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Proceedings ArticleDOI

Comparison of Fuzzy and MPC based Flying Capacitor Multicell Inverter

01 Nov 2016-pp 70-76
TL;DR: In this paper, a model predictive controller (MPC) is considered to be in command of the RMS voltage of the Flying Capacitor Multicell Inverter and the outputs are measured up to with fuzzy controlled FCMI.
Abstract: The Flying Capacitor Multicell Inverter (FCMI) possesses an inherent balancing of voltage across the capacitors and it is used to distribute the input voltage and minimize the voltage stress. This FCMI's RMS is first controlled with fuzzy controller and the outputs are analyzed, then a suitable Model Predictive Controller (MPC) is considered to be in command of the RMS voltage of the Flying Capacitor Multicell Inverter and the outputs are measured up to with fuzzy controlled FCMI. The controller (Fuzzy and MPC) are considered for different levels of FCMI circuits and has been found that MPC controller gives better performance as compared to the fuzzy controlled FCMI. To weigh against the performance of Fuzzy and MPC controllers the Rise time, Settling time, Overshoot, Peak and Peak time are taken as comparison parameters and the comparison chart is presented for analysis.
Citations
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Journal ArticleDOI
TL;DR: In this paper , an enhanced control strategy for renewable energy resources connected to the grid through voltage-sourced converters (VSCs) in microgrids is presented. But, the proposed scheme contains a voltage control loop with the minimum inverter switching, a power-sharing controller with minimum inverters, a negative-sequence current controller, and a loop to identify the control system operation mode.
Abstract: This article presents an enhanced control strategy for renewable energy resources connected to the grid through voltage-sourced converters (VSCs) in microgrids. The proposed scheme contains a voltage control loop with the minimum inverter switching, a power-sharing controller with the minimum inverter switching, a negative-sequence current controller, and a loop to identify the control system operation mode. All the controllers are designed using the multipurpose finite control set-model predictive control (FCS-MPC) strategy. Since these controllers use the dynamic current and VSC voltage, they can be applied in grid-connected and island operation modes and transferred between them. The method uses voltage–frequency control instead of power control for VSCs. One inverter controls voltage, and the other controls current. The conventional FCS-MPC is enhanced to reduce the computation power by eightfold. This improvement is significant because the maximum switching frequency is limited in practical implementations. Also, the superiority of the proposed multipurpose control scheme is proved theoretically. Simulation is implemented using MATLAB software and compared with methods in the literature. The simulation demonstrates that the presented control strategy is efficient, authentic, and compatible. The proposed method is also tested and validated in hardware experiments.

7 citations

Proceedings ArticleDOI
01 Oct 2018
TL;DR: Some of the most challenges and trends through an overview on papers that were published recently inredictive control are introduced.
Abstract: Predictive control is a new control strategy that has been applied to control power electronics applications, in the last decades. Many researches are working on the improvement of this control method such as reduction of computations cost, increasing accuracy, etc. This paper introduces some of the most challenges and trends through an overview on papers that were published recently.

3 citations


Cites background or methods from "Comparison of Fuzzy and MPC based F..."

  • ...In order to obtain current detecting [7], a cost function has been presented which can control the floating capacitor voltages of a 3-phase flying capacitor converter....

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  • ...A similar FCS-MPC method to the FC converter has been applied [6], [7]....

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Journal ArticleDOI
TL;DR: In this article , an enhanced control strategy for renewable energy resources connected to the grid through voltage-sourced converters (VSCs) in microgrids is presented. And the proposed scheme contains a voltage control loop with the minimum inverter switching, a power-sharing controller with minimum inverters, a negative-sequence current controller, and a loop to identify the control system operation mode.
Abstract: This article presents an enhanced control strategy for renewable energy resources connected to the grid through voltage-sourced converters (VSCs) in microgrids. The proposed scheme contains a voltage control loop with the minimum inverter switching, a power-sharing controller with the minimum inverter switching, a negative-sequence current controller, and a loop to identify the control system operation mode. All the controllers are designed using the multipurpose finite control set-model predictive control (FCS-MPC) strategy. Since these controllers use the dynamic current and VSC voltage, they can be applied in grid-connected and island operation modes and transferred between them. The method uses voltage–frequency control instead of power control for VSCs. One inverter controls voltage, and the other controls current. The conventional FCS-MPC is enhanced to reduce the computation power by eightfold. This improvement is significant because the maximum switching frequency is limited in practical implementations. Also, the superiority of the proposed multipurpose control scheme is proved theoretically. Simulation is implemented using MATLAB software and compared with methods in the literature. The simulation demonstrates that the presented control strategy is efficient, authentic, and compatible. The proposed method is also tested and validated in hardware experiments.

3 citations

Proceedings ArticleDOI
01 Nov 2018
TL;DR: An overview of the predictive control technique applied to flying capacitor multilevel converters is carried out.
Abstract: Multilevel converters play an important role in power electronics applications due to their numerous advantages. In this way, a flying capacitor (FC) multilevel converter is an interesting option as it can create high-quality load waveforms and achieve high power levels. The predictive control technique is a control method for power converters that has attracted attention in the research community. This report carries out an overview of the predictive control technique applied to flying capacitor multilevel converters.

2 citations


Cites background or methods from "Comparison of Fuzzy and MPC based F..."

  • ...In order to obtain current detecting [7], a cost function has been presented which can control the floating capacitor voltages of a 3-phase flying capacitor converter....

    [...]

  • ...A similar FCS-MPC method to the FC converter has been applied [6], [7]....

    [...]

Proceedings ArticleDOI
07 Sep 2021
TL;DR: In this paper, a method of balancing of multi-level flying capacitor converters is described, where the balancing block is placed behind PWM modulator instead of balancing table and it is independent on type of modulator nor number of levels.
Abstract: This paper describes novel method of balancing of multi-level flying capacitor converters. The proposed balancing block is placed behind PWM modulator instead of balancing table and it is independent on type of modulator nor number of levels. The method was simulated in ModelSIM and PLECS/Simulink and implemented in VHDL to FPGA. Function of the proposed algorithm was verified on the real seven level flying capacitor converter with three phase asynchronous machine.
References
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Book
01 Dec 1994
TL;DR: This chapter discusses Fuzzy Systems Simulation, specifically the development of Membership Functions and the Extension Principle, and some of the methods used to derive these functions.
Abstract: About the Author. Preface to the Third Edition. 1 Introduction. The Case for Imprecision. A Historical Perspective. The Utility of Fuzzy Systems. Limitations of Fuzzy Systems. The Illusion: Ignoring Uncertainty and Accuracy. Uncertainty and Information. The Unknown. Fuzzy Sets and Membership. Chance Versus Fuzziness. Sets as Points in Hypercubes. Summary. References. Problems. 2 Classical Sets and Fuzzy Sets. Classical Sets. Operations on Classical Sets. Properties of Classical (Crisp) Sets. Mapping of Classical Sets to Functions. Fuzzy Sets. Fuzzy Set Operations. Properties of Fuzzy Sets. Alternative Fuzzy Set Operations. Summary. References. Problems. 3 Classical Relations and Fuzzy Relations. Cartesian Product. Crisp Relations. Cardinality of Crisp Relations. Operations on Crisp Relations. Properties of Crisp Relations. Composition. Fuzzy Relations. Cardinality of Fuzzy Relations. Operations on Fuzzy Relations. Properties of Fuzzy Relations. Fuzzy Cartesian Product and Composition. Tolerance and Equivalence Relations. Crisp Equivalence Relation. Crisp Tolerance Relation. Fuzzy Tolerance and Equivalence Relations. Value Assignments. Cosine Amplitude. Max Min Method. Other Similarity Methods. Other Forms of the Composition Operation. Summary. References. Problems. 4 Properties of Membership Functions, Fuzzification, and Defuzzification. Features of the Membership Function. Various Forms. Fuzzification. Defuzzification to Crisp Sets. -Cuts for Fuzzy Relations. Defuzzification to Scalars. Summary. References. Problems. 5 Logic and Fuzzy Systems. Part I Logic. Classical Logic. Proof. Fuzzy Logic. Approximate Reasoning. Other Forms of the Implication Operation. Part II Fuzzy Systems. Natural Language. Linguistic Hedges. Fuzzy (Rule-Based) Systems. Graphical Techniques of Inference. Summary. References. Problems. 6 Development of Membership Functions. Membership Value Assignments. Intuition. Inference. Rank Ordering. Neural Networks. Genetic Algorithms. Inductive Reasoning. Summary. References. Problems. 7 Automated Methods for Fuzzy Systems. Definitions. Batch Least Squares Algorithm. Recursive Least Squares Algorithm. Gradient Method. Clustering Method. Learning From Examples. Modified Learning From Examples. Summary. References. Problems. 8 Fuzzy Systems Simulation. Fuzzy Relational Equations. Nonlinear Simulation Using Fuzzy Systems. Fuzzy Associative Memories (FAMS). Summary. References. Problems. 9 Decision Making with Fuzzy Information. Fuzzy Synthetic Evaluation. Fuzzy Ordering. Nontransitive Ranking. Preference and Consensus. Multiobjective Decision Making. Fuzzy Bayesian Decision Method. Decision Making Under Fuzzy States and Fuzzy Actions. Summary. References. Problems. 10 Fuzzy Classification. Classification by Equivalence Relations. Crisp Relations. Fuzzy Relations. Cluster Analysis. Cluster Validity. c-Means Clustering. Hard c-Means (HCM). Fuzzy c-Means (FCM). Fuzzy c-Means Algorithm. Classification Metric. Hardening the Fuzzy c-Partition. Similarity Relations from Clustering. Summary. References. Problems. 11 Fuzzy Pattern Recognition. Feature Analysis. Partitions of the Feature Space. Single-Sample Identification. Multifeature Pattern Recognition. Image Processing. Summary. References. Problems. 12 Fuzzy Arithmetic and the Extension Principle. Extension Principle. Crisp Functions, Mapping, and Relations. Functions of Fuzzy Sets Extension Principle. Fuzzy Transform (Mapping). Practical Considerations. Fuzzy Arithmetic. Interval Analysis in Arithmetic. Approximate Methods of Extension. Vertex Method. DSW Algorithm. Restricted DSW Algorithm. Comparisons. Summary. References. Problems. 13 Fuzzy Control Systems. Control System Design Problem. Control (Decision) Surface. Assumptions in a Fuzzy Control System Design. Simple Fuzzy Logic Controllers. Examples of Fuzzy Control System Design. Aircraft Landing Control Problem. Fuzzy Engineering Process Control. Classical Feedback Control. Fuzzy Control. Fuzzy Statistical Process Control. Measurement Data Traditional SPC. Attribute Data Traditional SPC. Industrial Applications. Summary. References. Problems. 14 Miscellaneous Topics. Fuzzy Optimization. One-Dimensional Optimization. Fuzzy Cognitive Mapping. Concept Variables and Causal Relations. Fuzzy Cognitive Maps. Agent-Based Models. Summary. References. Problems. 15 Monotone Measures: Belief, Plausibility, Probability, and Possibility. Monotone Measures. Belief and Plausibility. Evidence Theory. Probability Measures. Possibility and Necessity Measures. Possibility Distributions as Fuzzy Sets. Possibility Distributions Derived from Empirical Intervals. Deriving Possibility Distributions from Overlapping Intervals. Redistributing Weight from Nonconsonant to Consonant Intervals. Comparison of Possibility Theory and Probability Theory. Summary. References. Problems. Index.

4,958 citations


"Comparison of Fuzzy and MPC based F..." refers background in this paper

  • ...[15] Timothy J Ross, ‘Fuzzy Logic with Engineering Applications’, McGraw Hill, Inc, 1997....

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  • ...Rule base block is designed such that the error signal becomes zero for both the positive and negative errors [15]....

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Journal ArticleDOI
TL;DR: In this article, a theoretical basis for model predictive control (MPC) has started to emerge and many practical problems like control objective prioritization and symptom-aided diagnosis can be integrated into the MPC framework by expanding the problem formulation to include integer variables yielding a mixed-integer quadratic or linear program.

2,320 citations


Additional excerpts

  • ...When a future response of the system needs to predicted over a given time period, then MPC is the best tool [8] [9] [17] [18]....

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  • ...[18] Morari, M & Lee, JH, ‘Model predictive control: past, present and future’, Computers and Chemical Engineering, vol....

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Journal ArticleDOI
TL;DR: In this paper, the most relevant characteristics of multilevel converters, to motivate possible solutions, and to show that energy companies have to bet on these converters as a good solution compared with classic two-level converters.
Abstract: This work is devoted to review and analyze the most relevant characteristics of multilevel converters, to motivate possible solutions, and to show that we are in a decisive instant in which energy companies have to bet on these converters as a good solution compared with classic two-level converters. This article presents a brief overview of the actual applications of multilevel converters and provides an introduction of the modeling techniques and the most common modulation strategies. It also addresses the operational and technological issues.

1,847 citations


"Comparison of Fuzzy and MPC based F..." refers background in this paper

  • ...[3] Franquelo, LG, Rodríguez, J, Leon, JI, Kouro, S, Portillo, R & Prats, MAM, ‘The age of multilevel converters arrives’, IEEE Ind....

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  • ...Researchers are always in pursuit of a developing a semiconductor device to withstand a high-current and high-voltage and hence to drive high power systems [3]....

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Journal ArticleDOI
TL;DR: The feasibility and great potential of FCS-MPC due to present-day signal-processing capabilities, particularly for power systems with a reduced number of switching states and more complex operating principles, such as matrix converters are found.
Abstract: This paper presents a detailed description of finite control set model predictive control (FCS-MPC) applied to power converters Several key aspects related to this methodology are, in depth, presented and compared with traditional power converter control techniques, such as linear controllers with pulsewidth-modulation-based methods The basic concepts, operating principles, control diagrams, and results are used to provide a comparison between the different control strategies The analysis is performed on a traditional three-phase voltage source inverter, used as a simple and comprehensive reference frame However, additional topologies and power systems are addressed to highlight differences, potentialities, and challenges of FCS-MPC Among the conclusions are the feasibility and great potential of FCS-MPC due to present-day signal-processing capabilities, particularly for power systems with a reduced number of switching states and more complex operating principles, such as matrix converters In addition, the possibility to address different or additional control objectives easily in a single cost function enables a simple, flexible, and improved performance controller for power-conversion systems

1,554 citations


"Comparison of Fuzzy and MPC based F..." refers background in this paper

  • ...[11] Samir Kouro, Patricio Cortes & Rene Vargas, ‘Model Predictive Control-A Simple and Powerful Method to Control Power Converters’, IEEE Transactions on Industrial Electronics, vol....

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  • ...Only the first value of the sequence is applied, and the algorithm is calculated again every sampling period [11]....

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Journal ArticleDOI
TL;DR: A simple classification of the most important types of predictive control is introduced, and each one of them is explained including some application examples.
Abstract: Predictive control is a very wide class of controllers that have found rather recent application in the control of power converters. Research on this topic has been increased in the last years due to the possibilities of today's microprocessors used for the control. This paper presents the application of different predictive control methods to power electronics and drives. A simple classification of the most important types of predictive control is introduced, and each one of them is explained including some application examples. Predictive control presents several advantages that make it suitable for the control of power converters and drives. The different control schemes and applications presented in this paper illustrate the effectiveness and flexibility of predictive control.

1,389 citations