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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.

A Nested Control Strategy for Single Phase Power Inverter Integrating Renewable Energy Systems in a Microgrid

ABSTRACT:  

In this paper a nested power-current-voltage control scheme is introduced for control of single phase power  inverter, integrating small-scale renewable energy based power generator in a microgrid for both stand-alone and grid-connected modes. The interfacing power electronics converter raises various power quality issues such as current harmonics in injected grid current, fluctuations in voltage across the local loads, voltage harmonics in case of non-linear loads and low output power factor.

The proposed nested proportional resonant current and model predictive voltage controller aims to improve the quality of grid current and local load voltage waveforms in grid-tied mode simultaneously by achieving output power factor near to unity. In stand-alone mode, it strives to enhance the quality of local load voltage waveform. The nested control strategy successfully accomplishes smooth transition from grid-tied to stand-alone mode and vice-versa without any change in the original control structure. The performance of the controller is validated through simulation results.

KEYWORDS:
  1. Microgrid
  2. Stand-alone mode
  3. Grid-connected mode
  4. Voltage harmonics
  5. Current harmonics
  6. Proportional resonant control
  7. Model predictive control

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. Block diagram of MPVC scheme

EXPECTED SIMULATION RESULTS:

 Fig. 2(a). Steady state grid voltage, load voltage and grid current waveforms with resistive load

Fig. 3(b). Steady state grid voltage, load voltage and grid current waveforms with non-linear load

Fig. 4. THD values of voltage and current waveforms in grid connected mode

Fig. 5(a). Steady state grid voltage, load voltage and filter current waveforms with resistive load

Fig. 6 (b). Steady state grid voltage and load voltage waveforms with non-linear Load

Fig. 7. THD values of load voltage waveform in stand-alone mode

Fig. 8(a). Transient state grid voltage, load voltage and grid current waveforms with change in active power reference

Fig. 9(b). Transient state grid voltage, load voltage and grid current waveforms with change in reactive power reference

Fig. 10(c). Grid voltage, load voltage and grid current waveforms during voltage Sag

(a) Transfer from stand-alone to grid-tied mode

(b) Transfer from grid-tied to stand-alone mode

Fig.11. Grid voltage, load voltage, filter inductor current, grid current

Waveforms

(a) Transfer from stand-alone to grid-tied mode

(b) Transfer from grid-tied to stand-alone mode

Fig.12. Grid current tracking error waveforms

CONCLUSION:

 

In this paper, a nested proportional resonant current and model predictive voltage controller is introduced for control of single phase VSI integrating a RES based plant in a microgrid. This strategy improves the quality of local load voltage and grid current waveforms with both linear and non linear loads. A non-linear load such as the diode bridge rectifier introduces voltage harmonics, but this scheme is successful in achieving low THD values for inverter local load voltage and grid current simultaneously. Simulation results validates the outstanding performance of the proposed controller in both steady state and transient state operations. A smooth transfer of operation modes from stand-alone to grid-tied and vice versa is also achieved by the nested control scheme without changing the control algorithm.

 REFERENCES:

[1] H. Farhangi, “The path of the smart grid,” IEEE Power and Energy Magazine, vol. 8, no. 1, pp. 18-28, Jan/Feb. 2010.

[2] F. Blaabjerg, Z. Chen, and S. B. Kjaer, “Power electronics as efficient interface in dispersed power generation systems,” IEEE Trans. Power Electron., vol. 19, no. 5, pp. 1184–1194, Sep. 2004.

[3] F. Blaabjerg, R. Teodorescu, M. Liserre, and A. V. Timbus, “Overview of control and grid synchronization for distributed power generation systems,” IEEE Trans. on Ind. Electron., vol. 53, no. 5, pp. 1398–1409,  Oct. 2006.

[4] Q. C. Zhong and T. Hornik, “Cascaded Current–Voltage Control to Improve the Power Quality for a Grid-Connected Inverter With a Local  Load,” IEEE Transactions on Ind. Electron., vol. 60, no. 4, pp. 1344- 1355, April 2013.

[5] Y Zhilei, X Lan and Y Yangguang, “Seamless Transfer of Single-Phase Grid-Interactive Inverters Between Grid-Connected and Stand-Alone  Modes,” IEEE Transactions on Power Electronics, vol. 25, no. 6, pp. 1597-1603, June 2010.