Performance Evaluation of Variable Structure Controller Based on Sliding Mode Technique for a Grid-Connected Solar Network

Feshara, Hazem F.; Ibrahim, Amr M.; El-Amary, Noha H.; Sharaf, Soliman M.

doi:10.1109/access.2019.2924592

Cited by 21 publications

(17 citation statements)

References 35 publications

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“…Figure 6 shows the actuator scheme, which adjusts the power in the boiler heating resistor by regulating the voltage with a DC-DC converter. This is a simplified model of the buck-boost converter [21], widely used for the analysis of its operation and control [22][23][24][25][26]. DC-DC converters can regulate the output voltage to the desired value from the switching of electronic devices, usually diodes and transistors.…”

Section: Heating Actuator (Boiler) Modelmentioning

confidence: 99%

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Analysis and Comparison of Fuzzy Models and Observers for DC-DC Converters Applied to a Distillation Column Heating Actuator

Heras-Cervantes

Téllez-Anguiano

Anzurez-Marin

et al. 2020

MCA

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In this paper, as an introduction, the nonlinear model of a distillation column is presented in order to understand the fundamental paper that the column heating actuator has in the distillation process dynamics as well as in the quality and safety of the process. In order to facilitate the implementation control strategies to maintain the heating power regulated in the distillation process, it is necessary to represent adequately the heating power actuator behavior; therefore, three different models (switching, nonlinear and fuzzy Takagi–Sugeno) of a DC-DC Buck-Boost power converter, selected to regulate the electric power regarding the heating power, are presented and compared. Considering that the online measurements of the two main variables of the converter, the inductor current and the capacitor voltage, are not always available, two different fuzzy observers (with and without sliding modes) are developed to allow monitoring the physical variables in the converter. The observers response is compared to determine which has a better performance. The role of the observer in estimating the state variables with the purpose of using them in the sensors fault diagnosis, using the analytical redundancy concept, likewise, from the estimation of these variables other non-measurable can be determined; for example, the caloric power. The stability analysis and observers gains are obtained by linear matrix inequalities (LMIs). The observers are validated by MATLAB® simulations to verify the observers convergence and analyze their response under system disturbances.

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Section: Heating Actuator (Boiler) Modelmentioning

confidence: 99%

“…Tanaka, in [18], demonstrates the stability of the fuzzy observer as long as there is a P matrix that satisfies the LMI's expressed in (25):…”

Section: Fuzzy Observermentioning

confidence: 99%

Analysis and Comparison of Fuzzy Models and Observers for DC-DC Converters Applied to a Distillation Column Heating Actuator

Heras-Cervantes

Téllez-Anguiano

Anzurez-Marin

et al. 2020

MCA

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“…[12] Performs a short-term solar forecast based on unsupervised clusters that has three parts: grouping, forecasting, and modeling, using time series and vector support machines. [13] investigates and evaluates a solar energy generation system, taking the maximum power point (MPP) as the main variable, the results are obtained on simulations. Similarly, [14] predicts solar radiation in Malaysia using a combination of wave transforms and two neural network models: multilayer perceptron and radial base function neural network, indicating that the second network obtains better results.…”

Section: Introductionmentioning

confidence: 99%

Selection of Characteristics by Hybrid Method: RFE, Ridge, Lasso, and Bayesian for the Power Forecast for a Photovoltaic System

et al. 2021

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Currently, the generation of alternative energy from solar radiation with photovoltaic systems is growing, its efficiency depends on internal variables such as powers, voltages, currents; as well as external variables such as temperatures, irradiance, and load. To maximize performance, this research focused on the application of regularization techniques in a multiparametric linear regression model to predict the active power levels of a photovoltaic system from 14 variables that model the system under study. These variables affect the prediction to some degree, but some of them do not have so much preponderance in the final forecast, so it is convenient to eliminate them so that the processing cost and time are reduced. For this, we propose a hybrid selection method: first we apply the elimination of Recursive Feature Elimination (RFE) within the selection of subsets and then to the obtained results we apply the following contraction regularization methods: Lasso, Ridge and Bayesian Ridge; then the results were validated demonstrating linearity, normality of the error terms, without autocorrelation and homoscedasticity. All four prediction models had an accuracy greater than 99.97%. Training time was reduced by 71% and 36% for RFE-Ridge and RFE-OLS respectively. The variables eliminated with RFE were Total Energy, Daily Energy, and Irradiance, while the variable eliminated by Lasso was: "Frequency". In all cases we see that the root mean square errors were reduced for RFE. Lasso by 0.15% while for RFE-Bayesian Ridge by 0.06%.

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“…Based on the dissimilar control object as well as entire and partial system-state data, an exhaustive control design for DC-to-AC inverters can be developed. However, the algorithm employs customary SMC and has arduous realization, thus displaying the distortion of the output voltage during steady-state and a transient state [11]. The sliding-mode tracking controller with the constant switching-frequency method has been recommended to control the output of the DC-to-AC inverter.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fixed-Time-Convergent Sliding Mode Technology Using Improved Quantum Particle Swarm Optimization for Renewable Energy Inverters

Chang¹

2020

Sustainability

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This paper describes a robust feedback technique involving novel fixed-time-convergent sliding mode technology (NFTCSMT) using improved quantum particle swarm optimization (QPSO) to obtain high-performance renewable energy inverters. Customary SMT encounters long time convergence towards the origin and the influence of the dithering. The NFTCSMT can rapidly impel system-following movement to approach the sliding manifold and effectively accelerate the convergence speed to equilibrium states. However, the NFTCSMT cannot easily select the global optimum of the controller parameters subject to large parameter changes and nonlinear interventions, leading to the dither phenomenon/steady-state error still being caused. The dither inflicts decreased control accuracy, high voltage harmonics, major harm in relation to switching components, and great thermal losses in power electronic converters. The improved QPSO including the unique property of a random compression/expansion factor is used to find optimal parameters of the NFTCSMT in practical applications, for the reason that it importantly mitigates the dither and amends convergent speed as well as guaranteeing global convergence. The presented alliance amid NFTCSMT and improved QPSO achieves faster response time and singularityless, and also yields high-accuracy tracking and dither attenuation. The robust stability using Lyapunov theorem of the suggested system has provided precise mathematical derivations. Simulations show that the suggested controller offers less than 0.1% voltage THD (total harmonic distortion) which exceeds IEEE standard 519 under heavily distorted rectifier loads, and less than 10% voltage dip which surpasses IEEE standard 1159 during step load transients. Experimental tests of an algorithmically controlled laboratory prototype (1 kW, 110 Vrms/60 Hz) of a renewable energy inverter (REI) based on digital signal processing manifest less than 0.05% voltage THD in the face of great inductor-capacitor alterations, and less than 10% voltage dip in the face of transient load scenarios.

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Performance Evaluation of Variable Structure Controller Based on Sliding Mode Technique for a Grid-Connected Solar Network

Cited by 21 publications

References 35 publications

Analysis and Comparison of Fuzzy Models and Observers for DC-DC Converters Applied to a Distillation Column Heating Actuator

Analysis and Comparison of Fuzzy Models and Observers for DC-DC Converters Applied to a Distillation Column Heating Actuator

Selection of Characteristics by Hybrid Method: RFE, Ridge, Lasso, and Bayesian for the Power Forecast for a Photovoltaic System

A Novel Fixed-Time-Convergent Sliding Mode Technology Using Improved Quantum Particle Swarm Optimization for Renewable Energy Inverters

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