Starting from the principle of instantaneous power theory, this article explores various direct power control (DPC) strategies for three-phase two-level pulsewidth modulation (PWM) converters. After summarizing the fundamental power formula of PWM rectifiers, this article studies the operating principle of the conventional table-based approach and its related improvements. It further looks into the advanced counterparts employing space vector modulation and different nonlinear control strategies. The emphasis is put on the prevailing predictive DPC. Besides, the voltage-sensorless and robust DPC methods based on the virtual flux concept and the state observer or estimator are investigated. Critical issues, including the sample delay, constant switching frequency, duty cycle optimization, objective function, and unbalanced operation are examined.

A Review on Direct Power Control of Pulsewidth Modulation Converters

Strong parameter dependence and large current ripple are two shortcomings that obstruct the development of model predictive control for voltage source inverters (VSIs). To solve these problems, in this article,a double-vector model-free predictive control (MFPC) method for VSI with visualization analysis is proposed. First, the ultralocal model of the VSI is established and a full-order sliding model observer is designed to estimate the lumped disturbance. MFPC is achieved with enhanced parameter robustness. Then, a double-vector MFPC method is further proposed. By applying two vectors per control period, the current ripple is reduced significantly. A detailed visualization analysis method is proposed, which verifies the effectiveness of the proposed double-vector MFPC method in theory. Besides, the proposed visualization analysis method has the potential to be used to analyze the effectiveness of other types of multivector model predictive control method with different cost functions. Detailed comparative experimental studies verify the effectiveness of the proposed double-vector MFPC method. 

Double-Vector Model-Free Predictive Control Method for Voltage Source Inverter With Visualization Analysis

Except for the benefits of mimicking synchronous generators, virtual synchronous generators (VSGs) still face particular small- and large-disturbance stability problems in practical applications. Therein, the large-disturbance stability is a much more dramatic and intractable problem, such as a short circuit or line tripping. Accordingly, this article explores this challenging problem from the perspective of output impedance optimization for VSGs. First, focusing on the potential risk of overcurrent during the electromagnetic process, the dynamic performances of different virtual impedance control structures used in VSGs are comparatively analyzed, and the output impedance boundary for suppressing the transient current is derived. Then, the output impedance boundaries for maintaining electromechanical transient stability are discussed in depth by using Lyapunov stability theory, which provides novel virtual impedance optimal design guidelines exclusively for the large-disturbance stability. Furthermore, a large-disturbance stability enhancement strategy based on adaptive virtual impedance control is proposed, which can limit the electromagnetic transient current and improves the electromechanical transient stability simultaneously. Finally, experiments validate the accuracy of the theoretical analysis and the effectiveness of the proposed strategy.

Analysis and Improvement of Large-Disturbance Stability for Grid-Connected VSG Based on Output Impedance Optimization

To simplify the tuning process and enhance the parameter robustness of the model predictive control (MPC) for grid-connected inverters, an improved phase-locked loop (PLL) and a new inductance online identification method are proposed in this article. First, an improved finite control set-based PLL is presented, which removes the PI controller, simplifying the tuning process. Next, a new inductance online identification method based on a model reference adaptive system is presented, which takes the actual grid voltage and the estimated one as the reference model and adjustable model, respectively. Here, a sliding mode observer (SMO) is proposed to estimate the grid voltage, and the inductance adaptive law is deduced by exploring the effects of the inductance on the estimated grid voltage. Furthermore, the sensitivity of the proposed SMO to frequency deviation is also analyzed, which shows that a proper sliding mode gain can be selected to reduce the effects of frequency deviation. Thus, a fixed grid frequency of 50 Hz can be used in the proposed SMO, indicating that it is not required to calculate the actual grid frequency using differential operation any more. Finally, experimental studies verify the validity of the proposed strategies.

An Inductance Online Identification-Based Model Predictive Control Method for Grid-Connected Inverters With an Improved Phase-Locked Loop

An augmented state observer-based sensorless control of grid-connected inverters under grid faults

A model predictive control (MPC) strategy for grid-connected power converters is proposed in this article to reduce the current ripples without using ac voltage sensors. First, a sliding-mode observer is designed to estimate the grid voltage with an adaptive compensation algorithm. Thus, grid-voltage sensors are removed. The novelty of the proposed grid-voltage observation method is that it is inherent frequency adaptive without using the accurate grid angular frequency. By implementing the MPC strategy using the estimated grid voltage, the current ripples are reduced, especially under distorted grid-voltage conditions because of the low-pass filter used in the observer. Next, to further reduce the current ripples, a double-voltage vector-based MPC strategy is proposed based on the principle of the modulated MPC. Detailed theoretical analyses are also carried out to show its effectiveness for the first time. Finally, experimental studies are carried out to verify the validity of the proposed ac voltage sensorless MPC strategy.

A Model Predictive Control Method for Grid-Connected Power Converters Without AC Voltage Sensors

Recently, model predictive control has been widely used to control grid-connected inverters due to its advantages. However, the conventional model predictive control methods usually require two AC voltage and current sensors to sample the grid voltages and currents. Particularly, the DC voltage sensor is also required to calculate the values of the voltage vectors. The inverter will lose its stability once these sensors fail. Thus, in this study, to improve the operational reliability of the grid-connected inverters, a sliding mode observer-based AC voltage sensorless model predictive control is proposed. First, a sliding mode observer is designed to estimate the grid voltage. Next, a new adaptive compensation strategy is proposed to remove the phase and amplitude errors caused by the low-pass filter. The main novelty of this compensation strategy is that it is immune to the actual grid frequency. Therefore, the grid voltage can be identified accurately, even under frequency deviation conditions. Besides, the stability and parameter designing method of the sliding mode observer is also analysed to provide a theoretical basis for its implementation. Finally, based on the observed grid voltage, the AC voltage sensorless model predictive control is achieved. Detailed comparative experimental results show the validity of the proposed method.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-pel.2019.1075

Sliding mode observer-based AC voltage sensorless model predictive control for grid-connected inverters

Conventional model predictive control (MPC) strategy for voltage source inverters has large current ripple since only one voltage vector is applied in each control cycle. Although the multi-vector modulated MPC strategy can reduce the current ripple, its lack of theoretical basis makes its optimality questionable. To solve this problem, a new visualization analysis method is proposed in this article to evaluate the effectiveness of the conventional multi-vector modulated MPC strategy, based on which a new hybrid multi-vector modulated MPC strategy is further presented to improve the current control performance. First, the principle of the proposed visualization analysis method is introduced, based on which it is verified that the conventional multi-vector modulated MPC has better control performance than the single-vector MPC. Second, based on the proposed visualization analysis method, it is also found that both the conventional double-vector and triple-vector modulated MPC strategies cannot achieve optimal control. Thus, a new hybrid multi-vector modulated MPC strategy is further proposed. By selecting an optimal voltage vector from 18 alternative voltage vector combinations, the current control performance is improved. Detailed comparative experimental results based on experimental platform and StarSim hardware-in-the-loop platform validate the effectiveness of the proposed hybrid multi-vector modulated MPC strategy.

Hybrid Multi-Vector Modulated Model Predictive Control Strategy for Voltage Source Inverters Based on a New Visualization Analysis Method

In recent years, virtual synchronous generator (VSG) control algorithm has been widely studied in distributed grid-connected power inverters to deal with active and reactive power adjustment by simulating the operation principle of the actual synchronous generator. Nevertheless, once the current sensor fails, the grid-connected power inverter will lose its stability. To enhance the fault-tolerant operation ability of the grid-connected power inverter, a new inverter side current sensor-less model predictive control method based on sliding mode observer (SMO) is proposed. The stability of the SMO is analyzed by using the Lyapunov stability criterion. The gains of the SMO are then designed by analyzing their influences on the current and voltage observation based on the closed-loop transfer functions. Besides, to show the influences of the capacitance changes on the current observation, a detailed parameter sensitivity analysis is also carried out. Then, by using the estimated inductance current from the SMO, a current sensor-less model predictive control method is finally proposed. The main novelty of the proposed method is that both the output voltage of the inverter and the inverter side inductance are not required in the proposed SMO. So, the accuracy of current sensor-less control is improved. To verify the effectiveness of the proposed method, detailed experimental studies are carried out based on a Typhoon and PE-Expert 4 based experimental platform.

/pdf/current-sensor-less-virtual-synchronous-generator-model-2dslrjt535.pdf

Yanyan Li

Papers

A Model Predictive Control Method for Grid-Connected Power Converters Without AC Voltage Sensors

An Inductance Online Identification-Based Model Predictive Control Method for Grid-Connected Inverters With an Improved Phase-Locked Loop

Sliding mode observer-based AC voltage sensorless model predictive control for grid-connected inverters

Hybrid Multi-Vector Modulated Model Predictive Control Strategy for Voltage Source Inverters Based on a New Visualization Analysis Method

Current Sensor-Less Virtual Synchronous Generator Model Predictive Control Based on Sliding Mode Observer