This book introduces a family of large-signal stability-based control methods for different power inverters (grid-connected inverter, standalone inverter, single-phase inverter, and three-phase inverter) in practical applications. Power inverters have stability issues, which include the inverter's own instability as well as the inverter's instability in relation to the other power electronic devices in the system (i.e., weak grid and the EMI filter). Most of the stability analyses and solutions are based on small-signal stability technology. Unfortunately, in actuality, the majority of practical instability concerns in power inverter systems are large-signal stability problems, which, when compared to small-signal stability problems, can cause substantial damage to electrical equipment.
As a result, researchers must conduct a comprehensive investigation of the large-signal stability challenge and solutions for power inverters. This book can be used as a reference for researchers, power inverters manufacturers, and end-users. As a result, the book will not become obsolete in the near future, regardless of technology advancements.
Author(s): Xin Zhang, Jinsong He, Hao Ma, Zhixun Ma, Xiaohai Ge
Publisher: Springer
Year: 2022
Language: English
Pages: 174
City: Singapore
Preface
Contents
About the Authors
1 Introduction
1.1 Significance of DG in MGs
1.2 Categories of MGs
1.2.1 AC MG
1.2.2 DC MG
1.2.3 Hybrid MG
1.3 The Cornerstone of MGs: Power Inverters
1.3.1 Grid-Connected Inverters: L or LCL Filtered Inverters
1.3.2 Standalone Inverters: LC-Filtered Inverter
1.3.3 Grid-Connected and Standalone Inverters Cascaded with LC Input Filters
1.4 The Necessity of Large-Signal Stability Analysis in Control of Inverters
1.4.1 Stability Problems of Inverters and the Existing Small-Signal Stability Analysis
1.4.2 The Necessity of Large-Signal Stability Analysis
1.4.3 Existing Large-Signal Stability Analysis of Inverters Via Lyapunov’s Theory
1.4.4 The Motivation of This Book: Advanced Control Strategies for the Power Inverter to Improve Its Large-Signal Stability
References
2 Adaptive Backstepping Current Control of Single-Phase LCL-Grid-Connected Inverters to Improve Its Large-Signal Stability in the Presence of Parasitic Resistance Uncertainty
2.1 Introduction
2.2 Mathematical Modelling
2.3 Derivation of Proposed Control Scheme
2.3.1 Step I: Derivation of Pseudo Reference x2ref(t) and Adaptive Law 1
2.3.2 Step II: Derivation of Pseudo Reference x3ref(t) and Adaptive Law 2
2.3.3 Step III: Derivation of Control Law µ(t) and Adaptive Law 3
2.4 Test Results
2.5 Conclusion
References
3 An Adaptive Dual-Loop Lyapunov-Based Control Scheme for a Single-Phase Stand-Alone Inverter to Improve Its Large-Signal Stability
3.1 Introduction
3.2 Mathematical Modelling
3.2.1 Average Model of the Investigated System
3.2.2 Load Voltage Reference
3.2.3 Current-Loop Reference
3.2.4 Model of the Load Current
3.3 Proposed Adaptive Dual-Loop Lyapunov-Based Control Scheme
3.3.1 The Proposed Lyapunov Function
3.3.2 Derivation of the Adaptive Dual-Loop Control Law
3.3.3 Implementation of Proposed Control Scheme
3.4 Stability Analysis and Robustness Verification
3.4.1 Stability Analysis
3.4.2 Robustness Against Plant Parametric Variations
3.5 Test Results
3.5.1 Steady-State and Dynamic Performance Evaluation
3.5.2 Overload and Recovery Scenario
3.6 Conclusion
References
4 Lyapunov-Based Control of Three-Phase Stand-Alone Inverters to Improve Its Large-Signal Stability with Inherent Dual Control Loops and Load Disturbance Adaptivity
4.1 Introduction
4.2 Preliminary of the Proposed Adaptive Dual-Loop Lyapunov-Based Control: Mathematical Modelling
4.2.1 Average Model of the Investigated System
4.2.2 Load Voltage References vodref, voqref, and Inductor Current References iLdref, iLqref
4.2.3 Model of the Load Currents and Proposed Adaptive Laws
4.2.4 Modified Inductor Current References iLdref, iLqref Incorporated with Adaptive Laws
4.3 Derivation of Proposed Adaptive Decoupled Dual-Loop Lyapunov-Based Control Scheme
4.3.1 Proposed Weighted All-in-One Lyapunov Function V
4.3.2 Derivation of the Switching Functions and Adaptive Laws
4.4 Implementation of Proposed Control Scheme and Its Resulted dq Decoupled Error Dynamics
4.4.1 Block Diagram of the Proposed Control Scheme
4.4.2 Decoupled Error Dynamics in d Frame and q Frame
4.4.3 Recommended Way to Set Load Voltage References
4.5 Stability Analysis and Controller Design Guidelines
4.5.1 Closed-Loop System Stability Proof
4.5.2 Power Loss Analysis, Switching Frequency (fs) Selection and Output LC Filter Design
4.5.3 Controller Gains Selection Via Poles Placement
4.6 Test Results
4.6.1 Performance of Proposed Approach
4.6.2 Comparisons Between the Proposed Approach and Existing Control Schemes
4.7 Conclusion
References
5 An Ellipse-Optimized Composite Backstepping Control Strategy for a Point-of-Load Inverter to Improve Its Large-Signal Stability Under Load Disturbance in the Shipboard Power System
5.1 Introduction
5.2 Preliminary of the Ellipse-Optimized Composite Backstepping Controller: Mathematical Modelling
5.2.1 Dynamic Equations of the Investigated POL Inverter
5.2.2 Control Objectives: Load Voltage References x1*, x3*
5.3 Recursive Derivation and Implementation of the Proposed Composite Backstepping Controller
5.3.1 Two-Step Backstepping Derivation in d Frame
5.3.2 Two-Step Backstepping Derivation in q Frame
5.3.3 Design of the Kalman Filter to Estimate and Feedforward the Load Currents for Load Disturbance Rejection
5.3.4 Implementation of the Proposed Composite Backstepping Controller with a Kalman Filter
5.4 Ellipse-Based Controller Gains Optimization, Feedback Gains Matrix Selection, and Robustness Analysis
5.4.1 Proposed Intuitive Ellipse-Based Strategy to Optimize the Controller Parameters with Fully Consideration of ξ and ωn
5.4.2 Quantitative Selection of the Feedback Gain Matrix G of the Kalman Filter Aided by Ellipse-Optimized Strategy
5.4.3 Robustness Analysis of the Proposed Control Scheme Under Parametric Variations and Measurement Errors
5.5 Test Results
5.5.1 Effectiveness of the Proposed Ellipse-Optimized Controller Gains Selection Strategy
5.5.2 Robustness Tests Under Plant Parametric Variations
5.5.3 Performance Evaluation Under Linear/Nonlinear Load Step, Reference Step, Overload and Recovery
5.5.4 Comparisons Between Existing Lyapunov-Based Approaches and the Proposed Control Scheme
5.6 Conclusion
References
6 Stability Constraining Dichotomy Solution Based Model Predictive Control for the Three Phase Inverter Cascaded with Input EMI Filter in the MEA
6.1 Introduction
6.2 Instability Problem of the Researched AC Cascaded System in MEA
6.2.1 Instability Problem Description
6.2.2 The Instability Reason of CPL with LC Input Filter
6.3 Preliminary of the SCDS-MPC Method: Mathematical Modeling of the Researched AC Cascaded System in MEA
6.3.1 Conventional Inverter Mathematical Model
6.3.2 Improved Mathematical Model with Consideration of the Inverter and Input EMI Filter for Stability Analysis
6.4 The Proposed SCDS-MPC Method
6.4.1 Conventional Model Predictive Control Scheme
6.4.2 Proposed Dichotomy Solution (DS) Based Model Predictive Control
6.4.3 Proposed System Stability Constraining Cost Function Definition
6.4.4 Sensitivity Analysis of Model Parameters Variation
6.5 Test Results
6.6 Conclusion
References
7 Composite-Bisection Predictive Control to Stabilize the Three Phase Inverter Cascaded with Input EMI Filter in the SPS
7.1 Introduction
7.2 Mathematical Modeling
7.3 Conventional FCS MPC and Problem Formulation
7.4 Proposed Composite Bisection Predictive Control
7.4.1 Structure of the Proposed CB-PC Scheme
7.4.2 Improved Generic DC-Link Stabilization Strategy Based on Instantaneous Power Theory
7.4.3 Indirect Voltage Control Strategy to Achieve Better Transient Response Inspired by the Deadbeat Control
7.4.4 Modified Bisection Algorithm
7.5 Test Results
7.5.1 Effectiveness of the Improved Generic Stabilization Method
7.5.2 Transient Performance of the Indirect Voltage Control in Comparison with the Existing Direct Voltage Control
7.5.3 Performance of the Proposed CB-PC Under Droop-Akin Strategy With/Without Delay Compensation
7.5.4 Comparisons Between the Proposed CB-PC and Existing MPC
7.6 Conclusion
References
8 Reinforcement Learning Based Weighting Factors’ Real-Time Updating Scheme for the FCS Model Predictive Control to Improve the Large-Signal Stability of Inverters
8.1 Introduction
8.2 Weighting Factors Selection in FCS MPC Affects System Stability
8.2.1 Particular Case: WFstability Selection is a Trade-Off Between DC-Link Stabilization and Load Voltage Tracking
8.2.2 Generalized Case: WFs Selection Affects System Stability (WFstability in Particular)
8.3 WFs’ Real-Time Updaing Via the Reinforcement Learning-Based Approach to Improve System Stability
8.3.1 Structure of the Proposed Approach
8.3.2 RL Agent and Its Selection
8.3.3 RL-Based Approach Using a DDPG Agent and Artificial Neural Networks
8.4 Verification on the Particular Case: Improving Tracking Accuracy While Ensuring DC-Link Stabilization
8.4.1 Configuration of the Observation, Reward, and ANN
8.4.2 Parameter Settings and Training Results
8.4.3 Test Results
8.5 Conclusion
References
