Transforming existing grid-following (GFL) doubly-fed induction generator (DFIG) wind farms into grid-forming (GFM) DFIG ones is essential for enhancing system frequency support. Studying frequency response and parameters tuning in hybrid wind farms with both unit types establishes a foundation for frequency control. Frequency response modeling for GFL units is well established, small-signal models of GFM DFIG units fail to capture frequency-active power dynamics due to multi-variable coupling, hindering unified hybrid wind farm modeling. Furthermore, frequency control parameters tuning relies on inefficient trial-and-error methods. These defects limit the application of hybrid wind farms. This paper proposes a comprehensive frequency response model for hybrid wind farms and an efficient parameter tuning method. By analyzing the frequency support process in GFM DFIG turbines, a frequency-active power response model is derived. Considering system inertia sources, wind farm outputs are transformed and aggregated to build a system frequency response model that includes hybrid DFIG wind farms. A multi-objective function based on this model integrates the maximum frequency variation rate, deviation, and steady-state deviation. The improved genetic particle swarm optimization (GPSO) algorithm is applied for parameters tuning under generator output and stability constraints. Simulation results demonstrate that the proposed model accurately captures frequency and turbine output dynamics, and the tuning method improves system frequency response and stability.

Aiming at the problem that the interaction between the source-side converter and the load-side converter easily causes system oscillation and system instability in the DC distribution system, this paper designs a phase compensation control link for the source-side converter in the master-slave control mode. Firstly, the impedance models of the source and load subsystems of the DC power distribution system are established, and the negative damping characteristics of the output impedance of the source converter are analyzed as an important cause of system oscillation. Then, the corresponding compensation control link is designed to realize the phase compensation of the output impedance of the source side converter. The compensated impedance is positively damped in the whole frequency band, and the phase difference of the impedance intersection is reduced, thus improving the system’s stability. The introduced compensation link is further reduced and simplified to facilitate practical engineering application. Finally, the effectiveness of the designed compensation link is verified by simulation and hardware-in-the-loop experiments.

The interaction amomg multi-parallel grid-connected converters (MPGCC) in weak grid often leads to stability challenges. This paper presents a converter admittance-reshaping control method designed to improve the stability of the MPGCC system. Firstly, a generalized mathematical model of a three-phase MPGCC system, which incorporates the interaction between converters, is established and the small-signal model is emplogyed for analysis. It reveals that kdq1(s) and kdq2(s) must satisfy the Nyquist criterion in a MPGCC system, where kdq1(s) represents the series interaction between the main grid and the MPGCC system, and kdq2(s) represents the interaction amomg the converters. Considering the uniqueness of stability analysis for MPGCC systems, this study integrates admittance reshaping-matrices into the control structure of the converters using the positive real method. This approach ensures that both the output admittance matrix of the converter and its inverse matrix are strictly positive real, thereby achieving passivity in the two-dimensional admittance model of the three-phase MPGCC output. This theoretically guarantees the asymptotic stability of the MPGCC system, thereby improving system stability. Finally, simulations and experimental tests are conducted on a device omprising three parallel-connected grid-connected converters to validate the effectiveness of the proposed control method. This paper is accompanied by a video that demonstrates the validation results.

Grid voltage background harmonics can induce harmonic currents in the energy storage converter (ESC). Furthermore, the frequency-coupling characteristic of the battery energy storage system (BESS), which induces new harmonic current components, can result in current ripple during battery charging. This paper proposes an admittance-reshaping method to mitigate the influence of grid background harmonics on the battery charging current of the BESS. Firstly, the small-signal admittance model of the ESC is established. Then, a theoretical analysis and mathematical derivation of the charging current ripple are conducted. The general forms of the inner current-control loop and outer power-control loop admittance-reshaping factors are then developed to reshape the admittance model into a symmetrical system within the dq frame, as a result, the harmonic components are reduced. Furthermore, the admittance-reshaping factors' parameters are appropriately selected to reduce the magnitude of harmonics. By decoupling the system and lowering the admittance value of the ESC, the quality of the grid-connected current is improved, while simultaneously suppressing the charging current ripple. The feasibility and effectiveness of the proposed admittance-reshaping method are verified by the presented simulation and experimental results.

Large-scale integration of renewable energy sources and power-electronic equipment introduces substantial background harmonics into the grid and, at the same time, gives rise to weak-grid operating conditions with a low short-circuit ratio, thereby degrading the power quality and stability of grid-connected converters. This paper investigates a three-phase LCL-type grid-connected converter and establishes a dq-domain admittance model that incorporates the DC-voltage outer loop, the phase-locked loop (PLL), and grid-voltage feedforward. On the basis of admittance-reshaping theory, a method is proposed to suppress the influence of background harmonics and enhance system stability. First, the frequency coupling caused by the structural asymmetry of the PLL and the voltage outer loop is decoupled to reduce the harmonic components in the grid current. Then, based on the decoupled model, the grid-voltage feedforward path is compensated to eliminate the negative-damping region in the converter output admittance and thus improve system stability under weak-grid conditions. Finally, simulation and experimental results verify the effectiveness of the proposed method.

Virtual resistance (VR) has been shown to have a positive effect on the small-signal stability of grid-connected systems controlled by virtual synchronous generator (VSG). However, VR exacerbates the coupling effects between the active and reactive power loops, leading to larger oscillation amplitudes and longer settling times. In severe cases, voltage and current limits may be violated, potentially resulting in system shutdown. To address this issue, the dynamic power coupling matrix model and the steady-state equilibrium equations of the grid-connected VSG system are established to analyze both the dynamic and steady-state coupling characteristics between active and reactive power, and the different coupling effects between VR and line resistance are revealed. Voltage and power-angle feedforward decoupling factors incorporating VR are then developed to suppress the dynamic coupling induced by VR and line resistance. Furthermore, a voltage compensation block is developed to mitigate the steady-state coupling effect of active power on reactive power. Finally, experimental results verify that the proposed decoupling method achieves effective decoupling, significantly improving both the dynamic and steady-state regulation performance of the system, while preserving the positive effect of VR on grid-connected stability.

Line-commutated-converter based high voltage direct current (LCCHVDC) systems are susceptible to commutation failure (CF) under AC faults, posing a severe threat to power grid stability. Under asymmetric faults, different commutation processes (CP) exhibit varying CF risks due to significant differences in commutation voltages. This paper analyzes the impact of advanced firing on commutation processes and the CF risk for individual CPs under asymmetric faults, pointing out that for low-risk CPs, the benefit of reduced reactive power consumption without advanced firing outweighs the benefit of implementing advanced firing to ensure successful commutation. Thus, a novel method is proposed to enhance CF resistance capability by optimizing advanced firing application. To verify the effectiveness of the proposed method, it is applied to both direct and indirect advanced firing control approaches. Simulations are conducted in PSCAD/EMTDC using the CIGRE benchmark model and a dual-infeed HVDC model. The results of waveforms and Commutation Failure Immunity Index (CFII) comprehensively demonstrate that the proposed method effectively mitigates CF while maintaining good applicability across diverse operational scenarios.

As a key link at the end of the power grid, the faults of distribution networks are prone to cause power outage accidents, and the traditional BP neural network in the fault diagnosis of distribution networks has the problems of low diagnostic accuracy and large error due to the randomness of the initial weights and thresholds. In this paper, we propose a genetic algorithm (GA) optimized BP neural network fault diagnosis method for distribution networks, which uses the global search capability of GA to optimize the initial weights and thresholds of BP network and construct the diagnosis model. The model is compared with the traditional BP neural network through examples to analyze the simulation errors and verify the fault tolerance. The results show that the GA-optimized BP neural network significantly reduces the testing and training errors, has higher diagnostic accuracy, and can accurately locate the faults when the FTU information is misreported or omitted, which provides an effective technical solution for fault diagnosis of distribution networks.

Accurately detecting and classifying faults are essential for fault clearance and recovery of the system. One of the biggest challenges in fault diagnosis is dealing with fault class imbalances, where some fault types occur less frequently than others. The class imbalance makes the classifier prefer the majority class, which will cause the deterioration of classification performance. To solve the problem of serious class imbalances in fault natures, fault causes, and fault phases for the actual power system, this article presents a transmission line fault classification method based on the squeeze and excitation (SE)-Inception-residual network (ResNet) model with focal loss (FL). In this method, the FL is introduced to focus more on hard-to-classify faults by reducing the weight of easy-to-classify faults automatically. Also, the proposed model integrates the SE-Inception module and the SE-ResNet module, which can exploit the advantages of each to achieve better classification accuracy along with good computational efficiency due to the lighter network structure. To verify the effectiveness of the proposed method, various types of faults with unequal numbers are generated based on the 735-kV three-phase transmission line model, and three-phase signals are converted into 2-D time-frequency maps using continuous wavelet transform (CWT) for extraction of the deeper and higher level features. Experimental results show that the proposed method achieves 98.5% classification accuracy and above 92% F1-score, which indicates the improvement of classification performance in the case of serious class imbalance. Also, the effectiveness of this proposed method is verified by the real recording fault data. In addition, the comparisons with other convolutional neural networks (CNNs) and the cross-entropy (CE) demonstrate its superiority. The interpretability of the proposed networks for fault recognition is provided based on the gradient-weighted class activation mapping (Grad-CAM) visualization, and it reveals the inherent mechanism of its good classification performance from time-frequency features.

To reduce the distortion effects of traveling wavefronts for fault location in distribution networks, a novel fault traveling wave detection method employing Robust Local Mean Decomposition (RLMD), Teager Energy Operator (TEO) and Incremental Difference Ratio (IDR) is proposed. According to the dispersion characteristic of traveling waves with multi-branch lines, the sampled faulty traveling wave signal is filtered using the RLMD method, then the TEO is used to determine the first half-wave time interval of the fault traveling wave. Finally, the IDR is proposed to detect the initial moment of the faulty traveling wave. And according to the fault traveling wave transmission path of multi-branch lines, the complex multi-branch line fault location problem is transformed into a three-terminal fault location problem. The fault traveling wave location method is proposed based on the three-terminal fault matrix, which achieves the fault location through the gradual reduction of the fault area range. Simulation and experiment results indicate that the method proposed in this paper can effectively calculate the arrival time of faulty traveling waves, and is more suitable for fault detection in multi-branch distribution lines compared to existing methods. The new proposed faulty location method can achieve reliable location to reduce location errors.

To improve the accuracy of fault location for distribution network, a novel fault location method is proposed in this paper. According to transmission characteristics of traveling wave, multi-branch distribution network can be divided into multi-level T -branches. And a three-terminal fault matrix is defined. By the analysis of fault characteristic in the three-terminal fault matrix, an identification principle of potential fault line is built. Then, according the potential fault line, a new three-terminal fault matrix is established. The scope of fault line is gradually narrowed until the potential fault line is the real fault line. Simulations show that the proposed method can locate faults accurately and improve reliability of fault location for distribution network.

Different line resistances between battery energy storage systems (BESSs) and the bus cause the problem of state-of-charge (SOC) unbalance between the batteries. SOC unbalance brings about battery over-charge or over-discharge, which reduces the battery life. This paper proposes an SOC feedback control strategy to achieve both output power sharing and SOC equalization between the BESSs. The average SOC of the batteries is set as the reference of each SOC control loop, and the control objectives are achieved by regulating the output voltage of the energy storage converters. The state space model of the proposed control method is established for stability analysis and control parameter design. The parameters are then designed in detail according to the dynamic and steady-state performance. Simulation and experiment verified that the proposed control strategy can achieve accurate SOC equalization and output power sharing when the line resistances and the battery capacities are different.

Conventional cascaded H-bridge power amplifier (CHB-PA) with N H-bridge power modules (HBPMs) could create 2N+1 level for output voltage at most and the output voltage level directly affects the sinusoidal characteristic and fidelity performance of power amplifier. With the same number of cascaded HBPMs, this paper proposes an asymmetrical cascaded multilevel power amplifier (ACM-PA) in which the voltage of one HBPM is one-third of the voltage of the other HBPMs, reducing the withstand voltage level of the HBPM. A virtual carrier phase shift pulse width modulation (VCPS-PWM) strategy is also proposed for ACM-PA to increase the output voltage level up to 6N-3 with the HBPM number as the same as conventional CHB-PA. Comparative simulation and experimental results are included to validate that the proposed ACM-PA and VCPS-PWM could obviously increase the output voltage level and decrease the total harmonic distortion (THD) of load current, improving the fidelity performance of power amplifier.

Abstract Aiming at the problems of noise interference and too many network parameters for power quality disturbances' (PQDs') classification based on deep learning, the lightweight convolutional neural network combining maximum likelihood Kalman filter and continuous wavelet transform is proposed. In this proposed method, the disturbed PQD signals are denoised by maximum likelihood Kalman filter, and then the denoised PQDs are converted to time‐frequency diagrams, which can provide rich time and frequency domain information, and finally the lightweight convolution neural network is used for automatically extracting and classifying multiple PQDs. To verify the effectiveness and superiority of the proposed method, a variety of PQDs were tested under different noise levels, the experiment results indicate that the average classification accuracy can reach more than 99% even in the case of 10 dB noise. Compared with the existing classification methods, the accuracy and noise immunity ability are improved. Additionally, the proposed method has decided advantages, as evidenced by its low parameter count of 0.73M and short average test time with only 0.7 ms.

This article proposes a decoupling control method to eliminate frequency coupling in three-phase grid-tied system. Firstly, the admittance model of three-phase LCL -type three-phase grid-tied converter (GTC) considering DC-bus voltage control (DVC) loop and phase-locked loop (PLL) is established in dq -domain frame, and the influence of asymmetrical DVC and PLL control structure on frequency coupling oscillation is analyzed. Then decoupling factors are added to the DVC loop and current control (CC) loop to achieve that the off-diagonal elements of the sequence admittance matrix of the GTC are 0, which means the frequency coupling in the GTC is eliminated. Therefore, the GTC system can be regarded as a single input single output (SISO) system, and the stability can be analyzed by the classic Nyquist criterion in the sequence-domain frame. Compared with the double input double output (DIDO) system, the resulted SISO system convenient for calculating the stability margin of the system and guiding parameter design. In addition, the SISO system is easier to be analyzed on the oscillation and resonance mechanism, and the analysis results can be used to optimize the design of the converter controller, grid planning and operation. Finally, simulation and experiment verify the feasibility and effectiveness of the proposed decoupling control method.

Accurate fault detection and classification help to analyze fault causes and quickly restore faulty phases. Deep learning can automatically extract fault features and identify fault types from the original three-phase voltage and current signals. However, this still imposes challenges such as recognition accuracy and computational complexity. More importantly, high level fault features cannot be extracted in the one-dimensional time series. This paper presents a robust fault classification method based on SA-MobileNetV3 for transmission systems. Considering that the SE (Squeeze-and-Excitation) attention module cannot aggregate the spatial dimension information on the channel, SA (shuffle attention) module is introduced into MobileNetV3, which can effectively fuse the importance of pixels in different channels and in different locations at the same channel. Also, transforming the time series three-phase voltage and current signals into two-dimensional images based on CWT (continuous wavelet transform) makes the proposed method be similar to image recognition, which can mine high level fault features and classify the faults visually. To verify the effectiveness of the method, a 735kV transmission line model is built for data generation through Simulink. Various kinds of fault conditions and factors are considered to verify the adaptability and generalizability. Simulation results show that the method can quickly and accurately identify 11 types of faults, and the accuracy rate is as high as 99.90%. A comparison between the proposed method and other existing techniques shows the superiority of the proposed SA- MobileNetV3, and better anti-noise performance makes it more suitable for real fault signals taken on-site.

Type identification and time location of power quality disturbances (PQDs) is the key to adopting corresponding measures to suppress disturbances. More complex multiple disturbances caused by the overlapping of different micro-grids make it a challenging task. The paper proposes a hybrid approach combing KF-ML (Kalman filter based on maximum likelihood) with deep belief network (DBN) for dealing with PQDs. To be specific, the KF-ML is firstly applied to reduce noise from the original distorted signal, and the innovation sequence obtained by KF-ML can be used to locate starting-ending times of PQDs. Then, the DBN, which fuses feature extraction and classification into a single block, is capable of recognizing the type of PQDs accurately. To verify the effectiveness of the proposed method, 20 classes of PQDs with noise interference are tested, and experiment results show that the detection time of the proposed method is very close to the set time, and the absolute error of time location is less than 0.3 ms. The average classification accuracy at different noise levels reaches about 95%, and is very high even with more disturbances combined. Thus, the proposed method is immune to noise and less affected with more disturbances combined relative to other methods.

区别于交流配电网,直流配电网中电源具有低惯性特性,当配电网负载突变时,直流母线电压波动较大,母线电压质量面临巨大挑战.在解决直流配电网惯性低的问题上,可采用超级电容增加物理惯性或者采用虚拟电容(需要变流器配置冗余容量提供)增加虚拟惯性,配置的电容越大,供电质量越高,但成本也会越高.然而,目前关于电容对电压跌落幅度抑制作用的研究以定性分析为主,缺乏定量计算.为此,该文对辐射型直流配电网中直流母线电压在负载突增下的暂态响应进行分析,求得母线电压跌落峰值与联络变流器直流侧电容值之间的数学关系,为系统惯性参数设计提供理论支持.由于获得的数学关系式比较复杂,为方便工程应用,设计简化方法,得到近似的代数关系.最后,利用仿真和实验对电压跌落峰值计算方法进行了验证,仿真与实验结果验证该文所提计算方法的正确性.

Due to its unique topology, modular multilevel converter (MMC) is very suitable for AC/AC conversion applications in distribution network and has broad development prospects. Aiming at the problems of multivariable coupling and multi-objective accurate control of single-phase direct AC/AC-MMC, a model predictive control (MPC) strategy based on current decomposition model is proposed in this paper. Firstly, according to the relationship between bridge arm electrical quantity and input and output, the equivalent current decomposition model of decoupling input circuit, output circuit and circulating circuit is derived. Then, the evaluation function with the output current, input current, circulating current and submodule (SM) capacitor voltage as the rolling optimization objective is established, the rolling optimization process of MPC is carried out according to the equivalent current model and simplified by selecting the optimal bridge arm levels, so as to realize the multi-objective accurate and fast control of AC/AC-MMC. Finally, the proposed MPC control strategy is simulated and compared with the traditional MPC control strategy and PI control strategy to verify the feasibility and effectiveness of the proposed control method.

In the stability analysis of DC (direct-current) power grids, AC (alternating current)/DC converters are usually treated as linear proportional amplifiers or inertial links. However, this approximation will have a considerable effect on the DC-side impedance model of a constant active power control converter and then affect the accuracy of the stability analysis of DC grids. In this paper, in view of the DC-side of the voltage fluctuations, the DC-side admittance model is established after the nonlinear relationship between the port voltage and the modulation signal function is determined. A comparison is made between the traditional modeling method and the proposed modeling method in the analysis of the phase–frequency characteristics and amplitude–frequency characteristics of the admittance. Results show that the proposed small-signal model can better fit the measured admittance in the low-frequency bands. The proposed admittance model in this paper is further applied to assess the small-signal stability of a DC grid. Finally, PSCAD/EMTDC simulation and lab-scaled prototype experiment are used to verify the correctness and effectiveness of the established admittance model. Results show that the small-signal model deduced in this paper can better fit and measure the admittance in the low-frequency band, and can more accurately judge the stability of the DC system.