Optimal Modulation Scheme Research Articles

A novel eight-switch nine-level modified ANPC inverter topology and its optimal modulation strategy

An eight-switch nine-level modified ANPC inverter (8S-9L-MANPC) is designed and simulated in this paper. With this topology, only one DC voltage source and eight active semiconductor switches are required, significantly reducing system size, weight, and cost. The structure consists of a five-level ANPC inverter cascaded with a two-level leg converter. This arrangement extends the 5L-ANPC structure to a 9L configuration without requiring the use of additional capacitors. To maintain the balancing of the flying capacitor (FC), a modulation strategy using a switching state redundancy-based control method is employed. Recent 9L inverters incorporating flying capacitors and based on a hybrid structure have been compared with the proposed structure. By conducting this comparison, we can highlight the potential improvements offered by the proposed structure over the recent 9L inverters in terms of reduced component count, simplified design, and cost savings. The proposed 8S-9L-MANPC inverter is tested under various operating situations in MATLAB/Simulink simulation.

Modulation strategy design method for single-inductor multi-port converter based on reinforcement learning

Single-inductor multi-port converters have the characteristics of "more silicon and less magnetism" and have great application potential in many fields. However, single-inductor multi-port converters have many switching modes and the modulation strategy design is complex. The current design method is to manually select the switching mode sequence and perform modal analysis. The design process requires power electronics expertise and experience. This paper proposes a modulation strategy design method for single-inductor multi-port converters based on reinforcement learning. This method uses a neural network to generate the modulation strategy and adopts a set of simple rules to provide rewards for training the neural network. Through reinforcement learning, the neural network can summarize experience in trial and error without human intervention and finally generate the optimal modulation strategy. This method has many advantages: (1) It only requires known conditions such as port voltage and converter structure as input, and some simple constraints as rewards, avoiding complicated manual design; (2) It can use a neural network to generate the optimal modulation strategy under different operating conditions, and has strong adaptability. Based on this method, a modulation strategy design for a single-inductor multi-port converter is carried out, and the effectiveness of the method is verified by experiments.

Deep reinforcement learning-aided minimum current control for the DBSRC based on harmonic analysis method

Aiming to optimize the modulation efficiency for the dual-bridge series-resonant converter (DBSRC), this paper proposes an deep reinforcement learning (DRL) aided EPS (DEPS) modulation scheme for minimum current operation based on the harmonic analysis method. Using the deep deterministic policy gradient (DDPG) algorithm as an advanced DRL algorithm, the scheme obtains the optimized modulation scheme DEPS through offline training of the agent, which can adopt the extended-phase-shift (EPS) modulation scheme and consider the zero-voltage-switching (ZVS) constraints. Thus, the trained agent of the DDPG which likes an implicit function, can provide optimal phase shift angle for the DBSRC in real-time with the minimum current and soft switching performance in the continuous operation range. Compared with the existing EPS modulation schemes using First Harmonic Approximation (FHA), the DEPS modulation scheme has similar operational efficiency and performance of the converter, while also possessing the ability to obtain modulation angles in real-time based on environmental parameters. Finally, PSIM simulation verifies the effectiveness of the proposed optimization scheme.

A Collaborative Modulation Method of Dual-Side Backflow Power Optimization and Zero-Voltage Switching for Dual Active Bridge

Dual active bridge (DAB) converters are widely used in DC microgrids because of their superior bidirectional energy flow regulation capability and characteristics, such as wide voltage gain and zero-voltage switching (ZVS). However, due to the inherent contradiction between the minimum backflow power and the ZVS of the power switches, the existing modulation methods are difficult to optimize and coordinate. Most of the studies increase the complexity of energy flow regulation in the optimization process. To solve the above problems, this paper proposes a collaborative modulation method of dual-side backflow power optimization and ZVS for DAB. The method constructs a dual-side backflow power optimization modulation strategy that is simple to control and uniform in all working conditions by analyzing the mathematical model of backflow power. Meanwhile, based on this optimized modulation strategy, a regulatory factor of phase-shift ratio is introduced to collaborate with the ZVS of the power switches, which reduces the backflow power while ensuring the ZVS of the primary and secondary sides. Finally, a 500W DAB prototype is built, and the experimental results verify the feasibility and effectiveness of the proposed modulation method.

Simultaneous analysis of cannabinoids and terpenes in Cannabis sativa inflorescence using full comprehensive two-dimensional liquid chromatography coupled to smart active modulation

Nowadays, the higher peak capacity achievable by comprehensive two-dimensional liquid chromatography (LC×LC) for the analysis of vegetal samples is well-recognized. In addition, numerous compounds may be present in very different amounts. Cannabinoids and terpenes represent the main components of Cannabis sativa inflorescence samples, whose quantities are relevant for many application purposes. The analyses of both families are performed by different methods, at least two different separation methodologies, mainly according to their chemical characteristics and concentration levels. In this work, concentration differences and sample complexity issues were addressed using an LC×LC method that incorporates an optimized modulation strategy, namely smart active modulation, for the simultaneous analysis of cannabinoids and terpenes. The system was built by interposing an active flow splitter pump between both dimensions. This set up aimed to exploit the known advantages of LC×LC. In addition, here we proposed to use the splitter pump for online control over the splitting ratio to facilitate the selective dilution of different eluted fractions containing compounds with highly different concentrations.This work represents the first application and demonstration of smart active modulation (SAM) in LC×LC to simultaneously determine analytes with significant differences in concentration levels present in complex samples. The proposed method was tested with eight different strains, from which fingerprints were taken, and numerous cannabinoids and terpenes were identified in these samples. With this strategy, between 49 and 54 peaks were obtained in the LC×LC chromatograms corresponding to different strains. THCA-A was the main component in six strains, while CBDA was the main component in the other two strains. The main terpenes found were myrcene (in five strains), limonene (in two strains), and humulene (in one strain). Additionally, numerous other cannabinoids and terpenes were identified in these samples, providing valuable compositional information for growers, as well as medical and recreational users. The SAM strategy here proposed is simple and it can be extended to other complex matrices.

A Si IGBT/SiC MOSFET Hybrid Isolated Bidirectional DC–DC Converter for Reducing Losses and Costs of DC Solid State Transformer

The DC solid state transformer (DCSST) is a crucial component for connecting buses of different voltage levels in the DC distribution grid. This paper proposes a Si IGBT/SiC MOSFET hybrid isolated bidirectional DC–DC converter and an optimized modulation strategy (OMS) to reduce the losses and costs of DCSST. Based on the analysis of topology and operating principles, a duty-cycle modulation strategy is proposed and the converter is modeled by the time domain analysis (TDA) method. Through the analysis of switching characteristics, an optimization problem is established, which aims to reduce the conduction losses of switches while ensuring zero-voltage switching (ZVS) for all switches and low-current turn-off for IGBTs simultaneously. The optimization problem is solved by the augmented Lagrangian genetic algorithm (ALGA), and the OMS for the proposed converter is deduced. Finally, a 2 kW experimental prototype with the primary voltage of 405–495 V and the secondary voltage of 150 V is built to verify the effectiveness of the proposed topology and OMS. The switching costs of the proposed converter is reduced by 27.3% and the efficiency is improved by up to 4.04% compared to the existing method.

A Variable Switching Frequency-Based Triple-Phase-Shifted Loss Optimized Modulation Strategy for DAB-Based DC–AC Microinverter

A Variable Switching Frequency-Based Triple-Phase-Shifted Loss Optimized Modulation Strategy for DAB-Based DC–AC Microinverter

High Efficiency Dual-Active-Bridge Converter with Triple-Phase-Shift Control for Battery Charger of Electric Vehicles

An optimal modulation scheme with triple-phase-shift (TPS) control could increase the efficiency in the entire load range for a dual-active-bridge (DAB) converter under wide output voltage range conditions. Therefore, this study proposes a convergent approach to TPS mode selection, coupled with an optimal modulation scheme, ensuring the circuit’s efficiency over the entire range in the realm of a high-power and high-efficiency battery charger for electric vehicles. The convergent approach to TPS mode selection also reduces the numerous cases for small-signal analysis through general average modeling. After verifying the small-signal models under various voltage transfer ratios and load conditions to verify the stability, a converter prototype with a rated power of 15 kW is built and tested. Thus, a peak efficiency of 97.7% can be achieved.

A Hybrid Modulation Strategy for LCC–LCC Compensated Bidirectional Wireless Power Transfer System to Achieve High Efficiency in the Whole Operating Range

A hybrid modulation strategy is proposed for the LCC-LCC compensated bidirectional wireless power transfer system (LCC-BWPT), which aims to boost the efficiencies in the whole operating range. The basic operating modes of LCC-BWPT with the hybrid modulation strategy are analyzed, and the unified discrete-time model is then established based on the full-mode operation, which can accurately describe the state variables under all operating modes. Based on the model, the criteria for optimal operation are put forward in consideration of three working processes of LCC-BWPT, i.e., the constant current (CC) charging process, the constant voltage (CV) charging process, and the CC discharging process. The optimal control variables in three working processes are derived respectively, which realize full zero-voltage switching (ZVS) operation and minimize the reactive current. On this basis, an optimal modulation strategy featuring high efficiency performance is proposed for LCC-BWPT. Besides, a control method independent of the real-time wireless communication is developed for LCC-BWPT with the optimal modulation strategy. Finally, a 1.2kW prototype is applied, and experimental results verify the effectiveness of the proposed optimal modulation strategy, where the efficiencies are improved in the whole operating range.

An Optimal VF-TPS Modulation Scheme for CLLC Converter With Minimized RMS Current

An Optimal VF-TPS Modulation Scheme for CLLC Converter With Minimized RMS Current

A Unified Optimal Modulation Strategy for DAB Converters to Tradeoff the Backflow Power Reduction and All ZVS in the Full Operating Range

A Unified Optimal Modulation Strategy for DAB Converters to Tradeoff the Backflow Power Reduction and All ZVS in the Full Operating Range

Dendrogram-based Heterogeneous Learners for Automatic Modulation Classification in DSTBC-OFDM Systems

Software-Defined Radios (SDRs) have a crucial role in dynamic spectrum sensing and sharing as they can quickly adapt to changes in their environment. Digital Modulation Recognition (DMR) enables this process by giving SDR the ability to recognize the received signal modulation format to make intelligent and autonomous decisions to switch on the optimal modulation scheme. However, with the deployment of the Distributed Space-Time Block Coding Orthogonal Frequency Division Multiplexing (DSTBC-OFDM) signals that support MIMO systems, that provides spatio-temporel multiplexing, new and challenging signal identification problems have emerged.This paper proposes a robust DMR method for relaying Space-Time Block Coding (STBC) Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed classifier is built using heterogeneous base classifiers namely, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RFC), based on a Dendrogram or decision tree structure.The Dendrogram-based Heterogeneous Learners (DHL) classifier were obtained via Ascendant Hierarchical Clustering (AHC) and by employing Higher-Order Moment (HOMs) and Higher-Order Cumulants (HOCs) as features. Moreover, we propose a grid search-based hyperparameter tuning for DHL to produce the best structure and optimize the performance.

Accelerated 2D Cartesian MRI with an 8-channel local B0 coil array combined with parallel imaging.

In MRI, the magnetization of nuclear spins is spatially encoded with linear gradients and radiofrequency receiverssensitivity profiles to produce images, which inherently leads to a long scan time. Cartesian MRI, as widely adopted for clinical scans, can be accelerated with parallel imaging and rapid magnetic field modulation during signal readout. Here, by using an 8-channel local coil array, the modulation scheme optimized for sampling efficiency is investigated to speed up 2D Cartesian scans. An 8-channel local coil array is made to carry sinusoidal currents during signal readout to accelerate 2D Cartesian scans. An MRI sampling theory based on reproducing kernel Hilbert space is exploited to visualize the efficiency of nonlinear encoding in arbitrary sampling duration. A field calibration method using current monitors for local coils and the ESPIRiT algorithm is proposed to facilitate image reconstruction. Image acceleration with various modulation field shapes, aliasing control, and distinct modulation frequencies are scrutinized to find an optimized modulation scheme. A safety evaluation is conducted. In vivo 2D Cartesian scans are accelerated by the local coils. For 2D Cartesian MRI, the optimal modulation field by this local array converges to a nearly linear gradient field. With the field calibration technique, it accelerates the in vivo scans (i.e., proved safe) by threefold and eightfold free of visible artifacts, without and with SENSE, respectively. The nonlinear encoding analysis tool, the field calibration method, the safety evaluation procedures, and the in vivo reconstructed scans make significant steps to push MRI speed further with the local coil array.

Optimal Modulation Strategy for Current-Source Converter to Reduce DC-Link Current Ripple

As one of the most important performance index of a current source converter (CSC), DC-link current ripple is closely related to the modulation strategy. Excessive DC-link current ripple not only reduces the system efficiency, but also increases the total harmonic distortion (THD) of grid current and leads to more electromagnetic interference. To get around this problem, optimizing modulation strategy is more attractive compared with increasing the inductor or switching frequency. To this end, based on detailed analysis of the mechanism to reduce the DC-link current ripple and the inherent deficiency of current ripple continuous increase phenomenon between two adjacent sequences, a novel modulation strategy by employing optimized switching sequence according to the modulation index is proposed. By applying the proposed modulation strategy, the current ripple is effectively reduced in the full range of modulation index. Furthermore, the proposed scheme is easy to implement in practice without increasing the switching frequency or inductor. Finally, selected experimental results confirm the validity of theoretical analysis.

A New Current Ripple Suppression Strategy for DC Link Inductance of Three-Phase Current Source Converter

In this paper, a new modulation method is proposed to reduce the dc link inductor current ripple of a three-phase current source converter. The excessive current ripple of the dc link inductor of the current source converter will bring a series of hidden dangers to the system: the total harmonic distortion of current increases, the system stability decreases, and the electromagnetic interference increases. The traditional method to reduce the inductor current ripple is to increase the inductance value or increase the switching frequency of the system. Using this method reduces the system efficiency and power density and increases the cost. Compared with the above methods, the optimized modulation strategy appears more attractive. This paper first analyzes the mechanism of reducing current ripple by traditional methods, and then proposes a new vector action sequence and vector action time algorithm. Finally, an experimental platform is built to compare the new scheme with the traditional scheme. The experimental results prove the correctness of the new modulation strategy.

Switching Frequency Minimized Harmonic Mitigation: A Multiobjective Optimized Modulation Strategy for High- Power Converters

In order to explore the minimum switching frequency required to regulate certain amount of harmonic components, a novel switching frequency minimized harmonic mitigation (SFMHM) model is proposed in this paper, which can regulate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \mathit{N}\mathbf{-1}$</tex-math></inline-formula> harmonics with far less than <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> switching angles. By flexibly adjusting the threshold values, the proposed model can achieve both harmonic elimination and mitigation objectives. Based on PWM discretization and supported by quadratic programming, the switching frequency in the proposed model is expressed as a quadratic objective function of the PWM waveform. The objectives as fundamental control and selected harmonic mitigations are realized by treating the numerical approximations of the Fourier coefficients as constraints, which are finally transformed into an optimization model with binary variables and can be easily solved by some optimization toolboxes such as YALMIP. Some computing results show that, compared with the conventional selective harmonic elimination/mitigation (SHE/SHM) methods, the number of switching angles required to mitigate the same number of harmonics under the proposed method has been significantly reduced. The switching frequency can be reduced a lot compared with the conventional SHE/SHM methods, e.g., by 40% for some modulation indexes. Simulations and experiments verify the correctness of proposed SFMHM model.

Optimal Modulation Strategy for Ripple Current Reduction of DC-Link Capacitor in Multidrive Systems

Optimal Modulation Strategy for Ripple Current Reduction of DC-Link Capacitor in Multidrive Systems

Optimized Synchronous SPWM Modulation Strategy for Traction Inverters Based on Non-Equally Spaced Carriers

The switching frequency of high-power inverters, such as those used in rail transit traction systems, is low, due to switching loss and heat dissipation limitations. This can result in considerable output voltage harmonics. This paper proposes an optimal SPWM strategy for traction inverters based on non-equally spaced carriers to address this issue. By dividing the fundamental wave cycle into regions according to the principle of three-phase, half-wave, and quarter-wave symmetry, the carrier width is symmetrically changed in each region, and a switching angle sequence is generated by comparing the fundamental wave with the non-isometric carrier. The proposed optimal modulation strategy has lower harmonic content than traditional strategies within a specific modulation range. Enhancing the inverter output waveform leads to increased torque accuracy, lowering additional losses in the motor and avoiding overheating. This results in improved performance, enhanced efficiency, and extended service life of the motor system. To further reduce voltage harmonics across the full-speed range, a multi-mode segmented synchronous modulation strategy is designed based on the optimal modulation strategy for different modulation ranges. Appropriate switching points are selected to improve the stability of the traction drive system across the full-speed range. The effectiveness of the proposed method is verified through simulation and experimental results.

A MIMO-Enabled Free Space Optical Link under Log-Normal Fading/Gamma-Gamma Channel: Exploring an Optimal Modulation Scheme

The technology of free-space optical communication (FSOC) systems has some distinctive merits compared to other technologies. Its use is extremely beneficial to meet the exigencies of optical telecommunications and wireless networks (OTWNs). However, since the OTWNs transport a lot of data, the choice of a reliable modulation scheme is highly crucial. To this end, the focus of this paper is an in-depth study of a Point-to-Point Optical Link (P2P-OL) system under a FSOC-Multiple-Input Multiple-Output (MIMO) channel using an optimal modulation scheme. Furthermore, atmospheric turbulence (AT) effects over the FSOC-MIMO channel are incorporated in the proposed system to obtain substantial results. The performance analysis test of the proposed high-rate P2P-OL system is validated under the case that the channel decreases significantly when the AT gets strong regimes. Finally, the proposed system uses an optimal Non-Return to Zero Pulse Generator-Mach-Zehnder Modulator ((NRZPG-MZM)) scheme, which displays acceptable performance levels in a dust-fog meteorological environment under a LNF FSOC-9 × 9 channel with the attenuation value of 59.66 dB/km (i.e., max OSNR = 24.9 dB, min BER = 1e − 09, and max Q-factor = 6), whereas with the same environment under a G-G FSOC-9 × 9 channel, the attenuation value is 58.55 dB/km (i.e., max OSNR = 24.67 dB, min BER = 1e − 09, and max Q-factor = 6).

AI-aided power electronic converters automatic online real-time efficiency optimization method

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future.