Direct predictive speed control of permanent magnet synchronous motor fed by matrix converter

Neya, Najmeh Movahhed; Saberi, Sajad; Mozafari, Babak

doi:10.11591/ijpeds.v11.i4.pp2183-2193

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…As mentioned earlier, the current reference can be obtained by imitating the connection between the inverter voltage, filter currents, and the capacitor voltage, shown in Figure 1, which can be represented in a stationary frame (αβ) with (7).…”

Section: The Proposed Multivariable Control Active Damping Methodsmentioning

confidence: 99%

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Active damping method for voltage source inverter-based distributed generator using multivariable finite-control-set model predictive control

Jongudomkarn

Kampeerawat

2021

IJPEDS

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Despite its advantages, the LCL filter can significantly distort the grid current and constitute a substantially more complex control issue for the grid-connected distributed generators (DGs). This paper presents an active damping approach to deal with the LCL filter's oscillation for the finite-control-set model predictive control (FCS-MPC)-three-phase voltage source inverters (VSIs)-based DG. The new approaches use the multivariable control of the inverter side's filter current and capacitor voltage to suppress the LCL filter resonance. The proposed method has been tested in steady-state and under grid voltage disturbances. The comparative study was also conducted with the existing virtual resistance active damping approaches for an FCS-MPC algorithm. The study validates the developed control schemes' superior performance and shows its ability to eliminate lower-order grid current harmonics and decrease sensitivity to grid voltage distortion.

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Section: The Proposed Multivariable Control Active Damping Methodsmentioning

confidence: 99%

“…Let indicate the virtual stator reactance, , indicate the filter reactance and symbolize filter resistance. If we equalize the , in (7) to the voltage command of the VSG control, the currents command * and * can be described as ( 8)…”

Section: The Proposed Multivariable Control Active Damping Methodsmentioning

confidence: 99%

Active damping method for voltage source inverter-based distributed generator using multivariable finite-control-set model predictive control

Jongudomkarn

Kampeerawat

2021

IJPEDS

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“…There are several algorithms for discovering knowledge in data mining, each of which is used for a specific purpose. Clustering is one of the most important data mining algorithms and has many applications in data analysis, document retrieval and image processing 5,6 …”

Section: Introductionmentioning

confidence: 99%

“…Clustering is one of the most important data mining algorithms and has many applications in data analysis, document retrieval and image processing. 5,6 One of the most popular methods of learning without an observer is clustering. 7 Clustering groups samples according to their properties.…”

mentioning

confidence: 99%

An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

Liu

Osmani

2022

Trans Emerging Tel Tech

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Ensemble clustering is an efficient unsupervised learning technique that has attracted a lot of attention. The purpose of this technique is to aggregate the results of several basic clustering algorithms in order to create a better clustering. This is not only possible, but has been developed with many techniques in recent years. However, there are still challenges such as similarity measure, agreement function and how to implement clustering algorithms. To address these challenges, this article proposes an ensemble clustering algorithm based on the consistency cluster consensus approach and the MapReduce model. In addition, the proposed algorithm is equipped with a new membership similarity measure. The consistency cluster consensus is defined as a new agreement function for the consensus of the results of the basic clustering methods. Besides, the proposed similarity measure consists of two factors: one is cluster similarity and the another is membership similarity. The process of the proposed ensemble clustering method is summarized in four steps. In the first step, partitions are generated by applying a number of individual clustering algorithms to the data. The second step is to convert the primary clusters from the partitions to a binary representation. The third step is to identify the candidate primary clusters for the consensus, where a subset with highest similarity between the clusters is emphasized. In the fourth step, the consistency cluster consensus is performed through a new agreement function. In addition, we use the MapReduce model to implement basic clustering algorithms to reduce computational complexity. Our method is evaluated on several real-world datasets and their performance was compared with other clustering ensemble methods, such as TWCE and RDPC-DSS. Experiments show that our method is more efficient and accurate and is 7% to 15% better than the best method compared. INTRODUCTIONData mining is the process of discovering regular and hidden patterns and processes in big data and distributed using a wide range of algorithms based on mathematical and statistical sciences. 1,2 These algorithms are usually applied to numeric and nontextual values and textual algorithms are used for textual data. Data mining uses sciences such as artificial intelligence, machine learning, statistics, operational research and database management to build models

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A continuous control set of the model predictive controller of PMA-SynRM machine for high-performance flywheel energy storage system

Moghaddam

Rezaei

Vahedi

et al. 2022

Int. J. Dynam. Control

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Direct predictive speed control of permanent magnet synchronous motor fed by matrix converter

Cited by 5 publications

References 24 publications

Active damping method for voltage source inverter-based distributed generator using multivariable finite-control-set model predictive control

Active damping method for voltage source inverter-based distributed generator using multivariable finite-control-set model predictive control

An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

A continuous control set of the model predictive controller of PMA-SynRM machine for high-performance flywheel energy storage system

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