Converter Feeding Constant Power Loads Research Articles

Mismatched Disturbance Compensation Enhanced Robust H$_\infty$ Control for the DC-DC Boost Converter Feeding Constant Power Loads

A new control scheme to enhance the robustness and disturbance rejection ability of a DC-DC boost converter feeding constant power loads (CPLs) is proposed in this paper via introducing mismatched disturbance compensation into the robust H <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{_\infty}$</tex-math></inline-formula> control. In cascaded DC power converter systems, the load side converters, when tightly controlled, behave as CPLs for their source side converters, where the nonlinearities could result in severe instability. Besides, for power converters in DC microgrids, both the uncertainties/disturbances and the unpredictable load access could result in severe challenges to achieving higher control performance. In particular, the unknown CPLs must be considered as part of disturbances that form the so-called mismatched disturbances. To counteract these negative factors, a practical exact feedback linearization method is used to transform the control problem of a nonlinear system into that of a linear system, and the compensation of mismatched uncertainties/disturbances is introduced into the robust H <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{_\infty}$</tex-math></inline-formula> control design. Moreover, sufficient conditions to ensure the asymptotic stability and robustness of system are established. Both simulation and experimental results confirm the efficiency.

Quantum Power Electronics: From Theory to Implementation

While impressive progress has been already achieved in wide-bandgap (WBG) semiconductors such as 4H-SiC and GaN technologies, the lack of intelligent methodologies to control the gate drivers has prevented exploitation of the maximum potential of semiconductor chips from obtaining the desired device operations. Thus, a potent ongoing trend is to design a fast gate driver switching scheme to upgrade the performance of electronic equipment at the system level. To address this issue, this work proposed a novel intelligent scheme for the control of gate driver switching using the concept of quantum computation in machine learning. In particular, the quantum principle was incorporated into deep reinforcement learning (DRL) to address the hardware limitations of conventional computers and the growing amount of data sets. Taking potential benefit of the quantum theory, the DRL algorithm influenced by quantum specifications (referred to as QDRL) not only ameliorates the performance of the native algorithm on traditional computers but also enhances the progress of relevant research fields like quantum computing and machine learning. To test the practicability and usefulness of QDRL, a dc/dc parallel boost converter feeding constant power loads (CPLs) was chosen as the case study, and several power hardware-in-the-loop (PHiL) experiments and comparative analysis were performed.

A Variable Self-Tuning Horizon Mechanism for Generalized Dynamic Predictive Control on DC/DC Boost Converters Feeding CPLs

In high-power electronic industrial systems, constant power loads (CPLs) can affect the system performance or even cause instability due to its inherent negative impedance characteristics. Regarding this control issue, model predictive control (MPC) methods are generally applied to dc/dc converters feeding CPLs, aiming to achieve satisfactory control performance. However, they are mostly based on a fixed horizon design, whose optimal performance may be far from the desired one due to the change of operating conditions, e.g., different variation levels of CPLs. In this context, a novel generalized dynamic predictive control (GDPC) strategy employing a simple self-tuning horizon mechanism is proposed for the first time, allowing the controller to adaptively optimize the system transient-time performance even under largely-varied operating conditions. First, a disturbance observer is utilized to reconstruct the lumped disturbances within the system, which are subsequently brought into the control design through feedforward compensation loops. Second, an adaptive horizon is introduced into the generalized predictive control (GPC) design and rigorous stability analysis based on Lyapunov theorem is given. The effectiveness and performance improvement of the proposed regulation methodology are verified by both simulation and experimental studies in reference to the double-loop proportional-integral (PI) control and a benchmark GPC algorithm.

In-Depth Analysis of Smooth and Nonsmooth Bifurcations for an Open-Loop Boost Converter Feeding Constant Power Loads in Discontinuous Conduction Mode

This paper presents a study of the existence conditions of limit cycles and mechanisms when losing their stability in an open-loop DC–DC boost converter loaded with a constant power load and operating in discontinuous conduction mode. First, the existence conditions are determined. Then, boundaries associated with different kinds of instabilities such as the classical smooth fold and period-doubling bifurcations and nonsmooth border-collision bifurcations such as fold border-collision bifurcations are elucidated. To perform the stability analysis, an implicit 1D map for the system is derived. From this model, it is obtained that smooth period-doubling and fold bifurcations may occur at certain values of the circuit parameters. Some results from numerical simulations performed on the circuit-based switched model are provided showing consistency with the theoretical analysis from the derived model.

A novel fixed‐time dynamic surface DC/DC SEPIC converter controller loaded by uncertain buck‐based constant power loads

The stability analysis of the single-ended primary-inductor converter DC/DC converters feeding constant power loads (CPLs) is of major importance. The nonlinear behaviour of CPLs and their negative incremental impedance, which impose adverse effects on system damping and stability margins, necessitate developing proper strategies for an efficient implementation framework of DC microgrid. In this paper, a fixed-time sliding mode disturbance observer is first addressed to provide an estimation of the power flow moving along the uncertain CPLs with time-varying nature within a fixed time. It not only expedites the estimation rate but also improves the robustness against physical parameter variation. The fixed-time dynamic surface control law is then developed based on the estimated load power for the duty cycle of the switch such that the entire power grid becomes stable and the desired voltage of the DC bus is tracked within a fixed time irrespective of the initial conditions. A rigorous Lyapunov-based approach is represented to guarantee the semi-global fixed-time uniform ultimate boundedness of the proposed scheme. Finally, in order to verify the proposed methodology's strengths, Model-in-the-Loop real-time simulations are performed under various operating case studies.

Source-Side Virtual RC Damper-Based Stabilization Technique for Cascaded Systems in DC Microgrids

Cascaded connection of power converters is a dominant connection form in DC microgrids. In such systems, despite the possible instability caused by the impedance interactions between the individually designed converters, tightly regulated load converters acting as constant power loads (CPLs) tend to destabilize the system owing to their negative resistance characteristics. Hence, this paper proposes a new virtual series RC damper in parallel with the source-side converter's capacitor without compromising the load's dynamic performance. Using this design-oriented active damping method, which utilizes a simple control structure with a more straightforward tuning of the control parameter, the stability and performance of the system are guaranteed. The feasibility and robustness of the suggested active stabilization idea against unanticipated variations in input voltage amplitude, and CPL power rating (load changes) as well as step changes in output voltage reference, are also authenticated. The control and operation principles, as well as the circuit physical meaning realized by the presented technique for three cascaded systems comprising the basic DC/DC converters feeding CPLs, are theoretically analyzed. Simulation and experimental results are provided to validate the effectiveness of the proposed active stabilizer.

Prescribed Performance Controller Design for DC Converter System With Constant Power Loads in DC Microgrid

In this paper, a composite prescribed performance control strategy is developed for stabilizing dc/dc boost converter feeding constant power loads. First, by employing the exact feedback linearization technique, the nonlinear uncertain dc converter system is first transformed into the Brunovsky’s canonical form. Then, a nonlinear disturbance observer is utilized to evaluate the dynamic change of load power and the accuracy of output voltage regulated by feedforward compensation. Next, the prescribed performance controller is elaborately designed to ensure that the tracking error of output voltage is always within the margin of predefined error bounds. Based on the backstepping design approach, the composite nonlinear controller with prescribed performance is determined. Finally, the numerical simulation results are presented to demonstrate the tracking performance of the proposed controller.

Power shaping control of DC–DC converters with constant power loads

This paper deals with the stabilization of DC–DC converters feeding constant power loads (CPLs). It is shown that power/energy dynamics are easily computed via the image of the input/output voltages/currents of the converter. Such image is represented by a high-order differential operator, obtained from first principles, acting on the variables of a stabilizing interconnected controller. This approach is based upon the premise that the destabilizing effect of CPLs, is caused due to a power imbalance in a dissipation equality, which prevents passivity. The proposed stabilizing mechanism shapes the power dynamics via an interconnected controller, inducing a feasible power flow and thus passivity. To do so, we developed a stability test, as well as control design tools in terms of linear matrix inequalities (LMIs), constructed from coefficient matrices of the system dynamics. As a practical advantage, the proposed power shaping approach permits to link stability conditions with the nominal power rate specification of power converters. Finally, we show that the proposed framework can also accommodate other traditional frequency domain, eigenvalue and immittance criteria. The main results are validated via a theoretical analysis and experimental results.

A Novel Deep Learning Controller for DC–DC Buck–Boost Converters in Wireless Power Transfer Feeding CPLs

Wireless power transfer (WPT), in a general scene, is the transmission of electrical energy for various technologies by means of magnetic couple resonance. Its potential applications change from low power electric equipment (like smartphones) to high-power devices over short distances of centimeters to tens of centimeters. In this letter, a pulsewidth-modulation-based adaptive data-driven strategy is newly developed for the output voltage regulation of the series–series (SS) compensated WPT. In an attempt to achieve a fast and adaptive voltage regulation, a deep deterministic policy gradient (DDPG) based on an ultralocal model (ULM) control scheme is applied to the dc–dc buck–boost converter feeding constant power loads installed in the receiver side of the WPT. In the suggested scheme, an intelligent feedback controller based on the ULM is used to stabilize the voltage response of the buck–boost converter while the unknown dynamics of the converter are estimated by a sliding mode observer (SMO). An auxiliary deep DDPG algorithm with Actor-Critic architecture is implemented to employ its adaptive capability to eliminate the SMO estimation error and improve the voltage regulation of the WPT plant. Some experimental outcomes on a laboratory testbed of the WPT plant are presented to assess the merits and real-time implementation of the adaptive data-driven scheme from a systematic perspective.

IoT-Based DC/DC Deep Learning Power Converter Control: Real-Time Implementation

Recently, a modularized smart grid (SG) architecture, entitled the Internet of Things (IoT) grid, is developed that accommodates the IoT technology into the dc-dc converters to build a programmable grid with a single voltage bus. This modern architecture can be established with low computing hardware that facilitates the control and management of the IoT-based grids. Due to the uncertainties originated from the integration of the IoT technology and power electronic converters, the deterministic methodologies are unable to precisely model the SG anymore. In response to these challenges, this article addresses a novel adaptive data-driven method based on the active disturbance rejection controller (ADRC) for the voltage regulation of an IoT-based dc-dc buck converter feeding constant power loads. In particular, a deep deterministic policy gradient (DDPG) with the actor-critic architecture is adopted for the online adjusting of the ADRC controller. The established DDPG takes into account the ADRC controller coefficients into the design objective and offers the ADRC controller with the online coefficient setting ability through the neural network learning. The IoT-based system is tested on a real-time testbed with the constrained application protocol protocol and IEEE 802.11 (Wi-Fi) network to assess the applicability of the suggested controller in the presence of network degradations. The impact of both packet loss and interfering traffic on the reduction performance of the DDPG adaptive ADRC controller is investigated, simultaneously. The supremacy of the suggested adaptive data-driven controllers is verified by a comprehensive comparative analysis with the state-of-the-art methodologies.

An Offset-Free Composite Model Predictive Control Strategy for DC/DC Buck Converter Feeding Constant Power Loads

The high penetration of power electronic converters into dc microgrids may cause the constant power load stability issues, which could lead to large voltage oscillations or even system collapse. On the other hand, dynamic performance should be satisfied in the control of power electronic converter systems with small overshoot, less oscillations, and smooth transient performance. This article proposes an offset-free model predictive controller for a dc/dc buck converter feeding constant power loads with guaranteed dynamic performance and stability. First, a receding horizon optimization problem is formulated for optimal voltage tracking. To deal with the unknown load variation and system uncertainties, a higher order sliding mode observer is designed and integrated into the optimization problem. Then an explicit closed-loop solution is obtained by solving the receding horizon optimization problem offline. A rigorous stability analysis is performed to ensure the system large signal stability. The proposed controller achieves optimized transient dynamics and accurate tracking with simple implementation. The effectiveness of the proposed controller is validated by simulation and experimental results.

Nonlinear Model Predictive Stabilization of DC–DC Boost Converters With Constant Power Loads

This article addresses the problem of stabilization of dc–dc converters feeding constant power loads (CPLs). An explicit model predictive control is developed based on an average model of a boost converter. The optimal control law is obtained offline by solving a multiparametric nonlinear programming problem over a simplex partition of the state space. The performance is validated through simulation and experimental studies. A delay compensation scheme is included for online implementation. The control is able to drive the output voltage to the desired level and stabilize it despite the instability effects produced by the presence of CPLs. It also features robustness against abrupt changes on the input voltage and the load.

Comparison of integer and fractional order robust controllers for DC/DC converter feeding constant power load in a DC microgrid

Abstract DC/DC converter’s when feeding the constant power loads (CPLs) are sensitive to the instabilities due to the induced negative impedance characteristics. An efficient control system is crucial to overcome the de-stabilization problem for the DC/DC converters. This article proposes an integer and fractional order terminal sliding mode (TSMC) controllers for DC/DC boost converter feeding the constant power load (CPL) in a typical DC microgrid configuration. Novel integer and fractional order nonlinear sliding manifolds are proposed and based on the Lyapunov theorem terminal sliding mode controllers (TSMC) are formulated and its stability is guaranteed. Finally, numerical simulations are presented to compare the performance of the integer TSMC, fractional TSMC and classical SMC.