Wind power connected to the grid via MMC-HVDC systems is prone to sub-synchronous oscillations (SSOs), and wind power fluctuations can induce modal frequency drift, leading to complex oscillation characteristics. This paper first analyzes the oscillation distribution patterns of MMC at the sending and receiving ends, and explores the propagation characteristics of system SSOs. Taking into account both the steady-state variation of wind power and the dynamic characteristics of MMC, an equivalent small-signal model of the system under wind farm fluctuation scenarios is established. Based on eigenvalue analysis, the mechanism of multimodal frequency drift SSO is revealed, and simulation verification is conducted using the PSCAD platform. The results show that the established model is accurate and effective, and can provide a reference for SSO stability analysis and suppression in wind power MMC-HVDC grid-connected systems.

With the connection of a large number of flexible loads and distributed generations to meshed distribution networks, the interaction between sources and loads has become more complex. Traditional evaluation systems are no longer applicable to the differentiated evaluation of meshed distribution networks. Therefore, this paper establishes a new indicator system and proposes an evaluation method based on variable weight theory (VWT) and coupling coordination degree (CCD). First, a new indicator system is established by analyzing the impact of multiple types of source-load connections on the power grid. Second, a weighting model based on the principle of minimum discriminative information (PMDI) and variable weight theory is established. Compared with the weighting results calculated by game theory (GT), the proposed model exhibits less volatility in weighting and enhances the applicability of evaluating different power supply grids.. Finally, the CCD was used to evaluate the level of coordinated development among indicators. The results show that the proposed indicator system and comprehensive evaluation method outperform others in evaluating the source-load indicator levels of various power supply grids. The method effectively identifies the coordinated development level of power supply grids under source-load interaction, providing valuable reference for the construction of meshed distribution networks.

The railway static power conditioner (RPC) is typically connected to the traction power grid through step-up transformers. However, the DC component in the RPC output voltage introduces a DC bias in these transformers, degrading the RPC’s compensation performance. This paper investigates the mechanism of DC bias generation, the transformer current characteristics, and their coupling with the RPC. Through the analysis of the RPC topology, compensation principle, and equivalent circuit, the formation of DC bias is clarified. When DC bias causes core saturation, the transformer excitation current exhibits a sharp peak waveform, preventing the secondary current from effectively restoring the primary current. The study further examines the operational behavior of the step-up transformer and RPC under non-ideal conditions, revealing that the DC bias significantly affects the transformer’s no-load current ratio and, consequently, the RPC’s compensation capability. Moreover, the boundaries and constraints of DC bias for the step-up transformer are established. Results show that maintaining the RPC output current within 0.3 % of the transformer’s rated current ensures operation within the permissible DC bias region, where the excitation current remains nearly symmetrical and current recovery is achieved. Experimental results validate the proposed mechanism and theoretical analysis.

With the rapid development of large-scale renewable energy integration and flexible DC transmission technologies, modular multilevel converter (MMC)-based high-voltage direct current (HVDC) systems have emerged as a core solution for next-generation DC transmission projects, owing to their modular structure, active/reactive power decoupling control capability, and absence of commutation failure risks. This technology plays a pivotal role in long-distance offshore wind power transmission. However, existing studies predominantly focus on submodule capacitor voltage balancing and arm circulating current suppression, while paying insufficient attention to the reactive power support and dynamic voltage regulation capabilities of MMC during receiving-end grid faults. Current control strategies primarily aim to ensure MMC’s stable operation, failing to fully leverage its rapid power regulation characteristics to provide STATCOM-like dynamic reactive power compensation for the receiving-end grid, thereby limiting system fault recovery capabilities. To address these challenges, this paper proposes a hybrid MMC-STATCOM control strategy integrated with submodule fluctuation suppression. First, a common-mode voltage injection and circulating current suppression method based on a quasi-proportional resonant (quasi-PR) controller is designed to effectively regulate submodule capacitor voltage fluctuations and circulating currents. Second, a phase-angle control variable (λ) is introduced into the conventional power outer-loop control as an additional control parameter. By adjusting the modulation wave phase angles of the upper and lower arms, dynamic power balance is achieved, forming the MMC-STATCOM composite control framework. This strategy enables proactive reactive power support during receiving-end grid short-circuit faults while maintaining continuous active power transmission, significantly enhancing the transient voltage stability and fault ride-through capability of interconnected grids. Finally, a simulation model of the receiving-end grid integrated with offshore wind farms via MMC-HVDC is established in Simulink. Experimental results validate the effectiveness of the proposed strategy, and provide a new technical path for the flexible and direct system to participate in the active grid support.

This study effectively analyzed the impact of microplastic release. Plastic containers are widely used in the food delivery industry, but the released microplastic particles can pose a threat to human health and the environment. The study employs lensless digital holography, which utilizes the principles of near-field light scattering and optical interference to rapidly detect microplastic particles. By adjusting the reconstruction distance, the technique can differentiate microplastic particles from other impurities, achieving precise detection and analysis of microplastic particles. The results showed that the release of microplastic particles from plastic bags at room temperature was about 5.25 times that of plastic boxes. In the experiment of releasing microplastics from plastic boxes, the increase was 1158.82% after heating for 60 seconds, 132.48% after three heating cycles, 141.18% after refrigeration, and 21.37% after refrigeration before heating. This study reveals the release of microplastics under different treatment conditions, providing a reliable basis for reducing the harm of microplastics.

The chemical integrity of power system liquids, such as coolants and transformer oils, is critical for the reliable operation of energy systems. Contaminants such as carbon, iron, copper, and tin can compromise cooling efficiency, increase failure risks, reduce equipment lifespan, and cause electrical malfunctions, thereby threatening the safety and stability of these systems. This study presents an innovative approach that integrates hyperspectral imaging (HSI) with machine learning (ML) algorithms to identify and quantify impurities in these liquids. A weighted ensemble model, referred to as the WeightedEnsemble_L2 model, has been developed and optimized. This model utilizes thirteen advanced machine-learning algorithms to identify impurities by analyzing spectral signatures across a broad wavelength range. The implemented artificial intelligence (AI) model demonstrates 90 % accuracy on the training set and 87.53 % on the validation set. This novel approach offers a robust solution for impurity detection in power system liquids, supporting predictive maintenance and enhancing the safety and stability of energy systems through the practical application of AI technology.

Proteins undergo a series of conformational changes when affected by the applied electric field, which changes their functions and properties. The conformational changes in proteins in various electric fields are different due to their internal structures. This study simulates the molecular dynamics of proteins in different amounts and directions of electric fields with gromacs software. According to the root mean square deviation, hydrogen bond, dipole moment, and solvent accessible surface area, it is proved that the conformation change in proteins is more drastic under the simultaneous action of multiple electric fields under various directions, and different fragments unfold with divergent electric fields combined, which is of great importance to control protein function, improve biochemical research and production efficiency in the food and drug safety field.

The increasing penetration rate of distributed energy brings more complex problems of voltage quality, safety and stability to the distribution network. A single optimal configuration of reactive power or energy storage is difficult to meet the increasingly diversified needs of modern power grids. This paper proposes a configuration strategy combining energy storage and reactive power to meet the needs of new energy distribution networks in terms of active power regulation and reactive power compensation, and to achieve tradeoff optimization in flexibility, voltage quality and economy, so as to adapt to the influence of new energy with different permeability. Firstly, the safety and stability evaluation system of distribution network is established with the target of flexibility demand and reactive power demand. Secondly, considering the coupling of planning layer and operation layer, a two‐layer model of energy‐reactive power optimization is established. After that, the gray wolf algorithm is used to solve the model, which enhances the global space exploration ability and reduces the possibility of falling into the local optimal. Finally, the improved IEEE33 node power distribution system is used for simulation test to verify the rationality and effectiveness of this scheme. The simulation results show that the scheme proposed in this paper can effectively improve the economy, security and stability of the distribution network, and can obtain an optimal configuration scheme with multi‐objective considerations under different new energy penetration rates. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

In order to solve the problem of sub-synchronous oscillation (SSO) of wind power grid-connected system with modular multilevel converter based on high voltage direct current transmission (MMC-HVDC), which is characterized by frequency drift due to the fluctuation of wind power output and the dynamic interaction of MMC. By considering the coupling characteristics of wind power volatility and the internal harmonic dynamics of MMC, we establish a small-signal model of the interconnection system between the equivalent wind farm and MMC-HVDC, study the dominant factors triggering the system oscillation, and use model predictive control (MPC). The results show that the wind farm output, wind farm terminal voltage and terminal current are the dominant factors leading to SSO frequency drift. It is also verified that the MPC-SSDC control not only effectively suppresses SSOs with single frequency drift, but is also effective when multiple SSOs with different frequencies exist simultaneously. It is shown that MPC-SSDC can self-adapt to the multi-oscillation scenario of wind power grid-connected system to provide frequency and damping support for the system.

Accurate partial discharge (PD) measurement is critical to ensure the stable operation of transformers. The ultrasonic method is a low-cost, safe, and reliable technology that is widely available and provides real-time monitoring capability. The PD ultrasonic signals propagation is complex and severely attenuated in the transformer, which greatly affects the measurement accuracy of the sensor. In order to improve the accurate monitoring of PD in complicated transformer environments, an optimization monitoring method based on sub-scene detection and quantitative analysis and evaluation is proposed in this paper. Firstly, to address this concern, a sub-scene monitoring method is designed and explores the optimal monitoring points separately. In addition, establish the partition model of an oil-immersed power transformer, and compare the ultrasonic wave propagation characteristics and sound pressure attenuation characteristics of different monitoring points. Then, analyzed by wavelet transform algorithm and Pearson correlation coefficient to determine the best monitoring point location for each scene. Finally, we further tested the proposed method through extensive experiments based on simulations, testbed, and trial deployment. The experimental results have demonstrated the feasibility and accuracy of the proposed method in transformer PD monitoring under complicated environments.

Aiming at the high content of third harmonics in traditional parallel VOC inverters and the problem of frequency and phase offset when the output is connected to an inductive load, a nonlinear frequency modulation method and pre-synchronization method based on the perspective of resonant circuits are proposed. This is a control method without interconnection lines. It can avoid power calculation. The two control objectives can be achieved only by adjusting the virtual capacitance. For the frequency modulation control method, after the phase adjustment is completed, the influence of phase adjustment on virtual capacitance will become transparent. Finally, the algorithm is verified by simulation, effectively reducing the third harmonic, suppressing system circulating current, and achieving power equalization.

Maximum power point tracking (MPPT) is one of the key technologies in photovoltaic power systems, aiming to improve system energy conversion efficiency. This article presents a study and improvement on the voltage-based MPPT control algorithm, and its effectiveness and feasibility are experimentally validated in current-mode micro-inverters. This algorithm adopts an optimization strategy combining segmented control and adaptive variable step size control technology to improve the tracking speed and accuracy of MPPT, in order to improve the energy conversion efficiency and stability of photovoltaic power generation systems. Experimental results demonstrate that the algorithm achieves rapid and accurate maximum power point tracking under different illumination and temperature conditions, significantly improving the overall system performance. Moreover, the algorithm exhibits strong robustness and adaptability, making it suitable for a wide range of photovoltaic power systems. With a rapid response to external environmental changes, the overall tracking efficiency of this method reaches 99.9%.

In view of the low accuracy of transformer fault diagnosis using support vector machine (SVM), This paper proposes a transformer fault diagnosis with support vector machine optimized by slime mold algorithm (SMA) based on principal component analysis (PCA). Firstly, PCA was used to reduce the dimensionality of high-dimensional data features and extract key data features. Secondly, SMA was used to optimize the internal parameters of SVM to improve the diagnosis performance of vector machine. Finally, the extracted key data features were input into the SMA-SVM fault diagnosis model for transformer fault diagnosis. The example analysis shows that the proposed method has high diagnostic accuracy and strong practicability, and provides a way to solve the practical engineering problems of fault diagnosis.

Dempster-Shafer(D-S)证据理论是在不考虑先验概率的情况下建模和处理不确定信息的有效数学工具.当两证据之间高度冲突时,Dempster组合规则会产生不合理的结果.针对这一问题,提出了一种基于散度差异值测度和信念熵的多传感器数据融合方法.提出了一种距离公式-单焦元三角散度来衡量两证据之间的距离,并由此计算出各证据的可信度.在D-S证据理论框架中提出了一种信念熵测度来度量各证据中包含的不确定信息,综合各证据的可信度和不确定度来计算各证据的权值,由此得到加权平均证据.利用Dempster组合规则融合加权平均证据得到最终融合结果.利用所提出的方法来解决变压器在线监测故障诊断实际应用问题,实验结果表明,提出的方法具有较快的收敛速度和较高的诊断精度,优于其他方法.

Weak points in product design are detected by electrodynamic vibration tables to improve and enhance product reliability. Therefore, the performance of the vibration table power supply directly determines the quality of the testing products. The current control of the vibration table power supply has problems such as large steady-state error, slow dynamic tracking speed, and serious phase lag at high-frequency output, which affect the product testing effect. To this end, this paper proposes an improved Proportional Integral Multi-Resonant (PIMR) control strategy to optimize the above problems by increasing the input feedforward channel and using a zero-phase shift notch filter. Simulation verifies the effectiveness of the proposed control strategy in this paper

Aiming at the problems of low diagnosis accuracy and unstable signal characteristics of rolling bearings, a fault diagnosis model based on Zebra algorithm optimized variational mode decomposition (ZOA-VMD) and optimized support vector machine (ZOA-SVM) was proposed. Firstly, the vibration signal is decomposed by ZOA-VMD, and the features of intrinsic mode function (IMF), energy, energy entropy, arrangement entropy and multi-scale arrangement entropy are extracted, and the feature vector is constructed. Then, the Zebra algorithm is used to optimize the core parameters of SVM, so as to avoid the uncertainty caused by artificial parameter setting, and the feature vector is input into the fault diagnosis model of ZOA-SVM, to improve the diagnosis accuracy and achieve better fault classification effect. Finally, by analyzing the simulation results of the bearing data of Case Western Reserve University in the United States, it is shown that the average test set diagnostic accuracy of the proposed ZOA-VMD and ZOA-SVM methods is up to 95%, which can effectively identify the fault types of rolling bearings, and has certain value in practical engineering applications.

When model predictive current control (MPCC) is applied to four-switch buck-boost (FSBB) converters, the dynamic performance of the converters will be degraded due to the discontinuity of duty cycle working area and inaccurate model parameters. Therefore, this paper introduces load current feedforward and disturbance observer to study the improvement strategy of MPCC. Firstly, an improved duty cycle prediction model with simplified Buck-Boost mode is proposed to solve the problem that the output voltage ripple of mode switching point increases due to discontinuous duty cycle working area. The dynamic response performance of the system to unknown load disturbance is improved by establishing a feedforward channel of load current. Then, according to the state space model of FSBB converter, an observer is constructed to correct the current prediction model in real time to eliminate the errors caused by inaccurate model parameters, and realize the multi-mode stable operation and smooth mode switching of FSBB converter. Finally, the simulation model and experimental platform are built to verify the superiority of the proposed model and control strategy.

针对配网开关柜温度存在误报警、漏报警,提出可用于在线监测的组合优化温度预测模型以及对应的预警机制优选方案.使用Savitzky-Golay算法将原温度序列分解成线性和非线性温度序列,对线性温度序列利用移动平均差分自回归(ARIMA)进行预测分析,对非线性温度序列使用极限学习机(ELM)进行预测,利用海洋捕食者算法对ELM的关键参数进行自动寻优,预测结果显示均方根误差为1.172℃.依托组合预测模型输出的高精度温度预测值并结合传统预警机制,形成预测温度评价指标,通过独立性权系数法对预测温度评价指标进行权值排序,筛选出最贴近实际工况的预警机制.

As a new renewable energy source, wind energy has developed rapidly in various countries in recent years, and the wake effect caused by its volatility and randomness brings wind speed differences to wind turbines inside wind farms, increasing the nonlinearity and uncertainty of the system. However, in the face of large-scale wind turbine cluster wind power system, traditional frequency control methods rarely consider the different wind conditions inside the wind farm, resulting in power imbalance between different wind turbines, increasing the actual frequency change rate of the system, and then limiting the limit ratio of wind turbines. Therefore, starting from the core problem of increasing the proportion of wind power that can be accommodated in the power grid, this paper proposes a doubly-fed wind turbine frequency rolling optimization strategy based on distributed model predictive control (D-MPC), which uses the internal optimization zoning rules of the wind farm to aggregate wind turbines with similar wind conditions, and incorporates the dynamic frequency of each zone into the top-level design of D-MPC. In addition, the maximum proportion of wind power connected to the grid is taken as the objective function of the system frequency control, and the MHE evaluator is constructed to estimate the unbalanced power in the wind farm in real time, which effectively reduces the curtailment rate of the power grid. Finally, with the help of PSCAD/EMTDC simulation platform, a power system model suitable for high proportion of wind power is constructed, and the simulation verifies that the proposed control strategy can maximize the proportion of wind power connection while ensuring the safe and stable operation of the power grid.