Bearings are vital components of motor drive systems and are widely used in various industrial applications. Bearing failures can lead to system collapse and pose a risk to human safety. Therefore, real-time monitoring and diagnosis of multi-fault bearings are crucial. This paper proposes a Mel-spectrum-based contrastive self-supervised Transformer (Mel-CSST) model to efficiently detect multiple bearing faults in electric motors through vibration noise signals. Among them, the contrastive self-supervised Transformer model (CSST) can be pre-trained without the need for labeled data, significantly improving the fault detection accuracy of the target bearing after transfer learning using the parameter-frozen domain-adversarial (PFDA) method. Mel-spectrums are converted from a mass of sub-signals generated by the random-masked sliding window (RMSW) method, providing training data sample pairs for the CSST model. Mel-spectrums can analyze significant vibration noise signals at lower frequencies in more detail, revealing the fault features missed by the standard fast Fourier transform. Furthermore, the encoder part of Mel-CSST uses a modified Transformer network to ensure the feature extraction effectiveness of CSST. The proposed method can be easily transferred to be used on target bearings without expensive labelling data in practical applications. Experiments using two real bearing datasets measured from two test rigs, along with comparison experiments with other existing methods, validate the effectiveness of the proposed method.

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A multi-port energy router (MER) is an important infrastructure for power management and energy storage after an unexpected power outage. In addition, MERs can relate to various emergency electric power sources (EEPSs) and power grids at the same time. Moreover, by putting an MER in mobile energy storage, an MER allows for more flexible energy management and regulation in urban cities. This paper proposes a new topology of MERs composed of a bidirectional AC/DC converter and a partial power processing-based triple active bridge (PPP-based TAB) converter to supply emergency power for an unexpected power outage. This design of an MER improves the power efficiency significantly to realize peer-to-peer (P2P) trading which realizes the bidirectional power transmission among the main power grid, EEPSs such as the battery of electric vehicles (EV), and clients’ loads. The control methods of the power transmission of two power electronic converters in MER are also illustrated in this paper to realize the power transmission among the main power grid, clients’ loads, and EEPSs. This allows any power source connected to the MER to be used as an input or output to receive or send out power, which greatly improves the utilization of power. In conclusion, the MATLAB/Simulink and experiment test are executed and meticulously presented to validate the practicality of the proposed configuration.

With their multidirectional power flow capability, dual active bridge (DAB) and triple active bridge (TAB) converters find application in energy routers as DC/DC transfer components for emergency energy supply during significant power outages. These converters ensure stable sustainable power transmission across various energy sources while enabling high-power conversion. However, controlling power direction poses a challenge in DAB/TAB converters for emergency energy supply, typically a rapid change of power direction of any port of DAB/TAB converters. To address this problem, this study proposes a novel automatic power direction control method for DAB/TAB converters, enabling bidirectional power transmission without manual intervention based on the state of charge (SOC) of battery for emergency energy supply. This method realizes the change of power direction of each port in DAB/TAB converter automatically according to different situations, even in emergencies. Given the now widespread shortage of emergency energy and the higher cost of labor regulation, this approach simplifies operations and enhances system safety and sustainability by eliminating the need for human supervision. A well-implemented automatic control method ensures efficient and consistent power transfer within the system by change the direction in about 3 s, whenever power direction adjustment is necessary.

Multi-port bidirectional energy router is an important infrastructure for peer-to-peer (P2P) energy trading after power supply interruption. The state-of-the-art multi-port bidirectional energy router lacks the traceability function of an energy source. A triple active bridge (TAB) converter with partial power processing (PPP) progress is employed as multi-port bidirectional energy router. This proposed multi-port bidirectional energy router integrates the energy conversion and signal transmission in one converter, which supports peer-to-peer trading. In detail, the controlling approach of power transmission and the phase perturbation modulation (PPM) method of signal transmission, which realize the energy traceable functionality, are also illustrated in this paper. Finally, Simulation is performed and presented as proof of the feasibility of the designed method.