Diagnosis and localization of fault for a neutral point clamped inverter in wind energy conversion system using artificial neural network technique

Introduction. In this paper, to maximize energy transmission in wind power system, various Maximum Power Point Tracking (MPPT) approaches are available. Among these techniques, we have proposed the one based on typical fuzzy logic. Despite the somewhat reduced performance of fuzzy MPPT. For a number of reasons, fuzzy MPPT can replace conventional optimization techniques. In practice, the effectiveness of conventional MPPT methods depends mainly on the accuracy of the information given and the wind speed or knowledge of the aerodynamic properties of the wind system. Novelty. Our new MPPT for monitoring the maximum power point has been proposed. We developed an algorithm to improve control performance and govern the stator’s developed active and reactive power using the typical fuzzy logic 2 and enable robust control of a grid-connected, doubly fed induction generator. Purpose. MPPT which implies the wind turbine’s rotating speed should be modified in real time to capture the most wind energy, is necessary to achieve high efficiency for wind energy conversion, according to the aerodynamic characteristics of the wind turbine. Methods. Developing a mathematical model for a wind energy production system is complex, can be strongly affected by wind variation and is a non-linear problem. Thanks to these characteristics, thus, the Lyapunov technique is combined with a sliding mode control to ensure overall asymptotic stability and robustness with regard to parametric fluctuations in order to accomplish this goal. We contrasted our fuzzy type-2 algorithm’s performance with that of the fuzzy type-1 and Perturbation & Observation (P&O) suggested in the literature. Practical value. The simulation results demonstrate that the control performance is satisfactory when using the fuzzy logic technique. From these results, it can be said for the optimization of energy conversion in wind systems, the fuzzy type-2 technique may offer a workable option. Since it presents a great possibility to avoid problems either technical or economics linked to conventional strategies.

Section: Fig 5 Diagram Design Of Robust Controllersmentioning

confidence: 99%

Maximum power point tracking improvement using type-2 fuzzy controller for wind system based on the double fed induction generator

Kaddache,

Drid,

Khemis

et al. 2024

“…To avoid such issues, the technique of fault diagnosis and identification has become a crucial step in protecting this sort of electrical machines. The sorts of faults often relate to diagnostic techniques [6][7][8][9].…”

mentioning

confidence: 99%

Wavelet packet analysis for rotor bar breakage in an inverter induction motor

Ibaid

Belhamdi

Abid

et al. 2023

Introduction. In various industrial processes, squirrel cage induction motors are widely employed. These motors can be used in harsh situations, such as non-ventilated spaces, due to their high strength and longevity. These machines are subject to malfunctions such as short circuits and broken bars. Indeed, for the diagnosis several techniques are offered and used. Novelty of the proposed work provides the use of wavelet analysis technology in a continuous and discrete system to detect faults affecting the rotating part of an induction motor fed by a three-phase inverter. Purpose. This paper aims to present a novel technique for diagnosing broken rotor bars in the low-load, stationary induction machine proposed. The technique is used to address the problem of using the traditional Techniques like Fourier Transforms signal processing algorithm by analyzing the stator current envelope. The suggested method is based on the use of discrete wavelet transform and continuous wavelet transform. Methods. A waveform can be monitored at any frequency of interest using the suggested discrete wavelet transform and continuous wavelet transform. To identify the rotor broken bar fault, stator current frequency spectrum is analyzed and then examined. Based on a suitable index, the algorithm separates the healthy motor from the defective one, with 1, 2 and 3 broken bars at no-load. Results. In comparison to the healthy conditions, the recommended index significantly raises under the broken bars conditions. It can identify the problematic conditions with clarity. The possibility of detecting potential faults has been demonstrated (broken bars), using discrete wavelet transform and continuous wavelet transform. The diagnostic method is adaptable to temporary situations brought on by alterations in load and speed. Performance and efficacy of the suggested diagnostic method are demonstrated through simulation in Simulink® MATLAB environment.

“…Using NN models for measuring, observing and diagnosis can solve many problems of classical modeling. These models allow global monitoring for complex systems, and offer the possibility of fault isolation with necessary decisions [15].…”

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confidence: 99%

Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines

Sakhara¹,

Brahimi²,

Nacib³

et al. 2023

Introduction. Nowadays, fault diagnosis of induction machines plays an important role in industrial fields. In this paper, Artificial Neural Network (ANN) model has been proposed for automatic fault diagnosis of an induction machine. The aim of this research study is to design a neural network model that allows generating a large database. This database can cover maximum possible of the stator faults. The fault considered in this study take into account a short circuit with large variations in the machine load. Moreover, the objective is to automate the diagnosis algorithm by using ANN classifier. Method. The database used for the ANN is based on indicators which are obtained from wavelet analysis of the machine stator current of one phase. The developed neural model allows to taking in consideration imbalances which are generated by short circuits in the machine stator. The implemented mathematical model in the expert system is based on a three-phase model. The mathematical parameters considered in this model are calculated online. The characteristic vector of the ANN model is formed by decomposition of stator current signal using wavelet discrete technique. Obtained results show that this technique allows to ensure more detection with clear evaluation of turn number in short circuit. Also, the developed expert system for the taken configurations is characterized by high precision.