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The application of virtual synchronous generator (VSG) control in flywheel energy storage systems (FESS) is an effective solution for addressing the challenges related to reduced inertia and inadequate power supply in microgrids. Considering the significant variations among individual units within a flywheel array and the poor frequency regulation performance under conventional control approaches, this paper proposes an adaptive VSG control strategy for a flywheel energy storage array (FESA). First, by leveraging the FESA model, a variable acceleration factor is integrated into the speed-balance control strategy to effectively achieve better state of charge (SOC) equalization across units. Furthermore, energy control with a dead zone is introduced to prevent SOC of the FESA from exceeding the limit. The dead zone parameter is designed based on the SOC warning intervals of the flywheel array to mitigate its impact on regular operation. In addition, VSG technology is applied for the grid-connected control of the FESA, and the damping characteristic of the VSG is decoupled from the primary frequency regulation through power differential feedback. This ensures optimal dynamic performance while reducing the need for frequent involvement in frequency regulation. Subsequently, a parameter design method is developed through a small-signal stability analysis. Consequently, considering the SOC of the FESA, an adaptive control strategy for the inertia damping and the P/ω droop coefficient of the VSG control is proposed to optimize the grid support services of the FESA. Finally, the effectiveness of the proposed control methods is demonstrated through electromagnetic transient simulations using MATLAB/Simulink.

Recent years, there have been several high-frequency oscillation events in modular multilevel converter (MMC) based DC current projects. Considering the wideband oscillation characteristics of this issue, passive MMC impedance shaping methods are superior solutions for their advantages over the active methods in enhancing the broadband damping level. However, the optimization of parallel passive devices mainly focused on reactive power compensation and the suppression of characteristic harmonics generated by low-level inverters, which is not applicable to MMC systems. To address the optimization design problem of passive damping devices, this paper proposes an optimized design method for passive filters by analyzing the relationship between the passive damping device and the impedance shaping effect of the inverter. The proposed method reduces the capacity of the passive damping device while ensuring system stability margin. Finally, the effectiveness of the proposed passive impedance shaping strategy is verified through time-domain simulations considering changes in AC side operating conditions and power reduction operation of the MMC.

Abstract The problem of submodule switching frequency reduction in half‐bridge modular multilevel converters (HB‐MMCs) with capacitor voltage self‐balancing control is considered and explored in this paper. A selection principle of submodule switching state vectors is proposed based on the voltage self‐balancing switching state matrix, aiming to lower submodule switching frequency and device losses. The relationship between system stability and submodule switching signals is revealed according to the capacitor voltage self‐balancing characteristics, and the full‐rank constraints on the voltage self‐balancing switching state matrix are proposed. Considering the tradeoff between switching loss and capacitor voltage fluctuation, the evaluation indexes of voltage self‐balancing control effect are determined. The selection principle of submodule switching state vectors and the optimized construction method of switching state matrix are presented. Voltage self‐balancing HB‐MMC models are built in MATLAB/Simulink, and it is verified that the submodule switching state vector selection principle proposed in this study can effectively reduce switching frequency while meeting the evaluation requirements of practical engineering projects, so as to achieve the balance between switching loss and steady‐state capacitor voltage fluctuation.

A converter valve is the core equipment of HVDC transmission system, whose operating temperature threshold is strictly constrained. This paper proposes a novel prediction method for outlet water temperature of converter valve based on F-BP network, which aims to accurately predict the outlet water temperature and assists the operation and maintenance personnel to take measures in time so that the temperature of the converter valve will not exceed its preset threshold when the operation condition has changed. Firstly, the principle and method of the construction of typical operation databases of converter valve is stated, including data standardization, the calculation of the optimal clustering category number and the final clustering process. Then, the steps of using the typical operation databases and BP neural network to make predictions are presented. Using MATLAB, we predicted the outlet water temperature of a converter valve in Chuxiong Converter Station with F-BP method and two other existing methods in comparison. The results indicate that the proposed approach’s prediction accuracy increases by 0.9141 °C and 0.9938 °C respectively compared with the simple BP neural network and linear regression, which contributes to the prediction application of the outlet water of a converter valve.

In natural environments, the connecting bolts of overhead lines and power towers are prone to loosening and missing, posing potential risks to the safe and stable operation of the power system. This paper reviews the challenges in bolt defect detection using power vision technology, with a particular focus on unmanned aerial vehicle (UAV) images. These UAV images offer a cost-effective and flexible solution for detecting bolt defects. However, challenges remain, including missed detection due to the small size of bolts, false detection caused by dense and occluded bolts, and underfitting resulting from imbalanced bolt defect datasets. To address these issues, this paper summarizes solutions that leverage deep learning algorithms. An experimental analysis is conducted on a dataset derived from UAV inspections, comparing the detection characteristics and visualizing the results of various algorithms. The paper also discusses future trends in the application of UAV-based power vision technology for bolt defect detection, providing insights for the advancement of intelligent power inspection.

Abstract With the increasing proportion of renewable power generations in the power system, the power grid impedance may fluctuate greatly, and it is difficult for the virtual synchronous generator (VSG) with a single control structure to meet the stability requirements. Thus, the control principles of the two types of VSGs are compared to conclude that there are similarities in the control structure of the two VSGs. And the small signal models of the two types of VSGs are established to analyze the stability boundaries, coming to the conclusion that the stability region is complementary. Based on the conclusions, the paper puts forward the idea of switching operation modes: switch to the U‐VSG mode when the grid strength weakens and switch to the PQ‐VSG mode when greater, so that the inverter can keep stable in a wider range of grid strength. Therefore, the characterization method of the switching boundary with hysteresis properties is proposed. Then the improved recursive least squares (RLS) algorithm is introduced to identify the power grid impedance online without additional injected disturbance, based on which the adaptive multi‐mode smooth switching control strategy is proposed. Finally, the effectiveness of the analysis and the control strategy are verified by simulations.

To achieve real-time safety management of on-site power system work, the object detection algorithm of the cloud platform is transplanted into the edge computing device. However, due to the limited capacity and arithmetic power of the AI chip carried in the edge computing device, the algorithm needs to be studied in a lightweight manner. Therefore, this paper proposes an improved edge-side lightweight YOLOv4 (You Only Look Once version 4) algorithm, which is compressed and accelerated by improving the network structure and the confidence loss function of the algorithm without losing too much detection accuracy. Firstly, a lightweight convolutional neural network based on depthwise separable convolution and mobile inverted bottleneck convolution is constructed as the backbone network of YOLOv4, and the depthwise separable convolution is used instead of the conventional convolution, which can greatly reduce the parameters number and computation of the algorithm. Secondly, an improved bidirectional feature fusion network is constructed as the neck network of YOLOv4 algorithm to improve the information availability of multi-semantic features and enhance the extraction capability of small-scale features. Then, the confidence loss function of the algorithm is improved to alleviate the problems of uneven distribution of positive samples and negative samples and uneven distribution of easy-to-classify samples and hard-to-classify samples. Finally, the improved YOLOv4 algorithm is tested on an edge computing device equipped with an NVIDIA Jetson Xavier NX chip, and the dataset based on the compliance of on-site dress code in the electric power system field is tested. The experimental results show that, compared with the original YOLOv4 algorithm, the improved YOLOv4 algorithm reduces the parameters number by 93.11%, and the real-time video stream detection speed on the edge computing device is increased by 22.00%, reaching 15.05 FPS. The lightweight improved YOLOv4 algorithm is suitable for deployment at the edge end and can achieve real-time accurate identification of risks in the on-site work.

The existing photovoltaic frequency regulation strategies do not take into account the differences in the frequency regulation capability. So they cannot fully exploit the support capacity to the grid frequency. For the purpose of improving the control precision and regulation effect of PV, a comprehensive control strategy for photovoltaic virtual synchronous generator (PV-VSG) is proposed, which accurately considers the frequency regulation capability. First, the inertia regulation margin, power regulation speed and power regulation margin are taken as the evaluation index of frequency regulation capability of PV. Then, the frequent deviation-free control strategy with integral link and the multiple control modes switching strategy are proposed, whose control parameters are designed according to the frequency regulation capability. In the simulation verification, the proposed method reduces the frequency deviation by 0.11 Hz compared to existing strategies, and can quickly recover to the normal value. At the same time, the frequency regulation utilizes the capability of the PVs, which also validates the effectiveness of the proposed strategy.

A “cloud-edge-end” collaborative system architecture is adopted for real-time security management of power system on-site work, and mobile edge computing equipment utilizes lightweight intelligent recognition algorithms for on- site risk assessment and alert. Owing to its lightweight and fast speed, YOLOv4-Tiny is often deployed on edge computing equipment for real-time video stream detection; however, its accuracy is relatively low. This study proposes an improved YOLOv4-Tiny algorithm based on attention mechanism and optimized training methods, achieving higher accuracy without compromising the speed. Specifically, a convolution block attention module branch is added to the backbone network to enhance the feature extraction capability and an efficient channel attention mechanism is added in the neck network to improve feature utilization. Moreover, three optimized training methods: transfer learning, mosaic data augmentation, and label smoothing are used to improve the training effect of this improved algorithm. Finally, an edge computing equipment experimental platform equipped with an NVIDIA Jetson Xavier NX chip is established and the newly developed algorithm is tested on it. According to the results, the speed of the improved YOLOv4-Tiny algorithm in detecting on-site dress code compliance datasets is 17.25 FPS, and the mean average precision (mAP) is increased from 70.89% to 85.03%.

Abstract The Vienna rectifiers are widely used in industrial applications. The computationally efficient optimal switching sequence model predictive control directly optimizes the switching sequences. Thus, it requires to store the switching states of 25 voltage vectors, which increases the memory usage. Moreover, the neutral‐point (NP) voltage is regulated by redundant vector preselection. As only one redundant vector is applied during the whole sampling period, the NP voltage ripple is relatively large, and the grid current performance is still not satisfactory. To overcome these drawbacks, this paper proposes a model predictive duty cycle control strategy for three‐phase Vienna rectifiers, which directly optimizes three‐phase duty cycles. The three‐phase duty cycles are first obtained by predicting the effects of three‐phase duty cycles on instantaneous current variations and minimizing a cost function. To guarantee the grid current performance, a novel three‐phase duty cycles modification method is proposed by considering the structural characteristics of the Vienna rectifier. To suppress the NP voltage ripple, the obtained three‐phase duty cycles are further optimized. With the proposed method, low memory usage, low harmonic distortion in the grid current, and low NP voltage ripple are achieved simultaneously. Experiments are conducted to verify the effectiveness of the proposed method.

The interactions between power quality in the AC-DC distribution network segments contribute to the distributed propagation of power quality anomalies throughout the entire network. Focusing on the photovoltaic multifunctional grid-connected inverter (PVMFGCI), this study deeply explores a collaborative governance strategy for optimizing regional power quality. Initially, the analysis dissects the DC ripple generation mechanism corresponding to harmonics and asymmetry in AC subnetwork voltages. Subsequently, a strategy is proposed for partitioning comprehensive control regions for AC-side power quality, taking into account photovoltaic governance resources based on insights from photovoltaic control realms and power quality classifications. Further, a collaborative allocation model for governance resources incorporating active optimization potentials of grid-connected converters is established based on the governance capabilities and residual capacities of PVMFGCI. Finally, the effectiveness of the proposed approach is validated through a MATLAB-based example analysis.

In the combined operation system of variable speed pumped storage and direct drive wind turbine, multiple power electronic devices are coupled with each other, and the operating conditions are complex, it is necessary to conduct deep research on the stability of the system. In the synchronously rotating coordinate d-q-0 system, this paper introduces the speed of the doubly-fed machine as a variable, and adds the small signal model of the pump turbine, and establishes a joint operation system suitable for 0.1 Hz–200 Hz stability analysis broadband impedance model. Based on the generalized Nyquist theory, the corresponding stability analysis of the system under different working conditions and different parameters can be carried out.

Aiming at the difficulties of few real samples for defective insulators and complex background of aerial images, this paper proposes a detection method based on target search and cascade recognition. Using the SINet framework, we apply fine-grained texture enhancement to different sizes of receptive fields. Through nearest-neighbor decoding and grouping reverse attention, the more recognizable features are guided to aggregate and generate a refined location area map by performing cascading purification operations. Additionally, we integrate the classification network to complete the solution. Experimental results show that the AUC value is up to 99.82%, which demonstrates the effectiveness and superiority of the proposed method on insulator defect detection.

To realize the accurate identification and segmentation of the insulator string in the complex background image with diverse appearance and obscuration, this paper proposes an insulator segmentation method based on improved U-Net. The algorithm embeds the attention mechanism ECA-Net (Efficient Channel Attention Neural Networks) in the coding stage of U-Net to improve the model’s ability to extract semantic features, thereby improving the accuracy of insulator detection. Experimental results show that the average overlap IOU of the proposed method is 96.8%, which can more accurately segment different types of insulators in complex backgrounds.

The intelligent fault diagnosis of main circulation pumps is crucial for ensuring their safe and stable operation. However, limited research has been conducted on this topic, and applying existing fault diagnosis methods designed for other equipment may not yield optimal results when directly used for main circulation pump fault diagnosis. To address this issue, we propose a novel ensemble fault diagnosis model for the main circulation pumps of converter valves in voltage source converter-based high voltage direct current transmission (VSG-HVDC) systems. The proposed model employs a set of base learners already able to achieve satisfying fault diagnosis performance and a weighting model based on deep reinforcement learning that synthesizes the outputs of these base learners and assigns different weights to obtain the final fault diagnosis results. The experimental results demonstrate that the proposed model outperforms alternative approaches, achieving an accuracy of 95.00% and an F1 score of 90.48%. Compared to the widely used long and short-term memory artificial neural network (LSTM), the proposed model exhibits improvements of 4.06% in accuracy and 7.85% in F1 score. Furthermore, it surpasses the latest existing ensemble model based on the improved sparrow algorithm, with enhancements of 1.56% in accuracy and 2.91% in F1 score. This work presents a data-driven tool with high accuracy for the fault diagnosis of main circulation pumps, which plays a critical role in maintaining the operational stability of VSG-HVDC systems and satisfying the unmanned requirements of offshore flexible platform cooling systems.

The accurate and efficient detection of power lines and towers in aerial drone images with complex backgrounds is crucial for the safety of power grid operations and low-altitude drone flights. In this paper, we propose a new method that enhances the deep learning segmentation model UNet algorithm called TLSUNet. We enhance the UNet algorithm by using a lightweight backbone structure to extract the features and then reconstructing them with contextual information features. In this network model, to reduce its parameters and computational complexity, we adopt DFC-GhostNet (Dubbed Full Connected) as the backbone feature extraction network, which is composed of the DFC-GhostBottleneck structure and uses asymmetric convolution to capture long-distance targets in transmission lines, thus enhancing the model’s extraction capability. Additionally, we design a hybrid feature extraction module based on convolution and a transformer to refine deep semantic features and improve the model’s ability to locate towers and transmission lines in complex environments. Finally, we adopt the up-sampling operator CARAFE (Content-Aware Re-Assembly of FEature) to improve segmentation accuracy by enhancing target restoration using contextual neighborhood pixel information correlation under feature decoding. Our experiments on public aerial photography datasets demonstrate that the improved model requires only 8.3% of the original model’s computational effort and has only 21.4% of the original model’s parameters, while achieving a reduction in inference speed delay by 0.012 s. The segmentation metrics also showed significant improvements, with the mIOU improving from 79.75% to 86.46% and the mDice improving from 87.83% to 92.40%. These results confirm the effectiveness of our proposed method.

Appropriate cooling of the converter valve in a high-voltage direct current (HVDC) transmission system is highly significant for the safety, stability, and economical operation of a power grid. The proper adjustment of cooling measures is based on the accurate perception of the valve’s future overtemperature state, which is characterized by the valve’s cooling water temperature. However, very few previous studies have focused on this need, and the existing Transformer model, which excels in time-series predictions, cannot be directly applied to forecast the valve overtemperature state. In this study, we modified the Transformer and present a hybrid Transformer–FCM–NN (TransFNN) model to predict the future overtemperature state of the converter valve. The TransFNN model decouples the forecast process into two stages: (i) The modified Transformer is used to obtain the future values of the independent parameters; (ii) the relation between the valve cooling water temperature and the six independent operating parameters is fit, and the output of the Transformer is used to calculate the future values of the cooling water temperature. The results of the quantitative experiments showed that the proposed TransFNN model outperformed other models with which it was compared; with TransFNN being applied to predict the overtemperature state of the converter valves, the forecast accuracy was 91.81%, which was improved by 6.85% compared with that of the original Transformer model. Our work provides a novel approach to predicting the valve overtemperature state and acts as a data-driven tool for operation and maintenance personnel to use to adjust valve cooling measures punctually, effectively, and economically.

For the high-frequency resonant accident of modular multilevel converter (MMC) in real engineering, in this paper, the 10th order state-space model of MMC is established based on the dynamic phasor method. The state-space model of MMC controller for power control is established according to the practical engineering. Then the time delay of input and output is considered in the controller, and the Pade fitting in state-space model applicable to the eigenvalue method is introduced to the complete state space model of MMC. Based on the proposed model, the MMC high-frequency resonance phenomenon is analyzed by the eigenvalue method, and the accuracy of the model is verified by simulation results. Besides, it is demonstrated that high time delay causes high-frequency resonance in the MMC system based on the participation factor and the system eigenvalue trajectory. Then, the coupling relationship between the input and output time delay inside the controller and the d-q axis time delay is investigated, and a new time delay compensation method to suppress high-frequency resonance is proposed based on Pade fitting, which can be used jointly with the traditional method with good effect.

An interval power flow (IPF) method that considers the interval correlations of input random variables is proposed to improve the calculation accuracy of IPF, i.e., as correlated distributed generations (DGs) and some correlated DGs-loads are integrated into distribution system. The interval correlation for input variables is described by parallelogram model (PM), whose shape and size are determined by the interval correlated level. Based on affine arithmetic (AA) method, the IPF is solved through nonlinear optimization instead of traditional interval iterative computations. The optimization model of IPF is established, and the interval correlations of input variables (DGs-DGs and DGs-loads) are added into the IPF optimization problem in the form of additional constraint, to make the power flow solutions, i.e., bus voltage magnitude, voltage angle, active and reactive power of branches, more accurate. Finally, several cases, i.e., numerical case, IEEE33-bus, PG&E69-bus and IEEE118-bus distribution system, not only demonstrate the effectiveness of the proposed method, but also indicate that the IPF results are affected by uncertainty level, and the widths of IPF increase with the increasing of uncertainty level.

A combined algorithm based on traveling wave (TW) theory and time-frequency characteristics is proposed to estimate the DC line fault location in modular multilevel converter-high voltage direct current (MMC-HVDC) systems. The relationship between instantaneous frequency and TW propagation velocity is analysed, and the local voltage signal is utilized to extract fault features. A TW distortion-based scheme is improved to obtain the approximate fault location and accurately detect TW arrival times. The arrival times of the initial TW from the fault point and opposite terminal are determined using intrinsic mode functions under variational mode decomposition. Given the attenuation property of the TW-based method, the Teager energy operator is used to overcome the weakness in fault feature extraction. The instantaneous frequency of the TW front is obtained via ensemble empirical mode decomposition and Hilbert-Huang transform. The fault location is predicted with the detected TW arrival times and corresponding TW propagation velocity. The effectiveness and superiority of this method are verified through simulations in PSCAD/EMTDC and computation in MATLAB.

Edge computing can avoid the long-distance transmission of massive data and problems with large-scale centralized processing. Hence, defect identification for insulators with object detection models based on deep learning is gradually shifting from cloud servers to edge computing devices. Therefore, we propose a detection model for insulators and defects designed to deploy on edge computing devices. The proposed model is improved on the basis of YOLOv4-tiny, which is suitable for edge computing devices, and the detection accuracy of the model is improved on the premise of maintaining a high detection speed. First, in the neck network, the inverted residual module is introduced to perform feature fusion to improve the positioning ability of the insulators. Then, a high-resolution detection output head is added to the original model to enhance its ability to detect defects. Finally, the prediction boxes are post-processed to incorporate split object boxes for large-scale insulators. In an experimental evaluation, the proposed model achieved an mAP of 96.22% with a detection speed of 10.398 frames per second (FPS) on an edge computing device, which basically meets the requirements of insulator and defect detection scenarios in edge computing devices.

Power line segmentation is very important to ensure the safe and stable operation of unmanned aerial vehicles in intelligent power line inspection. Although the power line segmentation algorithm based on deep learning has made some progress, it is still quite difficult to achieve accurate power line segmentation due to the complex and changeable background of aerial power line images and the small power line targets, and the existing segmentation models is too large and not suitable for edge deployment. This paper proposes a lightweight power line segmentation algorithm—G-UNets. The algorithm uses the improved U-Net of Lei Yang et al. (2022) as the basic network (Y-UNet). The encoder part combines traditional convolution with Ghost bottleneck to extract features and adopts a multi-scale input fusion strategy to reduce information loss. While ensuring the segmentation accuracy, the amount of Y-UNet parameters is significantly reduced; Shuffle Attention (SA) with fewer parameters is introduced in the decoding stage to improve the model segmentation accuracy; at the same time, in order to further alleviate the impact of the imbalanced distribution of positive and negative samples on the segmentation accuracy, a weighted hybrid loss function fused with Focal loss and Dice loss is constructed. The experimental results show that the number of parameters of the G-UNets algorithm is only about 26.55% of that of Y-UNet, and the F1-Score and IoU values both surpass those of Y-UNet, reaching 89.24% and 82.98%, respectively. G-UNets can greatly reduce the number of network parameters while ensuring the accuracy of the model, providing an effective way for the power line segmentation algorithm to be applied to resource-constrained edge devices such as drones.

DC fault protection is a major issue in multiterminal high-voltage DC current transmission systems. Most studies have focused on primary protection, which considers a different theory. However, the poor sensitivity of primary protection under high-resistance fault and maloperation still affect the protection feasibility to a certain extent. Thus, backup protection is definitely required to enhance the stability of an entire system considering the limit in primary protection. This paper proposes a backup-protection strategy based on wavelet-packet-energy entropy as mitigation for primary protection. The pole-to-ground (PG) fault with high resistance is highlighted because detecting the fault transient of PG fault with high resistance is difficult in the most primary relay. Fault detection and classification are achieved using the proposed backup-protection method based on theoretical study and simulation verification. The proposed method also presents the expected robustness of fault resistance, fault distance and different operating conditions.

Aerial insulator defect images have some features. For instance, the complex background and small target of defects would make it difficult to detect insulator defects quickly and accurately. To solve the problem of low accuracy of insulator defect detection, this paper concerns the shortcomings of IoU and the sensitivity of small targets to the model regression accuracy. An improved SIoU loss function was proposed based on the regular influence of regression direction on the accuracy. This loss function can accelerate the convergence of the model and make it achieve better results in regressions. For complex backgrounds, ECA (Efficient Channel Attention Module) is embedded between the backbone and the feature fusion layer of the model to reduce the influence of redundant features on the detection accuracy and make progress in the aspect. As a result, these experiments show that the improved model achieved 97.18% mAP which is 2.74% higher than before, and the detection speed could reach 71 fps. To some extent, it can detect insulator and its defects accurately and in real-time.

In the field of wind turbine surface defect detection, most existing defect detection algorithms have a single solution with poor generalization to the dilemma of insufficient defect samples and have unsatisfactory precision for small and concealed defects. Inspired by meta-learning ideology, we devised a cross-task training strategy. By exploring the common properties between tasks, the hypothesis space shrinks so that the needed sample size that satisfies a reliable empirical risk minimizer is reduced. To improve the training efficiency, a depth metric-based classification method is specially designed to find a sample-matching feature space with a good similarity measure by cosine distance. Additionally, a real-time feedback session is innovatively added to the model training loop, which performs information enhancement and filtering according to the task relevance. With dynamic activation mapping, it alleviates the information loss during traditional pooling operations, thus helping to avoid the missed detection of small-scale targets. Experimental results show that the proposed method has significantly improved the defect recognition ability under few-shot training conditions.

Smart substation is the key part of smart grid. The reliability of smart substation is extremely important to the safe and stable operation of the smart grid. Smart substation is a cyber-physical system (CPS). Hence, this paper conducts reliability evaluation study for smart substation from the perspective of CPS. Firstly, the basic reliability indices of cyber and physical elements of smart substation are presented. The reliability index of one cyber element takes into account the reliability factors of data leakage, tampering, loss and delay, etc., on the cyber side. Then, the cyber-physical interactions of smart substation are analyzed. It is concluded that the effect of the cyber side and cyber-physical interactions on the reliability of smart substation is reflected in the effect of measurement and control messages on circuit breaker operation. And, the new reliability indices considering cyber-physical interactions are proposed. Furthermore, the MALI-hybrid method, which combines the Monte Carlo method, analytical method, Latin hypercube sampling method, and important sampling method, is presented for evaluating the reliability of smart substation. Finally, the rationality of the proposed reliability indices, the efficiency, and correctness of MALI-hybrid method are verified by case studies.

Abstract The presence of distributed generators (DGs) with high penetration poses new challenges in the management and operation of electrical grids. Due to the local character of DGs, they could in principle be used in emergency situations to prevent a voltage instability event of the grid. In this paper, a certain method is proposed to coordinate the operation of virtual power plant (VPP) and conventional voltage regulation device to improve the static voltage stability of distribution network with the multi-agent framework. The concept and the general framework of this coordinated control system is introduced, and the voltage instable nodes are determined based on the voltage instability indicator. The voltage coordinated control model of the distribution system is established according to the multi-agent consistency control theory and the coordinated controllers for agents are designed by solving a problem with bilinear matrix inequality constraints. The suggested method is implemented on an IEEE 33 nodes test system and the simulation results show its efficiency and validity.

Determining the voltage ratio change is one of the core issues in the traceability of the DC voltage divider. Basing on the previous research results, this study proposes an improved DC voltage summation method to evaluate the voltage division ratio error of 1000 kV DC resistance divider. The principle of the method is to calibrate the voltage divider with rated voltage 2U by using two auxiliary voltage dividers which are with rated voltage U, wherein the high-voltage (HV) arm and the low-voltage arm of the auxiliary voltage divider can be separated. Research results show that compared with the conventional method, the method can reduce one measurement variable when determining the divider's ratio change, thus simplifying the calibration process. The voltage ratio of 100 kV measured by the method of this study was well-verified by the calibration results from the National Institute of Metrology (NIM, China) and Physikalisch-Technische Bundesanstalt (PTB, Germany). Using the proposed method, the ratio change of DC voltage divider at an applied voltage of 1000 kV was effectively obtained and the uncertainty of 2.5 μV/V was achieved. Research results can provide technical guarantee for the accurate measurement of HVDC magnitude.

Single-phase FACTS device has a bulky and short-life electrolytic capacitor to absorb the ripple pulsating at twice of the line frequency in DC side, resulting in lower power density. This paper introduced a structure of three-phase bridge which added an additional leg connected to an AC capacitor based on single-phase H-bridge. The 2-ripple energy of the electrolytic capacitor in single-phase H-bridge could be transformed to the film capacitor of the AC side in three-phase H bridge. The size of single-phase FACTS is reduced by ten times compared to the single-phase H-bridge. A simple control strategy had been studied, through two kinds of controllers: the digital quais-PR controller had been used to control gird current and AC capacitor voltage and current; the two cosine controller had been used to eliminate the rest of two-ripple harmonic in the DC side, there was no controller to maintain DC voltage. The experiments verified the feasibility of the control strategy.

Bipolar VSC-HVDC scheme is one of the most feasible transmission technologies to integrate offshore wind energy source. While one pole stops by suffering even a transient fault, the other pole will block soon due to the overcurrent, then the wind turbines will trip which would cause a critical impact on power system. A novel coordinating fault ride through strategy is proposed to solve the problem here. First, the operational behaviour of DFIG under the integration voltage regulation is analysed; Second, based on the power generation characteristics of DFIG with different terminal voltage, a coordinating strategy without any additional communications requirements is presented, utilising the flexibility power electronics converters both in DFIG and VSC-HVDC; Finally, the adaptive control scheme considering the constraints of both wind farms and DC system is verified by PSCAD/EMTDC, and the results show that the strategy can effectively regulate the current through the non-fault pole and provide active support for the AC grid during transient faults.

The signal spectral leakage and fence effect are prone to take place during the power harmonic analysis by fast fourier transform (FFT) under asynchronous sampling. The inhibiting ability of classical window functions is restricted by side-lobe behaviors. A new type window function called time domain mutual-multiplication window, is obtained by multiplication operation of several window functions. A novel approach of harmonic analysis is developed through analyzing performances of main-lobe and side-lobe of the new window. Simulation results show that the time domain mutual-multiplication window can significantly suppress frequency spectral leakage and improve the accuracy of harmonic parameter estimation.

It is a common practice that one converter controls DC voltage and the other controls power in two-terminal voltage source converter (VSC)−based high voltage DC (HVDC) systems for AC gird interconnection. The maximum transmission power from a DC-voltage-controlled converter to a power-controlled converter is less than that of the opposite transmission direction. In order to increase the transmission power from a DC-voltage-controlled converter to a power-controlled converter, an improved virtual impedance control strategy is proposed in this paper. Based on the proposed control strategy, the DC impedance model of the VSC−HVDC system is built, including the output impedance of two converters and DC cable impedance. The stability of the system with an improved virtual impedance control is analyzed in Nyquist stability criterion. The proposed control strategy can improve the transmission capacity of the system by changing the DC output impedance of the DC voltage-controlled converter. The effectiveness of the proposed control strategy is verified by simulation. The simulation results show that the proposed control strategy has better dynamic performance than traditional control strategies.

The main weakness of the half-bridge modular multilevel converter-based high-voltage direct current (MMC-HVDC) system lies in its immature solution to extremely high current under direct current (DC) line fault. The development of the direct current circuit breaker (DCCB) remains constrained in terms of interruption capacity and operation speed. Therefore, it is essential to limit fault current in the MMC-HVDC system. An enhanced fault current-limiting circuit (EFCLC) is proposed on the basis of fault current study to restrict fault current under DC pole-to-pole fault. Specifically, the EFCLC consists of fault current-limiting inductance L F C L and energy dissipation resistance R F C L in parallel with surge arrestor. L F C L reduces the fault current rising speed, together with arm inductance and smoothing reactor. However, in contrast to arm inductance and smoothing reactor, L F C L will be bypassed via parallel-connected thyristors after blocking converter to prevent the effect on fault interruption speed. R F C L shares the stress on energy absorption device (metal oxide arrester) to facilitate fault interruption. The DCCB requirement in interruption capacity and breaking speed can be satisfied effortlessly through the EFCLC. The working principle and parameter determination of the EFCLC are presented in detail, and its effectiveness is verified by simulation in RT-LAB and MATLAB software platforms.

With the increasing penetration of power electronic equipment in modern residential distribution systems, harmonics mitigation through the distributed generation (DG) interfacing converters has received significant attention. Among recently proposed methods, the so-called active resonance damper (ARD) and harmonic voltage compensator (HVC) based on voltage detection can effectively reduce the harmonic distortions in selected areas of distribution systems. However, it is found out that when traditional ARD algorithm is used to eliminate harmonic current injected by non-linear loads, its performance is constrained by stability problems and can at most eliminate half of the load harmonic currents. Thus, inspired by the duality between ARD and HVC, this paper presents a novel improved resistive active power filter (R-APF) algorithm based on integral-decoupling control. The design guideline for its parameters is then investigated through carefully analyzing the closed-loop poles' trajectory. Computer studies demonstrate that the proposed algorithm can effectively mitigate the load harmonic currents and its performance is much better than traditional ARD based on proportional control.

The integration of a modular multilevel converter-based high-voltage direct current (MMC-HVDC) transmission system in power networks has led to a high requirement for the rapidity of fault recognition. This study focused on the rapid fault diagnosis of an alternating current (AC) line fault in a back-to-back (BTB) MMC-HVDC system via fault detection and classification. Discrete wavelet transform and modulus maxima were applied to extract the fault features. Phase-mode transformation and normalization were adopted to widen the application range. Simulation and calculation results indicated that the proposed method can detect all fault types in an AC transmission line on the basis of single-side fault information within 1 ms under different values of transition resistance, fault inception angle, and fault distance. The BTB MMC-HVDC model was built using real-time laboratory (RT-LAB) based on the matrix laboratory (MATLAB) software platform, and the fault diagnosis algorithm was performed in MATLAB.

Evaluations of electric power distribution network risks must address the problems of incomplete information and changing dynamics. A risk evaluation framework should be adaptable to a specific situation and an evolving understanding of risk.This study investigates the use of symbolic dynamics to abstract raw data. After introducing symbolic dynamics operators, Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy are used to quantitatively evaluate relationships between risk sub-factors and main factors. For layered risk indicators, where the factors are categorized into four main factors - device, structure, load and special operation - a merging algorithm using operators to calculate the risk factors is discussed. Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method.Distribution networks are exposed and can be affected by many things. The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic.