Power Quality Improvement of Constant Frequency Aircraft Electric Power System Using Genetic Algorithm and Neural Network Control Based Control Scheme

Khalid, Saifullah

doi:10.4172/2332-0796.1000206

Cited by 1 publication

(3 citation statements)

References 15 publications

(21 reference statements)

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“…The ABC algorithm performs the search for the controller parameters. The performance of the ABC-SAPF filter gives better results than the ANN-SAPF filter or the Fuzzy Logic-SAPF filter [76,81]. Table 6.…”

Section: Artificial Bee Colony (Abc) Algorithm For Sapfmentioning

confidence: 97%

“…The fuzzy logic control technique controls the voltage loop in the SAPF filter, and the Artificial Neural Network (ANN) technique controls and controls the operation of the SAPF to create a bias current that multiplies reactive power compensation, controls components harmonics generated in the current source while controlling the unbalance point in the source current, the GA-FL-ANN Combined Controller gives a THD i of 0.99%, and a THD v of 1.4% and a response time of 0.0058 s [75]. The compensating current-controlled GAs in the SAPF filter circuit improve the performance of reactive power compensation, control total harmonic distortion (THD) and speed up the frequency response [76].…”

Section: Genetic Algorithms (Gas) Algorithms For Sapfmentioning

confidence: 99%

“…The SAPF controller generates many uncertain and time-varying parameters. For the GAs technique to give good results, the ANN technique combined with GAs is a good direction to improve the performance of the model [76]. GA techniques can also be combined with other algorithms in meta-heuristics, such as the Bee familiarization method (QBGAs), to improve model performance [77].…”

Section: Stepmentioning

confidence: 99%

See 2 more Smart Citations

Harmonic Mitigation Using Meta-Heuristic Optimization for Shunt Adaptive Power Filters: A Review

Duc,

Hlavaty,

Bilik

et al. 2023

Energies

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Shunt Adaptive Power Filter (SAPF) is widely used in the performance of power quality improvement activities in the power supply industry for processing industries or civil power sources in the world today based on its simplicity, transparency, high reliability, efficiency, and reliability, and their powerful compensating current-providing nature. The PI controller integrated into the SAPF operation mechanism works with extra high efficiency in selecting the current to compensate for the lost current generated in the power supply due to harmonics generated by the parameter values. The system operates by the PWM method for bridge rectifier circuits that perform the function of selecting the appropriate compensating current, providing correct compensation for the amount of current loss in the power supply. Adjusting the parameter to reach the optimal value by different methods is a promising and popular research direction at present. The parameter serves the right purpose for the PI controller to generate enough PWM pulses to excite the bridge rectifiers to generate just the right amount of compensating current and enough current to be compensated on the power supply. The commonly used parameter adjustment methods include the Ziegler Nichols closed-loop vibration method, the P-Q theoretical method, and several other methods. This study conducts a comprehensive review of the literature on modern strategies for adjusting theparameters in the PI controller in the SAPF suite by using the meta-heuristic optimization method. This study performs classification according to the operation mode of meta-heuristic optimization methods to adjust the parameter to control the PI to select the correct PWM frequency to activate bridge rectifiers to select the most optimal compensation current to compensate for the loss of current on the power supply to meet the goal of improving power quality in accordance with IEEE 519-2022 standard, leading to the total harmonic distortion (THD) value is below 5%. The study presents in detail some meta-heuristic optimization algorithms, including applications, mathematical equations, and implementation of flow charts for SAPF and provides some open problems for future research. The main objective of this study is to provide an overview of applying meta-heuristic optimization algorithms to the parameter tuning of PI controllers.

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Section: Artificial Bee Colony (Abc) Algorithm For Sapfmentioning

confidence: 97%

Section: Genetic Algorithms (Gas) Algorithms For Sapfmentioning

confidence: 99%