The single-stage three-level converter with unbalanced dual DC ports offers both high efficiency and low cost, making it attractive for photovoltaic (PV)-battery integration. In such systems, interport power flow regulation typically relies on space vector pulsewidth modulation (SVPWM) or asymmetric carrier-based modulation, assisted by a PI-based port power distribution loop. However, these methods increase implementation complexity and compromise control simplicity, while their overall performance is unsatisfactory. To address this issue, a simplified direct power control is proposed by using symmetrical carrier-based virtual SVPWM. Specifically, the equivalent modulation waves, containing the zero-sequence voltage with the power regulation factor, are analytically derived. This derivation enables carrier-based implementation without sector determination and dwell time calculation of vectors. Furthermore, the mathematical relationship between the DC-ports power and the modulation wave is established, thereby providing intuitive guidance for the design of the power regulation factor. In this context, the power regulation bounds of the proposed method are quantitatively analyzed under different degrees of DC-voltage imbalance. Finally, experimental results obtained from a prototype verify the effectiveness of the proposed method.

With the increasing number of electric vehicles, charging demand in highway service areas has surged, posing significant challenges to the planning of energy facilities. This paper proposes a two-level robust optimization configuration method for highway Photovoltaic-Storage-Charging systems based on charging load forecasting. The upper-level model aims to maximize return on investment, employing a genetic algorithm for capacity optimization and constructing uncertainty sets for photovoltaic output to enhance robustness. The lower-level model utilizes the cell transmission model to simulate traffic flow and introduces cumulative prospect theory to capture the uncertainty of user charging behavior, enabling accurate charging load forecasting. Meanwhile, mixed-integer linear programming is adopted to optimize the intraday scheduling strategy of the energy storage system. The return on investment is fed back to the upper level via weighted robust aggregation, driving iterative evolution to obtain a robust optimal configuration that balances economic efficiency and service level. The proposed method is validated through a case study on the Wushen-Liangguang highways chain section. Simulation results show that the overall return on investment and photovoltaic consumption rate of the selected road section reach 20.85% and 98.53%, and the charging waiting time in most service areas is less than 5 minutes.

Port selection plays a critical role in the corrosion diagnosis of grounding grids. However, the lack of scientific guidance for port selection has restricted the application of network-based corrosion diagnosis methods. To address this issue, this paper proposes an iterative optimization method for port selection based on weighted coverage and multi-weight collaborative optimization. The method firstly establishes a port scoring model using the sensitivity matrix to quantify port values from three aspects: information complementarity, information richness and numerical stability. An iterative optimization strategy is then adopted, which greedily selects ports while predicting the resistance of grounding grid branches, thereby enabling recursive correction of the sensitivity matrix and gradually screening out port combinations with high-quality information. Finally, simulation experiments conducted on a simulated grounding grid with 53 branches show that, compared with the unweighted greedy selection with the same 14 selected ports, the weighted greedy selection method reduces the mean absolute percentage error from 34.83% to 5.7%. It is verified that the proposed method can effectively optimize the measuring port selection and improve the accuracy of grounding grid corrosion diagnosis. The research conclusions can provide theoretical guidance for port selection in corrosion diagnosis of large grounding grids.

Grid-connected inverter (GCI) with PQ control mode is widely used in energy storage, wind turbines, and photovoltaic power generation systems. However, the interaction among multiple factors, such as the power loop, phase-locked loop, and grid impedance, can easily lead to instability of the GCI. In engineering, GCI stability is usually improved by increasing or decreasing the controller parameters of the power loops. Nevertheless, the underlying mechanism of why such adjustments work remains unclear, which hinders parameter tuning by engineering technicians. Therefore, clarifying the influence mechanism and change rule of the controller parameters on GCI stability is essential. Hence, from a new perspective on positive and negative feedback loops (PNFL), this paper proposes an analysis method for GCI stability changes based on PNFL, and reveals the internal mechanism underlying the different influences of active power loop and reactive power loop controller parameters on GCI stability. Meanwhile, a criterion is proposed for determining the GCI stability based on whether the phase‑frequency characteristic of the feedback loop formed by the reactive power controller within the active power loop is greater than 0°. By cross‑validating the proposed criterion with the generalized Nyquist criterion derived from the impedance model, the underlying mechanism is revealed. It explains why the GCI stability demonstrates nonlinear behavior as the parameters of the reactive power loop controller are increased. Moreover, the effect of active power loop controller parameter on GCI stability is not as significant as that of reactive power loop controller parameter within the same range of parameter variation. Finally, simulations and experiments verify the proposed method.

For the multi-infeed system of grid-connected inverters (GCI), the interaction mechanism is highly complex, since it often involves key factors such as the control parameters, the power grid structure, and the impedance. The interaction among GCI can easily induce the multi-infeed system to lose stability due to harmonic oscillation. In this article, a harmonic oscillation source identification-based stability improving method for the GCI multiinfeed system is proposed. From the new perspective of constructing the closed-loop control of the node currents, this article first presents a current oscillation modal gain model of the GCI multi-infeed system with different control parameters considering the grid structure, line impedance, grid impedance, and other factors. Then, the key harmonic oscillation source and the corresponding oscillation frequencies are identified. What is more, the equivalent 'source-load' multiple-input multiple-output system for the oscillation source is constructed with the identified oscillation source inverter as the section. The harmonic oscillation phenomenon is also analyzed from the interaction characteristics of the main diagonal elements of the impedance matrix. Through the coordination of phase-locked loop positive feedback loop suppression and q-axis phase compensation, a stability improving method of asymmetric impedance reshaping is proposed, and the control parameters of the compensation unit are designed. Its characteristic lies in the ability to suppress harmonic oscillation effectively. Compared with the initial control strategy, the grid impedance range that the GCI multi-infeed system can adapt to is increased to nearly three times. Finally, the proposed method is verified by the simulations and experiments.

In this paper, an enhanced chaotic map with free control (ECMFC) is constructed based on the feedback from sinusoidal nonlinearity, which brings direct amplitude control, Lyapunov exponent rescaling, and offset boosting. In this case, two independent outstanding constants are found dealing with the special regime of the free control of offset boosting. The newly developed model can be employed as a powerful generator of pseudo-random numbers, which exhibits superior distribution uniformity, ergodicity, and pseudo-randomness, and thus can effectively assist optimization algorithms in escaping local optima. Due to its amplitude control and offset boosting, ECMFC enables the formation of targeted chaotic trajectories to rapidly steer the search process toward optimal regions. As a result, in robot path planning applications, the algorithm effectively identifies optimal paths.

Existing research on unconstrained in-the-wild head pose estimation suffers from the flaws of its datasets, which consist of either numerous samples by non-realistic synthesis or constrained collection, or small-scale natural images yet with plausible manual annotations. This makes fully-supervised solutions compromised due to the reliance on generous labels. To alleviate it, we propose the first semi-supervised unconstrained head pose estimation method SemiUHPE, which can leverage abundant easily available unlabeled head images. Technically, we choose semi-supervised rotation regression and adapt it to the error-sensitive and label-scarce problem of unconstrained head pose. Our method is based on the observation that the aspect-ratio invariant cropping of wild heads is superior to previous landmark-based affine alignment given that landmarks of unconstrained human heads are usually unavailable, especially for underexplored non-frontal heads. Instead of using a pre-fixed threshold to filter out pseudo labeled heads, we propose dynamic entropy based filtering to adaptively remove unlabeled outliers as training progresses by updating the threshold in multiple stages. We then revisit the design of weak-strong augmentations and improve it by devising two novel head-oriented strong augmentations, termed pose-irrelevant cut-occlusion and pose-altering rotation consistency respectively. Extensive experiments and ablation studies show that SemiUHPE outperforms its counterparts greatly on public benchmarks under both the front-range and full-range settings. Furthermore, our proposed method is also beneficial for solving other closely related problems, including generic object rotation regression and 3D head reconstruction, demonstrating good versatility and extensibility.

Capacitors are essential in industrial applications due to their ability to store and release electrical energy rapidly. However, capacitors, with their complex manufacturing processes and heterogeneous characteristics, pose two critical challenges to conventional detection algorithms: 1) the difficulty in detecting small-scale defects amid multiscale defect variations and 2) the inherently imbalanced distribution of defects. To address these issues, the GDTS-YOLOv8 detection framework is introduced, which leverages a gather-and-distribute (GD) mechanism and a two-stage self-fine-tuning (TS) strategy with a frozen backbone. First, GD is constructed to enhance the efficiency of multiscale feature extraction and fusion in the YOLOv8 feature fusion layer. Centered on optimizing cross-level information exchange and reducing information loss, this mechanism can effectively address the issue of poor detection performance for small targets in multiscale scenarios. Second, TS is proposed to address the inherent imbalance problem, comprising: 1) a rare-defect upweighting pretraining stage with label reduction of frequent defects and 2) a full-data fine-tuning stage integrating label reintroduction of frequent defects with backbone freezing for optimal performance. Experimental results confirm the proposed method's effectiveness, enhancing capacitor detection precision by over 5% (75.3% peak accuracy) and demonstrating significant improvements in small-defect detection and imbalanced sample handling.

Capacitors are essential in industrial applications due to their ability to store and release electrical energy rapidly. However, capacitors, with their complex manufacturing processes and heterogeneous characteristics, pose two critical challenges to conventional detection algorithms: 1) the difficulty in detecting small-scale defects amid multi-scale defect variations; and 2) the inherently imbalanced distribution of defects. To address these issues, the GDTS-YOLOv8 detection framework is introduced, which leverages a Gather-and-distribute mechanism (GD) and a two-stage self-finetuning strategy with a frozen backbone (TS). Firstly, GD is constructed to enhance the efficiency of multi-scale feature extraction and fusion in the YOLOv8 feature fusion layer. Centered on optimizing cross-level information exchange and reducing information loss, this mechanism can effectively address the issue of poor detection performance for small targets in multi-scale scenarios. Secondly, TS is proposed to address the inherent imbalance problem, comprising: (a) a rare-defect upweighting pre-training stage with label reduction of frequent defects; (b) a full-data fine-tuning stage integrating label reintroduction of frequent defects with backbone freezing for optimal performance. Experimental results confirm the proposed method's effectiveness, enhancing capacitor detection precision by over 5% (75.3% peak accuracy) and demonstrating significant improvements in small-defect detection and imbalanced sample handling.

Although existing transfer learning approaches have demonstrated their potential in cross-domain bearing fault diagnosis, they predominantly rely on single-source operating conditions and emphasize global feature alignment between domains. This single-domain paradigm introduces two critical limitations: insufficient adaptation of decision boundaries for target domain distributions, and neglect of discriminative local features across varying operational conditions. To overcome these challenges, a multi-operating-condition-guided approach with global-local contrastive learning for few-shot cross-domain fault diagnosis is proposed. Our methodology innovatively integrates multi-source domain supervision with contrastive feature learning through two key mechanisms: global contrastive alignment, which preserves condition-invariant characteristics across multiple operational domains, and local contrastive refinement, which enhances discriminative feature learning through fine-grained sample relationships. By jointly optimizing global and local contrastive objectives, the proposed method effectively bridges domain discrepancies while maintaining condition-specific discriminability, particularly in few-shot scenarios in which only limited labeled target samples are available. Comprehensive evaluations of two rotating machinery datasets demonstrated that the proposed method achieved superior cross-domain diagnostic accuracy and enhanced stability compared with other popular transfer learning methods. https://github.com/xinyeC/fircode1024cc/tree/main/GLCL.

The spatio-temporal distribution of electric vehicle charging loads is affected by the high uncertainty of road network traffic conditions. Therefore, the accuracy of prediction of charging load can be improved by combining the real-time road network traffic condition, which can provide a basis for the distribution network to cope with the charging load. We present a spatio-temporal charging load prediction method based on cellular traffic simulation. First, joint vehicle-road-network modeling and travel chain principles simulate user travel patterns. Next, a metacellular transmission model integrates traffic flow simulation to represent urban road dynamics, including vehicle behaviors (e.g., following, lane changes) and adaptive traffic flow updates. Road traffic indexes are derived, and an EV energy consumption model incorporating traffic parameters predicts charging load distribution. Simulation in a Hunan urban area validates the method’s feasibility.

Grid impedance and phase-locked loop (PLL) are critical factors for the stability of the grid-connected inverters (GCIs) in a weak grid. They are the positive feedback control loops formed by PLL in the GCI with grid impedance. It is prone to GCI instability, especially in the case of the higher PLL bandwidth. A novel impedance-phase and magnitude control strategy is proposed to improve stability of GCI with different grid impedance. Moreover, a detailed design of control loop and parameter calculation for the impedance-phase and magnitude control strategy are introduced. First, PLL output impedance is reshaped to broaden the frequency range of the GCI phase-frequency characteristic curve above the -90 degrees line towards the low-frequency band. In addition, current loop output impedance is reshaped to maintain the phase margin (PM) of the GCI near to 45 degrees. Meanwhile, the magnitude of GCI output impedance is also increased significantly. Stability of the GCI in a weak grid is enhanced by adopting the proposed control strategy. Simulation and experimental results verify the analysis and the proposed method.

Recently, data-driven methods have gained increasing prominence in the field of machinery intelligent fault diagnosis (IFD). Unfortunately, three main shortcomings of IFD models are exposed during the practical application process: (1) the new session necessitates a significant quantity of labeled fault samples; (2) the diagnostic model cannot maintain long-term diagnosis; (3) cross-machine fault diagnosis is not achievable. To overcome these drawbacks, a class- added continual learning framework based on Knowledge-informed Dual-branch Network (KDN) is proposed for continual fault diagnosis of mechanical equipment with limited samples. In particular, a category-knowledge distillation technique is employed to retain the diagnostic knowledge acquired from the previous session, while a knowledge-transfer regularization loss is applied to avoid overfitting of the diagnostic model. Furthermore, a self-adapting knowledge- weight allocation mechanism is introduced to automatically assign the relative weights of each loss function. In this way, the diagnostic model can continuously identify various fault categories, which substantially enhances its performance across different machines. Experiments on three rotating machinery datasets are conducted to validate the effectiveness and superiority of the proposed KDN. The experimental results demonstrate that the proposed KDN is capable of performing continual fault diagnosis on data from different machines, even with a limited number of samples in the new session.

Under the high penetration of new energy, multi-machine interconnected grid-connected inverters (GCIs) are prone to lose stability due to the interaction with the power grid. To enhance their adaptability to the grid, a stability improvement method for multi-machine interconnected GCI systems with flexible control bandwidth design is proposed. First, based on the design principles of the converter control bandwidth, the transfer function models of the voltage outer loop and current inner loop are constructed, and the inner and outer loop control bandwidths that meet the requirements are designed. Secondly, to mitigate the adverse effects caused by the interaction of control bandwidths among multi-machine interconnected inverters, By analyzing the influence mechanism of the inner and outer loop bandwidths on the stability of the multi-machine interconnection system under different ratios, a flexible control bandwidth allocation scheme for different inverters in the multi-machine interconnection scenario is designed., thereby enhancing the stability of multi-machine-interconnected GCI through differentiated control bandwidth design. Unlike methods that add extra control loops, the method proposed in this paper does not require sampling new physical variables or modifying the control structure. Instead, it only necessitates adjusting the controller parameters of the multi-machine interconnection system, specifically the optimized distribution of bandwidth, to enhance the stability of the multi-machine interconnection system. Finally, simulation results are presented to verify the correctness and effectiveness of the proposed control method.

Existing deep transfer learning methods assume that all labeled samples are correctly annotated. However, owing to factors such as human mistakes, measurement deviations, data transmission faults, and storage inaccuracies, it is unrealistic to accurately label all fault samples in actual industrial production. To solve this issue, a reliability confidence transfer learning framework (RCTLF) is proposed for crossdomain intelligent fault diagnosis (IFD) of electric motors in this study. Specifically, a reliability-aware evaluation mechanism is adopted to evaluate the reliability of each source sample. Meanwhile, a confidence estimation mechanism is utilized to assess the confidence level of each fault sample from the source and target domains. At last, a novel loss function is designed to train the constructed diagnostic model. To showcase the effectiveness of the proposed RCTLF in practical diagnostic scenarios, we conducted experiments on two electric motor datasets for IFD with noisy labels. The effectiveness of the proposed RCTLF is highlighted by comparisons with current advanced methods. The experiments confirm that it can still achieve satisfactory results, even when 80 % of the source domain data is mislabeled.

Based on the characteristics of electrolyzers, hydrogen storage tanks, and hydrogen fuel cells, this approach mitigates the impact on the upstream power grid caused by the current instability of new energy power generation. Installing hydrogen energy systems at distribution grid nodes incorporating photovoltaic power generation and connected to the upstream grid addresses this issue. The hydrogen energy system comprises three components: electrolyzers, hydrogen storage tanks, and hydrogen fuel cells. The system can draw electricity from the grid and also discharge electricity back into the grid. This paper utilizes the NSGA-II genetic algorithm to solve for the optimal configuration capacities of the electrolyzer, hydrogen storage tank, and hydrogen fuel cell components. The optimization is based on the daily load curve of the distribution network node, with the objective functions being the minimization of grid power purchase volatility and the maximization of renewable energy absorption rate. The solution aims to enhance the power quality of this distribution network node.

Since the voltage amplitude of the arc suppression device is different during the normal operation and single line-to-ground fault, the problems of high cost and low module utilization rate are serious. An integrated grid-connected converter (IGCC) with reactive power compensation and fault regulation ability is proposed. First, the topology and operation mechanism of IGCC are introduced in this article. A common unit combining neutral point clamped (NPC) and cascaded H-bridge is formed by improving the traditional arc suppression device. By adding a fourth leg in the NPC module and connecting with the arc suppression inductance, the integration of the two structures is realized. The access of NPC unit not only reduces the number of modules of traditional arc suppression device, but also provides an integrated port for arc suppression device and reactive power compensation device. Second, the parameters of active and passive part of IGCC are optimally designed to ensure the stable operation of IGCC. In addition, compared with existing schemes, the superiority of IGCC in cost and volume is proved. Finally, the correctness, feasibility and effectiveness of the proposed topology and functions are verified by the simulation and experiment results.