Optimal design of proportional‐resonant controller and its harmonic compensators for grid‐integrated renewable energy sources based three‐phase voltage source inverters

Ebrahim, Mohamed A.; Aziz, Beshoy Abdou; Nashed, Maged N. F.; Osman, Fawzy A.

doi:10.1049/gtd2.12108

Cited by 24 publications

(7 citation statements)

References 27 publications

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“…For a typical HIL simulation, the control strategies (namely, MPC and ANN-based) are implemented in an external target micro-controller kit (in our study, C2000TM-microcontroller-LaunchPadXL TMS320F28379D kit), while the three-phase four-level flying capacitor inverter and its different loads are simulated and hosted on the personal computer (i.e., Host-PC) as a model in the MATLAB. The HIL simulation requires cooperation between the Host-PC and the Target LAUNCHXL-F28379D, which is achieved using a virtual serial COM port [39]. To be more specific, the Host-PC transmits the measured signals (or, input features) such as V 1x , V 2x , i ref , i x , ∆V 1x , ∆V 2x , ∆i x to the LAUNCHXL-F28379D kit.…”

Section: Hil Validation and Resultsmentioning

confidence: 99%

An Artificial Neural Network-Based Model Predictive Control for Three-Phase Flying Capacitor Multilevel Inverter

Malidarreh¹,

Bakeer²,

Mohamed³

et al. 2022

IEEE Access

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Model predictive control (MPC) has been used widely in power electronics due to its simple concept, fast dynamic response, and good reference tracking. However, it suffers from parametric uncertainties, since it directly relies on the mathematical model of the system to predict the optimal switching states to be used at the next sampling time. As a result, uncertain parameters lead to an ill-designed MPC. Thus, this paper offers a model-free control strategy on the basis of artificial neural networks (ANNs), for mitigating the effects of parameter mismatching while having a little negative impact on the inverter's performance. This method includes two related stages. First, MPC is used as an expert to control the studied converter in order to provide a dataset, while, in the second stage, the obtained dataset is utilized to train the proposed ANN. The case study herein is based on a four-level three-cell flying capacitor inverter. In this study, MAT-LAB/Simulink is used to simulate the performance of the proposed method, taking into account various operating conditions. Afterward, the simulation results are reported in comparison with the conventional MPC scheme, demonstrating the superior performance of the proposed control strategy in terms of robustness against parameters mismatch and low total harmonic distortion (THD), especially when changes occur in the system parameters, compared to the conventional MPC. Furthermore, the experimental validation of the proposed method is provided based on the Hardware-in-the-Loop (HIL) simulation using the C2000TM-microcontroller-LaunchPadXL TMS320F28379D kit, demonstrating the applicability of the ANN-based control strategy to be implemented on a DSP controller.INDEX TERMS Model predictive control, Artificial neural network, Multilevel inverter, Total harmonics distortion, Hardware-in-the-Loop (HIL) simulation. I. INTRODUCTIONP OWER converters have been used in a wide range of applications in recent decades due to their improved performance and efficiency [1]. In this context, substantial attention has been paid to multilevel inverters (MLIs) that introduced with certain advantages, such as: (i) reducing the total harmonic distortion, (ii) reducing common-mode voltage, and (iii) reducing the dv/dt (that is, the rate of voltage change over time in switching instant) stress that leads to a reduction in electromagnetic emissions. There are a number of well-established and conventional MLI topologies that have been implemented and refined over time, such as: flying capacitor (FC), cascaded H-Bridge (CHB), neutral

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Section: Hil Validation and Resultsmentioning

confidence: 99%

An Artificial Neural Network-Based Model Predictive Control for Three-Phase Flying Capacitor Multilevel Inverter

Malidarreh¹,

Bakeer²,

Mohamed³

et al. 2022

IEEE Access

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“…In this section, classical PID control with AD, predictive current-voltage control, proposed bias-free FCS-MPC strategy for grid-tied LCL-filtered converter are validated by simulation and experiment data. The parameters of the setup are collected in Table I, where the simplified outer loop gain and weighting factors are tuned with the solutions proposed in [21] and [11], [20], respectively. Simulation is done via the PLECS environment, while the controller hardware for the experimental verification is B-BOX-RCP, an off-the-shelf product from Imperix.…”

Section: Effectiveness Verificationmentioning

confidence: 99%

Bias-Free Predictive Control of Power Converters with LCL Filter in Micro-Energy Systems

Xing

Zhang

Dragičević

et al. 2023

IEEE Trans. Ind. Electron.

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“…The proportional controller takes care of and reduces the steady state error in system response 62 – 65 . The integral controller reduces the settling time and the derivative controller is responsible for the stability of the entire system at the time of emergencies 66 – 69 . This combination reduces the consequence of a disturbance and shortens the time it takes for the frequency level to return to its set point in all critical situations 70 .…”

Section: Design Of Controller and Cost Functionmentioning

confidence: 99%

Modern PID/FOPID controllers for frequency regulation of interconnected power system by considering different cost functions

Mohamed,

Jagatheesan,

Anand

2023

Sci Rep

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This article presents frequency regulation of an interconnected three-area power system (Thermal + Wind + Hydro). Fractional Order PID (FOPID) and Proportional-Integral-Derivative (PID) controllers are applied as subsidiary regulators to control the electrical power interconnected system at the time of sudden load variation. To accomplish this study, Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Sine Cosine Inspired Algorithm (SCIA) and Atom Search Inspired Algorithm (ASIA) are implemented to optimize the secondary regulators' gains (PID and FOPID) by considering various cost functions such as Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), Integral Square Error (ISE), and Integral Time Square Error (ITSE). Performance analysis in this work is conducted using various cost functions based on GA, GWO, SCIA and ASIA. The comparative analysis of the attained results reveals that GWO-PID and ASIA–PID settle at (83.83 s) and (30.31 s), respectively and ASIA-FOPID at (25.12 s). The controllers based on ITSE as a cost function outperform the comptrollers with other cost functions (ISE, IAE and ITAE). In addition, the ISE-based GA–PID and SCIA–PID settle at (113.92 s) and (35.1 s), respectively and SCIA-FOPID at (24.78 s). The ISE-based regulators yield improved response equated to other cost functions (ITSE, IAE and ITAE) optimized controllers. The robustness test also is carried out to validate the effectiveness of the proposed optimization techniques by changing the system parameters within ± 25% and ± 50% from their nominal values as well as changing the load pattern.

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Optimal design of proportional‐resonant controller and its harmonic compensators for grid‐integrated renewable energy sources based three‐phase voltage source inverters

Cited by 24 publications

References 27 publications

An Artificial Neural Network-Based Model Predictive Control for Three-Phase Flying Capacitor Multilevel Inverter

An Artificial Neural Network-Based Model Predictive Control for Three-Phase Flying Capacitor Multilevel Inverter

Bias-Free Predictive Control of Power Converters with LCL Filter in Micro-Energy Systems

Modern PID/FOPID controllers for frequency regulation of interconnected power system by considering different cost functions

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