Predictive Control-Based DSTATCOM with a Multi-Criterion Decision-Making Method
By: Srikakolapu, Jayadeep
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Contributor(s): Arya, Sabha Raj.
Publisher: New York Springer 2022Edition: Vol,103(6), Dec.Description: 2097–2110 p.Subject(s): Electrical EngineeringOnline resources: Click here
In:
Journal of the institution of engineers (India): Series BSummary: four-wire distribution static compensator, an adaptive LMS algorithm called improved proportionate weight-constraint de-correlation normalized LMS (IPDNLMS) is employed. This improved control of basic LMS uses a proportionate approach to a normalized LMS to attain a better convergence rate. The IPDNLMS technique with fixed step value has made a compromise between fast convergence rate and small steady-state error. The learning rate control provides convergent results with accuracy. These advantages over traditional LMS led authors to use IPDNLMS-based control algorithm in DSTATCOM to compensate power quality problems. The suggested work differs in its use of distorted load current. In this study, finite control set model predictive control (FCS-MPC) of the DSTATCOM employing technique for order of preference by similarity to ideal solution (TOPSIS) is used. Proper selection of switching state decreases compensator current error to improve DSTATCOM power quality. This method employs TOPSIS to find the ideal switching state to minimize multi-constraint cost function. The recommended control approach improved grid current THD and reduced harmonic load-triggered reactive power. Complete control is validated in MATLAB–Simulink and tested with a d-SPACE 1104 processor.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2023-0039 |
four-wire distribution static compensator, an adaptive LMS algorithm called improved proportionate weight-constraint de-correlation normalized LMS (IPDNLMS) is employed. This improved control of basic LMS uses a proportionate approach to a normalized LMS to attain a better convergence rate. The IPDNLMS technique with fixed step value has made a compromise between fast convergence rate and small steady-state error. The learning rate control provides convergent results with accuracy. These advantages over traditional LMS led authors to use IPDNLMS-based control algorithm in DSTATCOM to compensate power quality problems. The suggested work differs in its use of distorted load current. In this study, finite control set model predictive control (FCS-MPC) of the DSTATCOM employing technique for order of preference by similarity to ideal solution (TOPSIS) is used. Proper selection of switching state decreases compensator current error to improve DSTATCOM power quality. This method employs TOPSIS to find the ideal switching state to minimize multi-constraint cost function. The recommended control approach improved grid current THD and reduced harmonic load-triggered reactive power. Complete control is validated in MATLAB–Simulink and tested with a d-SPACE 1104 processor.
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