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S.NO PROJECT TITLEJOURNALS
1An Improved Current-Limiting Strategy for Shunt Active Power Filter (SAPF) Using Particle Swarm Optimization (PSO)IEEE
2Transformerless Z-Source Four-Leg PV Inverter with Leakage Current ReductionIEEE
3Ensuring Power Quality and Stability in Industrial and Medium Voltage Public GridsIEEE
4A BL-CSC Converter Fed BLDC Motor Drive with Power Factor CorrectionIEEE
5Dual-Buck AC–AC Converter with Inverting and Non-Inverting OperationsIEEE
6Self-tuned fuzzy-proportional–integral compensated zero/minimum active power algorithm based dynamic voltage restorerIET
7Modeling, Implementation and Performance Analysis of a Grid-Connected Photovoltaic/Wind Hybrid Power SystemIEEE
8A Comparative Study of Different Multilevel Converter Topologies for Battery Energy Storage ApplicationIEEE
9A Low Cost Speed Estimation Technique for Closed Loop Control of BLDC Motor DriveIEEE
10A         Synchronous   Generator        Based  Diesel-PV Hybrid Micro-grid with Power Quality ControllerIEEE
11A         Synchronous   Generator        Based  Diesel-PV Hybrid Micro-grid with Power Quality ControllerIEEE
12An Intelligent Fuzzy Sliding Mode Controller for a BLDC MotorIEEE
13Analysis Of Solar Energy Embeded To Distribution Grid For Active & Reactive Power Supply To GridIEEE
14Cascaded Multilevel Inverter Based Electric Spring for Smart Grid ApplicationsIEEE
15Comparative Simulation Results of DVR and D- STATCOM to Improve Voltage Quality in Distributed Power SystemIEEE
16Design and Evaluation of a Mini-Size SMES Magnet for Hybrid Energy Storage Application in a kW-Class Dynamic Voltage RestorerIEEE
17Design of PID-Fuzzy for Speed Control of Brushless DC Motor in Dynamic Electric Vehicle to Improve Steady-State PerformanceIEEE
18Direct Torque Control of PM BLDC Motor Using Fuzzy ControllersIEEE
19Double Closed Loop Control for BLDC based on whole Fuzzy ControllerIEEE
20Dual-Bridge LLC Resonant Converter With Fixed- Frequency PWM Control for Wide Input ApplicationsIEEE
21A Two Degrees of Freedom Resonant Control Scheme for Voltage Sag Compensation in Dynamic Voltage RestorersIEEE
22High Performance Non-Salient Sensorless BLDC Motor Control Strategy from Standstill to High SpeedIEEE
23Indirect Speed Estimation of High Speed Brushless DC Motor Drive Using Fuzzy Logic Current CompensatorIEEE
24Modeling and Simulation of Closed Loop Speed Control for BLDC MotorIEEE
25Nine-level Asymmetrical Single Phase Multilevel Inverter Topology with Low switching frequency and Reduce device countsIEEE
26Novel Approach Employing Buck-Boost Converter as DC-Link Modulator and Inverter as AC-Chopper for Induction Motor Drive Applications: An Alternative to Conventional AC- DC-AC SchemeIEEE
27PWAM Controlled Quasi-Z Source Motor DriveIEEE
28Simulation and Control of Solar Wind Hybrid Renewable Power SystemIEEE
29Improved Dynamic Performance of Shunt Active Power Filter Using Particle Swarm OptimizationIEEE
30Particle Swarm Optimization Based Shunt Active Harmonic Filter for Harmonic CompensationIEEE
31Design and Performance Analysis of Three-Phase Solar PV Integrated UPQCIEEE
32Improved Fault Ride Through Capability in DFIG Based Wind Turbines Using Dynamic Voltage Restorer With Combined Feed-Forward and Feed- Back ControlIEEE
33Design and Evaluation of a Mini-Size SMES Magnet for Hybrid Energy Storage Application in a kW-Class Dynamic Voltage RestorerIEEE
34A Filterless Single-Phase AC-AC Converter Based on Coupled Inductors with Safe-Commutation Strategy and Continuous Input CurrentIEEE
35Novel Back EMF Zero Difference Point Detection Based Sensorless Technique for BLDC MotorIEEE
36A         Novel  DVR-ESS-embedded wind    energy conversion systemIEEE
37Dynamic Voltage Conditioner, a New Concept for Smart Low-Voltage Distribution SystemIEEE
38Transformer-less dynamic voltage restorer based on buck-boost converterIEEE
39A Generation of Higher Number of Voltage Levels by stacking inverters of lower multilevel structure with low voltage devices for drivesIEEE
40Optimal Pulse width Modulation of Medium- Voltage Modular Multilevel ConverterIEEE
41Novel Family of Single-Phase Modified Impedance-Source Buck-Boost Multilevel Inverters with Reduced Switch CountIEEE
42Adaptive Neuro Fuzzy Inference System Least Mean Square Based Control Algorithm for DSTATCOMIEEE
43An Islanding Detection Method for Inverter- Based Distributed Generators Based on the Reactive Power DisturbanceIEEE
44Quasi-Z-Source           Inverter           With    a          T- Type Converter in Normal and Failure ModeIEEE
45Real-Time Implementation of Model Predictive Control on 7-Level Packed U-Cell InverterIEEE
46High frequency inverter topologies integrated with the coupled inductor bridge armIET
47Dynamic voltage restorer employing multilevel cascaded H-bridge inverterIET
48Active power compensation method for single-
phase current source rectifier without extra active switches
IET
49Cascaded multilevel inverter using series connection of novel capacitor-based units with minimum switch countIET
50Design and Implementation of a Novel Multilevel DC-AC InverterIEEE
51A New Cascaded Switched-Capacitor Multilevel Inverter Based on Improved Series-Parallel Conversion with Less Number of ComponentsIEEE
52Circulating current derivation and comprehensive compensation of cascaded STATCOM under asymmetrical voltage conditionsIET
53Design and implementation of a novel three- phase cascaded half-bridge inverterIET
54Grid connected three-phase multiple-pole multilevel unity power factor rectifier with reduce components countIET
55Control of Ripple Eliminators to Improve the Power Quality of DC Systems and Reduce the Usage of Electrolytic CapacitorsIEEE
56Design of External Inductor for Improving Performance of Voltage Controlled DSTATCOMIEEE
57An Enhanced Single Phase Step-Up Five-Level InverterIEEE
58A Hybrid-STATCOM with Wide Compensation Range and Low DC-Link VoltageIEEE
59T-type direct AC/AC converter structureIET
60Modular Multilevel Converter Circulating Current Reduction Using Model Predictive ControlIEEE
61Parallel inductor multilevel current source inverter with energy – recovery scheme for inductor currents balancingIET
62Open-Circuit Fault-Tolerant Control for Outer Switches of Three-Level Rectifiers in Wind Turbine SystemsIEEE
63Enhancing DFIG wind turbine during three phase fault using parallel interleaved converters and dynamic resistorIET
64Load Model for Medium Voltage Cascaded H-Bridge Multi-Level Inverter Drive SystemsIEEE
65Development and Comparison of an Improved Incremental Conductance Algorithm for Tracking the MPP of a Solar PV PanelIEEE
66Impact of Switching Harmonics on Capacitor Cells Balancing in Phase-Shifted PWM Based Cascaded H-Bridge STATCOMIEEE
67Effect of circulating current on input line current of 12-pulse rectifier with active inter-phase reactorIET
68Modular Multilevel Converter-Based Bipolar High-Voltage Pulse Generator With Sensorless Capacitor Voltage Balancing TechniqueIEEE
69Power-Electronics-Based        Energy Management System With StorageIEEE
70Modulation     and      Control            of         Transformerless UPFCIEEE
71A Hybrid Simulation Model for VSC HVDCIEEE
72Switching Control of Buck Converter Based on Energy Conservation PrincipleIEEE
73A Three-Phase Multilevel Hybrid Switched- Capacitor PWM PFC Rectifier for High-Voltage- Gain ApplicationsIET
74A         dc-Side            Sensorless        Cascaded        H-Bridge Multilevel Converter Based Photovoltaic SystemIEEE
75Phase angle calculation dynamics of type-4wind turbines in rms simulations during severe voltage dipsIET
76A Multi-Level Converter with a Floating Bridge for Open-Ended Winding Motor Drive ApplicationsIEEE
77Model  Predictive        Control            of         Quasi-Z- Source Four-Leg InverterIEEE
78Using Multiple Reference Frame Theory for Considering Harmonics in Average-Value Modeling of Diode RectifiersIEEE
79Cascaded Dual Model Predictive Control of an Active Front-End RectifierIEEE
80Simple Time Averaging Current Quality Evaluation of a Single-Phase Multilevel PWM InverterIEEE
81Nonlinear Control of Single-Phase PWM Rectifiers With Inherent Current-Limiting CapabilityIET
82Impact of SFCL on the Four Types of HVDC Circuit Breakers by SimulationIEEE
83An Adaptive SPWM Technique for Cascaded
Multilevel Converters with Time-Variant DC Sources
IEEE
84Model-Based  Control            for       a          Three-Phase Shunt Active Power FilterIEEE
85Design of a multi-level inverter with reactive power control ability for connecting PV cells to the gridIEEE
86DSTATCOM supported induction generator for improving power qualityIET
87Improved equal current approach for reference current generation in shunt applications under unbalanced and distorted source and load conditionsIET
88A Hybrid-STATCOM With Wide Compensation Range and Low DC-Link VoltageIEEE
89Design of External Inductor for Improving Performance of Voltage-Controlled DSTATCOMIEEE
90Full-Bridge Reactive Power Compensator With Minimized-Equipped Capacitor and Its Application to Static Var CompensatorIEEE
91A New Cascaded Switched-Capacitor Multilevel Inverter Based on Improved Series–Parallel Conversion With Less Number of ComponentsIEEE
92Efficient Implicit Model Predictive Control of Three Phase Inverter with an Output LC FilterIEEE
93Single-stage Three-phase Differential-mode Buck-Boost Inverters with Continuous Input Current for PV ApplicationsIEEE
94Soft-start control strategy for the three phase grid-connected inverter with LCL filterIEEE
95High-Gain Single-Stage Boosting Inverter For Photovoltaic ApplicationsIET
96Multilevel Inverter Topologies With Reduced Device Count: A ReviewIEEE
97Real time implementation of unity power factor correction converter based on fuzzy logicIEEE
98Power Factor Correction in BLDC motor Drives Using DC-DC ConvertersIEEE
99Transformerless Single-Phase Universal Active Filter With UPS Features and Reduced Number of Electronic Power SwitchesIEEE
100PI tuning of Shunt Active Filter using GA and PSO algorithmIEEE
101PSO – PI Based DC Link Voltage Control Technique for Shunt Hybrid Active
Power Filter
IEEE
102Artificial Neural Network based Three Phase Shunt Active Power FilterIEEE
103Cascaded        open    end      winding           transformer based DVRIEEE
104Brushless DC motor drive with power factor regulation using Landsman converterIET
105Comparative Analysis of 6, 12 and 48 Pulse T-STATCOMIEEE
106A Superconducting Magnetic Energy Storage- Emulator/Battery Supported Dynamic Voltage RestorerIEEE
107Compensation of Voltage Distribunces In SMIB System Using ANN Based DPFC ControllerIEEE
108Commutation Torque Ripple Reduction in BLDC   Motor  Using   Modified         SEPIC
Converter and Three-level NPC Inverter
IEEE
109Novel Cascaded Switched-Diode Multilevel Inverter for Renewable Energy IntegrationIEEE
110A Highly Reliable Single-Phase High- Frequency Isolated Double Step-Down AC- AC Converter with Both Non-Inverting and Inverting OperationsIEEE
111Dynamic Voltage Restorer Using Switching Cell Structured Multilevel AC-AC ConverterIEEE
112Time-Varying and Constant Switching Frequency Based Sliding Mode Control Methods for Transformerless DVR Employing Half-Bridge VSIIEEE
113Evaluation of DVR Capability Enhancement – Zero Active Power Tracking TechniqueIEEE
114Sensitive          Load    Voltage           Compensation Performed by a Suitable Control MethodIEEE
115A         High    Gain    Input-Parallel  Output-Series DC/DC Converter with Dual Coupled InductorsIEEE
116A High Step-Up Converter with Voltage- Multiplier Modules for Sustainable Energy
Applications
IEEE
117A High Step-Up DC to DC Converter Under Alternating Phase Shift Control for Fuel Cell Power SystemIEEE
118High-Efficiency     MOSFET     Transformerless
Inverter           for       Non-isolated   Micro inverter Applications
IEEE
119A Multi-Input Bridgeless Resonant AC-DC Converter     for       Electromagnetic          Energy
Harvesting
IEEE
120A Novel Control Method for Transformerless
H-Bridge         Cascaded        STATCOM     with     Star Configuration
IEEE
121A Novel High Step-up DC/DC Converter Based on Integrating Coupled Inductor and Switched- Capacitor Techniques for Renewable Energy
Applications
IEEE
122A         Function          Based  Maximum        Power  Point Tracking Method for Photovoltaic SystemsIEEE
123A Three-Phase Grid Tied SPV System With Adaptive DC Link Voltage for CPI Voltage VariationsIEEE
124Design   of   External Inductor for Improving Performance of Voltage Controlled DSTATCOMIEEE
125Grid-Connected PV Array with Supercapacitor Energy Storage System for Fault Ride ThroughIEEE
126Grid-Connected PV-Wind-Battery based Multi- Input Transformer Coupled Bidirectional DC-DC Converter for household ApplicationsIEEE
127MPPT with Single DC-DC Converter and Inverter for Grid Connected Hybrid Wind-Driven PMSG-PV SystemIEEE
128Application     of         Neural Networks        in         Power QualityIEEE
129Neuro Fuzzy based controller for Power Quality ImprovementIEEE
130Single- and Two-Stage Inverter based grid connected Photovoltaic Power Plants with Tide- Through under Grid FaultsIEEE
131Power Quality Improvement of PMSG based DG set feeding Three-phase loadsIEEE
132A Five Level Cascaded H-Bridge Multilevel STATCOMIEEE
133MPPT with Single DC–DC Converter and Inverter for Grid-Connected Hybrid Wind-Driven PMSG– PV SystemIEEE
134Versatile Unified Power Quality Conditioner Applied to Three-Phase Four-Wire Distribution Systems Using a Dual Control StrategyIEEE
135A Novel Five-Level Voltage Source Inverter With Sinusoidal Pulse Width Modulator for Medium- Voltage ApplicationsIEEE
136Development and Comparison of an Improved Incremental Conductance Algorithm for Tracking the MPP of a Solar PV PanelIEEE
137A Single DC Source Cascaded Seven-Level Inverter Integrating Switched Capacitor TechniquesIEEE
138Improving the Performance of Cascaded H-bridge based Interline Dynamic Voltage RestorerIEEE
139Integrated Photovoltaic and Dynamic Voltage Restorer System ConfigurationIEEE
140Multilevel        Cascaded-Type           Dynamic          Voltage Restorer with Fault Current Limiting FunctionIEEE
141Dynamic Voltage Restorer Based on Three-Phase Inverters  Cascaded        Through           an        Open-End Winding TransformerIEEE
142Design Considerations of a Fault Current Limiting Dynamic Voltage Restorer (FCL-DVR)IEEE
143A Modified Three-Phase Four-Wire UPQC Topology With Reduced DC-Link Voltage RatingIEEE
144FPGA-Based Predictive Sliding Mode Controller of a Three-Phase InverterIEEE
145Pulse width Modulation of Z-Source Inverters With Minimum Inductor Current RippleIEEE
146Improving the Dynamics of Virtual-Flux-Based Control of Three-Phase Active RectifiersIEEE
147Sensorless Induction Motor Drive Using Indirect Vector Controller and Sliding-Mode Observer for Electric VehiclesIEEE
148Back-Propagation Control Algorithm for Power Quality Improvement Using DSTATCOMIEEE
149A Zero-Voltage Switching Three-Phase InverterIEEE
150Control of Reduced-Rating Dynamic Voltage Restorer With a Battery Energy Storage SystemIEEE
151A Combination of Shunt Hybrid Power Filter and Thyristor-Controlled Reactor for Power QualityIEEE
152A Transformerless Grid-Connected Photovoltaic System Based on the Coupled Inductor Single- Stage Boost Three-Phase InverterIEEE
153LCL Filter Design and Performance Analysis for Grid-Interconnected SystemsIEEE
154An Inductively Active Filtering Method for Power-Quality Improvement of Distribution Networks With Nonlinear LoadsIEEE
155A Bidirectional-Switch-Based Wide-Input Range High-Efficiency Isolated Resonant Converter for Photovoltaic ApplicationsIEEE
156Analysis and Implementation of an Improved Flyback Inverter for Photovoltaic AC Module ApplicationsIEEE
157Speed Sensorless Vector Controlled Induction Motor Drive Using Single Current SensorIEEE
158A Novel Design and Optimization Method of an
LCL Filter for a Shunt Active Power Filter
IEEE
159An Active Harmonic Filter Based on One-Cycle ControlIEEE
160A Nine-Level Grid-Connected Converter Topology for Single-Phase Transformerless PV SystemsIEEE
161Modeling and Design of Voltage Support Control Schemes for Three-Phase Inverters Operating Under Unbalanced Grid ConditionsIEEE
162Cascaded Two-Level Inverter-Based Multilevel STATCOM for High-Power ApplicationsIEEE
163A Voltage-Controlled DSTATCOM for Power-Quality ImprovementIEEE
164Solar PV and Battery Storage Integration using a New Configuration of a Three-Level NPC Inverter With Advanced Control StrategyIEEE
165A Current Control MPPT Method in HighA Novel Five-Level Inverter for Solar System Power Solar Energy Conversion SystemIEEE
166A Single-Stage Three-Phase Grid-Connected Photo-Voltaic System With Fractional Order MPPTIEEE
167Design and Implementation of Sliding Mode and PI Controllers based Control for Three Phase Shunt Active Power FilterIEEE
168Implementation of Adaptive Filter in Distribution Static CompensatorIEEE
169A Comparison of Soft-Switched DC-to-DC Converters for Electrolyzer ApplicationIEEE
170Adaptive fuzzy controller based MPPT for photovoltaic systemsIEEE
171Design of Fuzzy Logic Based Maximum Power Point Tracking Controller for Solar Array for Cloudy Weather Conditions.IEEE
172Dynamic Behavior of DFIG Wind Turbine Under Grid Fault ConditionsIEEE
173Fuzzy-Logic-Controller-Based SEPIC Converter for Maximum Power Point TrackingIEEE
174Performance Improvement of Direct Power Control of PWM Rectifier With Simple CalculationIEEE
175FLC-Based DTC Scheme to Improve the Dynamic Performance of an IM DriveIEEE
176Single Phase Grid-Connected Photovoltaic Inverter for Residential Application with Maximum PowerPoint TrackingIEEE
177Improved Active Power Filter Performance for Renewable Power Generation SystemsIEEE
178Micro Wind Power Generator with Battery Energy Storage for Critical LoadIEEE
179Power Conditioning in Distribution Systems Using ANN Controlled Shunt Hybrid Active Power FilterIEEE
180Power Quality Improvement Using UPQC Integrated with Distributed Generation NetworkIEEE

Improvement of PMSM Sensorless Control Based on Synergetic and Sliding Mode Controllers Using a Reinforcement Learning Deep Deterministic Policy Gradient Agent

ABSTRACT:

PMSM Sensorless The field‐oriented control (FOC) strategy of a permanent magnet synchronous motor (PMSM) in a simplified form is based on PI‐type controllers. In addition to their low complexity (an advantage for real‐time implementation), these controllers also provide limited performance due to the nonlinear character of the description equations of the PMSM model under the usual conditions of a relatively wide variation in the load torque and the high dynamics of the PMSM speed reference.

FOC

PMSM Sensorless Moreover, a number of significant improvements in the performance of PMSM control systems, also based on the FOC control strategy, are obtained if the controller of the speed control loop uses sliding mode control (SMC), and if the controllers for the inner control loops of id and iq currents are of the synergetic type. Furthermore, using such a control structure, very good performance of the PMSM control system is also obtained under conditions of parametric uncertainties and significant variations in the combined rotor‐load moment of inertia and the load resistance.

RL

PMSM Sensorless To improve the performance of the PMSM control system without using controllers having a more complicated mathematical description, the advantages provided by reinforcement learning (RL) for process control can also be used. This technique does not require the exact knowledge of the mathematical model of the controlled system or the type of uncertainties. The improvement in the performance of the PMSM control system based on the FOC‐type strategy, both when using simple PI‐type controllers or in the case of complex SMC or synergetic‐type controllers, is achieved using the RL based on the Deep Deterministic Policy Gradient (DDPG).

SMC

PMSM Sensorless This improvement is obtained by using the correction signals provided by a trained reinforcement learning agent, which is added to the control signals ud, uq, and iqref. A speed observer is also implemented for estimating the PMSM rotor speed. The PMSM control structures are presented using the FOC‐type strategy, both in the case of simple PI‐type controllers and complex SMC or synergetic‐type controllers, and numerical simulations performed in the MATLAB/Simulink environment show the improvements in the performance of the PMSM control system, even under conditions of parametric uncertainties, by using the RL‐DDPG.

KEYWORDS:

  1. Permanent magnet synchronous motor
  2. Sliding mode control
  3. Synergetic control
  4. Reinforcement learning
  5. Deep neural networks

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Figure 1. Block diagram for FOC‐type control of the PMSM based on PI‐type controllers using RL.

EXPECTED SIMULATION RESULTS:

Figure 2. Time evolution for the numerical simulation of the PMSM control system based on the FOC‐type strategy.

Figure 3. Time evolution for the numerical simulation of the PMSM control system based on the RL‐TD3 agent for the correction of iqref.

Figure 4. Time evolution for the numerical simulation of the PMSM control system based on the

RL‐TD3 agent for the correction of udref and uqref.

Figure 5. Time evolution for the numerical simulation of the PMSM control system based on the

RL‐TD3 agent for the correction of udref, uqref, and iqref.

Figure 6. Time evolution for the numerical simulation of the PMSM control system based on control

using SMC and synergetic controllers.

Figure 7. Time evolution for the numerical simulation of the PMSM control system based on control

using SMC and synergetic controllers using an RL‐TD3 agent for the correction of iqref.

CONCLUSION:

PMSM Sensorless Sliding Mode Controllers This paper presents the FOC‐type control structure of a PMSM, which is improved in terms of performance by using a RL technique. Thus, the comparative results are presented for the case where the RL‐TD3 agent is properly trained and provides correction signals that are added to the control signals ud, uq, and iqref. The FOC‐type control structure for the PMSM control based on an SMC speed controller and synergetic current controller is also presented.

PMSM

PMSM Sensorless Sliding Mode Controllers To improve the performance of the PMSM control system without using controllers having a more complicated mathematical description, the advantages provided by the RL on process control can also be used. This improvement is obtained using the correction signals provided by a trained RL‐TD3 agent, which is added to the control signals ud, uq, and iqref. A speed observer is also implemented for estimating the PMSM rotor speed.

LOAD

PMSM Sensorless Sliding Mode Controllers The parametric robustness of the proposed PMSM control system is proved by very good control performances achieved even when the uniformly distributed noise is added to the load torque TL, and under high variations in the load torque TL and the moment of inertia J. Numerical simulations are used to prove the superiority of the control system that uses the RL‐TD3 agent.

REFERENCES:

1. Eriksson, S. Design of Permanent‐Magnet Linear Generators with Constant‐Torque‐Angle Control for Wave Power. Energies 2019, 12, 1312.

2. Ouyang, P.R.; Tang, J.; Pano, V. Position domain nonlinear PD control for contour tracking of robotic manipulator. Robot. Comput. Integr. Manuf. 2018, 51, 14–24.

3. Baek, S.W.; Lee, S.W. Design Optimization and Experimental Verification of Permanent Magnet Synchronous Motor Used in Electric Compressors in Electric Vehicles. Appl. Sci. 2020, 10, 3235.

4. Amin, F.; Sulaiman, E.B.; Utomo, W.M.; Soomro, H.A.; Jenal, M.; Kumar, R. Modelling and Simulation of Field Oriented Control based Permanent Magnet Synchronous Motor Drive System. Indones. J. Electr. Eng. Comput. Sci. 2017, 6, 387.

5. Mohd Zaihidee, F.; Mekhilef, S.; Mubin, M. Robust Speed Control of PMSM Using Sliding Mode Control (SMC)—A Review. Energies 2019, 12, 1669.

Improvement of PMSM Control Using Reinforcement Learning Deep Deterministic Policy Gradient Agent

ABSTRACT:

PMSM Control Based on the advantage of using the reinforcement learning on process control, provided by the fact that it is not necessary to know the exact mathematical model and the structure of its uncertainties, this article approaches the possibility of improving the performances of the Permanent Magnet Synchronous Motor (PMSM) control system based on the Field Oriented Control (FOC) type control strategy

DDPG

By using the correction signals provided by a trained reinforcement learning agent, which will be added to the control signals ud, uq, and iqref . The type of reinforcement learning used is the Deep Deterministic Policy Gradient (DDPG). The combination possibilities of these control structures are presented, and their superiority over the FOC type control strategy is validated by numerical simulations.

KEYWORDS:

  1. Permanent magnet motors
  2. Field oriented control
  3. Reinforcement learning
  4. Intelligent agent
  5. Deep neural networks

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. Block diagram for FOC-type control of the PMSM based on reinforcement learning.

EXPECTED SIMULATION RESULTS:

Fig. 2. Time evolution for the numerical simulation of the PMSM control system based on the FOC-type strategy.

Fig. 3. Time evolution for the numerical simulation of the PMSM control system based on TD3 agent for the correction of udref and uqref .

Fig. 4. Time evolution for the numerical simulation of the PMSM control system based on TD3 agent for the correction of iqref.

Fig. 5. Time evolution for the numerical simulation of the PMSM control system based on TD3 agent for the correction of udref, uqref and iqref.

CONCLUSION:

PMSM Control This article presents the FOC-type control structure of a PMSM, which is improved in terms of performance by using a reinforcement learning technique. Thus, the comparative results are presented for the case where the reinforcement learning agent is properly trained and provides correction signals that will be added to the control signals ud, uq, and iqref.

PMSM

PMSM Control Numerical simulations are used to demonstrate the superiority of the control system that uses the reinforcement learning, and the following papers will study the possibilities for optimization in terms of the implementation of the reinforcement learning in the PMSM control.           

REFERENCES:

[1] B. Wu and M. Narimani, Control of Synchronous Motor Drives, in High-Power Converters and AC Drives , Wiley-IEEE Press, 2017, pp.353-391.

[2] B. K. Bose, Modern power electronics and AC drives, Prentice Hall, Knoxville, Tennessee, USA, 2002.

[3] H. Wang and J. Leng, “Summary on development of permanent magnet synchronous motor,” Chinese Control And Decision Conference (CCDC), Shenyang, China, 2018, pp. 689-693.

[4] Z. Liu, Y. Li, and Z. Zheng, “A review of drive techniques for multiphase machines,” in CES Transactions on Electrical Machines and Systems, vol. 2, pp. 243-251, June 2018.

[5] S. Sakunthala, R. Kiranmayi, and P. N. Mandadi, “A Review on Speed Control of Permanent Magnet Synchronous Motor Drive Using Different Control Techniques,”International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, China , 2018, pp. 97-102.

Research on Anti DC Bias and High Order Harmonics of Fifth Order Flux Observer for IEEE Electrical Projects

ABSTRACT:

DC Bias Due to various nonideal factors, including the motor parameter mismatches, detection errors, converter nonlinearities, noise, etc. and high order harmonics exist in flux model, which make the traditional flux observer estimation inaccurate. In order to suppress DC bias and high order harmonics, an interior permanent magnet synchronous motors (IPMSM) sensorless drive method based on a fifth order flux observer (FOFO) is proposed in this paper.

DC Bias

DC Bias The proposed FOFO can completely remove DC bias and has strong filtering ability for high order harmonics. Additionally, the parameters of the FOFO are set through s-domain analysis. Then, the discrete FOFO is obtained to better implementation in digital systems. The proposed FOFO is verified by experiments on a 2.0-kW IPMSM drive platform.

KEYWORDS

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. The block diagram of IPMSM sensorless drive based on the FOFO.

EXPECTED SIMULATION RESULTS:

Fig. 2. Estimated rotor flux and position estimation error at 1000 r/min. (a) SOFO. (b) FOFO.

Fig. 3. Eestimated rotor flux and position estimation error at 1800 r/min. (a) SOFO. (b) FOFO.

Fig. 4. Estimated rotor flux and position estimation error at 50 r/min. (a) SOFO. (b) FOFO.

CONCLUSION:

DC Bias In this paper, to further improve the performance of the flux observer to suppress DC bias and high order harmonics, a FOFO is proposed. Theoretical analysis shows that proposed FOFO has strong attenuation ability against the DC bias and harmonics, and motor parameters mismatch and additional interference can be avoided.

IPMSM

DC Bias Additionally, the parameters of FOFO are set through s-domain analysis. Moreover, for better implementation in digital systems, the structure of discrete FOFO is obtained. The effectiveness of the proposed FOFO has been verified at a 2.0-kW IPMSM sensorless drive. Compared with the SOFO sensorless drive

FOFO

the proposed FOFO method has strong performance of suppressing stator voltage DC bias and stator current DC bias, and the proposed method has better suppression of stator resistance mismatch and q-axis inductance mismatch under the condition of stator current DC bias.

DC

DC Bias The main advantages of the proposed FOFO are: 1) it is insensitive to DC bias; 2) it has strong suppression ability to high order harmonics; 3) it has high rotor position estimation accuracy. Our future research work will further study the application of FOFO in synchronous reluctance motors.

REFERENCES:

[1] S. Kim, J. Im, E. Song, and R. Kim, “A new rotor position estimation method of IPMSM using all-pass filter on high-frequency rotating voltage signal injection,” IEEE Trans. Ind. Electron., vol. 63, no. 10, pp. 6499-6509, Oct. 2016.

[2] H, Zhang, W. Liu, Z. Chen, S. Mao, T. Meng, J. Peng, and N. Jiao, “A time-delay compensation method for IPMSM hybrid sensorless drives in rail transit applications,” IEEE Trans. Ind. Electron., vol.66, no. 9, pp. 6715-6726, Sept. 2019.

[3] R. Antonello, L. Ortombina, F. Tinazzi, and M. Zigliotto, “Enhanced low-speed operations for sensorless anisotropic PM synchronous motor drives by a modified back-EKF observer,” IEEE Trans. Ind. Electron., vol. 65, no. 4, pp. 3069-3076, Apr. 2018.

[4] C. Li, G.Wang, G. Zhang, N. Zhao, and D. Xu, “Adaptive pseudorandom high-frequency square-wave voltage injection based sensorless control for SynRM drives,” IEEE Trans. Power Electron., vol. 36, no. 3, pp. 3200-3210, Mar. 2021.

[5] G. Zhang, G. Wang, H. Zhang, H. Wang, G. Bi, X. Zhang, and D. Xu, “Pseudo-random-frequency sinusoidal injection for position sensorless IPMSM drives considering sample and hold effect,” IEEE Trans. Power Electron., vol. 34, no. 10, pp. 9929-9941, Oct. 2019.

PMSM System Reduced-Order Feedback Linearization for Independent Torque Control electrical projects

ABSTRACT:

PMSM System in parallel to a 2-level 3-leg inverter gives a way to build up a high power-density driving system using existing electronic devices. But this type of system has a nature of nonlinearity that creates an obstacle in high performance control and the original system cannot be feedback-linearized directly.

Controller

This article presents a reduced-order feedback-linearization method. In the first place, an extra order-reducing step that separates the system as a main system and an auxiliary system is applied. Then a feedback-linearization method is applied to the reduced-order system. With these effort, the original system can be converted into a linear time-invariant system bringing the controller design problem into the linear domain.

Load torque

In the last step, a linear robust state-feedback controller is used to achieve the speed control as well as compensate the unmeasurable external load torque. An extensive experiment is given to verify the feasibility and good performance in a highly unbalanced load torque situation of the designed controller.

KEYWORDS:

  1. Parallel PMSM
  2.  Robust control
  3.  Feedback-linearization

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Figure 1. Proposed Controller Scheme.

EXPECTED SIMULATION RESULTS:

Figure 2. Speed And _D Response Of Speed Command Experiment.

Figure 3. Current Response Of Speed Command Experiment.

Figure 4. Speed And _D Response Of _D Command Experiment.

Figure 5. Current Response Of _D Command Experiment.

CONCLUSION:

In this article, we have presented a two-step state-feedback controller for the MIDPMSM system such that the two machines are carried out in closed-loop systems for handling the highly unbalanced load torque situation. This article proposes a new way to linearize a nonlinear system if feedback-linearization cannot be applied directly.

PMSM

The major contribution can be summarized in three aspects. First of all, a state-space description for the MID PMSM system is set up, and it is an affine nonlinear system with unknown inputs. And then, the original affine nonlinear system is linearized through two steps: order reducing and state-feedback linearization. With these two steps, the controller design problem is brought into the LTI system domain.

Polynomial

Secondly, in the state-feedback linearization stage, the stability of the constrained two-dimensional subsystem (7) is fully considered and dealt with. Indeed, in order to keep its stability, the calculation of the disturbance compensation gain k is given by analyzing eigenvalue constraints through solving its characteristic polynomial. Thirdly, based on the reduced-order linearized system, a state feedback controller together with an integrator is designed.

Loop

In this way, both goals, closed-loop stability and reference tracking, are reached. The experiment also proves that an open-loop machine can have the risk of becoming unstable when the “master-slave” method is used. The proposed controller can avoid this situation by putting both machines under closed-loop control. Although the proposed controller design method has shown its great advantages, at least one drawback of the controller is also left. This controller can hardly handle the singularity point of the system, which creates an obstacle. How to overcome the drawback becomes one of our next considerations.

REFERENCES:

 [1] Z. Deng and X. Nian, “Robust control of two parallel-connected permanent magnet synchronous motors fed by a single inverter,” IET Power Electron., vol. 9, no. 15, pp. 2833_2845, Dec. 2016.

[2] J. M. Lazi, Z. Ibrahim, M. H. N. Talib, and R. Mustafa, “Dual motor drives for PMSM using average phase current technique,” in Proc. IEEE Int. Conf. Power Energy, Kuala Lumpur, Malaysia, Nov. 2010, pp. 786_790.

[3] A. A. A. Samat, D. Ishak, P. Saedin, and S. Iqbal, “Speed-sensorless control of parallel-connected PMSM fed by a single inverter using MRAS,” in Proc. IEEE Int. Power Eng. Optim. Conf., Melaka, Malaysia, Jun. 2012, pp. 35_39.

[4] A. Del Pizzo, D. Iannuzzi, and I. Spina, “High performance control technique for unbalanced operations of single-vsi dual-PM brushles motor drives,” in Proc. IEEE Int. Symp. Ind. Electron., Bari, Italy, Jul. 2010, pp. 1302_1307.

[5] J. M. Lazi, Z. Ibrahim, M. Sulaiman, I. W. Jamaludin, and M. Y. Lada, “Performance comparison of SVPWM and hysteresis current control for dual motor drives,” in Proc. IEEE Appl. Power Electron. Colloq. (IAPEC), Johor Bahru, Malaysia, Apr. 2011, pp. 75_80.

Peak Current Detection Starting based Position Sensorless Control of BLDC Motor Drive Academic

ABSTRACT:

Peak Current Detection a single stage position sensorless control based solar power fed PMBLDC (Permanent Magnet Brushless DC) motor drive scheme for irrigation pump is proposed in this paper. The proposed system is designed without using any mechanical sensor to reduce the cost along with the complexity of the system with optimum utilization of the solar Photovoltaic (PV) power.

PMBLDC

Peak Current Detection The proposed system integrated with a PMBLDC motor drive coupled to a water pump is controlled by an inverter input voltage sensing based position sensorless control with high current detection and commutation point estimation based starting to wide speed range control .Elimination of position sensor, makes the system control compact and cheaper.

CURRENT

Peak Current Detection The peak current estimation based starting in sensorless mode, enables soft starting restricting high starting current with reliability like sensor based operation. The proposed drive is tested and validated on a developed laboratory prototype and its suitability is justified with different test results under steady state and dynamic operating conditions.

KEYWORDS:

  1. Peak Current detection based starting
  2.  Position sensorless control
  3. Incremental Conductance MPPT Algorithm
  4. PMBLDC motor drive
  5. Water pumping

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig.1 System configuration of position sensorless brushless DC motor drive operated aqua pumping

EXPECTED SIMULATION RESULT:

Fig.2 Solar PV Array Performance (a) Steady-state and Starting performance at 1000 W/m2 insolation (b) Dynamic performance varying from 500 W/m2 to 1000 W/m2

Fig. 3 BLDC motor performance at sensorless scheme(a)Zero starting and steady state performance at 1000W/m2 irradiance(b)Dynamic performance varying from 500 W/m2 to 1000 W/m2 irradiance

CONCLUSION:

Peak Current DetectionPosition sensorless control scheme of the BLDC motor has been presented for irrigation pump application. Sensorless control scheme has been justified for adverse environment application especially for rural areas. The performance of the proposed configuration has been evaluated satisfactory for water pumping application at different weather conditions.

REFERENCES:

[1] S. Jain, A. K. Thopukara, R. Karampuri and V. T. Somasekhar, “A Single-Stage Photovoltaic System for a Dual-Inverter-Fed Open-End Winding Induction Motor Drive for Pumping Applications, ” in IEEE Transactions on Power Electronics, vol. 30, no. 9, pp. 4809-4818, Sept. 2015

[2] L. An and D. D. Lu, “ Design of a Single-Switch DC/DC Converter for a PV-Battery-Powered Pump System With PFM+PWM Control, ” in IEEE Transactions on Industrial Electronics, vol. 62, no. 2, pp. 910- 921, Feb. 2015.

[3] J. V. M. Caracas, G. d. C. Farias, L. F. M. Teixeira and L. A. d. S. Ribeiro, “ Implementation of a High-Efficiency, High-Lifetime, and Low-Cost Converter for an Autonomous Photovoltaic Water Pumping System, ” in IEEE Transactions on Industry Applications, vol. 50, no. 1, pp. 631-641, Jan.-Feb. 2014.

[4] Tae-Hyung Kim and M. Ehsani, “Sensorless control of the BLDC motors from near-zero to high speeds, ” in IEEE Transactions on Power Electronics, vol. 19, no. 6, pp. 1635-1645, Nov. 2004.

[5] S. Dusmez, A. Khaligh, M. Krishnamurthy, E. Ugur and M. Uzunoglu, “Sensorless control of BLDCs for all speed ranges with minimal components, ”International Aegean Conference on Electrical Machines and Power Electronics and Electromotion, Joint Conference, Istanbul, 2011, pp. 626-631

Online Estimation Method of DC-Link Capacitors for Reduced DC-Link Capacitance IPMSM EEE

ABSTRACT:

DC-Link Capacitance In order to extend the lifetime and save the system cost, the film capacitor is applied in the DC-link of IPMSM drives. Many active damping control methods have been carried out to improve the drive system stability, which need the accurate value of the DC-link film capacitor.

DC-link

DC-Link Capacitance In this letter, an online DC-link capacitance estimation method is investigated for reduced capacitance IPMSM drives, which does not need any additional signal injection or sensor. The power coupling characteristics are analyzed to obtain the instantaneous power of the DC-link capacitor from the inverter and the grid sides.

Voltage

DC-Link Capacitance The band-pass filter is applied to extract the DC-link voltage and capacitor power with twice the frequency of the grid voltage. The DC-link capacitance could be estimated by the fundamental component of the DC-link voltage. The proposed method can be used for different kinds of load types and motor types of the drive system. Experimental results are performed to verify the estimation method, and the estimation error is within 1%.

KEYWORDS:

  1. Online capacitance estimation
  2. Motor drive
  3. Online capacitance estimation
  4. Reduced DC-link capacitance

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. Block diagram of DC-link capacitance estimation.

EXPECTED SIMULATION RESULTS:

Fig. 2. Experimental results when the motor operates at 1800rpm. (a) Waveforms of grid power, inverter power, capacitor power and its fundamental component. (b) Waveforms of the DC-link voltage, the product of DC-link voltage and its derivative, and the fundamental component of the product M. (c) Detailed waveform of the fundamental component of capacitor power, M, and the estimated DC-link capacitance.

Fig. 3. Experimental results when the motor operates at 4800rpm. (a) Waveforms of grid power, inverter power, capacitor power, and its fundamental component. (b) Waveforms of the DC-link voltage, the product of DC-link voltage and its derivative, and the fundamental component of the product M. (c) Detailed waveform of the fundamental component of capacitor power, M, and the estimated DC-link capacitance.

Fig. 4. Experimental results when the motor operates at 3000rpm and the DC-link capacitance is 59.5μF. (a) Waveforms of grid power, inverter power, capacitor power and its fundamental component. (b) Waveforms of the DC-link voltage, the product of DC-link voltage and its derivative, and the fundamental component of the product M. (c) Detailed waveform of the fundamental component of capacitor power, M, and the estimated DC-link capacitance.

CONCLUSION:

DC-Link Capacitance As for the reduced DC-link capacitance IPMSM drive system, a real-online DC-link capacitance estimation method is investigated in this letter, which does not need an additional signal injection. The power coupling characteristics are analyzed, and the instantaneous DC-link capacitor power is obtained.

Capacitance

DC-Link Capacitance The DC-link capacitance could be estimated by the ratio of the fundamental component of DC-link capacitor power and that of the product between DC-link voltage and its derivative term. Moreover, the proposed method only depends on the DC-link voltage and the instantaneous DC-link capacitor power,

Motor

DC-Link Capacitance which benefits its application in other motor type and load type reduced DC-link capacitance motor drive system. Experimental results verify the effectiveness of the proposed DC-link capacitance estimation method, which could realize the estimation precision within an error of 1% for the several tens micro far ad of DC-link capacitance.

REFERENCES:

[1] Y. Zhang, Z. Yin, J. Liu, R. Zhang and X. Sun, “IPMSM Sensorless Control Using High-Frequency Voltage Injection Method With Random Switching Frequency for Audible Noise Improvement,” IEEE Trans. Ind. Electron., vol. 67, no. 7, pp. 6019-6030, Jul. 2020.

[2] K. Liu and Z. Zhu, “Fast Determination of Moment of Inertia of Permanent Magnet Synchronous Machine Drives for Design of Speed Loop Regulator,” IEEE Trans. Control Syst. Technol., vol. 25, no. 5, pp. 1816-1824, Sept. 2017.

[3] J. Hang, H. Wu, S. Ding, Y. Huang and W. Hua, “Improved Loss Minimization Control for IPMSM Using Equivalent Conversion Method,” IEEE Trans. Power Electron., vol. 36, no. 2, pp. 1931-1940, Feb. 2021

[4] K. Abe, H. Haga, K. Ohishi and Y. Yokokura, “Current ripple suppression control based on prediction of resonance cancellation voltage for electrolytic-capacitor-less inverter,” IEEJ J. Ind. Appl., vol. 6, no. 1, pp. 1-11, 2017.

[5] Y. Zhou, W. Huang, and F. Hong, “Single-phase input variable-speed AC motor system based on electrolytic capacitor-less single-stage boost three-phase inverter,” IEEE Trans. Power Electron., vol. 31, no. 10, pp. 7043-7052, Oct. 2016.

Grid-Connected Induction Motor Using a Floating DC-Link Converter Under Unbalanced Voltage sag BTech/Mtech Final Year Electrical Projects

ABSTRACT:

Voltage sag This article proposes a series compensator with unbalanced voltage sag ride-through capability applied to grid connected induction motors. A conventional three-phase voltage source inverter (VSI) is intended to regulate the motor voltages. The VSI is connected in series with the grid and a three-phase machine with open-ended windings.

VSI

Voltage sag The proposed system is suitable for applications in which no frequency variation is required, like large pumps or fans. The VSI dc-link voltage operates as a floating capacitor through the energy minimized compensation (EMC) technique, in which there is no dc source or injection transformer. The motor load condition determines the minimum grid voltage positive component (sag severity) to keep EMC operation.

THD

Voltage sag Meanwhile, a voltage unbalance may increase the dc-link voltage requirements. A 1.5-hp four-pole induction motor has been used to verify the ride-through capability of the proposed compensator under grid voltage disturbances. A total harmonic distortion (THD) analysis of grid currents demonstrates that the proposed system provides low THD even if no passive filter is used.

CONTROL

Voltage sag The operating principle, converter output voltage analysis, pulse width modulation technique, control strategy, and components ratings are discussed as well. Simulation and experimental results are presented to demonstrate the feasibility of the system.

KEYWORDS:

  1. Floating capacitor
  2. Induction motor
  3. Series compensator
  4. Unbalanced voltage sag

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. Block diagram of feedback small-signal model.

EXPECTED SIMULATION RESULTS:

Fig. 2. Simulation waveforms at the steady state and half load with perphase grid (vga ) and load (vla ) voltages, as well as the converter’s line-to-line voltage (vcab ).

Fig. 3. Simulation waveforms with the proposed series compensator under balanced voltage sag at half load. (a) Rated grid voltages. (b) Three-phase voltage sag of 80%.

Fig. 4. Simulation waveforms with the proposed series compensator under unbalanced voltage sag:Fd = 15%and half load. (a) Grid voltages and currents. (b) DC-link voltage, torque, and speed.

CONCLUSION:

Voltage sag The proposed system has unbalanced voltage sag ride-through capability, being suitable for grid-connected induction motors applications. Indeed, the simulation and experimental results supported the theoretical analysis. A conventional three-phase VSI using a floating dc-link capacitor has been applied as a series compensator.

H-BRIDGE

Voltage sag Besides that, the proposed system does not require any additional passive filter, injection transformer, or extra power supply. A conventional three-phase H-bridge converter to compensate balanced grid voltage disturbances has recently been proposed in the literature. Compared to the conventional solution, the proposed one has a lower number of components, a single dc link, and can deal with unbalanced voltages without a complex control strategy.

MOTORS

Voltage sag The higher dc-link voltage requirement of the proposed series compensator was highlighted as its main drawback. Although the proposed solution provided higher THD of grid currents, its levels were acceptable. Hence, the proposed system can be easily integrated along with standard squirrel-cage induction motors when no frequency variation is required.

REFERENCES:

[1] H. G. Sarmiento and E. Estrada, “A voltage sag study in an industry with adjustable speed drives,” IEEE Ind. Appl. Mag., vol. 2, no. 1, pp. 16–19, Jan. 1996.

[2] K. Pietilainen, L. Harnefors, A. Petersson, and H. Nee, “DC-link stabilization and voltage sag ride-through of inverter drives,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1261–1268, Jun. 2006.

[3] A. H. Bonnett and H.M. Glatt, “Ten things you should know about electric motors: Their installation, operation, and maintenance,” IEEE Ind. Appl. Mag., vol. 24, no. 6, pp. 25–36, Nov. 2018.

[4] G.C. Jaiswal, M. S. Ballal, D. R. Tutakne, and H. M. Suryawanshi, “Impact of power quality on the performance of distribution transformers: A fuzzy logic approach to assessing power quality,” IEEE Ind. Appl.Mag., vol. 25, no. 5, pp. 8–17, Sep. 2019.

[5] “IEEE Recommended Practice for Monitoring Electric Power Quality”, IEEE Std 1159-2009 (Revision of IEEE Std 1159-1995), pp. c 1–81, Jun. 2009.

Current and Speed Sensor Fault Diagnosis Method Applied to Induction Motor Drive BTech/Mtech Final Year

ABSTRACT:

Induction Motor The paper proposes a novel approach based on a current space vector derived from measured stator currents to diagnose speed and current sensor failures in the field-oriented control of induction motor drives. A comparison algorithm between the reference and measured rotor speed is used to detect the speed sensor faults.

FTC

Induction Motor A counter is added to eliminate the influence of the encoder noise in the diagnosis method. In this approach, estimated quantities are not used in the proposed speed sensor fault diagnosis strategy, which increases the independence between the diagnosis stages in the fault-tolerant control (FTC) method.

MATLAB

Induction Motor Moreover, in order to discriminate between the speed sensor faults and the current sensor faults, a new approach combining the current space vector and a delay function is proposed to reliably determine the current sensor failures. The MATLAB-Simulink software was used to verify the idea of the proposed method.

DSP

Induction Motor Practical experiments with an induction motor drive controlled by DSP TMS320F28335 were performed to demonstrate the feasibility of this method in practice. The simulation and experimental results prove the effectiveness of the proposed diagnosis method for induction motor drives.

KEYWORDS:

  1. Fault-tolerant control
  2. Diagnosis
  3.  Induction motor
  4.  FOC
  5. Sensorless control

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Figure 1. Block Diagram of FTC Unit.

EXPECTED SIMULATION RESULTS:

Figure 2. Simulation Results – Speed Sensor Fault _ FTC.

Figure 3. Simulation Results _ Scaling Current Sensor Fault _ FTC.

Figure 4. Simulation Results _ Total Current Sensor Fault _ FTC.

CONCLUSION:

Induction Motor This paper presents a novel diagnosis method for the speed and current sensor fault-tolerant control of induction motor drives. The proposed method has proven its effectiveness in dealing with multi-type sensor failures. The speed sensor fault diagnosis algorithm can reliably detect the inaccuracy of the speed sensor signals without interference by random pulse noises.

FAULT

Induction Motor The loss of the current sensor signals, which is the most severe current sensor fault, is quickly detected by the delay-algorithm. Other types of current sensor failures is reliably identified without misunderstanding with a speed sensor fault. The proposed diagnosis algorithm is simpler than other existing detection methods, and thus

SENSER

the computational hardware system executes faster as well as cheaper due to the lower calculation burden for the same operating conditions. The simulation and experimental results have demonstrated the efficiency of the proposed method. Further research can be implemented to improve the diagnosis of the sensor faults in transient states.

REFERENCES:

[1] A. Gouichiche, A. Safa, A. Chibani, and M. Tadjine, “Global fault-tolerant control approach for vector control of an induction motor,” Int. Trans. Electr. Energy Syst., vol. 30, no. 8, Aug. 2020, Art. no. e12440, doi: 10.1002/2050-7038.12440.

[2] D. Diallo, M. E. H. Benbouzid, and M. A. Masrur, “Special section on condition monitoring and fault accommodation in electric and hybrid propulsion systems,” IEEE Trans. Veh. Technol., vol. 62, no. 3, pp. 962_964, Mar. 2013, doi: 10.1109/TVT.2013.2245731.

[3] A. A. Amin and K. M. Hasan, “A review of fault tolerant control systems: Advancements and applications,” Measurement, vol. 143, pp. 58_68, Sep. 2019, doi: 10.1016/j.measurement.2019.04.083.

[4] A. Raisemche, M. Boukhnifer, C. Larouci, and D. Diallo, “Two active fault-tolerant control schemes of induction-motor drive in EV or HEV,” IEEE Trans. Veh. Technol., vol. 63, no. 1, pp. 19_29, Jan. 2014, doi:  10.1109/TVT.2013.2272182.

[5] Y. Azzoug, A. Menacer, R. Pusca, R. Romary, T. Ameid, and A. Ammar, “Fault tolerant control for speed sensor failure in induction motor drive based on direct torque control and adaptive stator _ux observer,” in Proc. Int. Conf. Appl. Theor. Electr. (ICATE), Oct. 2018, pp. 1_6.

Combined Speed and Current Terminal Sliding Mode Control with Nonlinear Disturbance Observer for PMSM

ABSTRACT:

Speed and Current A terminal sliding mode control method based on nonlinear disturbance observer is investigated to realize the speed and current tracking control for PMSM drive system in this paper. The proposed method adopts the speed-current single-loop control structure instead of the traditional cascade control in the vector control of PMSM.

PMSM

Speed and Current Firstly, considering the nonlinear and the coupling characteristic, a single-loop terminal sliding mode controller is designed for PMSM drive system through feedback linearization technology. This method can make the motor speed and current reach the reference value in finite time, which can realize the fast transient response.

SLIDING MODE

Speed and Current Although the sliding mode control is less sensitive to parameter uncertainties and external disturbance, it may produce a large switching gain, which may cause the undesired chattering. Meanwhile, the sliding mode control cannot keep the property of invariance in the presence of unmatched uncertainties. Then, a nonlinear disturbance observer is proposed to the estimate the lump disturbance

CONTROL

Speed and Current which is used in the feed-forward compensation control. Thus, a composite control scheme is developed for the PMSM drive system. The results show that the motor control system based on the proposed method has good speed and current tracking performance and strong robustness.

KEYWORDS:

  1. PMSM drive
  2. Terminal sliding mode control
  3. Feedback linearization
  4. Nonlinear disturbance observer

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Figure 1: Block Diagram Of PMSM Control System

EXPECTED SIMULATION RESULTS:

Figure 2: The Motor Response Waveforms Of The Proposed Method: (A) Motor Speed (B) Dq-Axes Current (C) Phase Current

Figure 3: The Speed Waveforms Of Three Methods: (A) The Speed When The Motor Starts (B) The Speed With Load Disturbance

Figure 4: The Motor Waveforms With The Parameter Disturbance. (A) Motor Speed (B) Dq-Axes Current

Figure 5: The Contrastive Results With The Common Sliding Mode Control Method. (A) D-Axes Current (B) Q-Axes Current

CONCLUSION:

Speed and Current In this paper, a novel control method based on terminal sliding mode control through feedback linearization technology has been studied for PMSM drive system. The controller adopts the speed-current single-loop structure, which has the fast transient response.

SPEED

Speed and Current With the designed terminal sliding mode controller, the speed and current stabilizing control is achieved. Then, considering the lump disturbance in the drive system, a nonlinear disturbance observer is designed to deal with the mismatched disturbance, and it is used for the feed-forward compensation, and the robustness is improved effectively.

CURRENT

Speed and Current Simulation results have proved that the controller has good robust performance and speed tracking performance under various conditions. But the speed and current control problems in the flux-weakening control areas are not considered at present, which will be the future research emphases.

REFERENCES:

 [1] J. Yu, P. Shi and L. Zhao, “Finite-time command filtered backstepping control for a class of nonlinear systems,” Automatica, vol. 2018, no. 92, pp. 173–180, Jun. 2018.

[2] T. Li and Y. V. Rogovchenko, “Oscillation criteria for second-order superlinear Emden–Fowler neutral differential equations,” Monatshefte f´l´zr Mathematik, vol. 184, no. 3, pp. 489–500, Apr. 2018.

[3] A. Darba, F. D. Belie and P. D. Haese, “Improved dynamic behavior in BLDC drives using model predictive speed and current control,” IEEE Trans. On Industrial Electronics, vol. 63, no. 2, pp. 728–740, Sep. 2016.

[4] X. Lang, M. Yang and J. Long, “A novel direct predictive speed and current controller for PMSM drive,” Proceedings of 8th International Power Electronics and Motion Control Conference, Hefei, China, pp. 2551–2556, May. 2016.

[5] S. Katsuji, M. Yoshitaka and I. Toshiyuki, “Singularity-free adaptive speed tracking control for uncertain permanent magnet synchronous motor,” IEEE Trans. On Power Electronics, vol. 31, no. 2, pp. 1692–1701, Apr. 2015.