Experimental stand-alone self-excited induction generator driven by a diesel motor

Mhamdi, Taoufik; Sbita, Lassaâd

doi:10.1016/j.jesit.2016.08.005

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…The parameter under investigation is the acceleration or transient rotational speed. In the literature, several studies have been conducted to determine the minimum required capacitance value for ensuring self-excitation of an asynchronous generator at a given rotational speed [1], [15], [17], [19], [20]. The procedure involves setting the rotational speed and then varying the capacitance of the capacitors until self-excitation occurs [21].…”

Section: Introductionmentioning

confidence: 99%

Experimental determination of minimum capacitor for self-excitation of induction generators

Sibrahim,

Aissou,

Rouas

et al. 2024

IJPEDS

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This article addresses the issue related to determining the minimum capacitor required for the self-excitation of an induction generator. The determination of the minimum capacitance required for the self-excitation of a self-excited induction generator has already been the subject of several previous studies. It has been shown that the minimum capacitance depends on the rotation speed and the remanent magnetism. The study carried out in this paper shows that, in addition to the rotation speed and the remanent magnetism, there is a third parameter that has an influence on the self-excitation process, which is the acceleration or, in other words, the rotation speed ramp-up. In this paper, several experimental self-excitation tests for different values of the rotation speed ramp-up are carried out, leading to new characteristics of the minimum self-excitation capacitance as a function of the rotation speed. The results obtained from simulation and experimental studies prove the efficacy of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Experimental determination of minimum capacitor for self-excitation of induction generators

Sibrahim,

Aissou,

Rouas

et al. 2024

IJPEDS

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“…An induction motor is an essential part of numerous types of electromechanical systems, converting electrical energy into mechanical energy and thus propelling vehicles at impressive efficiency rates . The performance of these devices is not flawless, however, as induction motors produce heat during operation [4]- [6]. Without an effective system of thermal management, they are at risk of overheating, resulting in performance degradation .…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach on Data-Driven Thermal Monitoring of Induction Motors in Electrified Powertrains

Dexter Woodward

2024

jes

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The research is aimed to determine a data-driven approach towards the thermal monitoring of Induction Motors in the context of electrified powertrains. We apply the examination of the temperature data with the help of the appropriate set of sensors, including Thermocouples, Resistance Temperature Detectors , as well as Infrared sensors in combination with the mixture of the Machine Learning algorithms to predict the service requirements and operational needs of the examined system . The data, provided by the thermal sensors, makes it possible to predict the demand for the maintenance of the part or optimize the work of the coolant pumps. Thus, we conduct the performance analysis of several ML models, including Artificial Neural Networks , Support Vector Machines , Decision Trees , and Random Forests across the range of different epochs of the training. Our analysis has shown that ML-based approaches can be highly efficient in accurately predicting the forthcoming maintenance requirements using the sensor-based information. Precisely, the most efficient was the ANN model, producing the result of 97.65%. It was followed by SVM and DT, producing the results of 94.5% and 92.3%, respectively. Finally, RF was only able to produce the result of 90.25%, falling behind the other approaches. Our comparison has provided multiple implications to the relative strengths and weaknesses of the employed models, and the choice of whether they are appropriate must be conducted through the prism of these comparisons. In addition, the analysis of the performance across different epochs has shown that the subsequent ones have produced the better results, which makes the training process crucial.

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“…Several researchers in recent years have presented many techniques aimed at SEIG. These techniques include the use of SVC with a fuzzy controller [11]. The bank of capacitors and a variable inductor with a fuzzy controller [12], the Electrochemical Battery is used as an energy storage system (ESS) [13].…”

Section: Introductionmentioning

confidence: 99%

Performance investigation of stand-alone induction generator based on STATCOM for wind power application

Ali

Suliman

Khalaf

et al. 2020

IJECE

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Self-Excited induction generators (SEIG) display a low voltage and frequency regulation due to variable applied load and input rotation speed. Current work presents a simulation and performance analysis of a three-phase wind-driven, SEIG connect to a three-phase load. In addition, an investigation of the dynamic operation of the induction generator from starting steady state until no-load operation. It is assumed that the input mechanical power is constant where the rotor of the SEIG rotates at a constant speed. The value of the excitation capacitance which is necessary to the operation of the induction generator also computed to ensure a smooth and self-excitation starting. The output voltage of the generator is adjusted by varying the reactive power injected by STATCOM. A 3-phase IGBT voltage source inverter with a fuel cell input supply is connected as STATCOM which is used to compensate for the reduction in the supply voltage and its frequency due to variation occurred in the applied loads. This work includes introducing a neuro-fuzzyy logic controller to enhance the performance of the SEIG by regulation the generated voltage and frequency The dynamic model of SEIG with STATCOM and loads are implemented using MATLAB/SIMULINK

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Experimental stand-alone self-excited induction generator driven by a diesel motor

Cited by 5 publications

References 10 publications

Experimental determination of minimum capacitor for self-excitation of induction generators

Experimental determination of minimum capacitor for self-excitation of induction generators

A Machine Learning Approach on Data-Driven Thermal Monitoring of Induction Motors in Electrified Powertrains

Performance investigation of stand-alone induction generator based on STATCOM for wind power application

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