A Hybrid Intelligent Method for Compensation of Current Transformers Saturation Based on PSO-SVR

Gorji, Reza Taghipour; Hosseini, Mehdi; Abdoos, Ali Akbar; Ebadi, Ali

doi:10.3311/ppee.16248

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…Firstly, the Empirical Mode Decomposition (EMD) [17] is utilized for preliminary signal denoising, then a rotational factor is used to generate a delay compensation module to compensate for signal attenuation and time delays caused by metal reflections, measurement distance, and angle limitations. Improved SVR models [18] are employed for radar displacement signal compensation. Finally, cylinder simulation experiments are conducted for experimental verification.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Accumulator Performance in Hydraulic Systems through Support Vector Regression and Rotational Factors

Xu,

Zhou,

Chen

et al. 2024

Processes

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The piston-type accumulator is an energy storage device in hydraulic–pneumatic systems, playing a significant role in industries such as petrochemicals, heavy machinery, and steel metallurgy. The displacement parameters of the piston-type accumulator are vitally important for fault diagnosis and early warning in hydraulic systems. Traditional displacement measurement methods cannot meet the requirements of the internal testing environment of the accumulator. Therefore, this paper proposes an accumulator piston displacement signal compensation method based on rotational factors and support vector regression. Firstly, empirical mode decomposition is utilized to denoise the signal. Then, rotational factors are used to generate a delay compensation module to compensate for the signal attenuation and time delay caused by metallic reflection and scattering within the cylinder of the radar signal. The support vector regression model is improved based on a hash table to enhance its computational efficiency and achieve radar displacement signal compensation. Finally, a simulation experiment is designed to verify the effectiveness of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Optimizing Accumulator Performance in Hydraulic Systems through Support Vector Regression and Rotational Factors

Xu,

Zhou,

Chen

et al. 2024

Processes

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“…Furthermore, depending on their updates, some nodes will be classed as adaptive or fixed (Al-Mashhadany and Ahmed, 2020; Jaladi et al, 2020). Five layers were used to explain the stepwise of the architecture of ANFIS as shown in Figure 4 (Hemeyine et al, 2020; Taghipour Gorji et al, 2021).…”

Section: Sensor-less Control Speed Based On Mras Observermentioning

confidence: 99%

Robust sensor-less sliding mode of second-order control of doubly fed induction generators in variable speed wind turbine systems based on a novel MRAS-ANFIS observer

et al. 2023

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The current study proposed a robust sensor-less sliding mode second-order based on a super twisting algorithm (STA-SMSO) approach using a new observer Model Reference Adaptive System-Adaptative Neuro-Fuzzy Inference System (MRAS-ANFIS). This model was applied to a doubly fed induction generator (DFIG) wind turbine running under variable wind speed and DFIG fed with a power voltage source without a speed sensor, while the control objective was used to regulate independently, the active and reactive power DFIG stator were decoupled by using the field-oriented control technique. Additionally, this process reduced the cost of the control scheme and the size of DFIG by eliminating the speed sensor (encoder). In order to improve the traditional MRAS, the MRAS-ANFIS observer was proposed to replace the usual PI controller in the adaptation mechanism of MRAS with an Adaptative Neuro-Fuzzy Inference System (ANFIS) controller. The estimation of rotor position was tested and discussed under varying load conditions in low, zero, and high-speed region. The results mentioned that the proposed observer (MRAS-ANFIS) presented an attractive feature, such as guarantees finite time convergence, good response on speed wind variations, high robustness against machine parameter variations, and load variations compared to the conventional MRAS observer and MRAS-Fuzzy. Hence, the estimated rotor speed converged to their actual value has the capacity for estimating position in deferent region (low/zero/high) of speed.

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“…It should be noted that the simulation of the magnetic behavior of the core of current transformers is based on the Jiles-Atherton (JA) model. The JA model parameters used to simulate the CTs were introduced by Taghipour Gorji et al [13]. In simulations, the sampling frequency is set to 2500 Hz.…”

Section: Experimental Studymentioning

confidence: 99%

Discrimination between Inrush and Internal Fault Currents in Power Transformers Using Hyperbolic S-Transform

Yaseen,

Ebadi,

Abdoos

2023

IJE

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Numerous methods exist to distinguish between inrush current and internal faults, but these approaches have not yet become practical due to their inherent limitations. As a result, conventional methods, despite their well-known drawbacks, continue to be widely used in practice. In this paper, a new method based on time-frequency analysis is presented for detecting inrush current situations. To do this, a diverse array of scenarios involving a power transformer switching ON and internal fault cases are simulated using the PSCAD/EMTDC software package. Then, a hyperbolic S-transformer is employed to extract a determining index from the simulation results. Finally, a suitable threshold value for this index is computed so that inrush current can be distinguished from fault current by comparing the index with its threshold. Evaluation of the efficiency of the proposed method using simulation and real data confirms its excellent accuracy. Therefore, it can be used in algorithms for power transformer differential protection to improve their stability during inrush current transients.

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A Hybrid Intelligent Method for Compensation of Current Transformers Saturation Based on PSO-SVR

Cited by 6 publications

References 30 publications

Optimizing Accumulator Performance in Hydraulic Systems through Support Vector Regression and Rotational Factors

Optimizing Accumulator Performance in Hydraulic Systems through Support Vector Regression and Rotational Factors

Robust sensor-less sliding mode of second-order control of doubly fed induction generators in variable speed wind turbine systems based on a novel MRAS-ANFIS observer

Discrimination between Inrush and Internal Fault Currents in Power Transformers Using Hyperbolic S-Transform

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