Predictive Direct Power Control Research Articles

Neural Network Quadrature Signal Generator-Based Virtual Flux Estimation for Predictive Control of PWM Converters

In this paper, a frequency-adaptive neural network-based virtual flux (FANN-VF) estimator is developed for sensorless control of a pulse-width modulation converter under unbalanced and distorted grid conditions. This method exploits two parallel FANNs configured as quadrature signal generators. The VF fundamental positive and negative sequence components (PNSCs) are inherently separated in the estimator without employing any cascaded filters. A frequency-locked loop is introduced to accurately track the frequency variations. The estimated VF PNSCs are directly exploited to compute the power references in VF-based predictive direct power control scheme. The developed strategy ensures constant active power and sinusoidal current waveforms under nonideal grid conditions. Simulation and experimental tests are performed to verify the effectiveness of the proposed method.

High-Efficient Direct Power Control Scheme Using Predictive Virtual Flux for Three-Phase Active Rectifiers

In recent years, the pulse-width-modulation (PWM) converter has been found to have extensive applications in renewable energy, industrial fields, and others. The high efficiency requirement is crucial to operating a PWM rectifier in various applications, in addition to the fundamental control objectives of sinusoidal grid currents and the correct DC bus voltage. Additionally, in practical application, another issue arises when the grid voltage frequently experiences distortion, leading to a distorted grid current and a significant rise in total harmonic distortion (THD). To resolve these problems, a model predictive virtual flux-based direct power control (MPVFDPC) with improved power loss performance is proposed based on an integrated switching state predetermination strategy. The proposed MPVFDPC for PWM rectifier inherits the merits of both virtual flux control and direct power control, which have fast dynamic performance and the grid current THD is considerably decreased under distorted grid voltage states. The proposed technique aims to minimize switching loss under ideal and distorted grid voltage states by exploiting the discontinuous modulation concept by using a switching state predetermination strategy. The MPVFDPC with switching state predetermination strategy is proven by employing it in experiments as well as simulations in comparison with previous models: predictive direct power control (Conv. MPDPC) and conventional MPVFDPC (Conv. MPVFDPC). The acquired waveforms and quantitative data are employed to prove the effectiveness of the developed algorithm.

An enhanced control scheme for multifunctional grid connected PV system using fuzzy and predictive direct power control

This paper presents a combination between a fuzzy logic control (FLC) and a predictive direct power control for multifunctional grid connected photovoltaic (PV) system, to solve the oscillation problem in the DC link voltage of the in-verter caused by the fast irradiation changing. The whole system consists of a PV system which interface a DC-AC inverter, a FLC maximum power point tracking (MPPT) algorithm has been adopted to operate the DC-DC converter at the MPP. The predictive control strategy is applied to the DC-AC inverter with FLC in its voltage control loop to improve the power exchange between the grid and the PV system. Simulation results have been verified through MATLAB/Simulink software for the purpose of giving the effectiveness of the suggested control against existed controllers.

New combined control strategy of on-board bidirectional battery chargers for electric vehicles

This paper aims to develop a bidirectional on-board battery charger for electric vehicles (EVs). The studied battery charger is composed of a bidirectional ac-dc converter as the first stage of conversion and a bidirectional dc-dc converter as the second stage. The first one is controlled by a predictive direct power control strategy based on a space vector modulation technique known as P-SVM-DPC, and the second is used to regulate the battery current and regulate the power direction flow by using a direct current control technique. The choice of its topology has taken into consideration the grid-to-vehicles (G2V) and vehicle-to-grid (V2G) power flow directions. During charging or discharging, the DC/DC converter acts likes a buck or boost converter. Using MATLAB/Simulink software, the performance of the battery charger is examined in various operating modes, such as fast charging and quick discharging.

Model predictive direct power control of energy storage quasi-Z-source grid-connected inverter

Model predictive direct power control of energy storage quasi-Z-source grid-connected inverter

Mode Predictive Direct Power Control Scheme with Fixed Operation Frequency for a Vienna Rectifier Based on Voltage‐Sensorless

Model predictive direct power control (MPDPC) is a suitable method for the highly nonlinear system of a three‐level Vienna rectifier. However, the traditional MPDPC suffers from irregular switching states, which can decrease current tracking accuracy and generate high current ripple and electromagnetic noise. Additionally, the use of sensors can increase system hardware costs and complexity. To enhance operational reliability and reduce the fault impact of AC voltage sensors, a double closed‐loop control strategy based on voltage‐sensorless MPDPC in the inner loop and sliding mode control (SMC) in the outer loop is proposed. First, the virtual flux model of the three‐phase Vienna rectifier is established in the two‐phase static coordinate system, and the second‐order low‐pass filter is used to improve the voltage observer to estimate the grid‐voltage. Meanwhile, an improved grid voltage observer based on the second‐order low‐pass filter is designed to improve the observation effect. Second, to solve the variable switching operation, current fluctuation and wide harmonic spectrum caused by the traditional MPDPC effectively, a double closed‐loop control strategy with constant switching frequency based on voltage‐sensorless MPDPC in the inner loop and SMC control in the outer loop is proposed. Based on the principle of SVPWM modulation, the modulation voltage of SVPWM is calculated directly, in order to verify the correctness of the theoretical analysis, extensive simulation and matching experimental is presented to verify the correctness of the theoretical analysis. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Research on protection and control technology of power electronic transformers

As the key equipment in the modern power system, the power electronic transformer's performance and reliability play a vital role in the stable operation of the power system. This paper studies the soft switching technology, control technology and fault protection method of power electronic transformer. First, the soft switching technology of power electronic transformers is introduced, including zero voltage switching, zero current switching and so on. These soft switching technologies can effectively reduce the switching loss of the transformer, improve efficiency, and reduce electromagnetic interference and harmonic generation. Secondly, the control technology of power electronic transformer is introduced, including traditional proportional, integral, and derivative control (PID) control, model predictive control and direct power control. These control technologies can realize the precise control of the output voltage and current of the power electronic transformer, to meet the requirements of the power system for power quality and stability. In addition, this paper also introduces the fault protection methods of power electronic transformers, including short-circuit protection, over-voltage protection and over-medium current protection. These fault protection methods can effectively protect power electronic transformers from various faults and ensure their normal operation and safety.

Research on the overvoltage suppression strategy of single-phase photovoltaic grid-connected systems based on FCS-MPDPC

For the overvoltage problem brought by the access of high-penetration photovoltaic power plants to the low-voltage distribution network, this paper chooses to deal with it by using the reactive power regulation capability of photovoltaic power plant inverters, which reduces the equipment investment in distribution line voltage regulation. Based on the reactive power regulation theory of the inverter, a finite set model predictive direct power control method is proposed by constructing a virtual voltage vector, taking a single-phase cascaded 2H bridge grid-connected inverter as an example, to realize independent control of active and reactive power. By setting different power reference values, the inverter can be made to have two functions: grid-connected per-unit power factor and reactive power control during overvoltage. Finally, the two modes of inverter operation are verified by Simulink simulation, and the verification by example shows that the method can limit the grid-connected voltage deviation to within 7% and meet the requirements of grid-connected regulations.

Improving Photovoltaic Grid Integration under Partial Shading by Equilibrium Slime Mould Optimization

In the realm of photovoltaic grid integration with Shunt Active Power Filters operating under partial shading conditions, this study introduces an innovative approach aimed at minimizing both power consumption from the electrical grid and associated costs. The primary objective of this research is to maximize the efficiency of photovoltaic system output by implementing a novel algorithm known as the Equilibrium Slime Mould Optimization technique. This algorithm is employed to precisely track the global power point of the photovoltaic array under partial shading conditions, resulting in increased photovoltaic power injection and decreased grid-side consumption. The choice of the Equilibrium Slime Mould Optimization technique is motivated by its exceptional ability to efficiently explore the search space and avoid falling into local extrema. Additionally, this article incorporates Predictive Direct Power Control, one of the most contemporary Shunt Active Power Filter control techniques, to effectively eliminate harmonics and enhance overall system efficiency. To validate this proposed approach, a simulation setup was meticulously developed. The obtained results demonstrate a remarkable enhancement in the efficiency of photovoltaic power injection compared to the conventional sliding mode technique, which tends to get trapped at local maximum power point, thereby resulting in diminished power injection. This pioneering approach heralds a new era in the application of metaheuristic algorithms within practical systems, leading to enhanced productivity and reduced costs for consumers. Furthermore, it holds the potential to advance various categories of interconnected photovoltaic systems, ensuring improved performance across diverse operational scenarios.

Power-Compensated Triple-Vector Model Predictive Direct Power Control Strategy for Nonredundant Fault-Tolerant Rectifiers

A power compensated triple-vector model predictive direct power control (PCTV-MPDPC) strategy is proposed in this paper for non-redundant fault-tolerant three-phase rectifiers. The system fault tolerance ability and post-fault control performance are enhanced with low hardware complexity. By defining the negative conjugate of complex power as the control variable, a straight-forward pattern of power-voltage relationship analysis is obtained. Besides, a novel voltage vector selection method is put forward based on the location of the error vector between the reference and real control variables. The sector identification process can be omitted to derive a unified expression for duty ratio calculation. On top of that, an intuitive power compensation architecture is proposed to eliminate the dc-bus voltage deviation so that its utilization rate can be maximized. With the proposed PCTV-MPDPC strategy, the power ripples and input current harmonics can be significantly reduced. Superior steady-state and dynamic performance can be achieved compared with the conventional finite-control-set MPDPC (FCS-MPDPC) scheme and a recently proposed multiple-vector model predictive power control (MV-MPPC) method, along with excellent dc-bus voltage deviation suppression effect. Furthermore, simulation validation in Matlab/Simulink and experimental verification on a four-switch three-phase rectifier are carried out to demonstrate the effectiveness of the proposed PCTV-MPDPC strategy.

Fake algebraic Riccati equation applied to model predictive control for doubly fed induction generator direct power control

AbstractModel Predictive Control (MPC) and Direct Power Control (DPC) are widely used in Doubly Fed Induction Generator (DFIG) applications. However, in the context of DPC‐based MPC for DFIG, there has been a lack of literature focusing on the design of the MPC parameters. In this way, this article introduces a novel technique for designing weighing matrices in Direct Power Model Predictive Control using the Fake Algebraic Riccati Equation (FARE). As FARE is associated with the infinite receding horizon, a small receding horizon can be designed without compromising the robustness. Furthermore, this article presents a computationally efficient linear state‐space model for DPC that, when combined with a small receding horizon FARE design, offers a controller with low computational cost and robustness. The proposed theory is validated through experimental results under normal operation, variable speed operation, and parameter variations. Additionally, the proposed controller is compared to other MPC approaches.

An improved low-computation model predictive direct power control strategy for three-level four-leg active power filter

An improved low-computation model predictive direct power control strategy for three-level four-leg active power filter

Low Computational Burden Predictive Direct Power Control of Quasi Z-Source Inverter for Grid-Tied PV Applications

This paper proposes a simplified predictive direct power control for the grid-tied quasi Z-source inverter. The proposed control implements a model predictive control structure to achieve the maximum obtainable power from the collected PV source. The power delivered to the grid is managed to compensate for the reactive power and, as needed, to ensure the grid’s stability. A predictive power model for a quasi Z-source inverter is developed in which the proposed control can operate with a fixed switching frequency without a weighting factor. The simplified space vector modulation uses the three appropriate switching vectors that are selected and applied using precalculated switching times during each switching period, in which the required switching vectors are determined only from one sector in the space vector diagram, taking all of the information of the other sectors, which leads to reducing the computational burden. Simulation results and comparative study are used to confirm the proposed control performance for the grid-tied quasi Z-source inverter capable of tracking and generating the maximum power from PV with fast-tracking dynamics, ensuring the ac voltage desired, and better tracking of the active and reactive power reference with the lowest power ripple. The grid current harmonics were tested and conformed to the IEEE-519 standard. Additionally, the proposed simplified PDPC is experimentally validated using the Hardware-in-the-Loop emulator and the C2000TM-microcontroller-LaunchPadXL TMS320F28379D kit, establishing the usability and good result of our proposed control approach in terms of requirements.

Finite-Set Model Predictive Direct Power Control for DFIG with Reduced Number of Voltage Vectors


 This paper proposes a Simplified Finite-Set Model Predictive Power Controller (MPPC) to improve the performance of the Doubly-Fed Induction Generator (DFIG). The MPPC is implemented to regulate the stator active and reactive powers of DFIG, minimizing the error between them and their references, with an optimized null vector selection and a reduced number of active vectors, to decrease the computational burden. This reduction is applied in a two-level converter, reducing the test to 4 or 2 active vectors. Simulations and laboratory experiments were performed, showing results for a 0.56 kW DFIG. Finally, it was verified that the proposed system can maintain the stator powers at a reference.
 

Improved Model Predictive Direct Power Control for Parallel Distributed Generation in Grid-Tied Microgrids

This research proposes an improved finite control set direct power model predictive control method (FCS-DPMPC) for grid-tie distributed generation (DG). FCS-DPMPC predicts the system outcomes using the system model. During the next sampling time, a voltage vector is defined using the cost function to minimize the power ripple, consequently allowing flexibility for power regulation. Furthermore, the impact of implementing a one-step delay is studied and compensated through a model forecast pattern. In addition, a new two-step horizon technique has been developed to minimize switching frequency and computation burden. Simulation results for single DG and parallel operated DGs in a grid-tie manner confirm the effectiveness of the suggested control strategy, which signifies that this is an appropriate approach for distributed generation in microgrids.

Performance-Based Tuning for a Model Predictive Direct Power Control in a Grid-Tied Converter With L-Filter

Model predictive control (MPC) is a powerful and widely used technique to address the control challenges in power converters as the grid interface for renewable energy systems. This technique combines closed-loop control with error and control effort minimization; however, its design is challenging, and we know little about how the controller parameters affect the closed-loop performance of grid-connected voltage source converters. In this study, we applied an MPC direct power control with modulation for a grid-connected power converter with an inductive filter. For the controller design, we proposed an initial set based on the power converter’s nominal setup. Then, we define the range of settings to guarantee stability by analyzing the closed-loop poles of the system. The fine-tuning to improve the performance can be identified visually using the performance maps built from simulations of the control system, simultaneously sweeping the time horizons of the predictive model and the weight factors of the cost function. Experimental results on a low-power bench demonstrate the excellent performance of the designed controller, following and even outperforming the classical proportional-integral (PI) controller and other advanced control techniques.

A Novel Optimal Voltage Vector Selection Control Strategy for Vienna Rectifier

At present, model predictive control (MPC) is widely used in power electronic converters. The objective function is typically utilized to select the optimal voltage vector, but the switching frequency is not fixed or too high during the selection process. Therefore, to address this issue, a novel optimal voltage vector selection control strategy for Vienna rectifier is proposed in this paper. Specifically, hysteresis link is introduced into finite control set-model predictive direct power control (FCS-MPDPC). The optimization criterion is changed from the traditional objective function tracking error minimization to the longest extension step in the hysteresis loop. As a result, the switching frequency can be reduced and the control algorithm can be simplified by redefining the optimization criteria of the rectifier output voltage vector. Moreover, according to the power control model of Vienna rectifier, three-level space vector pulse width modulation (SVPWM) is used to generate the switching function for the next cycle. Finally, an experimental platform for Vienna rectifier based on dSPACE hardware is built to verify the feasibility of the proposed strategy. Experimental results show that the control strategy can ensure the rectifier to run normally and exhibit good steady state performance.

Hybrid Data-Model Predictive Control for Enabling Participation of Renewables in Regulating Reserves Service

A long-horizon hybrid data-model predictive direct power control (HD-MPDPC) scheme is proposed. The hybrid control scheme is for grid-side power electronic converters (PEC) in grid-connected renewable energy sources (RES). Computational burden of conventional MPDPC is alleviated by reducing the number of candidate voltage vectors to be examined in the cost function. This is achieved by employing data-driven forecast of RES output power to identify and eliminate the voltage vectors that do not counteract active and reactive power errors appropriately. RES power is forecasted using recurrent neural networks. The method for eliminating voltage vector candidates is inspired from direct power control, where output of active and reactive power hysteresis controllers along with the sector in which grid voltage vector lies are used to determine the switching states of the grid-side PEC. Thanks to reduced computational complexity, the hybrid predictive control scheme dispatches RES power more reliably over long horizons. This in turn enables RES as a regulating reserves service provider in power systems. Hardware-in-the-loop studies of a grid-connected wave energy conversion system verify reduced computational complexity of HD-MPDPC and its effectiveness in controlling RES output power over long horizons.

Multi-vector predictive direct power control for grid-tied three-level inverters with optimized vector application time calculations

Multi-vector predictive direct power control for grid-tied three-level inverters with optimized vector application time calculations

An Advanced Shunt Active Power Filter (SAPF) for Non-ideal Grid Using Predictive DPC

The usage of power electronic converters and other non-linear devices is very common in this modern era because of their flexibility in control. However, they inject harmonics into the power system. To deal with the problem, the use of filters has become almost inevitable. Active power filters controlled by direct power control (DPC) have overtaken this responsibility from the passive filters because of their numerous advantages. To deal with the real-world power system grid which is far from the idealized character of being balanced and sinusoidal, the proposed method uses the extended pq theory integrated with the dual second-order generalized integrator (D-SOGI) for the correct estimation of the all-important instantaneous powers. In contrast, the conventional pq theory is used in the existing works, which doesn’t hold in a non-ideal grid. In addition to that, here, for the selection of the switching vector, an advanced and more accurate predictive DPC method which is specifically designed to operate in a non-ideal grid, replaces the static look-up tables used in the previous works. Further, to make the filter designing easier, the proposed method focuses on constant switching frequency operation eliminating the problems arising from variable switching frequency used in literature. The proposed technique is tested in MATLAB/Simulink environment and the real-time system to validate its feasibility.