Optimized Sliding Mode Control to Maximize Existence Region for Single-Phase Dynamic Voltage Restorers

Biricik, Samet; Kömürcügil, Hasan

doi:10.1109/tii.2016.2587769

Cited by 111 publications

(51 citation statements)

References 43 publications

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“…The sliding-mode variable structure method is a well-known approach to cope with disturbances and modelling uncertainties [10]- [12] and has been widely applied in many fields. The sliding mode observer (SMO) ensures the robustness of the system when the modelling uncertainty exists and 2 a short convergence time can be achieved when combined with a finite-time stabilization technique [13].…”

Section: Introductionmentioning

confidence: 99%

Robust Excitation Force Estimation and Prediction for Wave Energy Converter M4 Based on Adaptive Sliding-Mode Observer

Zhang

Zeng

2020

IEEE Trans. Ind. Inf.

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The wave excitation force estimation and prediction plays an important role in improving the performance of causal and non-causal controllers for wave energy converters (WECs). This paper proposes a robust adaptive sliding-mode observer (ASMO) to estimate the wave excitation force subject to unknown disturbances and parametric uncertainties for a multi-motion multi-float WEC, called M4. Both the convergence time and the estimation error can be explicitly bounded within expected limits by tuning the ASMO parameters, which are essentially beneficial for causal controllers to maintain the control performance. A fixed-time convergent sliding variable is designed to drive the estimation error into a small region within a fixed time. Due to the adaptive law, the overall system is proven to be finite-time stable, which allows explicit formulations of the convergence time and the estimation error. Moreover, based on the wave force estimation by the ASMO, an improved Auto-Regressive (AR) model whose coefficients are updated by online training is developed to predict the wave excitation force. The prediction errors can also be explicitly estimated to achieve guaranteed control performance for the non-causal controller requiring future excitation force. From the comparison based on a realistic sea wave gathered from Cornwall, UK, it can be found that compared with the conventional Kalman Filter, the ASMO achieves a smaller steady-state estimation error and has satisfactory robustness performance against 30% model mismatch.

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Section: Introductionmentioning

confidence: 99%

Robust Excitation Force Estimation and Prediction for Wave Energy Converter M4 Based on Adaptive Sliding-Mode Observer

Zhang

Zeng

2020

IEEE Trans. Ind. Inf.

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“…In the DVR system, a state feedback controller and repetitive controller are implemented together to improve its performance [122]. The double band hysteresis control devised for the DVR can eliminate the chattering in the inverters [123]. The simulation results of a PV based DVR with hysteresis based control strategy is given in paper [124].…”

Section: Control Techniques Of the Dvrmentioning

confidence: 99%

Dynamic Voltage Restorer (DVR) – A Review

Remya

Parthiban

Ansal

et al. 2018

JGE

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Power quality (PQ) is gaining a great deal of importance as more sensitive loads are introduced into the utility grid. The degradation of product quality, damage of equipment and temporary shutdowns are the general issues associated with PQ problems in industries. Any mal-operation or damage of the industrial sensitive loads results in monetary losses disproportionately higher than the severity of the PQ issues. The evolution of power electronics technology replaced the traditional power quality mitigation methods with the introduction of Custom Power System devices (CUPS). The major power electronic controller based CUPS are DSTATCOM, DVR and UPQC. DVR is a pertinent solution for the economic losses caused by the PQ issues in the industries. Among the CUPS, DVR is the most cost-effective one. In the published literature, only a few papers correspond to the review of DVR technology. In this paper, a systematic review of published literature is conducted and a description is given on the design, standards and challenges in the DVR technology. A detailed survey is conducted on the published

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“…A sliding-mode observer (SMO) has not only better performance of sliding-mode control (SMC), like robustness to disturbances and parameter variations [17][18][19][20][21][22][23][24][25][26][27][28][29], but also provides estimation information of rotor position and speed for the sensorless control system. The dynamic equations of SMO are given based on (4),…”

Section: Iterative Sliding Mode Observermentioning

confidence: 99%

Sliding Mode and Neural Network Control of Sensorless PMSM Controlled System for Power Consumption and Performance Improvement

Wang

Tsai

2017

Energies

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This paper deals with the design of sliding mode control and neural network compensation for a sensorless permanent magnet synchronous motor (PMSM) controlled system that is able to improve both power consumption and speed response performance. The position sensor of PMSM is unreliable in harsh environments. Therefore, the sensorless control technique is widely proposed in industry. A sliding mode observer can estimate the rotor angle and has the robustness to load disturbance and parameter variations. However, the sliding mode observer is not conducive to standstill and low speed conditions because the amplitude of the back EMF is almost zero. As a result, this paper combines an iterative sliding mode observer (ISMO) and neural networks (NNs) as an angle compensator to improve the above problems. A dsPIC30F6010A-based PMSM sensorless drive system is implemented to validate the proposed algorithm. The simulation and experimental results prove its effectiveness.

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Optimized Sliding Mode Control to Maximize Existence Region for Single-Phase Dynamic Voltage Restorers

Cited by 111 publications

References 43 publications

Robust Excitation Force Estimation and Prediction for Wave Energy Converter M4 Based on Adaptive Sliding-Mode Observer

Robust Excitation Force Estimation and Prediction for Wave Energy Converter M4 Based on Adaptive Sliding-Mode Observer

Dynamic Voltage Restorer (DVR) – A Review

Sliding Mode and Neural Network Control of Sensorless PMSM Controlled System for Power Consumption and Performance Improvement

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