed Single-Source Voltage-Lift Multilevel Inverter Using Intelligent Controllers

Albert, Johny Renoald

doi:10.1007/s40313-021-00892-w

Cited by 56 publications

(4 citation statements)

References 35 publications

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“…The citations [30][31][32][33][34][35] are validating the vision sensor and the algorithm used in the proposed vision system. The citations [36][37][38][39][40] are validating the results and the percentage of error. The proposed system follows the same error pattern.…”

Section: Resultsmentioning

confidence: 85%

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

Ganesan¹,

Sankaranarayanan²,

Kumar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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This research introduces a challenge in integrating and cleaning the data, which is a crucial task in object matching. While the object is detected and then measured, the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage, abnormal stopping, and disaster. Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement. To solve all these problems, this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to a predetermined portion of the image. A novel white color sticker of the known dimensions marked with a color dot is pasted on the surface of an object for the best result in the template matching using the Improved Up-Sampled Cross-Correlation (UCC) algorithm. The vibration measurement is calculated using the Finite-Difference Algorithm (FDA), a machine vision system fitted with a macro lens sensor that is capable of capturing the image at a closer range, which does not affect the quality of displacement measurement from the video frames. The field test was conducted on the TAFE (Tractors and Farm Equipment Limited) tractor parts, and the percentage of error was recorded between 30% and 50% at very low vibration values close to zero, whereas it was recorded between 5% and 10% error in most high-accelerations, the essential range for vibration analysis. Finally, the suggested system is more suitable for measuring the vibration of stationary machinery having low frequency ranges. The use of a macro lens enables to capture of image frames at very close-ups. A 30% to 50% error percentage has been reported when the vibration amplitude is very small. Therefore, this study is not suitable for Nano vibration analysis.

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Section: Resultsmentioning

confidence: 85%

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

Ganesan¹,

Sankaranarayanan²,

Kumar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

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“…Currently, Artificial Neural Network (ANN) studies are employed in various fields and applications [27,28]. ANN provides an unsupervised control over the variables; the complexity of the network structure in the non-linear domain and confidence interval of the predicted results are challenging to obtain.…”

Section: Recent Literature Studymentioning

confidence: 99%

Breakdown Voltage Prediction by Utilizing the Behavior of Natural Ester for Transformer Applications

Pakianathan¹,

Maheswari²

2023

Intelligent Automation &Amp; Soft Computing

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This research investigates the dielectric performance of Natural Ester (NE) using the Partial Differential Equation (PDE) tool and analyzes dielectric performance using fuzzy logic. NE nowadays is found to replace Mineral Oil (MO) due to its extensive dielectric properties. Here, the heat-tolerant Natural Esters Olive oil (NE1), Sunflower oil (NE2), and Ricebran oil (NE3) are subjected to High Voltage AC (HVAC) under different electrodes configurations. The breakdown voltage and leakage current of NE1, NE2, and NE3 under Point-Point (P-P), Sphere-Sphere (S-S), Plane-Plane (PL-PL), and Rod-Rod (R-R) are measured, and survival probability is presented. The electric field distribution is analyzed using the Partial Differential Equation (PDE) tool. NE shows better HVAC with stand capacity under all the electrodes configuration, especially in the S-S shape geometry. The exponential function is developed for the oils under different electrode geometry; NE shows a higher survival probability. Likewise, the most influential dielectric properties such as breakdown voltage, kinematic viscosity, and water content are used to develop a Mamdani-based control system model that combines two variables to produce the surface model. The surface model indicates that the NE subjected for investigation is less susceptible to moisture effect and higher kinematic viscosity. Based on the surface models of PDE and fuzzy, it is concluded that NE possesses a high survival rate since its breakdown voltage does not react to changes in water content. Hence the application of NE in the transformer application is highly safe and possesses extended life.

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“…The NN, are considered as an adequate technique for the resolution of estimation and prediction problems as shown on several researches [24]- [27]. The definition of the network architecture is essential to obtain a performing system.…”

Section: Neural Network Model Nnmentioning

confidence: 99%

Estimation models of photovoltaic module operating temperature under various climatic conditions

Meflah,

Aouchiche,

Berkane

et al. 2023

IJEECS

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Accurate control of the operating temperature is essential for achieving optimal efficiency in the use of solar panels. The Normal Operating Cell Temperature (NOCT) is a widely used method for estimating module temperature, but it is not always accurate for all conditions. In this paper, the authors propose four different models for estimating module temperature using various techniques including Neural Networks, a linear method proposed by Ross, and a Fitting method. The objective is to find more accurate methods than the NOCT model. The results show that all four models provide good agreement between measured and calculated module temperatures, with R² > 0.91 and RMSE < 3.74 for clear days, and considering the resources of time, cost, and expertise necessary for simulating or evaluating the proposed model, the two models based on RN neural networks offers a more cost-effective solution, by utilizing straightforward mathematical skills, the RN model demonstrates applicability in any environment, making it a preferable choice over the NOCT model for predicting polycrystalline photovoltaic module temperature under different climatic conditions. The proposed models can be used to estimate PV module temperature with good precision under different climatic conditions.

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Design and Investigation of Solar PV Fed Single-Source Voltage-Lift Multilevel Inverter Using Intelligent Controllers

Cited by 56 publications

References 35 publications

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

Breakdown Voltage Prediction by Utilizing the Behavior of Natural Ester for Transformer Applications

Estimation models of photovoltaic module operating temperature under various climatic conditions

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