Fault Diagnosis Technology Based on Model Driven

Fault diagnosis is an important application of the power grids monitoring system. Under the situation of continuous development of smart grid, it brings new challenges to the fault diagnosis technology. A fault diagnosis expert system based on model driven approach is proposed in this paper. And the corresponding fault modeling technology based on Fault Logic Description Language (FLDL) is described step by step. Practices show that this system could meet the requirements of processing fault alarm information rapidly and reliably by operator.

Design and Application of Wind Power Peak Control Technology

Wind power control technology is an important part of intelligent control in wind farms. By the automatic calculation and implementation of control strategy, problems such as imprecise of manual control scheduling, slow adjust rate, heavy workload, etc. have been solved. It can improve the capacity of wind power grid, and it also has the important meaning to the safe and stable operation of power grid. This paper introduces wind power control system from certain aspects such as control mode, control principle, and so on.

An Image Fingerprint and Attention Mechanism Based Load Estimation Algorithm for Electric Power System

With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.

Distributed Graph Database Load Balancing Method Based on Deep Reinforcement Learning

This paper focuses on the scheduling problem of workflow tasks that exhibit interdependencies.Unlike indepen-dent batch tasks,workflows typically consist of multiple subtasks with intrinsic correlations and dependencies.It necessitates the distribution of various computational tasks to appropriate computing node resources in accor-dance with task dependencies to ensure the smooth completion of the entire workflow.Workflow scheduling must consider an array of factors,including task dependencies,availability of computational resources,and the schedulability of tasks.Therefore,this paper delves into the distributed graph database workflow task scheduling problem and proposes a workflow scheduling methodology based on deep reinforcement learning(DRL).The method optimizes the maximum completion time(makespan)and response time of workflow tasks,aiming to enhance the responsiveness of workflow tasks while ensuring the minimization of the makespan.The experimental results indicate that the Q-learning Deep Reinforcement Learning(Q-DRL)algorithm markedly diminishes the makespan and refines the average response time within distributed graph database environments.In quantifying makespan,Q-DRL achieves mean reductions of 12.4%and 11.9%over established First-fit and Random scheduling strategies,respectively.Additionally,Q-DRL surpasses the performance of both DRL-Cloud and Improved Deep Q-learning Network(IDQN)algorithms,with improvements standing at 4.4%and 2.6%,respectively.With reference to average response time,the Q-DRL approach exhibits a significantly enhanced performance in the scheduling of workflow tasks,decreasing the average by 2.27%and 4.71%when compared to IDQN and DRL-Cloud,respectively.The Q-DRL algorithm also demonstrates a notable increase in the efficiency of system resource utilization,reducing the average idle rate by 5.02%and 9.30%in comparison to IDQN and DRL-Cloud,respectively.These findings support the assertion that Q-DRL not only upholds a lower average idle rate but also effectively curtails the average response time,thereby substantially improving processing efficiency and optimizing resource utilization within distributed graph database systems.

Parallel naive Bayes algorithm for large-scale Chinese text classification based on spark

The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parallel naive Bayes algorithm(PNBA)for Chinese text classification based on Spark,a parallel memory computing platform for big data.This algorithm has implemented parallel operation throughout the entire training and prediction process of naive Bayes classifier mainly by adopting the programming model of resilient distributed datasets(RDD).For comparison,a PNBA based on Hadoop is also implemented.The test results show that in the same computing environment and for the same text sets,the Spark PNBA is obviously superior to the Hadoop PNBA in terms of key indicators such as speedup ratio and scalability.Therefore,Spark-based parallel algorithms can better meet the requirement of large-scale Chinese text data mining.

Station-and-network–coordinated planning of integrated energy system considering integrated demand response

The integrated energy system(IES)is an important energy supply method for mitigating the energy crisis.A station-and-network–coordinated planning method for the IES,which considers the integrated demand responses(IDRs)of flexible loads,electric vehicles,and energy storage is proposed in this work.First,based on load substitution at the user side,an energy-station model considering the IDR is established.Then,based on the characteristics of the energy network,a collaborative planning model is established for the energy station and energy network of the IES,considering the comprehensive system investment,operation and maintenance,and clean energy shortage penalty costs,to minimize the total cost.This can help optimize the locations of the power lines and natural gas pipelines and the capacities of the equipment in an energy station.Finally,simulations are performed to demonstrate that the proposed planning method can help delay or reduce the construction of new lines and energy-station equipment,thereby reducing the investment required and improving the planning economics of the IES.

Route optimization model for pedestrian evacuation in metro hubs

The route optimization problem for road networks was applied to pedestrian flow.Evacuation path networks with nodes and arcs considering the traffic capacities of facilities were built in metro hubs,and a path impedance function for metro hubs which used the relationships among circulation speed,density and flow rate for pedestrians was defined.Then,a route optimization model which minimizes the movement time of the last evacuee was constructed to optimize evacuation performance.Solutions to the proposed mathematical model were obtained through an iterative optimization process.The route optimization model was applied to Xidan Station of Beijing Metro Line 4 based on the actual situations,and the calculation results of the model were tested using buildingExodus microscopic evacuation simulation software.The simulation result shows that the proposed model shortens the evacuation time by 16.05%,3.15% and 2.78% compared with all or none method,equally split method and Logit model,respectively.Furthermore,when the population gets larger,evacuation efficiency in the proposed model has a greater advantage.

Research of Magnetic Bias Control System Based on STATCOM

As the most important style of reactive power compensation system, the research and design control system of static synchronous compensator (STATCOM) is an important aspect of keeping stable and normal operation. This paper analyzes the influences of bias magnetic to STATCOM, and proposes an effective magnetic bias control method and program realization, so reduced to producing two harmonics. It improves the quality and reliability of STATCOM output voltage;Finally, the tests are conducted in the ±500 kVar STATCOM, and the results show the validity and necessity of this compensation method.

Deep-learning-based methods for super-resolution fluorescence microscopy

The algorithm used for reconstruction or resolution enhancement is one of the factors affectingthe quality of super-resolution images obtained by fluorescence microscopy.Deep-learning-basedalgorithms have achieved stateof-the-art performance in super-resolution fluorescence micros-copy and are becoming increasingly attractive.We firstly introduce commonly-used deep learningmodels,and then review the latest applications in terms of the net work architectures,the trainingdata and the loss functions.Additionally,we discuss the challenges and limits when using deeplearning to analyze the fluorescence microscopic data,and suggest ways to improve the reliability and robustness of deep learning applications.

A probabilistic power flow calculation method considering the uncertainty of the static frequency characteristic

In a power system, power generation and load have frequency response characteristics, which randomly fluctuate with changes in operating status. This study investigates a probabilistic power flow method that considers the unit and load uncertainty of the static frequency characteristic. Firstly, a calculation model is established on the basis of the characteristics of the frequency modulation performance of the unit and load. Then a calculation method is developed using the concept of dynamic power flow in order to determine the probability distribution of the active power flow of each line under the occurrence of a fault in the system. In the method, Monte Carlo sampling with the semi-invariant method is applied for analysis and calculation. The IEEE-30-buses system is taken as an example to analyze the impact of different responses of units on the power flow distribution of various branches. The method discussed herein is compared with the Monte Carlo simulation method to verify its effectiveness.

Coordinated Control of Multi-FACTS to Enhance the Dynamic Stability of the Power System

This article introduces a FACTS coordinated control strategy with impedance/admittance measurement feedback. Then the effectiveness of this method is proved in mathematics with damp torque method. The control strategy effect is verified in a single machine infinite bus system and a four machine power system with PSASP6.26 (Power System Analysis Software Package). This coordinated control strategy has practical significance to improve system dynamic stability and theoretical significance to improve system transient stability.

Coordinate Control Strategy for Current Stabilization in an Aluminum Smelter Including on Load Tap Changer

The performance of the current stabilization control system in an aluminum smelter affects the quality and the quantity of the electrolytic products. This paper elaborates the power supply, in which the diode rectifiers and the self-saturable reactors could keep the series current stable, then describes the basic principle of the rectifier unit control and the series current control. A coordinate strategy is proposed to keep the series current stable, the self-saturable reactors are controlled by a proportional-integral control scheme and the on load tap changers of transformer are triggered by the errors between the setting value of the series voltage and the measured values of the series voltage. Simulation results on PSCAD/EMTDC show that the effectiveness of the proposed strategy to keep the series current stable.

A Combined Method of Temporal Convolutional Mechanism and Wavelet Decomposition for State Estimation of Photovoltaic Power Plants

Time series prediction has always been an important problem in the field of machine learning.Among them,power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies.Traditional power load forecasting often has poor feature extraction performance for long time series.In this paper,a new deep learning framework Residual Stacked Temporal Long Short-Term Memory(RST-LSTM)is proposed,which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences.The network framework of RST-LSTM consists of two parts:one is a stacked time convolutional memory unit module for global and local feature extraction,and the other is a residual combination optimization module to reduce model redundancy.Finally,this paper demonstrates through various experimental indicators that RST-LSTM achieves significant performance improvements in both overall and local prediction accuracy compared to some state-of-the-art baseline methods.

Opportunities and challenges of high-entropy materials in lithium-ion batteries

Lithium-ion batteries(LIBs)currently occupy an important position in the energy storage market,and the development of advanced LIBs with higher energy density and power density,better cycle life and safety is a hot topic for both academia and industry.In recent years,high-entropy materials(HEMs)with complex stoichiometric ratios have attracted great attention in the field of LIBs due to their various promising functional properties.The adjustability and synergistic effects of multiple elements in HEMs make them possible to break through the bottleneck of traditional electrode materials and electrolytes,providing new opportunities for the development of high-performance LIBs.This article provides an overview of the opportunities and challenges of HEMs in LIBs,including cathodes,anodes and electrolytes.The progress of HEMs in LIBs is first summarized and analyzed,then the potential advantages and limitations of HEMs used in LIBs are concluded,finally some envisioned solutions are proposed to develop more advanced LIBs through HEMs.

Mechanism Design for Ancillary Service Market Considering Social Welfare and Fairness

Increasing penetration of distributed energy resources in the distribution network(DN)is threatening safe operation of the DN,which necessitates setup of the ancillary service market in the DN.In the ancillary service market,distribution system operator(DSO)is responsible for safety of the DN by procuring available capacities of aggregators.Unlike existing studies,this paper proposes a novel market mechanism composed of two parts:choice rule and payment rule.The proposed choice rule simultaneously considers social welfare and fairness,encouraging risk-averse aggregators to participate in the ancillary service market.It is then formulated as a linear programming problem,and a distributed solution using the multi-cut Benders decomposition is presented.Moreover,successful implementation of the choice rule depends on each aggregator’s truthful adoption of private parameters.Therefore,a payment rule is also designed,which is proved to possess two properties:incentive compatibility and individual rationality.Simulation results demonstrate effectiveness of the proposed choice rule on improving fairness and verify properties of the payment rule.

Transient Stability Analysis of Converter-based Islanded Microgrids Based on Iterative Equal Area Criterion Considering Reactive Power Loop Dynamics and Varying Damping

With the rapid increase in the installed capacity of renewable energy in modern power systems,the stable operation of power systems with considerable power electronic equipment requires further investigation.In converter-based islanded microgrid(CIM)systems equipped with grid-following(GFL)and grid-forming(GFM)voltage-source converters(VSCs),it is challenging to maintain stability due to the mutual coupling effects between different VSCs and the loss of voltage and frequency support from the power system.In previous studies,quantitative transient stability analysis was primarily used to assess the active power loop of GFM-VSCs.However,frequency and voltage dynamics are found to be strongly coupled,which strongly affects the estimation result of stability boundary.In addition,the vary-ing damping terms have not been fully captured.To bridge these gaps,this paper investigates the transient stability of CIM consid-ering reactive power loop dynamics and varying damping.First,an accuracy-enhanced nonlinear model of the CIM is derived based on the effects of reactive power loop and post-disturbance frequency jump phenomena.Considering these effects will eliminates the risk of misjudgment.The reactive power loop dynamics make the model coefficients be no longer constant and thus vary with the power angle.To evaluate quantitatively the effects of re-active power loop and varying damping on the transient stability of CIM,an iterative criterion based on the equal area criterion theory is proposed.In addition,the effects of parameters on the stable boundary of power system are analyzed,and the dynamic interaction mechanisms are revealed.Simulation and experiment results verify the merits of the proposed method.

Optimal Initialization and Starting Approach of Synchronous Condenser Integrated Power Grid

Synchronous condensers(SCs)are generally used at the receiving-end stations of ultra-high-voltage direct current(UHVDC)transmission systems due to their strong reactive power support and flexible regulation of reactive power according to the interconnected grids operating conditions.In this paper,different starting control schemes of a SC integrated power grid are investigated providing four main contributions:1)The principle of reactive power support of the SC on the interconnected power grid is analytically studied,providing the establishment of mathematical models.2)Four different starting control schemes are developed for the initialization and SC integration,i.e.in Scheme 1,a preset initial falling speed is directly utilized without initialization;in Scheme 2,a black start sequential control approach with a static frequency converter(SFC)is proposed;in Scheme 3,PI/PD/PID controllers are respectively applied for the excitation device at the speed-falling stage;in Scheme 4,a pre-insertion approach of an energy absorption component with R/L/RL is utilized to suppress the surges at the SC integration instant.3)The dynamic behaviors of four different starting schemes at specific operating stages are evaluated.4)The success rate of SC integration is analyzed to evaluate starting control performance.Performance of the SC interconnected system with four different starting control schemes is evaluated in the timedomain simulation environment PSCAD/EMTDC^(TM).The results prove the superiority of the proposed starting control approach in Scheme 4.

Optimal Configuration of Multitype Energy Storage for Integrated Energy Systems Considering System Reserve Value

An integrated energy system(IES)contributes to improving energy efficiency and promoting sustainable energy development.For different dynamic characteristics of the system,such as demand/response schemes and complex coupling characteristics among energy sources,siting and sizing of multitype energy storage(MES)are very important for the economic operation of the IES.Considering the effect of the diversity of the IES on system reserve based on electricity,gas and heat systems in different scenarios,a two-stage MES optimal configuration model,considering the system reserve value,is proposed.In the first stage,to determine the location and charging/discharging strategies,a location choice model that minimizes the operating cost,considering the system reserve value,is proposed.In the second stage,a capacity choice model,to minimize the investment and maintenance cost of the MES,is proposed.Finally,an example is provided to verify the effectiveness of the MES configuration method in this paper in handling operational diversity and ensuring system reserve.Compared with the configuration method that disregards the system reserve value,the results show that the MES configuration method proposed in this paper can reduce the annual investment cost and operating cost and improve the system reserve value.

Optimal Operation Control Strategies for Active Distribution Networks Under Multiple States:A Systematic Review

With the large-scale integration of distributed renewable generation(DRG)and increasing proportion of power electronic equipment,the traditional power distribution network(DN)is evolving into an active distribution network(ADN).The operation state of an ADN,which is equipped with DRGs,could rapidly change among multiple states,which include steady,alert,and fault states.It is essential to manage large-scale DRG and enable the safe and economic operation of ADNs.In this paper,the current operation control strategies of ADNs under multiple states are reviewed with the interpretation of each state and the transition among the three aforementioned states.The multi-state identification indicators and identification methods are summarized in detail.The multi-state regulation capacity quantification methods are analyzed considering controllable resources,quantification indicators,and quantification methods.A detailed survey of optimal operation control strategies,including multi-state operations,is presented,and key problems and outlooks for the expansion of ADN are discussed.

Risk assessment framework for power control systems with PMU-based intrusion response system

Cyber threats are serious concerns for power systems.For example,hackers may attack power control systems via interconnected enterprise networks.This paper proposes a risk assessment framework to enhance the resilience of power systems against cyber attacks.The duality element relative fuzzy evaluation method is employed to evaluate identified security vulnerabilities within cyber systems of power systems quantitatively.The attack graph is used to identify possible intrusion scenarios that exploit multiple vulnerabilities.An intrusion response system(IRS)is developed to monitor the impact of intrusion scenarios on power system dynamics in real time.IRS calculates the conditional Lyapunov exponents(CLEs)on line based on the phasor measurement unit data.Power system stability is predicted through the values of CLEs.Control actions based on CLEs will be suggested if power system instability is likely to happen.A generic wind farm control system is used for case study.The effectiveness of IRS is illustrated with the IEEE 39 bus system model.