Flashover Characteristics of Dry Band-water band on Hydrophobic Surfaces

Due to their outstanding anti-flashover characteristics,composite insulators have been extensively applied in power systems.A lot of research has investigated flashover characteristics of hydrophobic specimens with artificial water droplets.However,the phenomenon is not consistent with that of the contaminated composite insulators.On the test specimens covered with water droplets,there is no obvious leakage current before the flashover and no obvious relationship between flashover voltage and the conductivity of water droplets.On the contaminated composite insulator surface,there are short continuous arcs on the insulators before critical flashover,making insulators look like a luminous lantern.Considering that under these two conditions,the proportion of water along the insulation distance is different,the flashover characteristic of“dry band-water band”on a hydrophobicity surface is analyzed in the present study.The influence of the water band parameter(including length,width,and conductivity)as well as the length of dry band is studied.On this basis,the arc generation and development process of the surface covered with“dry band-water band”is analyzed.The research results improve the understanding of the flashover process on contaminated composite insulators.

Resilience-oriented Hardening and Expansion Planning of Transmission System Under Hurricane Impact

In this paper,we propose a two-stage transmission hardening and planning(TH&P)model that can meet the load growth demand of normal scenarios and the resilience requirements of hurricane-induced damage scenarios.To better measure the resilience requirements,the proposed TH&P model includes two resilience assessment indexes,namely,the load shedding(LS)under the damage scenario and the average connectivity degree(ACD)at different stages.The first-stage model,which aims to meet the load growth demand while minimizing the LS,is formulated as a mixed-integer linear program(MILP)to minimize the total planning and hardening cost of transmission lines,the operating cost of generators,and the penalty cost of wind power and load shedding in both normal and damage scenarios.The second-stage model aims to further improve the ACD when the ACD of the scheme obtained from the first-stage model cannot reach the target.Specifically,the contribution of each transmission line to the ACD is calculated,and the next hardened line is determined to increase the ACD.This process is repeated until the ACD meets the requirements.Case studies of the modified IEEE RTS-24 and two-area IEEE reliability test system-1996 indicate the proposed TH&P model can meet the requirements for both normal and damage scenarios.

Advanced System-Level Model Reduction Method for Multi-Converter DC Power Systems

For dynamic stability analysis and instability mechanism understanding of multi-converter medium voltage DC power systems with droop-based double-loop control,an advanced system-level model reduction method is proposed.With this method,mathematical relationships of control parameters(e.g.,current and voltage control parameters)between the system and its equivalent reduced-order model are established.First,open-loop and closed-loop equivalent reduced-order models of current control loop considering dynamic interaction among converters are established.An instability mechanism(e.g.,unreasonable current control parameters)of the system can be revealed intuitively.Theoretical guidance for adjustment of current control parameters can also be given.Then,considering dynamic interaction of current control among converters,open-loop and closed-loop equivalent reduced-order models of voltage control loop are established.Oscillation frequency and damping factor of DC bus voltage in a wide oscillation frequency range(e.g.,10–50 Hz)can be evaluated accurately.More importantly,accuracy of advanced system-level model reduction method is not compromised,even for MVDC power systems with inconsistent control parameters and different number of converters.Finally,experiments in RT-BOX hardware-in-the-loop experimental platform are conducted to validate the advanced system-level model reduction method.

Capacity Market Mechanism Design,Considering Network Optimization and Energy Transition

The coordination of enrgy transition,fixed cost recovery,and sufficient generation supply leads to a new challenge for a traditional capacity market mechanism.Moreover,in order to better match network expansion at the same time,it is crucial to redesign the capacity market mechanism considering system topology.In this paper,a novel capacity market mechanism is proposed considering spot market operations,network expansion,and energy transition,which can minimize the total cost of capacity investment,network expansion,and generation operations,while satisfying the energy transition constraints and topology circumstances.Specifically,the capacity market mechanism co-ordinated with spot market operations is illustrated,in which the energy transition and network constraints are embedded.Then,a bi-level optimization model is established where the trade organizers minimize the total cost of both investment and operations,subject to the spot power market simultaneously minimizing the local dispatching costs.The numerical results of a test system show that more economical capacity portfolios can be obtained by constructing reasonable transmission lines,thereby obtaining a more optimal market cost.A detailed multi-scenario simulation is further analyzed to verify the effectiveness of the proposed market mechanism.

Coordinated Frequency Control for Isolated Power Systems with High Penetration of DFIG-Based Wind Power

This paper proposes a coordinated frequency control scheme for emergency frequency regulation of isolated power systems with a high penetration of wind power.The proposed frequency control strategy is based on the novel nonlinear regulator theory,which takes advantage of nonlinearity of doubly fed induction generators(DFIGs)and generators to regulate the frequency of the power system.Frequency deviations and power imbalances are used to design nonlinear feedback controllers that achieve the reserve power distribution between generators and DFIGs,in various wind speed scenarios.The effectiveness and dynamic performance of the proposed nonlinear coordinated frequency control method are validated through simulations in an actual isolated power grid.

Detection Method for Cascading Failure of Power Systems Based on Epidemic Model

The early detection of cascading failure plays an important role in the safe and stable operation of the power system with high penetration of renewable energy.This paper proposes a fault propagation dynamic model based on the epidemic model,and further puts forward a method to detect the development of cascading failures.Through the simulation of the IEEE 39-bus and 118-bus systems,this model is proven to be valid and capable of providing practical technical support for the prevention of cascading failures in power systems with high penetration of renewable energy.This paper also provides an analysis method for the choice of different protection and control measures at each stage of cascading failure,which has critical significance and follow-up value.

Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control

With integration of large-scale renewable energy,new controllable devices,and required reinforcement of power grids,modern power systems have typical characteristics such as uncertainty,vulnerability and openness,which makes operation and control of power grids face severe security challenges.Application of artificial intelligence(AI)technologies represented by machine learning in power grid regulation is limited by reliability,interpretability and generalization ability of complex modeling.Mode of hybrid-augmented intelligence(HAI)based on human-machine collaboration(HMC)is a pivotal direction for future development of AI technology in this field.Based on characteristics of applications in power grid regulation,this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence(HHI)system for large-scale power grid dispatching and control(PGDC).First,theory and application scenarios of HHI are introduced and analyzed;then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed.Key technologies are discussed to achieve a thorough integration of human/machine intelligence.Finally,state-of-theart and future development of HHI in power grid regulation are summarized,aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.

THz Wave Detection of Gap Defects Based on a Convolutional Neural Network Improved by a Residual Shrinkage Network

Internal air gap is a serious type of defect in the insulation equipment,which threatens the safe operation of the power grid.In order to diagnose the position and thickness of the internal air gap,this paper proposes a terahertz wave detection method based on wavelet analysis and a CNN(convolution neural network)model.According to the time-frequency characteristics of the wavelet cluster,the calculation method of air gap depth is proposed.To determine the thickness of the internal air gap,the performances of several classification methods,such as waveform feature analysis,Bayes,MLP(Multi-layer Perceptron),SVM(Support Vector Machine)and CNN are compared.The results show that the CNN modified by a residual shrinkage network and SVM(CNN-RSN-SVM)has the best performance.By adjusting the parameters,the classification accuracy of the CNN-RSN-SVM model can be elevated to 98.91%.Furthermore,the 3D imaging method of air gap defect based on wavelet analysis and CNNRSN-SVM classification model is formed.

Vulnerable Point Identification Using Heterogeneous Interdependent Node Theory for Distribution Systems

In order to reduce the occurrence or expansion of accidents and maintain safety in distribution networks,it is essential to find out the vulnerable points for the power system in time.In this paper,a vulnerable point identification method based on heterogeneous interdependent(HI)node theory and risk theory is proposed.Compared with the methods based on betweenness theory,the method based on HI nodes theory can deal with the shortcomings of the power flow shortest path,and consider the direct and indirect relationship of nodes.It is more suitable for identifying vulnerable points in a realistic power system.First,according to the analysis of heterogenous interdependent networks,the HI nodes are defined and used to evaluate the utility coupling value of each node.Then an identification indicator,which combines the utility coupling value and the risk indicators,is utilized to evaluate the vulnerability of each node.Results show that the proposed method is a suitable one to find the vulnerable points and better than betweennessbased methods for a distribution network.

Reconfiguration of PV Arrays(T-C-T,B-L,H-C)Considering Wiring Resistance

Partial shadings on the photovoltaic(PV)array causes reduction in maximum power generation.Reconfiguration of PV arrays plays an important role in increasing the maximum power generation from PV array configurations under partial shadings.In general,partial shadings on the PV arrays are concentrated on a group of modules.Therefore,the distribution of shading over the array increases the maximum power generation.This paper uses a modified Sudoku pattern to increase the maximum power generation from the PV array configurations.The PV array configurations are analyzed by considering column wiring resistance and cross ties resistance.The modified Sudoku pattern is applied to Total-Cross-Tied(T-C-T),Bridge-Link(B-L)and Honey-Comb(H-C)configurations and their performances are analyzed under various shading patterns,such as short narrow,short wide,long narrow,long wide,middle and diagonal.The specifications,such as Global Maximum Power(GMP),Mismatch losses,Fill Factor,Efficiency are considered to see the efficacy of various PV array configurations and their reconfigurations.From the results,it can be concluded that reconfigured T-C-T PV array generates the highest GMP compared to other configurations under considered shading patterns.

Information for Authors

CSEE Journal of Power and Energy Systems(JPES)is dedicated to reporting cutting-edge theories,methods,technologies and applications that will shape the development of power systems in energy transition.The journal is indexed by Science Citation Index Expanded(SCIE),INSPEC.DOAJ,ProQuest.Scopus,and Chinese Science Abstract Database(CSAD).It aims to provide an international platform for authors to maximize the reach and influence of their contributions,and present the latest research achievements,which have significant impacts on tlie advancement of knowledge in the fields.

An Algorithm for Practical Power Curve Estimation of Wind Turbines

Practical power curve estimation is necessary for evaluating the actual power output of a wind farm;since a power curve provided by the wind turbine manufacture will be different with the actual power curve following several years of operation.It can be estimated using the collected power output data including wind power generation and wind speed.This data is commonly ill-distributed due to a noticeable number of outliers,which impose a serious bias to the estimation models obtained from this data.It introduces an interesting challenge in estimation of a power curve.In this paper,an intelligent algorithm is proposed for estimating a power curve using the measured data while modeling and bias errors,imposed to the estimation model by the outliners,are minimized.More specifically,this algorithm is designed based on the Statistical Analysis Software(SAS)programming software package in order to facilitate analyzing and managing big datasets of wind speed and wind power generation.The effectiveness and practical application of the proposed algorithm is demonstrated using a real-world dataset.

An Assessment of Flexibility Reserves in Stochastic Modeling at Multiple Timescales

Due to recent technological achievements,stochastic optimization,which inherently captures the uncertainty of intermittent resources,is being used to capture the variability and uncertainty of wind and solar resources.However,due to persistent computational limitations,it is not practical to consider all possible variable generation scenarios.As a result,a reduced number of most likely scenarios is usually considered.While this helps reduce the computational burden,it also leaves the system operator vulnerable to some risk.In order to address this issue,this paper aims at providing insight into using an explicit reserve requirement in a stochastic modeling framework in order to provide system operators with greater confidence in stochastic dispatch solutions.This is accomplished by simulating a modified version of the IEEE 118 bus system in a fully stochastic,multi-timescale framework with flexibility reserve requirements.Results show that utilizing a stochastic flexibility reserve requirement within the stochastic modeling framework offers the most reliability benefit.

Impact of Grid Connection of Large-Scale Wind Farms on Power System Small-Signal Angular Stability

The grid connection of a large-scale wind farm could change the load flow/configuration of a power system and introduce dynamic interactions with the synchronous generators(SGs),thus affecting system small-signal angular stability.This paper proposes an approach for the separate examination of the impact of those affecting factors,i.e.,the change of load flow/configuration and dynamic interactions brought about by the grid connection of the wind farm,on power system smallsignal angular stability.Both cases of grid connection of the wind farm,either displacing synchronous generators or being directly added into the power system,are considered.By using the proposed approach,how much the effect of the change of load flow/configuration brought about by the wind farm can be examined,while the degree of impact of the dynamic interaction of the wind farm with the SGs can be investigated separately.Thus,a clearer picture and better understanding of the power system small-signal angular stability as affected by grid connection of the large-scale wind farm can be achieved.An example of the power system with grid connection of a wind farm is presented to demonstrate the proposed approach.

Hybrid Modular Smart Transformer for Asymmetrically Bidirectional Power Flow Operation

The presence of renewable energy resources in LV distribution networks may lead to a distribution transformer,also known as a Smart Transformer(ST),experiencing the bidirectional power flow.Therefore,the ST must have the capability to operate in both directions.However,the reverse power is less as compared to the forward power,thus the design of ST with the same capacity in both directions increases the hardware cost and decreases the system efficiency.This paper proposes a Hybrid-modular-ST(H-ST),composed of a mixed use of single active bridge-based series resonant converter and dual active bridge instead of complete use of uni-or bi-directional converter adopted in the conventional solid-state-transformer.Based on the proposed H-ST,the impacts of power imbalance among cascaded modules in reverse operation mode are analyzed and then an effective solution based on reactive power compensation combined with the characteristics of the proposed architecture is adopted.The simulation and experimental results clearly validate the effectiveness and feasibility of the theoretical analyses.

Micro-mechanism Study on the Different Antioxidants Protective Effect of Vegetable Oil by ReaxFF Molecular Dynamics

The aging problem of vegetable oil has severely restricted the popularization and application of vegetable oil transformers.Adding antioxidants is the most recommended solution.At present,there is insufficient research on the micromechanism application of the protective effect of antioxidants on vegetable oil.In this paper,multiple vegetable oil models with different types and concentrations of antioxidants are established.Then the molecular dynamics simulation based on ReaxFF is carried out at different temperatures and oxygen content.Results show that the protective mechanism of antioxidants is to release H and combine this with free radicals generated by the decomposition of unsaturated fatty acids(UFA)in the oil.In addition,the protective effects of four antioxidants show different results.The stronger ones are tert-butyl hydroquinone(TBHQ)and butylated hydroxytoluene(BHT),while the weaker ones are butylated hydroxyanisole(BHA)and propyl gallate(PG).TBHQ has a better protective effect at lower concentrations,but the decomposition of UFA is promoted at higher concentrations.When the temperature rises,the decomposition of UFA is promoted.With the addition of oxygen,smaller molecular compounds are easily oxidized,and the decomposition of UFA is accelerated.The above research could reveal the microscopic mechanism of antioxidant protection on vegetable oil,providing theoretical references for further exploration of effective vegetable oil aging protection technology.

Advanced Sensors Towards Ubiquitous Power Internet of Things

The ubiquitous power Internet of Things(UPIoT)uses modern information technology and advanced communication technologies to realize interconnection and human-computer interaction in all aspects of the power system.UPIoT has the characteristics of comprehensive state perception and efficient information processing,and has broad application prospects for transformation of the energy industry.The fundamental facility of the UPIoT is the sensor-based information network.By using advanced sensors,Wireless Sensor Networks(WSNs),and advanced data processing technologies,Internet of Things can be realized in the power system.In this paper,a framework of WSNs based on advanced sensors towards UPIoT is proposed.In addition,the most advanced sensors for UPIoT purposes are reviewed,along with an explanation of how the sensor data obtained in UPIoT is utilized in various scenarios.

Non-isolated Step-up DC-DC Converter Based on Switched Capacitor Cells

A non-isolated high gain step-up DC-DC converter for low power applications is suggested in this study.In the designed transformerless converter,the main switch current and voltage stress is reduced while maintaining high voltage gain.For instance,with a duty cycle of 0.5 a voltage gain equal to 5 is achieved while the normalized switch voltage stress is 0.4.Also,it decreases power losses of active and passive elements.In the proposed converter design,the switched-capacitor(SC)technique is used to obtain maximum voltage transfer gain using only one switch.The three modes of operation,i.e.,continuous conduction mode(CCM),boundary conduction mode(BCM),and discontinuous conduction mode(DCM),are studied in detail.The small signal analysis(SSA)of the designed converter is investigated,and its steady-state model is examined under CCM.Performance of the proposed converter proposed in this study is assessed and tested using a prototype.Efficiency of the converter is recorded above 94%in a wide range of output powers.Overall,compared to the other converters,the results suggest satisfactory performance of the designed converter.An issue of the proposed converter is that its input current is not smooth due to using the switched-capacitor cell in its structure.This issue is alleviated by using input filters.

Real-time Energy Management of Low-carbon Ship Microgrid Based on Data-driven Stochastic Model Predictive Control

With increasing restrictions on ship carbon emis-sions,it has become a trend for ships to use zero-carbon energy such as solar to replace traditional fossil energy.However,uncer-tainties of solar energy and load affect safe and stable operation of the ship microgrid.In order to deal with uncertainties and real-time requirements and promote application of ship zero-carbon energy,we propose a real-time energy management strategy based on data-driven stochastic model predictive control.First,we establish a ship photovoltaic and load scenario set consid-ering time-sequential correlation of prediction error through three steps.Three steps include probability prediction,equal probability inverse transformation scenario set generation,and simultaneous backward method scenario set reduction.Second,combined with scenario prediction information and rolling op-timization feedback correction,we propose a stochastic model predictive control energy management strategy.In each scenario,the proposed strategy has the lowest expected operational cost of control output.Then,we train the random forest machine learn-ing regression algorithm to carry out multivariable regression on samples generated by running the stochastic model predictive control.Finally,a low-carbon ship microgrid with photovoltaic is simulated.Simulation results demonstrate the proposed strategy can achieve both real-time application of the strategy,as well as operational cost and carbon emission optimization performance close to stochastic model predictive control.Index Terms-Data-driven stochastic model predictive control,low-carbon ship microgrid,machine learning,real-time energy management,time-sequential correlation.

Comprehensive Overview of Multi-agent Systems for Controlling Smart Grids

Agents are intelligent entities that act flexibly and autonomously and make wise decisions based on their intelligence and experience.A multi-agent system(MAS)contains multiple,intelligent,and interconnected collaborating agents for solving a problem beyond the ability of a single agent.A smart grid(SG)combines advanced intelligent systems,control techniques,and sensing methods with an existing utility power network.For controlling smart grids,various control systems with different architectures have already been developed.MAS-based control of power system operations has been shown to overcome the limitations of time required for analysis,relaying,and protection;transmission switching;communication protocols;and management of plant control.These systems provide an alternative for fast and accurate power network control.This paper provides a comprehensive overview of MASs used for the control of smart grids.The paper provides a wide-spectrum view of the status of smart grids,MAS-based control techniques and their implementation for the control of smart grids.Use of MASs in the control of various aspects of smart grids-including the management of energy,marketing energy,pricing,scheduling energy,reliability,network security,fault handling capability,communication between agents,SG-electrical vehicles,SG-building energy systems,and soft grids—have been critically reviewed.More than a hundred publications on the topic of MAS-based control of smart grids have been critically examined,classified,and arranged for fast reference.