Table of contents

This volume of Journal of Physics: Conference Series is dedicated to 2023 3^rd International Conference on Power System and Energy Internet (PoSEI 2023) supported by Murdoch University, Australia, held during 28^th–30^th July 2023 in Qingdao, China (virtual conference). The Conference, focused exclusively on recent trends of research in power system and energy Internet, attracted about 80 participants around the world.

The immediate purpose of this Conference was to bring together experienced as well as young scholars who are interested in working actively on various aspects of power system and energy Internet, in order to share and discuss recent related researches. And the ultimate plan is to make it a regular event every year based on the very positive response we received which was beyond our expectation.

The keynote speeches addressed major theoretical issues, current and forthcoming observational data as well as upcoming ideas in both theoretical and observational sectors. Keeping in mind the principle academic exchange first, the lectures were arranged in such a way that the young researchers had ample scope to interact with the stalwarts who are internationally leading experts in their respective fields of research.

Besides the keynote speeches, a good proportion of the participants also presented their work through contributory talks and posters on this big platform. This was particularly encouraging and of benefit to the young participants, given that there were a good number of scientists of international repute among the participants, the feedback from whom could guide them in the right direction. All the contributions were refereed by experts rigorously.

The major topics covered in the Conference are: Cogeneration and Distributed Generation, Power Quality and Electromagnetic Compatibility, Energy Efficiency and Energy Management, Information and Communication for Energy Internet, Sensor and Micro-machines, etc.

We are indebted to the Murdoch University, Australia for providing us with generous support to organize such a huge conference. We thank all the members of the Conference Committee who contributed their hard labour to make the Conference a great success. We gratefully acknowledge the experts and reviewers for their valuable suggestions on the review process of the papers submitted.

We sincerely thank the staff of Journal of Physics: Conference Series for their efforts in the publication of this issue. Last but not the least, we thank all the speakers and participants without whom the Program would not have been such a success. We hope we will have your active participation in the future conference versions of PoSEI as well.

The Committee of PoSEI 2023

List of Committee Member is available in this Pdf.

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.

• Type of peer review: Single Anonymous

• Conference submission management system: Morressier

• Number of submissions received: 138

• Number of submissions sent for review: 135

• Number of submissions accepted: 56

• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 40.6

• Average number of reviews per paper: 2

• Total number of reviewers involved: 15

• Contact person for queries:

Name: Xuexia Ye

Email: xx.ye@keoaeic.org

Affiliation: AEIC Academic Exchange Information Centre

To solve the problem that the insulator image taken by an unmanned aerial vehicle (UAV) in the process of power inspection cannot be accurately identified, located, and detected, a power insulator defect detection method based on multi-scale dense adaptive sensing is proposed. Firstly, a dense connection architecture based on multi-scale is proposed to improve the feature extraction ability of the algorithm for insulators. Secondly, an adaptive multi-scale weight transfer module is proposed to improve further the perception ability of the algorithm for insulator defect features and input multi-branch feature images. Finally, a multi-branch detection unit is proposed, and the parallel attention structure is used to improve the detection effect of the network on different scale branch insulator images. The experimental results in the existing insulator defect data set show that the proposed network can accurately identify and locate insulator defects, with an average detection accuracy of 95.6%, superior to the comparison algorithm in accuracy and speed.

The identification of line loss anomalies in low-voltage distribution networks has been a very challenging problem for a long time. The large-scale access of distributed photovoltaics to the distribution network changes the power flow distribution and further increases the difficulty of identifying abnormal line losses in low-voltage distribution areas. In this paper, an identification method for abnormal line loss in distributed photovoltaic access low-voltage distribution area is proposed. Firstly, we analyze the correlation between the line loss rate and other indicators in the area of distributed photovoltaic access. Secondly, according to the gray correlation degree results, the appropriate indicators are selected, and the k-means clustering algorithm is used to cluster these indicators, through the clustering results, we can detect outliers to determine whether there is a line loss anomaly in the area. Finally, by analyzing the time dispersion degree of the cluster where the outlier point is located, the anomaly coefficient of the area is obtained, and the line loss anomaly is judged according to the anomaly coefficient. To verify the effectiveness of this method, we conduct an empirical analysis of typical containing distributed photovoltaics. Experimental results show that this method can effectively identify the line loss abnormalities in the distribution area containing distributed photovoltaics.

The Optimal Tracking Rotor method, as one of the control methods for MPPT, increases the compensation coefficient to achieve an improvement in average wind energy capture efficiency, but it causes a significant increase in wind turbine load. There are differences in the energy contained between the high-wind-speed areas and the low-wind-speed areas. This paper proposes an improved optimal tracking rotor method by dynamically adjusting the compensation coefficient, to enhance the capture of wind energy in high wind speed areas and slow down the capture of wind energy in low wind speed areas. After research, it was found that the adjustment of the compensation coefficient varies with changes in wind speed and wind wheel acceleration. In response to this problem, the optimal change curve between the compensation coefficient and related parameters was determined. Fuzzy control was used to achieve dynamic adjustment of the compensation coefficient. Finally, the effectiveness of the proposed method was verified based on simulation results in Matlab/Simulink.

A dynamic voltage restorer (DVR) is often used for voltage compensation to reduce or eliminate the harm of voltage sag. The existing combined compensation strategy does not consider the specific process of voltage sag, so it cannot adapt to the change according to the drop depth and eliminate the phase abrupt change. In this paper, a flexible, adaptive combination compensation strategy is proposed, which adopts the appropriate compensation strategy according to the actual voltage change process, sets the transition period to avoid phase jump, and adopts the full voltage compensation strategy to fully recover the phase at the end of the voltage dip, to realize the adaptive, flexible compensation of voltage. The simulation results show that the compensation strategy varies with the voltage drop depth. Both the intermediate phase fluctuation and the energy consumption are smaller. The final compensation is complete.

Traditional phase-locked loops in three-level active neutral point-clamped inverter grid-connected systems for photovoltaic power generation exhibit a deficiency in the decoupling of positive and negative sequences under unbalanced power grid conditions. To address this issue, this study proposes the implementation of the fourth-order generalized integrator into the phase-locked loop to facilitate positive and negative sequence decoupling. Initially, a 3L-ANPC inverter grid connection system is established. However, under unbalanced grid voltage conditions, the PLL effect of the Decoupled Double Synchronous Reference Frame Phase-Locked Loop is found to be suboptimal, leading to considerable fluctuations and distortions in the output frequency signal. To address these complications, we propose an optimized phase-locked loop structure. In this improved design, the PLL first utilizes the FOGI to filter out multiple harmonics in the grid voltage prior to decoupling the positive and negative sequence voltage. This process effectively mitigates the effect on the PLL, thereby enabling rapid and precise tracking of the grid voltage. Simulation analysis is conducted by using Matlab/Simulink to substantiate the effectiveness of the proposed PLL, demonstrating its ability to reliably lock onto the frequency and phase of the fundamental voltage under conditions of unbalanced power grid voltage.

Given the problem that it is easy to receive thermal noise interference from the electronic circuit and equipment circuit of the detection system in the current feature frequency detection of Gas Insulated Switchgear (GIS) equipment, this paper proposes an ultra-high frequency (UHF) signal noise reduction method based on variational mode decomposition (VMD) combined with Savitzky-Golay (S-G) filter and tests it through simulation. By using VMD combine with the S-G filter method, the signal-to-noise ratio (SNR) of signals can be effectively improved and the interference of white noise to signals can be reduced.

With the advancement of energy transformation and new power systems, large-scale integration of new energy to replace synchronous generators is leading to insufficient inertia in the system, threatening stable frequency operation. Evaluating the minimum inertia requirements of power systems is beneficial for improving the integration capacity of new energy and ensuring secure and stable operation. This paper establishes a mathematical model of frequency dynamics after a fault and advances an approach to assess the minimum rotational inertia requirements. The proposed approach is tested on the modified WSCC-9 bus system to validate its efficacy.

To avoid a large number of harmonics generated by the subway train in the process of operation on the power grid security, the theoretical analysis and simulation experiments on the harmonics of the subway traction power supply system are carried out. Aiming at the problem of severe distortion and fluctuation of network side current at the early stage of train startup, a harmonic analysis and compensation control method based on the Sparrow Search Algorithm (SSA) optimized Adaline Algorithm is proposed, which can be employed to carry out real-time adaptive detection and suppression of harmonics generated by the subway traction power supply system. This method can form a closed-loop training loop with the subway system, and the sparrow search algorithm is used to optimize the initial weight value and learning rate hyperparameters of the Adaline model. Simulation results show that the proposed compensating control can reduce harmonics from 15.12% to 2.89% and the current distortion rate by up to 80.8%. Four error evaluation indexes were used to evaluate the errors before and after the optimization. This method is highly adaptive and can be employed to analyze and compensate system harmonics efficiently.

The gearbox is one of the crucial components in wind turbines, and its performance degradation would give rise to out-of-order or even damage. To accurately identify the fault of the gearbox, a novel fault diagnosis approach based on the ensemble model and the dung beetle optimization algorithm is introduced. Firstly, an ensemble learning model with different activation functions is established. Secondly, the dung beetle optimization is applied to select the base models for improving the performance and generalization ability of the ensemble model. Finally, the proposed method is tested with a gearbox dataset. The experimental results show that the proposed approach achieves higher diagnostic accuracy on the gearbox dataset.

The agricultural smart greenhouse electric load differs significantly from the traditional building electric load. It is more susceptible to the influence of meteorological conditions, featuring poor regularity and significant random fluctuations, so load forecasting faces new challenges. To address this challenge, this paper focuses on a variety of meteorological and historical similar feature extraction, innovates model updating strategies, and proposes a new ultra-short-term forecasting method for agricultural smart greenhouse electric loads. Firstly, an improved similar day selection (ISDS) method is designed. This method considers both trend similarity and magnitude similarity of time series. It sets weights according to the degree of influence of different meteorological features on load, thus improving the learning efficiency of the model. Next, a model dynamic update strategy (MDUS) is designed. This strategy consists of initial training of forecasting model parameters based on historical similar daily loads and online updating of forecasting model parameters based on adjacent daily load data. Then, the forecasting model is trained online and fine-tuned with the parameters based on the adjacent daily load data. The dynamically updated forecasting model is used to achieve ultra-short-term forecasting of the electric load to improve the forecasting accuracy and adaptability of the model. Finally, the effectiveness of the proposed method is verified by actual electrical load data, real-time meteorological data, and NWP data collected in an agricultural smart greenhouse in Shouguang, China.

Different load models of the same load have different effects on power flow calculation, short circuit calculation, voltage stability, and frequency stability after power system accidents. In this paper, some power load components in urban power grids are modeled by analyzing and fitting the measured power curve of load components, and they are divided into the power model, impact model, and harmonic model according to their characteristics. The power model includes the power models of some household appliances, industrial motors, and arc furnaces. The impact model includes the time series fitting model and probability fitting model. According to the measured power curve of load components, the load model established in this paper can avoid some risks of power calculation caused by the absence of a detailed load model.

Transmission sections have great significance in modern power systems. Given the phenomenon of frequent power outages caused by the chain tripping of transmission sections, a method for transmission section search based on the variable-scale affinity propagation clustering algorithm is proposed. Considering only the connection relationship, the power system model can be simplified to a topological map in graph theory for analysis. So firstly the algorithm considers the importance of each line from the perspectives of the structure of the graph and the actual operation of the power flow and then uses the variable-scale affinity propagation clustering algorithm for the network based on this value with the change of the number of cluster centers. The network will be partitioned at different scales, then find out the partition contact line at different scales, and judge whether it is a transmission cross-section by the power flow direction and the number of contact lines. This method can search out as many potential transmission sections of the power network as possible to provide more monitored objects for the staff and reduce the probability of large power outages. Finally, simulation in the IEEE-118-bus system and the IEEE-39-bus system verifies the effectiveness of the proposed method.

For the increasing proportion of building energy consumption in global energy consumption in recent years, this paper proposes a day-ahead economic dispatching method of building energy systems considering virtual energy storage. Firstly, virtual energy storage is modeled according to the thermal inertia of the building. Then, a building energy system model with a distribution grid, photovoltaic generator sets, and heating equipment (heat pump) is constructed. Finally, a day-ahead economic optimal dispatch of the building energy system considering virtual energy storage is modeled to achieve the charging and discharging management of virtual energy storage. Case study results show that under the precondition for meeting the thermal comfort of the occupants, the daily electric power purchase of the building energy system after considering virtual energy storage is reduced by 278.69 kW. The daily comprehensive operating cost is decreased by 8.3%. The validity and reliability of this suggested approach in this paper are proved.

An increasing number of renewable energy resources (RES) are connected to the distribution network, including wind power and photovoltaic. The volatility and randomness of its power output have brought some problems, including that the voltage exceeds the upper limit when its output is large, and the local power consumption is small. The use of distributed energy storage in the distribution network can effectively alleviate those problems. This paper proposes a method of distributed energy storage capacity allocation for the distribution network, based on probabilistic power flow (PPF). This capacity allocation based on PPF can more accurately describe the impact of the randomness of RES power output. Its validity and correctness are verified based on the improved RBTS-bus 6 model.

With the rapid development of society, traditional energy has become increasingly scarce and the problem of environmental pollution has intensified. By analyzing the objective functions of gas turbine, battery, photovoltaic power generation, and wind power generation, their constraints are established, and a microgrid optimal dispatching system is established. Using the idea of particle swarm optimization algorithm, an optimal dispatching model of microgrid with schedulable load is proposed. The particle swarm optimization algorithm runs in the optimal strategy algorithm of MATLAB to predict the optimal dispatching transmission time of the dispatchable load, and finally, minimize the total operating cost of the microgrid system by comparing the output before and after distributed generation in the system and the total load of the system.

Due to the layout differences between different areas of the distribution network, the electrical characteristics of each partition differ significantly. A flexible resource scheduling model considering distribution network functional zoning is proposed to address this issue. Firstly, electric vehicle charging congestion and willingness indices were proposed in different areas to guide electric vehicle charging, and corresponding controllable load models were established. A cross-regional charging model is proposed to guide users in charging, addressing the imbalance in the number and price of charging stations in each area. Then, a hierarchical scheduling model was developed based on the idea that renewable energy should be fully consumed in the upper layer to minimize the peak-to-valley disparity for the overall scheduling of flexible resources. They were scheduling controllable loads in each area in the lower layer and optimizing the charging positions of vehicles in each area. Finally, taking the IEEE33 node system as an example for simulation, the optimization effect of electric vehicles and controllable loads on the operation of the distribution network was analyzed. The results showed that the proposed method could leverage the resource characteristics of each partition, verifying the effectiveness of the proposed method.

To solve the problem that the voltage travelling wave protection at the AC bus is difficult to detect in the AC/DC interconnection network, a frequency protection principle of the line waveguide using the phenomenon of travelling wave catadioptric is proposed in this paper. According to the catadioptric phenomenon when the discontinuity of traveling wave impedance meets the wave, the fault area is identified by using the different refraction and catadioptric coefficients of faults inside and outside the region. In PSCAD/EMTDC environment, the AC-DC interconnection network model is established, and the protection principle is verified by MATLAB.

With the reform of the electricity market, the coupling interaction between different stakeholders becomes increasingly more obvious. This paper proposes a multi-time-scale optimal scheduling model for park-specific integrated energy system operator (IESO) considering the Stackelberg game given the different interest demands of different subjects in the park-specific integrated energy system (IES) and the accuracy of source-load forecasting. Firstly, a day-ahead Stackelberg game model is established with the park-specific IESO as the leader and the park users as followers. Afterward, taking into account the influence of large errors in source-load forecasts, an intraday dispatch model and a real-time correction model based on model predictive control were established. Through the analysis of calculation examples, it is verified that the multi-time scale optimal scheduling of park-specific IESO which considers the Stackelberg game can effectively reduce system operating costs, improve system economy, and stabilize fluctuations caused by source-load forecast errors.

In recent years, countries have advocated green development and gradually implemented new energy-based power grids. Due to the contradiction between the randomness of new energy generation and the growing demand for residential electricity consumption, the stable operation of power grids is facing a great challenge. Air-conditioning loads make up a large and increasing portion of residential electricity consumption, so monitoring it and establishing a corresponding model to guide residents to use electricity reasonably and ensure that the power grid operates stably is important. Many air conditioning model parameters are based on typical values or use offline identification and need higher accuracy. The paper presents a dynamic modeling approach for air conditioners based on the bat algorithm to realize a comprehensive view of the air conditioners and the buildings to which they belong. A model of the air conditioner and the building to which it belongs is created. Then the bat algorithm recognizes the model parameters and makes iterative corrections. The actual usage scenario is simulated in the form of simulation to generate air conditioner power consumption data and verify its recognition effect. The model is shown to identify the corresponding parameters accurately. It realizes the accurate monitoring of air conditioners in real application scenarios, which is important for comprehensively understanding air conditions.

The scenario analysis method is an important method to adapt to the optimal dispatch of power systems with a high percentage of new energy sources. As a hot research topic in scenario analysis methods, the significance of scenario reduction is to describe many complex scenarios that feature a small number of representative scenarios to achieve the purpose of reducing computational complexity. In this paper, a distribution network optimization dispatch method based on the hierarchical clustering algorithm for scenario reduction is proposed considering wind power output and photovoltaic output. Firstly, the original scenes are quickly categorized because of the hierarchical clustering algorithm to derive typical scenarios. Secondly, an optimal dispatch model of the distribution network containing renewable energy is established. Finally, the arithmetic example is verified by using the IEEE33 distribution network, and the experimental results validate the validity and superiority of the proposed scenario reduction method.

Insulation defects inside a Gas Insulated Switchgear (GIS) can generate partial discharges, and the current pulses caused by them can excite ultra-high frequency (UHF) electromagnetic waves. To study the attenuation characteristics of UHF signal propagation in GIS, simulation models of the straight cylinder, insulator, L-type, and T-type structures are established by CST simulation software. Then the propagation characteristics of partial discharge UHF signal are studied and analyzed. The simulation results show that the electromagnetic wave propagates in each structure inside the GIS with different degrees of attenuation. When passing through insulators continuously, the first insulator has the most serious attenuation; when passing through T-shaped structures, the attenuation of T-shaped vertical branches is more serious than linear branches.

Static Var Generator (SVG) consumes large amounts of electrical energy during its operation, generating high equipment operating costs. Therefore, it is necessary to quantify the operating SVG loss. However, the existing methods of loss estimation generally have notable problems, such as large deviations in loss estimation results. In this paper, we propose a method for calculating SVG loss and establish an SVG loss estimation model based on the extreme gradient boosting algorithm under different operating conditions. First, some data obtained by simulation experiments are input into the algorithm model for training, and the hyperparameters of the model are adjusted by the Bayesian optimization algorithm. Then, a new test set is constructed by superimposing the measurement errors with different signal-to-noise ratio noises on the test set data. Finally, the test set is fed into the trained model for testing to validate the effect of the present model. The experimental results show that the model can quickly and accurately achieve loss estimation of SVG devices with good engineering practicality.

The insulation performance of transformer oil has a great connection with the stable operation of power systems, and transformer oil will inevitably be mixed with water and other impurities in the actual operation. To explore the mechanism of the influence of suspended water droplets on the insulation performance of transformer oil, this paper uses the phase field method to simulate the interaction and phase transition process between oil and water phases in motion. They systematically analyse the influence of electric field and oil flow parameters on the deformation and coalescence of water droplets. The results indicate that droplets in the oil channel will deform along the direction of the electric field, and the degree of deformation gradually increases with the increase of the electric field strength. The electric field strength has an important impact on the degree of deformation of droplets in the oil channel. In addition, under the same electric field intensity, the radius of droplets is an important factor that leads to the length of aggregation time of droplets. Research has found that the aggregation time between droplets becomes shorter as the radius gradually increases.

Energy storage is an important way to solve the problems of system power support and new energy consumption. Based on the installed scale of various power sources and load forecast results in the planning year, the power balance calculation of typical methods is used to provide the basis for the calculation of the volume ratio relationship of thermal energy and energy storage. Then, a model is established for the relationship between thermal energy and energy storage to obtain a variety of thermal energy and energy storage installed capacity combinations. This paper proposes a system operation cost objective function to compare the annual cost and new energy consumption rate of thermal energy and energy storage. The aim is to obtain a combination of energy storage and thermal energy that meets the new energy consumption level of the system and has a relatively reasonable annual operation cost. This paper proposes a collaborative optimal allocation method for thermal energy and energy storage that takes into account the system operating costs. It will help guide the development of power resources in the region in the medium to long term and ensure the load supply requirements and power regulation capacity of the power system.

With the increasing application of lithium-ion batteries in energy storage (ES) and electric vehicles (EV), the operation state and the safety early warning are particularly important. Due to the complexity of lithium-ion batteries, predicting the state of charge (SOC) of batteries is still a great challenge. In this paper, an electrochemical impedance spectroscopy (EIS) based on the SOC estimation method was proposed. The EIS of different SOC under 25℃ was measured, and the variation characteristics of EIS were summarized from the aspects of amplitude and phase by using the full band information. Finally, the linear system and BP neural network model were constructed by selecting the characteristic parameters at a specific frequency. The results demonstrate that the prediction effect of the BP neural network is better, and the error is found to be less than 3%.

As the coupling degree of the natural gas pipeline network (NGPN) and the power system increase, unreliable gas supply risk related to random failures in NGPN has threatened the operation security of the power system. However, present studies seldom discuss the effect of natural gas supply reliability on power system operation. In this manner, a stochastic unit commitment model is presented with the consideration of long-term gas supply risk in NGPN. Firstly, with the combination of the Monte Carlo simulation (MCS) technique and the maximum-flow algorithm, the failure scenario set corresponding to gas supply risk can be obtained. Then, a two-stage model for unit commitment is established considering gas supply uncertainty based on the stochastic optimization model. Finally, case studies have shown that the power operation reliability has been improved with the increased reliability indices.

Sympathetic inrush is a phenomenon caused by the energization of the neighboring unloaded transformer. It may cause current differential protection to operate in the wrong way. Due to the lack of a special and continuous identification method, this paper studies the inherent feature of the two transformers during sympathetic inrush. Then a substation-area current signal based on the feature is constructed and its characteristics are analyzed. The time-frequency energy of the current signal is extracted based on the synchrosqueezed wavelet transform (SWT) since the SWT can process the transient signals accurately. The extracted energy is taken as the basis of the identification criterion. The simulation results conducted by PSCAD/EMTDC software prove the accuracy and effectiveness of the proposed method.

The large-scale wind power grid connection exacerbates the problem of uneven distribution of inertia between different regions, posing challenges to the frequency security defense of multi-area systems. This article proposes a two-stage robust optimization scheduling model that considers both the uncertainty of wind power output and the dynamic safety of multi-area frequencies. Firstly, according to the conditional value at risk theory, the operating cost and operating risk of the multi-region system are optimized, and the acceptable safety boundary of wind power is obtained. Secondly, multi-zone frequency dynamic safety verification is carried out in harsh wind power scenarios. Decouple the reserve capacity into non-accident reserve and accident reserve, and nest them separately in a two-stage model to achieve clear solution ideas. Finally, the two-stage optimization problem is solved through the column and constraint generation algorithm. The simulation results of numerical examples show that the model and method can effectively improve the frequency response capability of the multi-region system, so as to ensure the safe and economical operation of the system.

The operational status of power communication optical cables is directly related to the safety of power transmission lines and communication systems. Traditional cable maintenance modes face challenges in fault localization. This article proposes a platform for optical cable fault diagnosis and decision support, which is constructed at three levels: the data layer, ontology layer, and application layer. The key aspect of this platform is the utilization of a knowledge graph to map the status of the cable system. The method proposed in this article effectively addresses the pain points of traditional maintenance modes.

Under the pressure of global energy shortage and severe environmental pollution, using new energy in ships will become the mainstream direction of future development. Compared to traditional single fossil fuels, the hybrid system composed of clean energy improves overall cleanliness. Hybrid power design has also attracted widespread attention in academia and industry. Therefore, this article introduces a hybrid propulsion system for ships. It combines Proton-exchange membrane cell (PEMFC) and cell. A simulation model for the propulsion system of hybrid electric ships was established in the MATLAB/SIMULINK simulation environment. It analyzes the ship's operating speed and power under different operating conditions and allocates the operating power to the hybrid power system through an energy control strategy. Finally, it compares and analyzes the optimization potential and methods of hybrid power systems. The results indicate that the modeling method and energy management strategy used in this article can effectively simulate the load changes and energy allocation of ships under different operating conditions, improving the stability and efficiency of fuel cells.

The fluctuation of new energy output decreases distribution system reliability and increases the risk of a power outage. Based on this, considering the participation of new energy sources, the influence of load participation demand response on the distribution system is analyzed, the load demand response model is established, and the corresponding unit capacity compensation cost model is proposed. Considering the recovery income, operation cost, demand response compensation cost, and risk, a bi-level optimization model is established to solve the maximum recovery net income of the system. The upper layer aims at maximizing the expected net recovery income of the entire distribution system and proposes a distribution system partition scheme that takes into account the participation of new energy sources and mobile sources. The lower layer aims at maximizing the net recovery income of each partition and solves the recovery time and recovery amount of the load in each partition or the response time and response amount of the load participating in demand response under the condition of limited resources. Finally, the effectiveness of the power supply recovery optimization strategy of the distribution system proposed in this paper is verified by an example.

To meet the practical needs of effective failure management and safe operation of hydropower systems, the classification and description standardization of the failure modes are studied based on the statistical analysis of historical maintenance data of in-service hydropower equipment. The method of building the failure mode library is then offered, followed by the specification of failure mode codes. Given the characteristics of the hydropower equipment, a severity rating method and the severity measurement model of failure modes for hydropower equipment are proposed, with definition criteria built using fuzzy semantic techniques. Finally, a thrust bearing system of the hydraulic generator sets is used as an example to validate the method of failure mode analysis and severity measurement, and practical suggestions for implementation of severity measurement are given in this paper.

Electrical load forecasting is an essential foundation for power reliable and economical operation of the power grid. Most forecasting models regard the prediction results as deterministic variables, which ignores the randomness and volatility of the power load. At the same time, insufficient historical load data often lead to undertrained models, which affects the accuracy of capturing uncertain information. Therefore, we proposed an optimized transfer learning-based method for short-term load-interval prediction. A deep learning quantile regression model would be constructed by source domain data in the method, and the weights of the source model would be optimized to avoid negative transfer. Then, the target model is constructed by parameter transfer based on key layers and is tuned with hyperparameters by target domain data. From the experimental discussion, it is known that the model with an optimized transfer learning strategy can accurately quantify the fluctuation range of future power load.

Three-dimensional reconstruction and visualization of overhead transmission lines is an important foundation for grid operation and maintenance management. This paper introduces a 3D visualization method of transmission lines based on the unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) technology, which includes four steps: (I) Using UAV-LiDAR technology to obtain 3D point cloud data of power lines; (II) Using the least-squares method with loss minimization constraints to fit the point cloud data of the power lines, to obtain the accurate fitting function of the power lines; (III) Using the spline interpolation algorithm to draw the 3D scene in the 3D vectors, and combine with building information modeling (BIM) model to construct the transmission line visualization scene. The method of this paper can improve the authenticity and reliability of transmission line simulation and provide technical support for the digital platform of power grid management.