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Bipolar DC–DC converter plays an important role in data center and distributed renewable energy unit with the advantages of high efficiency and low cost. In order to improve the power density of bipolar DC–DC converter, a high frequency and high power density bipolar DC–DC converter based on Gallium Nitride High Electron Mobility Transistor (GaN HEMT) is proposed under the development trend of miniaturization, lightweight and high power density of power electronic converter. By using gallium nitride (GaN) switch device, the switching frequency of the converter is increased to 1 MHz, which effectively reduces the volume and weight of the device. In addition, through the design of high frequency driver circuit, two separate pull-up/pull-down outputs are provided for the control signal, and the rising and falling rates of the switch signal are controlled by adjusting the resistance to obtain good performance and driving stability. The experimental results show that the total weight of the converter with radiator is only 121.6 g, the power density is 35.23 W/in3, and the maximum efficiency can reach 92.7%. Compared with the same type of converter, the volume and weight of the converter are greatly reduced, effectively realizing the goals of miniaturization, lightweight and high power density of the converter.

A high-reliability SEPIC converter with reconfigurable voltage conversion gain is proposed, which is composed of a basic SEPIC converter and n extended units. Adopting an interleaved parallel structure on the input side of the converter not only achieves leg-level hardware redundancy but also reduces the input current ripple. When one branch of the converter suddenly changes or fails, due to the redundancy between branches, the converter can still to work, and the duty cycle of the other branches can be readjusted by using PI control without changing output voltage and power of the converter. This feature undoubtedly presents the reliability of the converter. On the contrary, when the converter works in normal operation, it can also actively control operation mode of each branch to achieve reconfigurable voltage conversion gain function. In addition, since the duty cycles of the switches are not limited, voltage conversion gain of the converter is widely, which makes it more suitable for the occasions where the input side fluctuates greatly. Section 2 details the working principle of the proposed converter and the voltage and current stress on components. Then, the reconfigurable voltage conversion gain of the converter and its high reliability characteristics are analyzed in Section 3. Finally, the correctness of the theoretical analysis is verified by experiments.

In order to address the energy imbalance issue of a series-connected lithium-iron battery pack, this paper proposes an active equalization method based on a reduced-order solving strategy for the Hanoi Tower problem. The proposed scheme utilizes a combined structure of a switching-network circuit and a bidirectional Cuk converter and leverages an ultracapacitor cell as the energy-transfer carrier. Simulation and comparison demonstrate that the exchange of unbalanced energy within the battery pack can be achieved. The proposed approach can effectively achieve various balancing modes such as cell-to-cell, cell-to-string, string-to-cell, and string-to-string with a relatively fast balancing speed.

Abstract This paper proposes an intelligent Deep Learning (DL) based approach for Data‐Driven Security‐Constrained Unit Commitment (DD‐SCUC) decision‐making. The proposed approach includes data pre‐processing and a two‐stage decision‐making process. Firstly, historical data is accumulated and pre‐processed. Then, the DD‐SCUC model is created based on the Gated Recurrent Unit‐Neural Network (GRU‐NN). The mapping model between system daily load and decision results is created by training the DL model with historical data and then is utilized to make SCUC decisions. The two‐stage decision‐making process outputs the decision results based on various applications and scenarios. This approach has self‐learning capabilities because the accumulation of historical data sets can revise the mapping model and therefore improve its accuracy. Simulation results from the IEEE 118‐bus test system and a real power system from China showed that compared with deterministic Physical‐Model‐Driven (PMD)‐SCUC methods, the approach has higher accuracy, better efficiency in the practical use case, and better adaptability to different types of SCUC problems.

Security-constrained unit commitment (SCUC) has been extensively studied as a key decision-making tool to determine optimal power generation schedules in the operation of electricity market. With the development of emerging power grids, fruitful research results on SCUC have been obtained. Therefore, it is essential to review current work and propose future directions for SCUC to meet the needs of developing power systems. In this paper, the basic mathematical model of the standard SCUC is summarized, and the characteristics and application scopes of common solution algorithms are presented. Customized models focusing on diverse mathematical properties are then categorized and the corresponding solving methodologies are discussed. Finally, research trends in the field are prospected based on a summary of the state-of-the-art and latest studies. It is hoped that this paper can be a useful reference to support theoretical research and practical applications of SCUC in the future.

How to obtain the optimal decision-making scheme based on the investment behavior of various stakeholders is an important issue that needs to be solved urgently in incremental distribution network planning. To this end, this article introduces the virtual player “Nature” to realize the combination of the game theory and robust optimization and proposes an incremental distribution network source–load–storage collaborate planning method with a multi-agent game. First, the planning and decision-making models of a DG investment operator, a distribution network (DN) company, power consumers, and a distributed energy storage (DES) investment operator are constructed, respectively. Then the static game behaviors between the DG investment operator and distribution network company, as well as the DG investment operator and the DES investment operator, are analyzed based on the transfer relations between these four participants. At the same time, robust optimization is used to deal with the uncertainty of the DG output, and the virtual player “Nature” is introduced to study the dynamic game behavior between the DG investment operator and the distribution company. Finally, a dynamic–static joint game planning model is proposed. The simulation results verify the correctness and effectiveness of the proposed method.

The fault of power systems introduces a severe challenge in terms of fault recording analysis, and the traditional Prony method cannot perform satisfactorily in the process of signal recordings fitting caused by faults. Therefore, an improved adaptive Prony algorithm is proposed in this article to study the characteristics of fault recordings. Specifically, the search step size is taken as an adaptive variable, and the mean square relative fitting error (MSRFE) is set as the criterion. Then, a large step is employed to rapidly determine an approximate segmentation point in the initial stage of the searching process, and its horizon is gradually reduced to establish an accurate subsegment point. Finally, the Prony algorithm is deployed to analyze the subsegment fitting original signal. The proposed approach has been simulated on an assumed fault signal, and the results validate the accuracy and efficiency of the method.