With the advantages of simple topology, voltage matching, electrical isolation, and bidirectional energy control, the triple active bridge (TAB) has become an active exploration and attempt for the flexible interconnection of dc ports. The high-frequency chain current (HFCC) calculation is the crucial step in determining zero-voltage-switching (ZVS) regions and current stresses, providing the basis for the subsequent performance optimization of TAB. Due to the number of switches increasing, TAB's degrees of freedom (the phase-shift angles and duty cycles) have multiplied compared with its two-port topology. The existing current calculation methods of TAB are challenging to balance accuracy and simplicity. Currently, the simple and accurate HFCC model, considering all the TAB variables, can hardly be found in the literature. This article proposes a unified and novel solution for TAB HFCC calculation based on the superposition theorem, Thevenin's theorem, and the time-domain method. It can get the uniform formula of the HFCC when all the control variables change. Based on the formula derived, this article further presents the corresponding ZVS and current stress analysis of TAB. A multiobjective optimal control method demonstrating efficiency improvement is proposed. Moreover, the tradeoffs between the control simplicity and optimization effect are analyzed based on the proposed HFCC model. The modulation pattern with the least algorithmic complexity for efficiency maximization is found. The validity and accuracy of the proposed method have been verified experimentally.

Identifying critical nodes in the power grid is a crucial aspect of power system security and stability analysis. However, the current methods for identification fall short in fully accounting for the power transfer characteristics between nodes and the consequences of node removal on the security and stability of power grid operation. To enhance the effective and accurate identification of critical nodes in the power grid, a method is proposed. This method is based on improved PageRank algorithm and node-weighted power flow transfer entropy, referred to as IPRA-PFTE. Firstly, based on the power flow and equivalent impedance between nodes, and the introduction of virtual nodes, an improved PageRank algorithm is obtained. Then the node-weighted power flow transfer entropy is derived by considering the uniformity of the transfer power flow distribution in the system following the removal of a node. Finally, the importance of nodes is obtained by combining the improved PageRank algorithm with the node-weighted power flow transfer entropy. The method’s effectiveness and accuracy are validated through simulation using the IEEE 39-bus example and subsequent comparison with existing methods.

Accurate power load forecasting is crucial for ensuring grid stability, optimizing economic dispatch, and facilitating renewable energy integration in modern smart grids. However, real load forecasting is often disturbed by the inherent non-stationarity and multi-factor coupling effects. To address this problem, a novel hybrid forecasting framework based on adaptive mode decomposition (AMD) and improved least squares support vector machine (ILSSVM) is proposed for effective short-term power load forecasting. First, AMD is utilized to obtain multiple components of the power load signal. In AMD, the minimum energy loss is used to adjust the decomposition parameter adaptively, which can effectively decrease the risk of generating spurious modes and losing critical load components. Then, the ILSSVM is presented to predict different power load components, separately. Different frequency features are effectively extracted by using the proposed combination kernel structure, which can achieve the balance of learning capacity and generalization capacity for each unique load component. Further, an optimized genetic algorithm is deployed to optimize model parameters in ILSSVM by integrating the adaptive genetic algorithm and simulated annealing to improve load forecasting accuracy. The real short-term power load dataset is collected from Guangxi region in China to test the proposed forecasting framework. Extensive experiments are carried out and the results demonstrate that our framework achieves an MAPE of 1.78%, which outperforms some other advanced forecasting models.

To solve the problems of large current stress, difficult soft-switching of all switches, and slow dynamic response of dual active bridge converters, a multi-objective unified optimal control strategy based on triple-phase-shift control was proposed. The forward power flow global modes of triple-phase-shift control were analyzed, and three high-efficiency modes were selected to establish the analytical models of current stress and soft-switching. Combined with these models, the optimal solutions in different modes were derived by using the cost function-optimization equation to overcome the limitation of the Lagrange multiplier method, such that the DAB converter achieved the minimum current stress, and all switches operated in the soft-switching state over the entire power range. At the same time, the virtual power component was introduced in the phase-shift ratio combination, which improved the dynamic response of output voltage under the input voltage or load steps changed by power control. The theoretical analysis and experimental results show that the proposed control strategy can optimize the performance of the DAB converter from three aspects, such as current stress, soft-switching, and dynamic response, which achieves multi-objective optimization of the steady-state and dynamic performance of DAB converters.

A triple-active bridge (TAB) can be used as a power conversion unit in a three-port DC energy router (DCER) such as a triple-active bridge-based DC energy router (TAB-DCER). The operational loss of a TAB can be seen as a key factor affecting the efficiency of a TAB-DCER. However, the RMS value of the inductor current of the TAB-DCER increases under single-phase shift (SPS) control, and this greatly increases the system operating losses. The use of phase-shifted plus PWM (PS-PWM) control can reduce the RMS value of the inductor current, but its mathematical model is complex, and involves difficult calculations. To address this problem, in the study reported here, we developed an optimal control strategy for the RMS value of the inductor current based on TAB-DCER. First, the working principle of a TAB-DCER under PS-PWM control was analyzed, and a circuit decomposition model was established. Second, the operating modes under PS-PWM control were analyzed, and corresponding expressions of port power and the RMS value of the inductor current were obtained. Third, an optimized mathematical model of the sum of squares of the RMS value of the inductor current of the TAB-DCER was constructed. Finally, a genetic algorithm was used to solve the mathematical model and derive the optimal phase shift angle; this resulted in a lower RMS value of the inductor current in the TAB-DCER and reduced the system operating losses. The simulation and experimental results show that the TAB-DCER used in the present study can reduce operating losses, improve system efficiency, and deliver coordinated power control.