Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Carbon Emission Factors Prediction of Power Grid by Using Graph Attention Network

Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice,which is of immense importance in mobilizing the entire society to reduce carbon emissions.The method of calculating node carbon emission factors based on the carbon emissions flow theory requires real-time parameters of a power grid.Therefore,it cannot provide carbon factor information beforehand.To address this issue,a prediction model based on the graph attention network is proposed.The model uses a graph structure that is suitable for the topology of the power grid and designs a supervised network using the loads of the grid nodes and the corresponding carbon factor data.The network extracts features and transmits information more suitable for the power system and can flexibly adjust the equivalent topology,thereby increasing the diversity of the structure.Its input and output data are simple,without the power grid parameters.We demonstrated its effect by testing IEEE-39 bus and IEEE-118 bus systems with average error rates of 2.46%and 2.51%.

Experimental study on the effect of H_(2)O and O_(2) on the degradation of SF_(6) by pulsed dielectric barrier discharge

SF_(6) has excellent insulation performance and arc extinguishing ability,and is widely used in the power industry.However,its global warming potential is about 23,500 times that of C0_(2),it can exist stably in the atmosphere,it is not easily degradable and is of great potential harm to the environment.Based on pulsed dielectric barrier discharge plasma technology,the effects of H_(2)O and 0_(2) on the degradation of SF_(6) were studied.Studies have shown that H_(2)O can effectively promote the decomposition of SF_(6) and improve its degradation rate and energy efficiency of degradation.Under the action of a pulse input voltage and input frequency of 15 kV and 15 kHz,respectively,when H_(2)O is added alone the effect of 1% H_(2)O is the best,and the rate and energy efficiency of degradation of SF_(6) reach their maximum values,which are 91.9% and 8.25 g kWh^(-1),respectively.The synergistic effect of H_(2)O and O_(2) on the degradation of SF_(6) was similar to that of H_(2)O.When the concentration of H_(2)O and O_(2) was 1%,the system obtained the best rate and energy efficiency of degradation,namely 89.7% and 8.05 g kWh~(-1),respectively.At the same time,different external gases exhibit different capabilities to regulate decomposition products.The addition of H_(2)O can effectively improve the selectivity of S0_(2).Under the synergistic effect of H_(2)O and O_(2),with increase in O_(2) concentration the degradation products gradually transformed into SO_(2)F_(2).From the perspective of harmless treatment of the degradation products of SF_(6),the addition of O_(2) during the SF_(6) degradation process should be avoided.

Transient Damping of Virtual Synchronous Generator for Enhancing Synchronization Stability during Voltage Dips

Virtual synchronous generators(VSGs)are widely introduced to the renewable power generation,the variablespeed pumped storage units,and so on,as a promising gridforming solution.It is noted that VSGs can provide virtual inertia for frequency support,but the larger inertia would worsen the synchronization stability,referring to keeping synchronization with the grid during voltage dips.Thus,this paper presents a transient damping method of VSGs for enhancing the synchronization stability during voltage dips.It is revealed that the loss of synchronization(LOS)of VSGs always accompanies with the positive frequency deviation and the damping is the key factor to remove LOS when the equilibrium point exists.In order to enhance synchronization stability during voltage dips,the transient damping is proposed,which is generated by the frequency deviation in active power loop.Additionally,the proposed method can realize seamless switching between normal state and grid fault.Moreover,detailed control design for transient damping gain is given to ensure the synchronization stability under different inertia requirements during voltage dips.Finally,the experimental results are presented to validate the analysis and the effectiveness of the improved transient damping method.

Localization method of subsynchronous oscillation source based on high-resolution time-frequency distribution image and CNN

The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation events.However,existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features,such as frequency drift,caused by the random volatility of wind farms when oscillations occur.This paper proposes a subsynchronous oscillation sourcelocalization method that involves an enhanced short-time Fourier transform and a convolutional neural network(CNN).First,an enhanced STFT is performed to secure high-resolution time-frequency distribution(TFD)images from the measured data of the generation unit ports.Next,these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization model.Ultimately,the trained CNN model realizes the online localization of subsynchronous oscillation sources.The effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design platform.Test results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms,thus providing a foundation for oscillation suppression in practical engineering scenarios.

Coordinated Voltage Control of Distribution Network ConsideringMultiple Types of Electric Vehicles

The couple between the power network and the transportation network(TN)is deepening gradually with the increasing penetration rate of electric vehicles(EV),which also poses a great challenge to the traditional voltage control scheme.In this paper,we propose a coordinated voltage control strategy for the active distribution networks considering multiple types of EV.In the first stage,the action of on-load tap changer and capacitor banks,etc.,are determined by optimal power flow calculation,and the node electricity price is also determined based on dynamic time-of-use tariff mechanism.In the second stage,multiple operating scenarios of multiple types of EVs such as cabs,private cars and buses are considered,and the scheduling results of each EV are solved by building an optimization model based on constraints such as queuing theory,Floyd-Warshall algorithm and traffic flow information.In the third stage,the output power of photovoltaic and energy storage systems is fine-tuned in the normal control mode.The charging power of EVs is also regulated in the emergency control mode to reduce the voltage deviation,and the amount of regulation is calculated based on the fair voltage control mode of EVs.Finally,we test the modified IEEE 33-bus distribution system coupled with the 24-bus Beijing TN.The simulation results show that the proposed scheme can mitigate voltage violations well.

The Short-Term Prediction ofWind Power Based on the Convolutional Graph Attention Deep Neural Network

The fluctuation of wind power affects the operating safety and power consumption of the electric power grid and restricts the grid connection of wind power on a large scale.Therefore,wind power forecasting plays a key role in improving the safety and economic benefits of the power grid.This paper proposes a wind power predicting method based on a convolutional graph attention deep neural network with multi-wind farm data.Based on the graph attention network and attention mechanism,the method extracts spatial-temporal characteristics from the data of multiple wind farms.Then,combined with a deep neural network,a convolutional graph attention deep neural network model is constructed.Finally,the model is trained with the quantile regression loss function to achieve the wind power deterministic and probabilistic prediction based on multi-wind farm spatial-temporal data.A wind power dataset in the U.S.is taken as an example to demonstrate the efficacy of the proposed model.Compared with the selected baseline methods,the proposed model achieves the best prediction performance.The point prediction errors(i.e.,root mean square error(RMSE)and normalized mean absolute percentage error(NMAPE))are 0.304 MW and 1.177%,respectively.And the comprehensive performance of probabilistic prediction(i.e.,con-tinuously ranked probability score(CRPS))is 0.580.Thus,the significance of multi-wind farm data and spatial-temporal feature extraction module is self-evident.

Power equipment vibration visualization using intelligent sensing method based on event-sensing principle

Vibration measurements can be used to evaluate the operation status of power equipment and are widely applied in equipment quality inspection and fault identification.Event-sensing technology can sense the change in surface light intensity caused by object vibration and provide a visual description of vibration behavior.Based on the analysis of the principle underlying the transformation of vibration behavior into event flow data by an event sensor,this paper proposes an algorithm to reconstruct event flow data into a relationship correlating vibration displacement and time to extract the amplitude-frequency characteristics of the vibration signal.A vibration measurement test platform is constructed,and feasibility and effectiveness tests are performed for the vibration motor and other power equipment.The results show that event-sensing technology can effectively perceive the surface vibration behavior of power and provide a wide dynamic range.Furthermore,the vibration measurement and visualization algorithm for power equipment constructed using this technology offers high measurement accuracy and efficiency.The results of this study provide a new noncontact and visual method for locating vibrations and performing amplitude-frequency analysis on power equipment.

Optimization of DC Resistance Divider Up to 1200 kV Using Thermal and Electric Field Analysis

Self-heating and electric field distribution are the primary factors affecting the accuracy of the Ultra High Voltage Direct Current(UHVDC)resistive divider.Reducing the internal temperature rise of the voltage divider caused by self-heating,reducing the maximum electric field strength of the voltage divider,and uniform electric field distribution can effectively improve the UHVDC resistive divider’s accuracy.In this paper,thermal analysis and electric field distribution optimization design of 1200 kV UHVDC resistive divider are carried out:(1)Using the proposed iterative algorithm,the heat dissipation and temperature distribution of the high voltage DC resistive divider are studied,and the influence of the ambient temperature and the power of the divider on the temperature of the insulating medium of the divider is analyzed;(2)Established the finite element models of 1200 kV and 2×600 kV DC resistive dividers,analyzed the influence of the size of the grading ring and the installation position on the maximum electric field strength of the voltage divider,and calculated the impact of the shielding resistor layer on the vicinity of the measuring resistor layer.The research indicates that:(1)The temperature of the insulating medium is linearly related to the horsepower of the voltage divider and the ambient temperature;(2)After the optimized design of the electric field,the maximum electric field strength of the 1200 kV DC resistive divider is reduced to 1471 V/mm,which is about 24% lower than that of the unoptimized design;(3)Installing the shielding resistor layer can significantly improve the electric field near the measuring resistor layer.This paper has an important reference function for improving the accuracy of the UHVDC resistive divider.

Research on Short-Term Load Forecasting of Distribution Stations Based on the Clustering Improvement Fuzzy Time Series Algorithm

An improved fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering method is used to cluster the data,and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division.On this basis,the data is fuzzed to form a fuzzy time series.Secondly,a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load,which is used to predict the short-term trend change of load in the distribution stations.Matlab/Simulink simulation results show that the load forecasting errors of the typical fuzzy time series on the time scale of one day and one week are[−50,20]and[−50,30],while the load forecasting errors of the improved fuzzy time series on the time scale of one day and one week are[−20,15]and[−20,25].It shows that the fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations.

Research on Optimal Configuration of Energy Storage in Wind-Solar Microgrid Considering Real-Time Electricity Price

Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids.In this paper,an improved energymanagement strategy based on real-time electricity price combined with state of charge is proposed to optimize the economic operation of wind and solar microgrids,and the optimal allocation of energy storage capacity is carried out by using this strategy.Firstly,the structure and model of microgrid are analyzed,and the outputmodel of wind power,photovoltaic and energy storage is established.Then,considering the interactive power cost between the microgrid and the main grid and the charge-discharge penalty cost of energy storage,an optimization objective function is established,and an improved energy management strategy is proposed on this basis.Finally,a physicalmodel is built inMATLAB/Simulink for simulation verification,and the energy management strategy is compared and analyzed on sunny and rainy days.The initial configuration cost function of energy storage is added to optimize the allocation of energy storage capacity.The simulation results show that the improved energy management strategy can make the battery charge-discharge response to real-time electricity price and state of charge better than the traditional strategy on sunny or rainy days,reduce the interactive power cost between the microgrid system and the power grid.After analyzing the change of energy storage power with cost,we obtain the best energy storage capacity and energy storage power.

Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms

At present,the proportion of new energy in the power grid is increasing,and the random fluctuations in power output increase the risk of cascading failures in the power grid.In this paper,we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering(DEC)algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids.First,considering the real-time operation status and system structure of new energy power grids,the scenario cascading failure risk indicator is established.Based on this indicator,the risk of cascading failure is calculated for the scenario set,the scenarios are clustered based on the DEC algorithm,and the scenarios with the highest indicators are selected as the significant risk scenario set.The results of simulations with an example power grid show that our method can effectively identify scenarios with a high risk of cascading failures from a large number of scenarios.

Research of Electric Cable Path Planning Based on Heuristic Optimization Algorithm in Mixed-Land Scenario

In order to improve the reliability of power supply,the sophisticated design of the structure of electric cable network has become an important issue for modern urban distribution networks.In this paper,an electric cable path planning model based on heuristic optimization algorithm considering mixed-land scenario is proposed.Firstly,based on different land samples,the kernel density estimation(KDE)and the analytic hierarchy process(AHP)are used to estimate the construction cost of each unit grid,in order to construct the objective function of comprehensive investment for electric cable loop network.Then,the ant colony optimization(ACO)was improved in pheromone concentration,factor increment and search direction to accelerate the solving speed,and the cable path planning result with minimum construction cost is obtained.Finally,the feeder’s tie line of the cable loop network is planned by the genetic algorithm(GA)to achieve the minimum operating cost.In the case analysis,compared with the traditional method,not only the subjective factors in the process of investment estimation can be avoided,but also the speed of model solving and the quality of the optimal solution are improved.

Virtual Synchronous Generator Adaptive Control of Energy Storage Power Station Based on Physical Constraints

The virtual synchronous generator(VSG)can simulate synchronous machine’s operation mechanism in the control link of an energy storage converter,so that an electrochemical energy storage power station has the ability to actively support the power grid,from passive regulation to active support.Since energy storage is an important physical basis for realizing the inertia and damping characteristics in VSG control,energy storage constraints of the physical characteristics on the system control parameters are analyzed to provide a basis for the system parameter tuning.In a classic VSG control,its virtual inertia and damping coefficient remain unchanged.When the grid load changes greatly,the constant control strategy most likely result in the grid frequency deviation beyond the stable operation standard limitations.To solve this problem,a comprehensive control strategy considering electrified wire netting demand and energy storage unit state of charge(SOC)is proposed,and an adaptive optimization method of VSG parameters under different SOC is given.The energy storage battery can maintain a safe working state at any time and be smoothly disconnected,which can effectively improve the output frequency performance of energy storage system.Simulation results further demonstrated the effectiveness of the VSG control theoretical analysis.

Methods Research to Calculate Equivalent of Environmental Cost in Power Industry

Power industry as an important means of primary energy consumption, occupies an important position in coal consumption and pollutant emissions, and causes a great impact on the environment. It is particularly important to define environmental costs while Environmental costs account for a heavy weight in the total cost of generating after the introduction of the Clean Development Mechanism (CDM) This paper analyzes the composition of environmental cost and its calculation methods, three cost equivalent pricing methods and its scope in environmental costs were proposed, there are Government pricing, market pricing and cost accounting, then analyses the cost of equivalent effect on electricity trade.

Method of Electricity Supply Reliability Need Assessment for Multi-type Power Users Area

With electric power outage influence on different type users analyzed, indexes for assessing supply reliability need level is designed. Based on fuzzy evaluation method, a regional power supply reliability level assessment method which utilizes qualitative data is presented.

Research on the Effects of the Most Used Market Power Mitigation Mechanisms Considering Different Market Environments

The market power mitigation method of the supply-side has become one of the key points affecting the stability of the electricity spot market.Different mitigation mechanisms are used in the current mature electricity markets of the world.However,the same market power mitigation mechanism shows different effects in different market environments.Every market operator in the world needs the most efficient way to mitigate market power.Considering that there is no relevant literature discussing the market power effects of different mitigation methods in detail,the mitigation effects need to be discussed and further researched.So,we analyze the effects of the most utilized market power mitigation mechanisms while considering different market environments.Firstly,we establish a Nash-Stackelberg interactive game model to simulate the competitive strategies of power suppliers.Secondly,the different market power mitigation approaches are modeled.Then,a multi-agent system(MAS)genetic interior-point algorithm is proposed to solve the problem of suppliers.Finally,through the simulation analysis,the market power mitigation effects of different mechanisms while considering three operation states of the system in two market structures are all analyzed.

Optimal guidance strategy for flexible load based on hybrid direct load control and time of use

The time-of-use(TOU)strategy can effectively improve the energy consumption mode of customers,reduce the peak-valley difference of load curve,and optimize the allocation of energy resources.This study presents an Optimal guidance mechanism of the flexible load based on strategies of direct load control and time-of-use.First,this study proposes a period partitioning model,which is based on a moving boundary technique with constraint factors,and the Dunn Validity Index(DVI)is used as the objective to solve the period partitioning.Second,a control strategy for the curtailable flexible load is investigated,and a TOU strategy is utilized for further modifying load curve.Third,a price demand response strategy for adjusting transferable load is proposed in this paper.Finally,through the case study analysis of typical daily flexible load curve,the efficiency and correctness of the proposed method and model are validated and proved.

Current Sharing Technology in Transmission Conductors of Cold Dielectric High Temperature Superconducting Cables Using Second-generation HTS Wires

冷绝缘高温超导电缆的导电层一般设计为多层结构以满足大电流载流特性，但伴随层数的增加，超导体上的集肤效应会引起电缆输电导体各层电流分布不均匀的问题，从而造成电缆损耗增加和传输性能下降。采用基于动态惯性权重因子的粒子群优化算法，提出了电缆导体层电流层间均流优化的设计方法。应用第2代高温超导材料钇钡铜氧涂层导体，通过建立超导电缆的等效电路模型，考虑电场、磁场等约束因素，对一根1km长，110kV／3kA等级的冷绝缘高温超导电缆进行优化设计，获得了电缆本体结构参数及输电导体层和屏蔽层的电流分布。比较优化前后层电流的结果可知，优化后超导电缆各导体层电流与平均电流相比最大不平衡率小于3．5％，各屏蔽层电流达到均布，较好地实现了电缆各导体层电流均匀分布的优化目标。最后，超导模型样缆载流特性实验也验证了优化设计方法的有效性。