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Talkative power conversion (TPC) is a promising communication solution for the dc distributed power system (DPS). It involves embedding data into the output power ripple of a converter by using the power and signal dual modulation (PSDM) method. This article proposes a frequency shift keying (FSK)-based method for active rectified LLC (ARLLC) resonant converters. The method integrates the communication function into the power conversion by modulating data into the switching carrier. The phase control of the secondary active rectified switching devices is employed to suppress the input–output voltage gain fluctuations caused by frequency modulation, thereby avoiding communication’s influence on power conversion. For data transmission, no additional hardware or cabling is required, reducing the complexity and costs. In addition, a detailed design guideline considering both communication and power conversion is provided, facilitating the general TPC-ARLLC system design. Finally, a 500 W prototype with a resonant frequency of 120 kHz is constructed, and the experiment achieves a 10-kb/s bitrate with a 0.33% maximum output voltage fluctuation ratio, demonstrating the correctness of the proposed modulation method.

In a photovoltaic (PV) system equipped with dc power optimizers (DCPOs), communication is a critical function for ensuring reliability. This article presents a talkative power conversion (TPC) method for DCPOs using power/signal dual modulation (PSDM) to achieve integrated dc power line communication (PLC). The method integrates modulated data signal into the power control loop of a DCPO, so no transmission circuit is added for communication. The proposed PV system consists of several series-connected DCPOs and a communication hub. A communication model of the system is built, based on which the modulation/demodulation method and noise distribution are discussed. Furthermore, the cross effects between communication and power transfer are analyzed in detail, especially in pass-through mode. At last, a 2.2 kW practical system including 12 PV panels is implemented with a 3.75 kb/s data rate. The communication performance is verified in long-term running under various environments.

Domain-adversarial learning-based fault diagnosis methods have shown prominent application prospects in the field of cross domain fault diagnosis. However, the existing domain-adversarial methods cannot simultaneously consider domain-level discrepancy, interclass and intraclass correlation of target domain samples, which result in poor feature representation ability of fault diagnosis model for ambiguous targets. For this reason, a fault diagnosis method is proposed by implicit discriminator domain adversarial residual network (DDARN). First, a unified domain adversarial framework based on the nuclear-norm Wasserstein discrepancy (NWD) is constructed to achieve domain-level feature alignment and adjust the inter class-intra class correlation for ensuring the prediction certainty. Additionally, improved deep residual network (IDRN) integrating the efficient activation function is designed to facilitate the propagation of information and enhance nonlinear representation ability of the network. Finally, case studies on public datasets and self-made KUST-SY bearing datasets indicate DDARN can achieve slightly better diagnostic performance over state-of-the-art methods.

The Hammerstein model has proven its ability for modeling many industrial servo systems, but the corresponding parameter estimation remains as a challenging problem, especially for the purpose of achieving fast convergence. This article presents a fixed-time (FxT) adaptive parameter estimation scheme for Hammerstein systems with an asymmetric dead-zone to ensure the convergence of estimated parameters in fixed-time. A continuous piecewise linear function neural network is adopted to model the nonlinear dead-zone dynamics to remedy the use of intermediate variable. To avoid using the system state information, a K-filter is used to construct a relationship between the unknown system parameters and the input/output signals. Then, a new adaptive law based on the parameter error and the FxT scheme is developed to online estimate the lumped unknown parameters and ensure the fixed-time convergence, where an online updated auxiliary variable of regressor is suggested to eliminate the impact of the regressor on the convergence performance. Both numerical simulations and experimental results are given to demonstrate the effectiveness of proposed methods.

Power and signal dual modulation (PSDM) integrates communication functions in power converters, providing an appropriate communication method for power electronics systems. This article discusses the theory and implementation of PSDM in resonant converters. The data are modulated into a resonant converter's gate signal and then transmitted along with power. The fundamental principles of the proposed PSDM modulation are analyzed in detail, and a novel PSDM scheme is proposed. The decoupling between data modulation and power control is realized in the communication process. Furthermore, the design considerations of converter parameters regarding their influence on the output voltage gain are given to evaluate the reliability of the proposed PSDM scheme. Finally, a 500 W prototype with a resonant frequency of 180 kHz is set up, and the experiment achieves a 4.5 kbps bitrate using the proposed modulation scheme, demonstrating the correctness of the scheme.