Jinbin Zhao

In [1], there are errors in the description of some parameter settings and descriptions. The authors would like to correct this terminology error.

Offshore wind power faces significant challenges in balancing cost and reliability, while most existing commercial or emerging technical solutions struggle to address both aspects simultaneously. The integration of wind farm centralized power conversion (CPC) technology with flexible low-frequency transmission presents a promising approach. This combined solution holds potential to minimize offshore power electronic devices, thereby reducing costs and enhancing reliability, though it first requires resolving the critical challenge of communication-free long-distance CPC. This paper begins by tracing the origin of power tracking control in variable-speed constant-frequency (VSCF) wind turbines. Analytical findings reveal that the maximum power point tracking (MPPT) control widely adopted in commercial turbines fundamentally operates as a model-based, power imbalance-driven rotational speed-wind speed progressive follow-up system, rather than a classical closed-loop feedback system. Such control methodology proves particularly advantageous for inherently huge-inertia wind turbines in achieving the comprehensive objectives of load reduction, power smoothing, and tracking efficiency. Building on this foundation, the study proposes a communication-free centralized power conversion (CPC) principle based on active power-frequency (P-f) progressive follow-up control, accompanied by communication-independent compensation control and technical feasibility analyses. Furthermore, a flexible dynamic low-frequency wind power system (DLFWPS) is developed, incorporating multi-perspective suitability evaluations of various generator types. Through comprehensive simulations and experimental validation, the feasibility and superiority of both the communication-free CPC principle and the DLFWPS are conclusively demonstrated. The proposed system achieves comparable power production to conventional VSCF wind farms while exhibiting enhanced cost-effectiveness, grid frequency support and operational reliability.

Estimating the state of health (SOH) of lithium-ion batteries (LIBs) based on data-driven methods are widely used by extracting health features (HFs) from complete charging measurements. Since the user’s charging habits are different, it is, however, difficult to obtain complete HFs under random charging conditions. This article proposes an SOH online estimation method based on random charging curves to solve this problem. First, rough localization of the random charging curve is accomplished to obtain its position relative to the full charging curve and get the data needed for the reconstruction model by backtracking the historical charging data. Second, the extreme learning machine optimized by genetic algorithm and hill climbing algorithm (GA-HC-ELM) is applied to reconstruct the partial charging curve. Then, HFs are extracted from the reconstructed curves, and Gaussian process regression (GPR) is applied for fast SOH estimation. Finally, three commonly used public datasets were selected to conduct a feasibility analysis of the proposed method. The results show that the charging curve reconstruction average root-mean-square error (RMSE) is within 1.01 mV in some typical cases, and the SOH estimation RMSE is stable within 1.22% in most cases, indicating that this method has a reliable SOH estimation effect under random charging conditions.

To solve the probleµ of large current ripple on the low-voltage side of the interleaved switched capacitor bidirectional converter in the energy storage systeµ, an interleaved parallel switched capacitor bidirectional DC/DC converter based on coupled inductors by using µagnetic integration technology is proposed. the µagnetic core can be effectively shared by integrating the discrete inductors on the low-voltage side of the converter into a coupled inductor, and the coupling coefficient is adjusted to fully utilize the core flux, thereby increasing the inductance value and iµproving the current ripple on the low-voltage side. Based on the analysis of the relationship aµong the inductance value, duty cycle and coupling coefficient under different µodes of the converter, this paper finds the functional relationship between the converter ripple current and the coupling coefficient. The siµulation results show that when the coupling coefficient is greater than 0, the current ripple decreases on the low-voltage side gradually as the coupling coefficient increases. Finally, a 150W experiµental prototype is built for verification. The efficiency of the converter is 91.28%. When the coupling coefficients are 0.15 ,0.63 and 0.82, the current ripple on the low-voltage side was reduced by 9.88%, 37.03% and 44.44%, respectively.

The mathematical mechanism model of modular multilevel converters (MMC) exhibits high-dimensional complexity, necessitating a trade-off between accuracy and efficiency during the modeling process. To address this issue, this article proposes a nonmechanism modeling approach for MMC, focusing on the mapping relationships between electrical quantities. The proposed method utilizes the convolutional neural network - long short-term memory - attention mechanism (CNN-LSTM-AM) neural network algorithm to drive the data-driven modeling of MMC. Operational data from the MMC system is utilized as the training set. The CNN layer selects input features with strong correlations, while the LSTM layer captures temporal dependencies among the features. The AM layer performs weighted aggregation on the LSTM output to enhance prediction accuracy. In addressing the challenge of fitting frequency, which cannot be directly achieved using electrical quantities, the frequency model incorporates a Hilbert–Huang transform (HHT) decomposition of voltage and current signals. The extracted instantaneous frequencies and amplitudes serve as additional input features to improve the fitting of output features. This approach effectively resolves the issue of inadequate prediction accuracy in the frequency model. Additionally, data-driven models for MMC inverters and rectifiers are trained using simulation data and evaluated for accuracy using a separate test set.

Traditional methods for flexible capacity allocation do not take into account the actual operation status of resources, and this can lead to redundancy of allocation results in a high renewable penetration power system. Using collaborative optimization during the flexibility resource planning stage can significantly improve the overall economics and flexibility. Therefore, a bilevel operation-planning joint optimization model for flexible capacity allocation is proposed in this paper. The aim is to optimize the annual total cost and flexibility of the system. The upper planning level introduces the economic costs, flexibility resource capacity, and flexibility index which are used as the evaluation index of system flexibility, while in the lower operation level, a morphological clustering algorithm based on the multiscale and entropy weight method is proposed for obtaining typical scenarios of flexibility demand. On this basis, the lower level simulates production to estimate daily operating costs. In addition, the model is solved iteratively using the nondominated sorting genetic algorithm-II (NSGA-II) and the linear programming method to obtain the Pareto solutions. Case studies are carried out based on a practical town area, and the results verify the validity and rationality of the proposed bilevel capacity allocation model.