Operating experience has shown that vibration can cause pressure fluctuations and initiate bubbles at the oil gap, which seriously threatens the insulation properties of oil-immersed power equipment. The main aim of this study was to determine the characteristics and mechanism of bubble initiation at the transformer oil gap under vibration in this paper. The results show the existence of the vibration-dependent bubbling phenomenon at the oil gap. Interestingly, bubbles initiate during the process of gap expansion, and the evolution of bubble morphology was further analyzed. The threshold characteristic of the bubble initiation process was found, and the relevant influencing factors were experimentally obtained. In the range considered in this study, higher gas content, higher temperature, and lower moisture content are conducive to bubble initiation at the oil gap under vibration, while vibrational frequency does not affect the threshold characteristic of bubble initiation process. The vibration-dependent bubbling phenomenon can be attributed to the vibration-induced cavitation process caused by the decrease in pressure at the oil gap. The influence mechanism of the above factors on the threshold characteristics of bubble initiation was further discussed. This study contributes to providing a reference for risk assessment of abnormal vibration of oil-immersed power equipment.

The commutation failure is the most prevalent fault in line-commutated converter based HVDC systems, which may result in transient overvoltage on the sending-side system. Overvoltage level evaluation has become a crucial task for power industries to assess the tripping risk of large-scale wind turbines and implement effective stability control measures. In this paper, decision tree (DT) model is adopted to extract the mapping relationship between transient overvoltage and massive electrical quantities of power grids. The common DT algorithm is transformed by modifying the error weight assignment, which reflects the error tolerances for different actual overvoltage regions. To compensate for potential inaccuracies in the data-driven method, a derivation of the mathematical relationship between the reactive power consumed by the rectifier and AC voltage is presented, along with an analytical expression for the peak value of transient overvoltage. On this basis, an overvoltage analysis method integrating the model-driven and data-driven techniques is proposed, and the improved DT algorithm is ap-plied to fast error correction, enhancing the interpretability of regression prediction results. Case studies were performed in the actual Northwest China local region hybrid AC/DC power grid with transient overvoltage problems, and the simulation results verified the effectiveness of the proposed method.

Adequacy is a crucial consideration in the planning and operation dispatching of the power system, especially in the power system with high penetration of renewable energy sources. Considering the multi-time scale characteristics of renewable energy power and the response characteristics of flexible regulation resources, a multi-time scale adequacy evaluation method based on empirical mode decomposition is proposed. In this method, a net load curve is decomposed into multiple component curves at a multi-time scale by the empirical mode decomposition (EMD) algorithm. As a result, the adequacy demand of the system at each time scale is obtained by waveform recognition. Moreover, the available adequacy resources of the system at different time scales are obtained according to the regulation models of flexible regulation resources. By analyzing the adequacy demand and available adequacy resources of the system at the same time scale, the adequacy evaluation indices at each time scale can be calculated and weighted to form the comprehensive indices. At last, taking a practical power system as a case, the adequacy evaluation indices at each time scale with different capacities of renewable energy sources and energy storage systems are compared and analyzed. Simulation results indicate the validity of the method.

Solar storms impact electrical power grids by causing DC neutral point currents in transformers. These currents lead to half-cycle saturation as well as other related and unwanted effects in the grid. To reduce the effect of these currents on the grid, DC-blocking devices can be installed or changes in the grid topology can be made. However, these counter measures often have unwanted side effects or cannot be applied to the grid due to operational restrictions. In this work, a novel mitigation approach, based on the distribution of currents on more transformers, is presented. The number and location of grounded transformer neutral points is optimized, taking grid related constraints such as the minimal number of transformer connections into account. It is shown that the algorithm can effectively reduce the stress on transformers without any additional assets and thus increase system security.

The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data-driven models currently used assume that the input data is complete, which has obvious limitations in real-world applications. This paper suggests an IVS-GAN model to assess power system stability using incomplete PMU measurement data with random loss. The proposed method combines the super-resolution perception technology based on Generative Adversarial Network (GAN) with a time-series signal classification model. The generator adopts a one-dimensional U-Net network and uses convolutional layers to complete and recover missing data. The discriminator adopts a new GRU-Attention architecture proposed in this paper to better extract voltage temporal variation features on key buses. The result of this paper is that the stability evaluation method outperforms other algorithms in high voltage data loss rates on the New England 10-machine 39-bus system.

The multi-scale flexibility coordination of multiple storages is a key technology to enhance the diversified regulation ability of the power system.This paper first considered the interaction mechanism of multi-type storage peak regulation time sequences based on the Euclidian distance, Dynamic time warping distance, and storage correlation distance. A matching index was proposed to consider the temporal correlation, overall distribution characteristics, and dynamic characteristics of the net load and energy storage. The multitype storage coordination mode, including battery storage, pumped storage, and electric vehicles, was formulated, and a collaborative optimal scheduling system architecture of source-grid-load-storage (SGLS) was constructed. To attain a low-carbon economy, a collaborative optimal scheduling model of SGLS considering the dynamic time-series complementarity of multiple energy storage systems was constructed. The Nash equilibrium theory was used to achieve friendly interaction among the source, grid, load, and storage. Then, an improved transfer reinforcement learning algorithm for SGLS was proposed, which used reinforcement learning and transfer learning algorithms combined with K-means clustering and dual-structure experience pool technology. The test results of actual regional power grid data indicated that the proposed strategy can effectively reduce the economic and carbon treatment costs of the system and improve the absorption capacity of renewable energy.

The voltage endurance coefficient n of cross-linked polyethylene (XLPE) insulation is an important indicator for the analysis of insulation failure mechanism. Accurately obtaining the value of n can provide technical support for the reliability evaluation of cable operation. The constant stress method and the step stress method are used to obtain the values of n, but the test parameters can influence the accuracy of the obtained n values. Two methods for selecting test parameters are proposed. The equivalence between the two methods is established, providing theoretical support for the accurate evaluation of the n value of solid insulation materials by using the step stress method. The accuracy of the evaluation results is verified by analyzing the test data. The test results show that the nstep=13.04 evaluated by the step stress test with the proposed parameter selection method is consistent with the test result of constant stress nconstant=12.83.

A power line carrier (PLC) communication characteristics-based method is proposed for single-phase-to-ground fault location in neutral isolated medium voltage (MV) meshed distribution networks in this paper. The carrier signals with a time-varying frequency and constant amplitude are processed by a set of PLC transmitters and receivers, whose placement is optimized by regarding the power network as an undirected graph. Two signal encoding and decoding algorithms for the PLC terminals are proposed to avoid using expensive timing systems between the terminals. The fault location technique is implemented by comparing the cosine similarity of amplitude attenuation (AA) and phase offset (PO) between the fault and a library. The node corresponding to the maximum cosine similarity of the characteristics between the present fault and the library is selected as the location of the current fault. Only one set of low-cost PLC communication terminals and the widely available power lines are needed in the fault location system, making this approach highly practical. Numerical simulations using MATLAB/Simulink have been performed to verify the technique’s feasibility. The results show that the method can accurately locate faults in neutral isolated MV meshed distribution networks. Besides, the presented approach achieves high accuracy in estimating transition resistance values.

This paper proposes a novel primary frequency regulation (PFR) scheme for Multi infeed HVDC (MIDC) system. First, a dynamic model for frequency response (FR) in MIDC system is carried out to involve LCC-HVDCs in frequency regulation. Then, a disturbance observer (DO) is proposed to estimate both the power imbalance and the full order states of the system. By combining a Kalman filter (KF), this DO gets an accurate estimation quickly with local frequency measurement. On the basis of the estimated disturbance and state variables, feedforward control schemes can be concluded to fully utilize the fast adjustability of LCC-HVDCs. In this paper, an model predictive control (MPC) based scheme is designed to coordinate LCC-HVDCs under constraints of HVDC capacity and frequency boundaries. As a result, the optimal power-sharing property of LCC-HVDCs is achieved to enhance the PFR dynamic performance of the AC main system. Further, the stability of the proposed method is analyzed using Lyapunov method. Finally, numerical simulations are concluded to verify the effectiveness and the merits of the proposed method, by comparing it to typical droop controls.

An explosion accident in an enclosed cable trench caused by the discharge of 10 kV three-phase cable joints is discussed. Combined with the disassembly analysis, the fault recording data analysis, and the validation of experiment, the evolution of the cable joint fault is deduced and discussed. The results show that the trigger of the cable joint fault is the creepage discharge at the XLPE-SiR insulation interface. This results in the partial breakdown and the grounding failure of three-phase cable joints. Under the long-term floating potential and current thermal effect, the insulations are gradually ablated and decomposed into a large amount of combustible gas. Finally, the accumulated combustible gas is ignited by the arc caused by a three-phase short circuit at the moment of the reclosing operation. The analytical method and conclusions proposed in this paper can provide suggestions and guidance for the analysis of similar fault accidents in the future.

Electric Vehicles (EV) have the potential to completely transform the transportation industry, slash carbon emissions significantly, and pave the road for significant climate progress. According to surveys, worries regarding range and charging accessibility are a significant barrier to EV customer adoption. The electricity grid will be significantly impacted by EV charging. A coordinated charging approach that facilitates EV charging while avoiding the power grid from overloading is required to manage EV charging. In this work, the EVs are classified based on their charging capacity. This classification includes Car, truck and bus. With this a chargingdischarging scheme has been established using Real Time Pricing (RTP) for each hour for a 20 min block. Augmenting this, in the present work the power loss is analyzed when Distributed Generation (DG) units are installed after the EVs are located in the system for charging and discharging. The DG units are installed considering the Voltage Stability Index (VSI) and the power losses incurred due to the presence of EVs. The Monetary benefit of such an investment is analyzed for different topologies of IEEE-33 and 69 bus systems and compared with the existing methods where DGs are installed before the EVs are located in the system for charging and discharging.

Driven by the demand for carbon emission reduction and environmental protection, battery exchange stations (BES) with battery energy storage stations (BESS) and renewable energy have become one of the key technologies to achieve the goal of emission peaking and carbon neutrality. Therefore, this paper proposes a strategy to optimize the operation of BES with Photovoltaics (PV) and BESS supplied by transformer spare capacity. Firstly, it introduces the operation mechanism of BES and uses the spare capacity of building special transformers and the roof PV to supply power to BES to avoid the investment of transformers. Secondly, this paper establishes the load model of BES and proposes the charging rules of battery exchange. Thirdly, a segmented pricing mechanism for the rental price of special transformers is formulated to guide BES operators to preferentially rent spare capacity during low load rate periods. Aiming at the maximum daily profit of BES, an optimization model is established to optimize the number of batteries to be charged and the charging status of BESS in each period, on this basis, the demand response model is proposed. Simulation results show that the proposed strategy can improve the daily profit of BES. And the configuration of BESS can improve the battery exchange capacity. Moreover, the exponential segmented pricing mechanism can effectively guide BES to rent the spare capacity under low and medium load rates of special transformers.

Most of the first eight pilot regions in China have entered the long-period continuous operation stage, and the second batch of six power spot pilots have started their simulation trial runs; however, the construction of power markets in the hydropower-rich regions of China (i.e., Sichuan and Yunnan) still faces multiple problems, including poor reliability of medium- and long-term power supplies, inefficient hydropower resource utilization and consumption scales, and unstable market prices. In this study, we establish a multi-scale joint clearing model for different market models (considering both long- and short-term operation strategies) and perform simulations of different market models at different time scales, to compare and analyze their optimization results and electricity price fluctuations. The model can stabilize the market price and reasonably reflect the true value of hydropower; it can also promote the consumption of clean energy and ensure the reliability of power supplies in the medium and long term. Finally, suggestions are made regarding the selection of electricity market models, the security of energy supplies, and the design of sustainable development mechanisms for hydropower in hydropower-rich regions, with a view to providing useful references for the development of China’s electricity market.

When acting as the shared energy storage and participating in electricity market services, the schedulable capacity that shared electric vehicle (shared EV) will provide to the grid needs in future time needs to be predicted accurately. This research first proposes a method to construct a schedulable capacity dataset for shared EVs based on publicly available shared vehicle rental service data. Secondly, a schedulable capacity evaluation model based on model-agnostic meta-learning, convolutional neural network, long short-term neural network and attention mechanism (MAML-CNN-LSTM-Attention) is proposed. Through the model, the aggregated schedulable capacity of shared EVs in different functional communities for the coming 60 minutes is predicted. Model uses MAML to fine-tune the meta-prediction network through multi-task training to quickly adapt to feature changes caused by different travel habits of different functional communities; CNN-LSTM is used to learn spatial features of schedulable capacity and efficiently extract high-dimensional temporal features from historical sequences; Attention mechanism is used to further improve model prediction accuracy. Simulations show that the model proposed in this paper outperforms other existing models and can reliably predict the schedulable capacity for different date types and functional areas, providing useful decision aids for shared EV operators to participate in market services.

This paper proposes a recurrent neural network (RNN) based model to segment and classify multiple combined multiple power quality disturbances (PQDs) from the PQD voltage signal. A modified bi-directional long short-term memory (BI-LSTM) model with two different types of attention mechanism is developed. Firstly, an attention gate is added to the basic LSTM cell to reduce the training time and focus the memory on important PQD signal part. Secondly, attention layer is added to the BI-LSTM to obtain the more important part of the voltage signal by assigning weightage to the output of the BI-LSTM model. Finally a SoftMax classifier is applied to classify the combined PQD signal in 96 different combinations. The performance of proposed BI-LSTM model with attention gate and attention layer mechanism is compared with the performance of baseline models based on recurrent neural network (RNN) and convolution neural network (CNN). With this model, the PQD signal is easily segmented from the voltage signal which makes the process of PQD classification more accurate with less computation complexity and in less time.

This paper considers the variability in the impact of multi-dimensional meteorological information on power load in different regions. To improve the accuracy of load forecasting in the spatial dimension, the method of spatio-temporal fusion (SF) of multi-dimensional meteorological information is proposed. The Copula theory is applied to analyze the nonlinear coupling of meteorological information such as wind speed, rainfall, temperature, and sunshine intensity from multiple meteorological stations with the power load and to achieve spatio-temporal fusion. In the time dimension, the core parameters of the variational mode decomposition (VMD) are improved by the marine predators algorithm (MPA). The weighted permutation entropy (WPE) is used to construct the MPA-VMD fitness function for the adaptive decomposition of the load sequence. In addition, the input sets of the LSTM model and MPA-LSSVM model are constructed by combining each component of the time dimension and each meteorological information of spatial dimension to obtain the prediction results of each component. The prediction model corresponding to each component is selected according to the evaluation index, and reconstructed to obtain the overall prediction results. The analysis results show that the proposed forecasting method is better than the traditional forecasting method and effectively improves the accuracy of power load forecasting.

In modern power systems, the impact of random power sources and loads on power systems is increasing, resulting in threatened system frequency security. However, traditional methods are often not comprehensive in modelling randomness, and there is no analytical method to assess the frequency response of power systems under uncertain power disturbances. In this paper, the power disturbance in a power system is regarded as an interval random quantity. Based on a general model of the system frequency response (SFR-G) and the theory of stochastic processes, an analytical prediction method of the power system frequency deviation under uncertain power disturbance is proposed. Through further deduction, the allowable range of power disturbance under the premise of secure frequency deviation can also be defined, which provides a reference for frequency security and accident prevention in power systems. Numerical simulations demonstrate that the proposed analytical method can delimit the response interval of system frequency deviation well, and the backstepping calculation can also delimit the allowable range of power disturbance to ensure the secure operation of the system.

Distributed energy resources (DERs) provide flexible load restoration strategies, which can effectively enhance the resilience of active distribution systems (ADSs). Whereas, the widespread DERs in ADSs complicate the supply-demand relationship and make the system resilience difficult to access. Therefore, this paper proposes a simulation-based resilience assessment algorithm of ADSs considering the microgrid formation based on grid-edge DERs. Microgrid formation is used to depict the resilience gain of grid-edge DERs on ADSs. Specifically, a resilience assessment framework for ADSs is firstly proposed, where the uncertainty of component state and supply-demand is modelled based on probability statistics. Then the mixed integer linear programming is used to search for optimal load restoration strategies with microgrid formation. On this basis, a set of resilience indices are defined to quantitatively analyse the resilience of ADSs, and a resilience assessment algorithm with uncertainty scenario generation is proposed to obtain these indices. Furthermore, extensive numerical results based on a modified IEEE 123-bus feeder validate the effectiveness of our proposed method.