Sliding mode control strategy of voltage source inverter based on load current sliding mode observer

Hou, Bo; Liu, Junwei; Feng-bin, Dong; Wang, Minglei; Mu, Anle

doi:10.1109/ipemc.2016.7512471

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…An individual or candidate solution to the problem is defined as a vector with the values [α 1 , α 2 , α 3 ]. In the first step, the initial population is created using random values of these coefficients, considering the constraints indicated by Equations ( 8) and (9).…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…The algorithm starts with a random generation of particles that are located within the search space. These particles must comply with the constraints given by Equations ( 8) and (9). Four characteristics are associated with each particle: a position representing a certain combination of values of the variables, the value of the objective function at the position where the particle is located, a velocity indicating how and where the particle is moving, and a record of the best position the particle has been to.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…Furthermore, the system is not limited to a single operating point. This condition allows for a wide control range in AC systems, where the signals of the current and voltages feature positive and negative values in the time function [3][4][5][6][7][8][9]. SMC research efforts are focused on DC/DC converters, where references and signals are constant over time, or AC systems that are linearized around an operating point.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Sliding-Mode Control of Semi-Bridgeless Boost Converters Considering Power Factor Corrections

Ortiz-Castrillón,

Saldarriaga-Zuluaga,

Muñoz-Galeano

et al. 2023

Energies

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Sliding-mode control (SMC) is a robust technique used in power electronics (PE) for controlling the behavior of power converters. This paper presents simulations and experimental results of an optimal SMC strategy applied to Semi-Bridgeless Boost Converters (SBBC), which includes Power Factor Correction (PFC). As the main contribution, the optimal coefficients of the SMC strategy are obtained using two metaheuristic approaches, namely the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The main objective is to obtain the sliding coefficients that ensure the best converter response in terms of the input current and output voltage, both during start-up and under disturbances (including changes in load, source, and references). The fitness function to be minimized includes two coefficients, namely the Integrative Absolute Error (IAE) and the Integral Time Absolute Error (ITAE), for both the input current and output voltage. These coefficients measure the converter’s effort to follow the control references. The IAE penalizes errors during start-up, whereas the ITAE penalizes errors in the steady state. The tests carried out demonstrated the effectiveness of the GA and PSO techniques in the optimization process; nonetheless, the GA outperformed the PSO approach, providing sliding coefficients that allowed for a reduction in the input current overshoot during start-up of up to 24.15% and a reduction in the setting time of the output voltage of up to 99%. The experimental results were very similar when tuning with the GA and PSO techniques; nevertheless, tuning with the GA technique produced a better response in the face of disturbances compared to the PSO technique.

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Section: Genetic Algorithmmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Sliding-Mode Control of Semi-Bridgeless Boost Converters Considering Power Factor Corrections

Ortiz-Castrillón,

Saldarriaga-Zuluaga,

Muñoz-Galeano

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

“…Various sliding mode control techniques were implemented for grid-connected single-phase inverters [9]. Chenchireddy et al [10] introduced second-order sliding mode control; while reference [11], [12] introduced adaptive fuzzy sliding mode control. The main objective of this method is to achieve precise control of the output current and reduce current harmonics in the 3-phase multilevel inverter.…”

Section: Introductionmentioning

confidence: 99%

Fundamental frequency switching strategies of a seven level hybrid cascaded H-bridge multilevel inverter

Chenchireddy,

Sreejyothi,

Ganesh

et al. 2024

IJAPE

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This paper presents a novel hybrid cascaded H-bridge multilevel inverter (HCHB MLI) designed to address the growing importance of multilevel inverters in the context of renewable energy sources such as solar, wind, and fuel cells. The proposed topology features eight insulated-gate bipolar transistor (IGBT) switches and utilizes two distinct input direct current (DC) sources: a battery and a capacitor, making it a hybrid system. The control strategy employed in this topology is based on fundamental switching frequency techniques. Simulation results of the proposed topology are conducted using MATLAB/Simulink software, while hardware experimentation with a single-phase H-bridge inverter is also demonstrated in the paper. For pulse generation and IGBT switch control, an Arduino UNO microcontroller is utilized. The output voltage of the single-phase H-bridge inverter is verified through experimentation using a cathode-ray oscilloscope (CRO).

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“…Using higher order SMC like twisting, super twisting, enhanced exponential reaching law based SMC, terminal SMC etc. the chattering can be reduced [5][6][7][8] to realize excellent control accuracy. These controllers based on SMC are developed by using integer order calculus.…”

Section: Introductionmentioning

confidence: 99%

Robust fractional order sliding mode control for solar based DC-AC inverter

Saheb¹,

Gudey²

2020

Journal of Energy Systems

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Fractional order sliding mode control (FOSMC) strategy for a solar based DC-AC inverter is presented in this work. First FOSMC is implemented to voltage source inverter with a fixed DC input voltage of 400V to drive a load of 2.3 kW at a power factor of 0.8 lag. Here load voltage and current through capacitor as state variables and a linear sliding surface is considered. FOSMC using Gao’s reaching law is derived for inverter circuit. FOSMC is implemented at load bus to control output voltage of inverter with linear and nonlinear loads to desired values. FOSMC controls the output voltage with good voltage regulation, less steady state error of 1.32 %, settling time of 0.15 ms, good dynamic response, and convergence to origin with less chattering compared to classical SMC. FOSMC based solar based VSI is presented. The maximum power from PV array is extracted using P&O MPPT algorithm. A boost converter is used to step up input voltage of 200 V to 400 V. P-V and I-V characteristics are obtained for a typical solar cell of 2.5 kW FOSMC requires less control efforts to obtain a pure sinusoidal output voltage waveform of 230 V (rms) with output voltage THD of 0.135% well within IEEE standards. PSCAD/EMTDC v4.6 is used for simulation work.

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Sliding mode control strategy of voltage source inverter based on load current sliding mode observer

Cited by 8 publications

References 8 publications

Optimal Sliding-Mode Control of Semi-Bridgeless Boost Converters Considering Power Factor Corrections

Optimal Sliding-Mode Control of Semi-Bridgeless Boost Converters Considering Power Factor Corrections

Fundamental frequency switching strategies of a seven level hybrid cascaded H-bridge multilevel inverter

Robust fractional order sliding mode control for solar based DC-AC inverter

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