Cervical spondylotic myelopathy (CSM) and parkinsonian syndromes (PS) present similar motor symptoms, often causing misdiagnosis due to current clinical diagnostic limitations. Misdiagnosis can exacerbate patient conditions or result in unnecessary surgical interventions, thereby increasing surgical risks and the likelihood of serious postoperative complications. This study aims to develop a mixed dual-branch network for classifying CSM patients, PS patients, and healthy individuals using gait data. This study recruits 51 CSM patients, 49 PS patients, and 33 healthy controls. The kinematic data are collected and used to calculate the time series of angle, angular velocity, and angular acceleration for the hip, knee, and ankle joints. From each time series, 20 features are extracted, including the time domain, frequency domain, time-frequency domain, and nonlinear features. A dual-branch model named DCDM-Net is proposed to classify subjects through collaborative decision making (CDM) method, with one branch using ResNet with convolutional block attention module (CBAM) and evidential deep learning (EDL) loss for analyzing time series, and the other employing multilayer perceptron (MLP) for dealing with multi-domain features. DCDM-Net achieves an ACC of 92.35% $\pm ~0.76$ % and an AUC of 96.70% $\pm ~0.47$ % in the three-class classification task. Additionally, in binary classification scenarios, the model demonstrates robust performance with an average ACC of 93.13% and AUC of 98.34%. Furthermore, comparative evaluations show that the integrated EDL module surpasses Softmax, MC-Dropout, and Deep Ensembles in uncertainty estimation, yielding the lowest Expected Calibration Error (ECE of 0.0304) and lower Brier score (0.1074), indicating superior reliability. However, cross-dataset OOD validation yielded an AUROC of $0.4022~\pm ~0.2481$ and an AUPR of $0.9699~\pm ~0.0162$ , revealing that restricting features to joint angles leads to significant distribution overlap; this conversely validates that angular velocity and acceleration are indispensable for preventing model overconfidence. Interpretable results obtained through the SHapley Additive exPlanations (SHAP) method and the integrated gradients (IG) method are confirmed by clinical findings. Our method provides a promising tool for diagnosing CSM and PS, with the potential to reduce misdiagnosis. The code implementation of this study is available at https://github.com/AImedcinesdu212/DCDM-Net.

Transient power quality disturbances (PQDs) are highly abrupt and destructive; therefore, their reliable monitoring is essential for the secure and stable power system operation. A wide variety of monitoring indices and the full-data upload architecture pose increasing challenges for lightweight monitoring of transient PQDs. Higher order statistics (HOS), especially kurtosis, can effectively capture abrupt waveform changes, making them suitable as unified monitoring indices of multitype transient PQDs. However, traditional kurtosis has reduced monitoring accuracy due to its phase sensitivity. To address this issue, a lightweight monitoring method for transient PQDs based on a phase-robust kurtosis (PRK) index is proposed. This method first introduces kurtosis as a lightweight monitoring index and explains the cause of phase sensitivity from both theory and waveform distribution characteristics. Then, a PRK index is proposed based on a phase-aligned coupling component to eliminate the phase sensitivity and accurately characterize the severity of transient PQDs. In addition, a cloud-edge-end collaborative lightweight monitoring method is developed to clarify the monitoring process of the PRK index, which effectively guides the lightweight acquisition and transmission of monitoring data and supports intelligent analysis. Both simulation and actual measured data demonstrate the significant capability of the proposed method to reduce data volume and improve monitoring accuracy.

With the increasing deployment of non-linear power electronic devices in distribution networks, the issue of excessive harmonics has become increasingly prominent. Accurate analysis and forecasting of harmonic currents are essential to better understand and mitigate harmonic-related problems. Common harmonic sources in power grids include various types of loads and renewable energy generation equipment, such as wind and photovoltaic systems. Accordingly, this paper focuses on forecasting harmonic currents for different types of aggregated harmonic sources. Considering the strong randomness, high time-variability, and coupling characteristics of harmonic currents, a data-driven probabilistic harmonic current forecasting method is proposed. First, the raw data are decomposed into deterministic and stochastic components. Next, a radial basis function autoregressive exponential model is employed to perform point forecasting of the deterministic component, accommodating multiple input factors and simultaneous multi-harmonic outputs while preserving model partial interpretability. Subsequently, the proposed multi-Gaussian fitting method and an improved Markov chain Monte Carlo approach are used for probabilistic forecasting of the stochastic component. Finally, the deterministic and stochastic component predictions are combined to yield the final probabilistic harmonic current forecast. Using actual harmonic measurement data, comparative analyses with multiple intelligent models are conducted to validate the effectiveness and accuracy of the proposed forecasting method. Furthermore, the harmonic current predictions are applied to forward probabilistic harmonic power flows, and corresponding forward and new probabilistic harmonic mitigation measures are proposed, demonstrating practical engineering applicability.

High-proportional renewable energy integration and rapid installation of new type loads, such as electric vehicles (EVs), into the power system are challenging the monitoring and operation of the power system. Different from the conventional system operation requirements, high-quality and real-time power system data become essential for system operators on key power equipment monitoring, power flow dispatching, and operation optimization. However, due to equipment limitations, bandwidth, storage and other factors, power system data is usually collected at lower sampling frequencies. Therefore, super-resolution reconstruction of these data is necessary to obtain higher frequency data to meet the system operation requirements. In this paper, a super-resolution reconstruction method based on weather-aware self-attention time series generative adversarial network (WA-TGAN) is proposed. The proposed method considers the influence of weather impacts and introduces the self-attention mechanism. The developed generator can more effectively capture long distance dependencies in power system data and improve the quality of generated data, which could help the operators obtain higher resolution and more realistic data and provide high quality data samples for tasks such as new energy forecasting and power quality assessment. The super-resolution reconstruction method proposed in this paper is validated based on actual distribution system data from northern China and publicly available datasets from Austria. The experiment results are validated through indicators such as reconstruction error and timing characteristic evaluation and compared with traditional super-resolution reconstruction methods. The experiment results indicated that the proposed method has better performance compared to the traditional methods.

The improvement of power quality for a hybrid grid connected system with grid-following (GFL) and grid-forming (GFM) converters becomes an essential issue due to their complex harmonic generation characteristics. Currently, traditional mitigation methods easily cause harmonic amplification or resonance due to ignoring the influence of harmonic coupling characteristics. The key to solving this issue is to establish the coupling model and a robust harmonic mitigation method suitable for different converters. Therefore, this paper first constructs a harmonic coupling admittance matrix (HCAM) model considering the GFL & GFM converters, and the line impedance based on theoretical derivation and measured data. Secondly, the performance on HCAM due to the traditional impedance reshaping method is analyzed, and its adverse effects on the harmonic admittance are quantified as equivalent delay. Finally, the harmonic vector control with a phase compensator is proposed, significantly improving the harmonic mitigation performance of a hybrid grid-connected system. Comparative experiments are provided by an experimental platform with GFL&GFM converters, which is driven by digital signal processing controllers. The results verify the validity of the analysis and the superiority of the proposed method.

A significant amount of nonlinear loads exacerbate the distorted degree of the grid, and the development of grid-forming (GFM) converters provides new opportunities for power quality improvement. Currently, the difficulty of GFM converters in independently realizing active mitigation of harmonic currents in the whole system is the state perception of other harmonic sources. Therefore, modeling the correlation between the harmonic states of the grid and the GFM converter based on the existing rather than additional sampling is the key factor in guiding harmonic suppression methods. In order to address this issue, this article first compares the existing GFM harmonic mitigation methods, clarifying the need for differential control of harmonics generated by the GFM converter and loads. Second, a coordinated control with a harmonic voltage compensator and virtual impedance is proposed in this article, and separately suppresses the harmonic components mentioned previously. This method only needs the initial harmonic phase of the grid and equates the load to a constant harmonic current source model. Third, this method is improved by considering the change of distorted voltage at point of common coupling on the load, and a more accurate compensator based on the harmonic Notorn model of the load is proposed to achieve better harmonic mitigation performance. Finally, the comparative steady-state and dynamic experiments are provided by a 10 kW GFM converter and rectifier load. The results prove the effectiveness and superiority of the proposed methods, and the harmonic currents injected into the grid can be decreased by over 90%.

Photovoltaic (PV) has become a new type of harmonic source due to its harmonic generation characteristics under both ideal and nonideal grid. The harmonic distortion becomes more serious due to an increase in PV penetration and the application of nonlinear loads. Furthermore, the research has found that the current total harmonic distortion (THD) trend behaves as an inverted "U" shape, and the harmonic mitigation method should be improved correspondingly to ensure that THD remains at a low level under complex power operating conditions. First, the relationship between harmonic percentage and PV penetration is modeled in this article. Second, a trapezoidal wave compensation method is proposed to suppress the harmonics of PV generated caused by the dead zone under an ideal grid. Third, a novel harmonic current feedback strategy is proposed to decrease the THD for the grid with significant background harmonics. Finally, comparative experiments are shown in a 10-kW PV converter experimental platform and a modified IEEE 33-bus distribution network (DN) model, and the simulation and experimental results prove the effectiveness and superiority of the proposed method.

With the rapid growth of urban load density and the explosive expansion of DC loads, traditional AC distribution networks face severe challenges in capacity and stability. Medium-voltage direct current (MVDC) intertied systems, integrating grid-following (GFL) and grid-forming (GFM) converters, have emerged as a promising solution to enhance the resilience and efficiency of urban AC/DC hybrid grids. However, the coordinated site selection and planning of GFL and GFM converters, balancing control stability and economic efficiency, remains a critical unresolved issue. This paper proposes a novel site selection and planning method for embedded urban MVDC intertied systems. First, a dual-stiffness index framework is developed, incorporating voltage stiffness (Kvtg) and current stiffness (Kctg) to quantify the stability margins of GFL and GFM modes, respectively. These index establish critical thresholds for stability boundaries, providing a quantitative basis for coordinated converter planning. Second, an integrated planning methodology is formulated to balance control benefits and economic considerations, where Kvtg and Kctg are embedded as stability constraints, alongside investment and operational costs, to achieve technical and economic synergy. Third, to improve planning efficiency, a neural network-based decoupled stability assessment model is designed. Latin hypercube sampling generates input features, and a dual-branch ReLU neural network learns the stiffness characteristics of GFL and GFM converters separately. The model is linearized using the Big-M method for compatibility with mixed-integer linear programming (MILP) optimization. Case studies on a 36-node urban distribution system in Dongying, China, validate the proposed method. Results show that the optimal planning of intertied system with converters at nodes 9 and 17 not only reduces voltage sag domains by up to 70.4% during faults but also significantly enhances frequency stability by limiting the Rate of Change of Frequency and improving the frequency nadir, while minimizing lifecycle costs. This method effectively coordinates GFL and GFM converters, ensuring stable operation and economic feasibility in complex urban grids.

The integration of grid-following (GFL) and grid-forming (GFM) controls leverages their strengths to enhance stability in renewable power systems. However, a critical limitation in current small-signal stability analyses is the common oversight of dynamic variations of the PCC voltage phase angle. The distinct responses of GFL and GFM converters during small-signal events are not adequately captured, which can lead to errors in stability assessment. This paper proposes an optimized small-signal model that explicitly includes the dynamic behavior of the PCC voltage phase angle. The accuracy and validity of the proposed model are verified through comparative simulations using a detailed MATLAB/Simulink electromagnetic transient (EMT) model.

Inverter-based resources (IBRs) are a new type of generators entering power systems. Industries are concerned about the harmonic impact caused by IBR plants. How to model IBRs for harmonic studies has, therefore, become an important topic. This industry application-oriented tutorial paper presents a comprehensive review and analysis of the harmonic behaviors of IBR units, covering their harmonic characteristics, advanced and practical harmonic models, methods for model parameter determination and more. Lab test results on a physical IBR unit are presented to substantiate the findings. It is hoped this paper will clarify the confusion between the harmonic models of VSC (voltage source converter) based IBR units versus LCC (line commutated converter) based loads. Furthermore, a comparison between IBRs and synchronous generators reveals many similarities in their harmonic behaviors.

The site selection of converter stations for voltage source converter‐based direct current (VSC‐DC) interties can significantly impact their control performance, particularly when both grid‐following control (GFL) and grid‐forming control (GFM) are configured. It is challenging to fully realise the maximum potential of both control modes at a single node within an urban power grid. This paper proposes a novel converter station site selection method to identify optimal connection points in urban grids, enabling efficient active power transfer while providing flexible active and reactive power support in interconnected urban areas. First, a comprehensive index system is developed to quantitatively assess the impact of VSC‐DC intertie converter station siting on urban grids under both GFL and GFM modes. The proposed method utilises voltage stability margins to quickly rank candidate nodes and combines Euclidean distance with grey relational analysis to determine the most advantageous locations. A case study demonstrates the effectiveness of the proposed approach. The results show that the novel site selection method ensures converter stations configured with both GFL and GFM are optimally sited, thereby maximising control performance, significantly enhancing power support capabilities, and improving resilience at the connected nodes.

To address the challenges in characterizing power system faults, this paper proposes a novel powerbased fault assessment methodology. First, building upon the instantaneous reactive power theory, the power decomposition framework is extended to unbalanced conditions, with a rigorous measurement methodology established. Second, critical parameters such as disturbance power and energy flow are defined as key metrics for evaluating power system faults. Finally, comprehensive analysis of fault identification, propagation, and localization is conducted from a power-based perspective. Results demonstrate that this methodology achieves precise fault localization and provides novel insights for fault assessment.

Due to large-scale power electronic equipment access, the problem of harmonic voltage distortion has become serious in the distribution network. Voltage-detecting active power filters (VDAPF) are widely used due to their harmonic mitigation performance at the control node. However, the influence of VDAPF on the adjacent nodes is generally ignored in traditional analysis methods, it decreases the efficiency of VDAPF configuration and harmonic mitigation. Therefore, a voltage harmonic mitigation evaluation method of VDAPF is proposed based on the harmonic network propagation equation. Firstly, the control structure of VDAPF is introduced, and the virtual conductance is used to express its harmonic mitigation performance on the distribution network. Secondly, a voltage harmonic mitigation evaluation method is proposed to model the influence on control nodes and controlled nodes. Finally, the correctness and validity of the proposed method are verified by the mathematical analysis and simulation in a 3-node system.

Harmonic pollution poses an increasingly serious threat to modern power systems, necessitating real-time, highprecision monitoring solutions. Traditional harmonic monitoring methods such as Fast Fourier Transform (FFT) suffer from issues such as high computational complexity, multi-cycle dependency, and poor noise immunity, making them unsuitable for achieving high-precision real-time online monitoring in complex harmonic environments. This paper proposes a lightweight evaluation method for harmonic monitoring indicator based on higher-order statistics (HOS), which achieves real-time online monitoring of harmonic distortion by optimizing data acquisition and processing strategies. This method combines time-domain waveform variance characteristics to construct a lightweight indicator VHD that is independent of harmonic order and phase, effectively reducing computational load and improving data stability. Simulation results demonstrate that the proposed indicator exhibits a good mapping relationship with the traditional total harmonic distortion (THD) indicator. Compared with the FFT-based THD method, the proposed method achieves an 87.6% reduction in average calculation time under the same hardware configuration. This lightweight advantage enables real-time online monitoring of harmonic distortion, providing efficient data support for harmonic control and optimization in power systems.

Unlike grid-following control, grid-forming (GFM) control provides voltage and frequency support to modular multilevel converter-based high voltage direct current (MMC-HVDC) systems, thereby enhancing system stability. However, the impact of the internal harmonic dynamics of the modular multilevel converter (MMC) and the critical control loops of GFM control on the stability of grid-connected systems has not been fully clarified. Therefore, a small-signal model of the MMC-HVDC system is developed to investigate the influence of grid impedance characteristics and key control loops on system stability.

The global rise of renewable energies has led to the widespread incorporation of inverter-based resources (IBRs), encompassing wind turbines, photovoltaic arrays, batteries, etc. To clarify the impact of IBR on power quality, it is critical to construct the harmonic model and identify its parameters. According to the IBR's harmonic model, the paper reviews three widely used experimental methods to determine harmonic model parameters, including the frequency scan method, the stateswitching method, and the measurement-based harmonic coupling modeling method. Different measurement approaches determine their suitability for analyzing harmonic impedance, harmonic sources, and harmonic coupling relationships. Experimental verifications are conducted on the IBR laboratory platform, wind turbine converter, and photovoltaic converter. Results indicate that utilizing the Norton equivalent model for the IBR unit can significantly simplify the process of determining harmonic model parameters while retaining relatively high accuracy. Recommendations are finally provided to determine available harmonic parameters from engineering perspectives.

High-proportional renewable energy integration and rapid installation of new type loads, such as electric vehicles (EVs), into the power system are challenging the monitoring and operation of the power system. Different from the conventional system operation requirements, high-quality and real-time power grid data become essential for system operators on key power equipment monitoring, power flow dispatching, and operation optimization. However, due to equipment limitations, bandwidth, storage and other factors, power grid data is usually collected at lower sampling frequencies. Therefore, super-resolution reconstruction of these data is necessary to obtain higher frequency data for meeting the system operation requirements. In this paper, a super-resolution reconstruction method based on self-attention-time series generative adversarial network (SA-TimeGAN) is proposed. The proposed method considers the influence of meteorological factors by introducing a self-attention mechanism. The developed generator can more effectively capture long-distance dependencies in power grid data and improve the quality of generated data, which could help the operators obtain higher resolution and more realistic data. The performance of the proposed super-resolution reconstruction method is verified based on the real distribution system data obtained from northern China. The experiment results are validated through indicators such as reconstruction error and timing characteristic evaluation and compared with traditional generative adversarial network (GAN) reconstruction methods. The experiment results indicated that the proposed super-resolution reconstruction method has better performance compared to the traditional methods.

Suppressing current harmonics is an essential issue for photovoltaics (PVs). The equivalent impedance or admittance expresses the interaction between the PV converter (PVC) and the background harmonics from the grid, where the control strategies and parameters play a crucial role in the process. Currently, multifrequency coupling characteristics have become more vital in harmonic analysis, and traditional selective harmonic suppression methods show imperfect performance. The key to solving this issue is constructing an adaptive controller for coupled harmonic mitigation according to grid conditions. Therefore, this article first constructs a harmonic coupling matrix model (HCMM) of the PVC to describe the harmonic coupling characteristics, which can represent the influence of topology and control parameters simultaneously. Second, the effect of the critical controller parameters and virtual impedance method on the coupled harmonic admittance of the HCMM is analyzed. Finally, coordinated control of adaptive parameter tuning and virtual impedance injection method is proposed to reduce coupled harmonics efficiently and to avoid the instability caused by the modulation voltage saturation. Comparative experiments are provided by a three-phase PVC with a digital signal processing (DSP) controller, and the results verify the validity of the coupled harmonic analysis and the effectiveness of the proposed method.

High penetration of photovoltaic (PV) integrated into the distribution network (DN) with limited consuming capacity results in serious voltage violation, posing a significant challenge to power quality. With the increase of electric vehicles (EVs), the scheduling of EVs has been recognized as a solution for mitigating voltage violation in DN. However, to realize violation mitigation, EVs are scheduled for charging or discharging, resulting in an adverse impact on the daily use of EVs. How to balance the usage of EVs as commuting devices with their effectiveness as a resource for mitigating voltage violations is the key issue. In this paper, a scheduling strategy for EVs is proposed to maximize the effectiveness of voltage violation mitigation while not influencing the daily use of EVs. Firstly, a representative scenario set reflecting the requirements for voltage violation mitigation is generated by the mean shift clustering algorithm. Secondly, the first stage model is developed to mitigate voltage violation, and the scheduling decision of the charge station applicable to the scenario set is solved. Finally, the second stage model is developed to schedule EVs with different temporal characteristics. In the second stage model, EVs are scheduled following the scheduling decisions of charging stations to ensure effective mitigation of violation. Meanwhile, the charging demands of different categories of EVs are incorporated into the objective of the second stage model to minimize the adverse impacts on their daily usage. The effectiveness of the proposed scheduling strategy is validated through a simulation constructed based on the historical dataset of real DN. The simulation results show that the proposed method effectively mitigates voltage violation while reducing the influence on the usage of EVs.