Adaptive Model Predictive Control for Three Phase Voltage Source Inverter using ADALINE estimator

Cherif, Ali Yahia; Remache, Seif El Islam; Barra, Kamel; Wira, Patrice

doi:10.1109/gpecom.2019.8778544

Cited by 4 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this application, a double algorithmic ADALINE will be used for harmonics extraction and processing, from the MV application for guaranteeing robust reference for MPC. Many papers discussed the idea of ADALINE (Widrow & Winter, 1988) (Cherif et al, 2019), but few papers talked about using ADALINE as a noise reference for MPC converter in SAPF application. Each of ADALINE, MMC, and MPC influences Harmonic elimination performance and cost which will be shown in the next sections.…”

Section: :Low Pass Passive Filtersmentioning

confidence: 99%

See 1 more Smart Citation

Modular Multilevel Converter Controlled by Daisy-Chain Model Predictive Control for Medium Voltage Shunt Active Filter

ElMansy¹

2021

Proceedings of ‏The 4th International Conference on Modern Research in Science, Engineering and Technology

View full text Add to dashboard Cite

Nowadays most of the loads exhibits a non-linear nature leading to sever distortion in the grid current. Mitigation of the adverse effects of such problems has been the focus of numerous researches throughout the past century. However, each genuine concept has its set-backs regarding energy consumption, reliability or protection. A Modular Multi-level converter (MMC) based Shunt Active Power Filter (SAPF) is introduced in this paper. The converter is to be connected to the medium voltage grid to compensate the reactive power required by the non-linear loads, thus eliminating the harmonics. The MMC out performs high-voltage/highpower converters in terms of quality output performance, high modularity, simple scalability, and low voltage and current rating demand for the power switches. A Model Predictive Control (MPC) algorithm is developed to control the converter. Least Mean Square Algorithm (LMS) based Adaptive Linear Neuron (ADALINE) network generates the reference current. The threephase load current is measured and analyzed to estimate the harmonics waveform, which is then fed to the MPC to generate the optimal switching sequence for the MMC. Simulation and results are presented to show the effectiveness of the proposed techniques.

show abstract

Section: :Low Pass Passive Filtersmentioning

confidence: 99%

“…At first, it's vital to say the ennobling idea of this technique that came from the concepts of the organic model of living organic neurons. ADALINE theory of operation in addition to electrical applications are mentioned and discussed in (Widrow & Winter, 1988) (Cherif et al, 2019).…”

Section: Adaline Least Mean Square (Lms)mentioning

confidence: 99%

Modular Multilevel Converter Controlled by Daisy-Chain Model Predictive Control for Medium Voltage Shunt Active Filter

ElMansy¹

2021

Proceedings of ‏The 4th International Conference on Modern Research in Science, Engineering and Technology

View full text Add to dashboard Cite

show abstract

“…Conversely, the Adaptive Linear Neuron (ADALINE) network corresponds to a simple single-layer neural network that does not require an offline training stage, being trained online. Therefore, its potential has been shown in several works regarding not only filter parameter estimation [2,12,24,26] but also current/voltage harmonic components identification [27,28] and flux estimation in electrical machines [23], which have reported a good estimation accuracy. Furthermore, according to [5,25], ADALINE-based parameter estimation techniques typically require lower computational burden and present faster convergence than more conventional estimation methods, including those based on EKFs [17,18,20] and MRAS [11,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Online Filter Parameters Estimation in a Double Conversion UPS System for Real-Time Model Predictive Control Performance Optimization

et al. 2022

View full text Add to dashboard Cite

Uninterruptible Power Supplies (UPS) represent the key technology to continuously feed and protect a wide range of critical applications in electric power systems. Due to their continuous operation, UPS components degrade over time, including their filtering elements, leading to decreased filtering capabilities. With these deviations, UPS performance is typically reduced. Moreover, if filter parameter deviations are not considered in the control system, performance degradation can be further increased and the critical load can be seriously compromised. This is especially important with Model Predictive Control (MPC), which heavily relies on system parameters accuracy. Thus, control performance optimization using estimated filter parameters in UPS systems can be extremely important. Nevertheless, this has not yet been covered in the literature for UPS systems. In light of these facts, this paper proposes a new mechanism that, by using online estimated filter parameters, optimizes the performance of an MPC strategy, in a UPS system. In the scope of this optimization mechanism, an estimation method that enables parameter identification and control optimization not only in balanced but also in highly unbalanced filter conditions (rarely studied in the literature) is proposed. Experimental results demonstrate the accuracy of the proposed estimators and the effectiveness of the proposed control performance optimization mechanism. Under severe filter parameter variations, the proposed performance optimization scheme enabled to reduce the degradation (caused by filter variation) of the grid current and load voltage THD by 29.41 % and 91.60 %, respectively. Furthermore, the degradation of the RMS load voltage value was also significantly reduced by 97.98 %.INDEX TERMS model predictive control, online inductance and capacitance estimation, power quality, real-time control performance optimization, uninterruptible power supply.

show abstract

“…In [20], an MPC based current control of a switched reluctance motor is proposed, in which an adaptive controller is used to dynamically estimate and modify the motor inductance based on Kalman Filter theory. Paper [21] proposes a FCS-MPC based control method for voltage source inverter. This method is able to correct the correct parameters based on ADALINE estimator.…”

Section: Introductionmentioning

confidence: 99%

“…This method is able to correct the correct parameters based on ADALINE estimator. However, the adaptive methods proposed in [20,21] require additional algorithm installation and processing in controller, which inevitably increases the computational burden.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Model Predictive Control Based Three-Phase Inverter Current Control Method

et al. 2019

View full text Add to dashboard Cite

This paper proposes a novel current control method based on Model Predictive Control (MPC) for three-phase inverters. The proposed method is based on an Adaptive MPC (A-MPC) with a PWM modulation. An innovative model parameter estimation and modification method is also proposed, leading to enhanced control accuracy. Comparing with traditional current control methods, such as PI and PR control, the proposed method has better dynamic performance. The transient dynamics, i.e., recovery time and overshoot, have been considerably improved. Simulation and experimental results are presented to validate the effectiveness of the proposal.

show abstract

Adaptive Model Predictive Control for Three Phase Voltage Source Inverter using ADALINE estimator

Cited by 4 publications

References 13 publications

Modular Multilevel Converter Controlled by Daisy-Chain Model Predictive Control for Medium Voltage Shunt Active Filter

Modular Multilevel Converter Controlled by Daisy-Chain Model Predictive Control for Medium Voltage Shunt Active Filter

Online Filter Parameters Estimation in a Double Conversion UPS System for Real-Time Model Predictive Control Performance Optimization

A Novel Adaptive Model Predictive Control Based Three-Phase Inverter Current Control Method

Contact Info

Product

Resources

About