Hysteresis current control of induction motor drives using dSPACE DSP controller

Talib, M. H. N.; Isa, Siti Noormiza Mat; Hamidon, H.; Ibrahim, Zulkifilie; Rasin, Zulhani

doi:10.1109/pecon.2016.7951617

Cited by 18 publications

(5 citation statements)

References 11 publications

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“…In this system, a fixed hysteresis band is applied for the current control of the rotor side converter (RSC). As shown in Figure 3, hysteresis current control is basically a feedback current control technique [19], [20]. A hypothetical control band surrounding the reference current controls the current error, which is the difference between the reference and inverter currents [21].…”

Section: Dfig Wind Turbine System Configurationmentioning

confidence: 99%

Real-time current sensor fault detection and localization in DFIG wind turbine systems

Touioui,

Ganouche,

Ahmida

et al. 2023

IJPEDS

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<span lang="EN-US">The degree of reliability is one of the main issues in having the uninterrupted operation of fault-tolerant measurement and control systems. This paper presents a technique for detecting and localizing current sensor fault in doubly fed induction generator wind turbine systems (DFIG-WT). For this purpose, a methodology divided into three steps has been carried out and is presented as follows: the first step is the application of the zero-sequence component as an indicator of the presence of the sensor fault. The second step is based on the application of Concordia transformation on the current signals to generate different criteria for localizing the faulty sensor. Finally, an artificial neural network model is developed to analyze the localization criteria and identify the faulty sensor. All simulations are carried out in MATLAB/Simulink environment. Results show that the proposed technique is effective and can be used in real-time fault detection and localization in the DFIG-WT.</span>

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Section: Dfig Wind Turbine System Configurationmentioning

confidence: 99%

Real-time current sensor fault detection and localization in DFIG wind turbine systems

Touioui,

Ganouche,

Ahmida

et al. 2023

IJPEDS

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“…The rules were developed by the phase-planetrajectory method [19] since this methodology offers an easy and systematic technique for relating the overall system dynamic performance with the fuzzy knowledge base. Due to implementation for real experimental with a high sampling rate, the simplified technique in [20] was applied to reduce the number of rules. Fig.…”

Section: Main Fuzzy Logic Controllermentioning

confidence: 99%

Experimental Investigations into the Behavior of Scaling Factors in a Fuzzy Logic Speed Control Induction Motor with Model Reference Adaptive Control

Ismail¹,

Talib²,

Ibrahim³

et al. 2022

ECTI-EEC

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This paper presents a self-tuning fuzzy logic speed controller (FLSC) with model reference adaptive control (MRAC) for an induction motor (IM) drive system. The MRAC is examined by output scaling the factor tuner for optimum motor speed performance. A detailed investigation is carried out on the scaling factor control of the input change error and main FLSC output increment. This proposed method utilizes seven simplified rules of the 5 × 5 matrix membership functions to minimize the computational burden and memory space limitations. All simulation work is conducted using Simulink and Fuzzy Tools in the MATLAB software and the experimental testing with the aid of a digital signal controller board, dSPACE DS1103. Based on the results, the output scaling factor makes a more significant impact on the performance effect compared to the input error scaling factor. The input change error and output SF also exhibit similar behavior, indicating that a large range of UoD tuners works well in terms of capability load rejection while a small range of UoD tuners performs well in terms of rise time. The analysis includes no-load and load tests to ascertain the overshoot percentage, rise time, and settling time for transient and steady-state conditions.

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“…The FOC drive system presented in Figure 2 is based on hysteresis current controller [21]. This is due to its simple structure, fast response and good accuracy.…”

Section: Figure 2 Induction Motor Drive Based Hysteresis Current Controllermentioning

confidence: 99%

Self-tuning Fuzzy Logic Controller Based on Takagi-Sugeno Applied to Induction Motor Drives

Farah

Talib

Ibrahim

et al. 2018

IJPEDS

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<span>Fuzzy logic controller has been the main focus for many researchers and industries in motor drives. The popularity of Fuzzy Logic Controller (FLC) is due to its reliability and ability to handle parameters changes during load or disturbance. Fuzzy logic design can be visualized in two categories, mamdani design or Takagi-Sugeno (TS). Mamdani type can facilitate the design process, however it require high computational burden especially with big number of rules and experimental testing. This paper, develop Self-Tuning (ST) mechanism based on Takagi-Sugeno (TS) fuzzy type. The mechanism tunes the input scaling factor of speed fuzzy control of Induction Motor (IM) drives Based on the speed error and changes of error. A comparison study is done between the standard TS and the ST-TS based on simulations approaches considering different speed operations. Speed response characteristics such as rise time, overshoot, and settling time are compared for ST-TS and TS. It was shown that ST-TS has optimum results compared to the standard TS. The significance of the proposed method is that, optimum computational burden reduction is achieved.</span>

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Hysteresis current control of induction motor drives using dSPACE DSP controller

Cited by 18 publications

References 11 publications

Real-time current sensor fault detection and localization in DFIG wind turbine systems

Real-time current sensor fault detection and localization in DFIG wind turbine systems

Experimental Investigations into the Behavior of Scaling Factors in a Fuzzy Logic Speed Control Induction Motor with Model Reference Adaptive Control

Self-tuning Fuzzy Logic Controller Based on Takagi-Sugeno Applied to Induction Motor Drives

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