Chuan Xiang’s research while affiliated with Dalian Maritime University and other places

Due to the influence of meteorological conditions, shipboard photovoltaic (PV) systems have problems such as large fluctuation and inaccurate prediction of the output power. In this paper, a short-term PV power prediction method based on a novel digital twin (DT) model and BiLSTM is proposed. Firstly, a PV mechanism model and a data-driven model were established, in which the data-driven model was updated iteratively in real time using the sliding time window update method; then, these two models were converged to construct a PV DT model according to the DS evidence theory. Secondly, a BiLSTM model was built to make short-term predictions of the PV power using the augmented dataset of the DT model as an input. Finally, the method was tested and verified by experiments and further compared with main PV prediction methods. The research results indicate the following: firstly, the absolute error of the DT model was smaller than that of the mechanism model and the data-driven model, being as low as 5.62 W after the data update of the data-driven model; thus, the DT model realized data augmentation and high fidelity. Secondly, compared to several main PV prediction models, the PV DT model combined with BiLSTM had the lowest RMSE, MAE, and MAPE; the best followability; and the smallest absolute error under different weather conditions, which was especially obvious under cloudy weather conditions. In summary, the method can accurately predict the shipboard PV power, which has great theoretical significance and application value for improving the economy and reliability of solar ship operation.

Due to the high penetration of virtual power plants (VPPs), the bi-directional power flow between VPPs and active distribution grids makes the grid operation complex. Without congestion management, the operation schedule only considers the economic benefits, and power flow constraints might be violated. Hence, it is necessary to conduct power interaction within the operation constraints. This paper proposes a coordinated economic dispatch method under voltage security constraints. The linear expressions were derived by simplifying the AC power flow equations to reduce the computation complicity. Then, optimal economic dispatch models with voltage security constraints were established for the active distribution grid and VPPs, respectively. Meanwhile, the transacted power and clearing price were set as the communication variables, and a coordinated strategy was proposed for the overall optimal goal. The modified IEEE 33-node and PG&E-node distribution grids were utilized for the simulations, and the results affirmed the validity of the proposed method.

The high load power variation due to the frequent change of working conditions will cause the DC bus voltage fluctuation of ship DC microgrid, decrease the stability of power supply system. This paper uses hybrid energy storage system (HESS) consisting of the supercapacitor and battery pack compensates or absorbs the power difference between generator and load, proposes a variable droop control strategy by taking consider the state of charge (SOC) of each battery, to suppress the fluctuation of power, and establishes Simulink simulation model for verification. Simulation results illustrate that: the proposed method can prevent voltage fluctuations from exceeding the limit, make super capacitor response high frequency power, battery pack response low frequency power; adjust the SOC of each battery pack unit, extend its operating life. The research results provide reference for the optimal operation control of ship DC microgrid.KeywordsShip DC microgridDroop controlHybrid energy storage systemState of charge

Aiming at the fault diagnosis accuracy of rolling bearings is not high enough, and unknown faults cannot be correctly identified. A priority elimination (PE) method is proposed in this paper. First, the priority diagnosis sequence of faults was determined by comparing the ratios of the inter-class distance to the intra-class distance for all faults. Then, the model training and fault diagnosis were carried out in order of the priority sequence, and the samples of the fault that had been identified were eliminated from the data set until all faults were diagnosed. For the diagnosis model, the stacked sparse auto-encoder network (SSAE) was selected to extract the features of the vibration signal. The extreme gradient boosting algorithm (XGBoost) was chosen to identify the fault type. Finally, the method was tested and verified by experimental data and compared with classical algorithms. Research results indicate the following: (1) with the addition of PE based on SSAE-XGBoost, the fault diagnosis accuracy can be improved from 96.3% to 99.27%, which is higher than other methods; (2) for the test set with the samples of unknown faults, the diagnosis accuracy of SSAE-XGBoost with PE can reach 92.34%, which is nearly 6% higher than that without PE and is also obviously higher than other classical fault diagnosis methods with or without PE. The PE method can not only improve the diagnosis accuracy of faults but also identify unknown faults, which provides a new method and way for fault diagnosis.

The bus voltage of the ship DC microgrid is sensitive to the change of loads, which has an influence on the power supply quality. This paper introduces a hybrid energy storage system (HESS) that is composed of a battery set and a supercapacitor set, and further studied the control method of HESS. First of all, the topological structures of the ship DC microgrid and HESS are described. Second, combined with the frequency division droop control and voltage PI control, a sliding mode control (SMC) method is proposed to control the charge and discharge of HESS based on an improved reaching law. Finally, the simulation model of the ship DC microgrid is established for the verification of the control method. Simulation results show that: (1) HESS can overcome the shortage of the dynamic response ability of the diesel rectifier generator to the steep change of load power. The supercapacitor set and the battery set successfully respond to the high-frequency and low-frequency components of the differential power in the system, respectively. (2) Compared with the traditional PI control method, SMC can reduce the current chattering of HESS and the voltage fluctuation amplitude of the DC bus. The proposed SMC method can provide a reference for the stable and reliable operation of the ship DC microgrid.

For the ship integrated power system (SIPS) with DC bus, the DC bus voltage fluctuates greatly with the load change causing by the switch of the ship navigation conditions. In this study, a hybrid energy storage system (HESS) was adopted to suppress the fluctuation of DC bus voltage, and an energy management strategy of HESS considering generator load rate (SVA) and state of charge (SOC) of the energy storage unit was proposed. The HESS can switch flexibly between the constant voltage mode and the charging mode according to SOC and SVA. The HESS supplies power to maintain the stability of DC bus voltage when the load exceeds the rated power of the rectifier generator under some extreme conditions

The capacitive voltage transformer (CVT) is a special measuring and protecting device, which is commonly applied in high-voltage power systems. Its measurement accuracy is affected seriously by the stray capacitances of the capacitance voltage divider (CVD) to ground and other charged parts. In this study, based on the boundary element method, a mathematical model was established firstly to calculate the stray capacitance. Then, the voltage distribution of the CVD was obtained by the CVD’s equivalent circuit model. Next, the effect of stray capacitance on the voltage distribution and the voltage difference ratio (VDR) of CVD was analysed in detail. We finally designed three types of shield and optimized their structure parameters to reduce VDR. The results indicated that the average deviation rate between calculated and experimental measured voltages is only 0.015%; that is to say, the method has high calculation precision. The stray capacitance of the CVD to ground is far larger than that of the CVD to the high-voltage terminal. It results in the inhomogeneous distribution of voltage and the increase of VDR. For the test CVT, its VDR exceeds the requirement of class 0.2. Among all of the three types of shield, the C type reduced the VDR of the test CVT the most. After optimizing the structure parameters of C-type shield, the VDR is further reduced to 0.08%. It is not only in accord with the requirement of class 0.2 but also has an adequate margin.

The rolling bearing is an extremely important basic mechanical device. The diagnosis of its fault play an important role in the safe and stable operation of the mechanical system. This study proposed an approach, based on the Fast Fourier Transform (FFT) with Decimation-In-Time (DIT) and XGBoost algorithm, to identify the fault type of bearing quickly and accurately. Firstly, the original vibration signal of rolling bearing was transformed by DIT-FFT and divided into the training set and test set. Next, the training set was used to train the fault diagnosis XGBoost model, and the test set was used to validate the well-trained XGBoost model. Finally, the proposed approach was compared with some common methods. It is demonstrated that the proposed approach is able to diagnose and identify the fault type of bearing quickly with almost 99% accuracy. It is more accurate than Machine Learning (89.88%), Ensemble Learning (93.25%), and Deep Learning (95%). This approach is suitable for the fault diagnosis of rolling bearing.