Review of the Analysis and Suppression for High-frequency Oscillations of the Grid-connected Wind Power Generation System

High-frequency oscillation(HFO)of gridconnected wind power generation systems(WPGS)is one of the most critical issues in recent years that threaten the safe access of WPGS to the grid.Ensuring the WPGS can damp HFO is becoming more and more vital for the development of wind power.The HFO phenomenon of wind turbines under different scenarios usually has different mechanisms.Hence,engineers need to acquire the working mechanisms of the different HFO damping technologies and select the appropriate one to ensure the effective implementation of oscillation damping in practical engineering.This paper introduces the general assumptions of WPGS when analyzing HFO,systematically summarizes the reasons for the occurrence of HFO in different scenarios,deeply analyses the key points and difficulties of HFO damping under different scenarios,and then compares the technical performances of various types of HFO suppression methods to provide adequate references for engineers in the application of technology.Finally,this paper discusses possible future research difficulties in the problem of HFO,as well as the possible future trends in the demand for HFO damping.

Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.

Utility and Application of a Versatile Analytical Method for MMF Calculation in AC Machines

A versatile analytical method(VAM) for calculating the harmonic components of the magnetomotive force(MMF) generated by diverse armature windings in AC machines has been proposed, and the versatility of this method has been established in early literature. However, its practical applications and significance in advancing the analysis of AC machines need further elaboration. This paper aims to complement VAM by augmenting its theory, offering additional insights into its conclusions, as well as demonstrating its utility in assessing armature windings and its application of calculating torque for permanent magnet synchronous machines(PMSM). This work contributes to advancing the analysis of AC machines and underscores the potential for improved design and performance optimization.

Decentralized Optimal Control and Stabilization of Interconnected Systems With Asymmetric Information

The paper addresses the decentralized optimal control and stabilization problems for interconnected systems subject to asymmetric information.Compared with previous work,a closed-loop optimal solution to the control problem and sufficient and necessary conditions for the stabilization problem of the interconnected systems are given for the first time.The main challenge lies in three aspects:Firstly,the asymmetric information results in coupling between control and estimation and failure of the separation principle.Secondly,two extra unknown variables are generated by asymmetric information(different information filtration)when solving forward-backward stochastic difference equations.Thirdly,the existence of additive noise makes the study of mean-square boundedness an obstacle.The adopted technique is proving and assuming the linear form of controllers and establishing the equivalence between the two systems with and without additive noise.A dual-motor parallel drive system is presented to demonstrate the validity of the proposed algorithm.

Set-Membership Filtering Approach to Dynamic Event-Triggered Fault Estimation for a Class of Nonlinear Time-Varying Complex Networks

The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks,utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism(DETM).In order to optimize communication resource utilization,the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not.To guarantee a satisfactory estimation performance for the fault signal,an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals.The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains.The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator,ensuring that the specified performance requirements are met under certain conditions.Then,the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided.Finally,a numerical example is conducted to demonstrate the effectiveness of the designed estimator.

Two-Stage Optimal Scheduling of Community Integrated Energy System

From the perspective of a community energy operator,a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads.The day-ahead scheduling analyzes whether various controllable loads participate in the optimization and investigates the impact of their responses on the operating economy of the community integrated energy system(IES)before and after;the intra-day scheduling proposes a two-stage rolling optimization model based on the day-ahead scheduling scheme,taking into account the fluctuation of wind turbine output and load within a short period of time and according to the different response rates of heat and cooling power,and solves the adjusted output of each controllable device.The simulation results show that the optimal scheduling of controllable loads effectively reduces the comprehensive operating costs of community IES;the two-stage optimal scheduling model can meet the energy demand of customers while effectively and timely suppressing the random fluctuations on both sides of the source and load during the intra-day stage,realizing the economic and smooth operation of IES.

A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant

With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)analysis.Recently,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring.Although the MHNBM is effective,it still has some shortcomings that need to be improved.For the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating variables.In addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical data.When the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of MHNBM.Here a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above shortcomings.For the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the model.At the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data samples.Compared with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China.

Multi-Objective Optimization Analysis of Auxiliary Flux Modulator Magnetic Gear with Unequal Magnetic Poles

To enhance the output torque and minimize the torque ripple of coaxial magnetic gear(CMG),a novel auxiliary flux modulator CMG with unequal magnetic poles is proposed.This design incorporates an inner rotor with an asymmetric sector and a trapezoidal combined N-S pole structure,featuring Halbach arrays for the arrangement of permanent magnets(PMs).The outer rotor PMs adopt a Spoke-type configuration.To optimize the CMG for high output torque and low torque ripple,a sensitivity analysis is conducted to identify key size parameters that significantly influence the optimization objectives.Based on the sensitivity hierarchy of these parameters,a multi-objective optimization analysis is performed using a genetic algorithm(GA)to determine the optimal structural parameter values of the CMG.In addition,a coaxial magnetic gear(CMG)topology with 4 inner and 17 outer pole pairs is adopted,and the parametric model is established.Finally,the electromagnetic properties of the CMG are evaluated using the finite element method.The results indicate a remarkable reduction in torque ripple,specifically by 46.15%.

Application of Hankel Dynamic Mode Decomposition for Wide Area Monitoring of Subsynchronous Resonance

In recent years,subsynchronous resonance(SSR)has frequently occurred in DFIG-connected series-compensated systems.For the analysis and prevention,it is of great importance to achieve wide area monitoring of the incident.This paper presents a Hankel dynamic mode decomposition(DMD)method to identify SSR parameters using synchrophasor data.The basic idea is to employ the DMD technique to explore the subspace of Hankel matrices constructed by synchrophasors.It is analytically demonstrated that the subspace of these Hankel matrices is a combination of fundamental and SSR modes.Therefore,the SSR parameters can be calculated once the modal parameter is extracted.Compared with the existing method,the presented work has better dynamic performances as it requires much less data.Thus,it is more suitable for practical cases in which the SSR characteristics are timevarying.The effectiveness and superiority of the proposed method have been verified by both simulations and field data.

Distributed Robust Optimal Dispatch for the Microgrid Considering Output Correlation between Wind and Photovoltaic

As an effective carrier of integrated clean energy,the microgrid has attracted wide attention.The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the economics and reliability of microgrids.This paper proposes an optimization scheme based on the distributionally robust optimization(DRO)model for a microgrid considering solar-wind correlation.Firstly,scenarios of wind and solar power output scenarios are generated based on non-parametric kernel density estimation and the Frank-Copula function;then the generated scenario results are reduced by K-means clustering;finally,the probability confidence interval of scenario distribution is constrained by 1-norm and∞-norm.The model is solved by a column-and-constraint generation algorithm.Experimental studies are conducted on a microgrid system in Jiangsu,China and the obtained scheduling solution turned out to be superior under wind and solar power uncertainties,which verifies the effectiveness of the proposed DRO model.

Loss Analysis of Magnetic Gear with Slotted in Magnetic Modulation Ring

In order to improve the operation efficiency of coaxial magnetic gear(CMG),in this paper,a CMG model with slotted in magnetic modulation ring is proposed.In this model,the permanent magnets(PMs)of internal and external rotors are distributed in Halbach array,the inner rotor PMs are equally divided into 3 small pieces,and the outer rotor PMs are equally divided into 2 small pieces.At the same time,the static magnetic modulation ring iron blocks are slotted,each iron block has 3 slots,the width of the slot is 0.4°,and the depth of the single side slot is 1mm.Finally,a two-dimensional model is established,and the eddy current loss and iron loss of the model are optimized,compared with the conventional CMG model,it is found that the changed pattern can increase the internal and external output torque by 4%and 4.12%,respectively.The eddy current loss is reduced by 66.57%,and the iron loss is reduced by 8.9%,which significantly improve the operation efficiency of the CMG.

Game-theoretical Model for Dynamic Defense Resource Allocation in Cyber-physical Power Systems Under Distributed Denial of Service Attacks

Electric power grids are evolving into complex cyber-physical power systems(CPPSs)that integrate advanced information and communication technologies(ICTs)but face increasing cyberspace threats and attacks.This study considers CPPS cyberspace security under distributed denial of service(DDoS)attacks and proposes a nonzero-sum game-theoretical model with incomplete information for appropriate allocation of defense resources based on the availability of limited resources.Task time delay is applied to quantify the expected utility as CPPSs have high time requirements and incur massive damage DDoS attacks.Different resource allocation strategies are adopted by attackers and defenders under the three cases of attack-free,failed attack,and successful attack,which lead to a corresponding consumption of resources.A multidimensional node value analysis is designed to introduce physical and cybersecurity indices.Simulation experiments and numerical results demonstrate the effectiveness of the proposed model for the appropriate allocation of defense resources in CPPSs under limited resource availability.

Relaxed Stability Criteria for Time-Delay Systems:A Novel Quadratic Function Convex Approximation Approach

This paper develops a quadratic function convex approximation approach to deal with the negative definite problem of the quadratic function induced by stability analysis of linear systems with time-varying delays.By introducing two adjustable parameters and two free variables,a novel convex function greater than or equal to the quadratic function is constructed,regardless of the sign of the coefficient in the quadratic term.The developed lemma can also be degenerated into the existing quadratic function negative-determination(QFND)lemma and relaxed QFND lemma respectively,by setting two adjustable parameters and two free variables as some particular values.Moreover,for a linear system with time-varying delays,a relaxed stability criterion is established via our developed lemma,together with the quivalent reciprocal combination technique and the Bessel-Legendre inequality.As a result,the conservatism can be reduced via the proposed approach in the context of constructing Lyapunov-Krasovskii functionals for the stability analysis of linear time-varying delay systems.Finally,the superiority of our results is illustrated through three numerical examples.

Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication constraints.These transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear dynamics.Through the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

Reliability Assessment of Microgrid Using Sequential Monte Carlo Simulation

Sequential Monte Carlo simulation method is introduced to the reliability assessment of microgrid,and a Weibull distribution wind speed model is built to simulate the hourly wind speed of a specific site.Wind turbine generator model combined with a two-state reliability model is applied to Monte Carlo simulation method,and results show that the wind turbine reliability model works well with sequential Monte Carlo simulation.A two-state reliability model of micro gas turbine and a load model from IEEE reliability test system （IEEE RTS） are also introduced to the reliability evaluation of microgrid.Case studies show that Monte Carlo simulation method is flexible and efficient dealing with microgrid consisting of renewable resources with fluctuation characteristics.

Electrical Tracking,Erosion and Fire Retardancy Performance of Silicone Rubber Insulation Containing Aluminum Trihydrate,Graphene and Glass Fiber Additives

Silicone rubber composite is a priority electrical insulating material used in high-voltage outdoor insulation applications.Low electrical tracking/erosion and poor flame resistance performance of silicone rubber once ignited,substantially reduce its working life.This paper attempts to investigate tracking/erosion performance of room temperature vulcanized(RTV)silicone rubber along with flame retardant parameters using aluminum trihydrate(ATH),graphene nanosheets(GN)and milled glass fiber(GF)additives.The inclined plane test(IPT)was performed in line with criteria defined in IEC 60587 using step-up tracking voltage method while flame retardancy is evaluated according to ASTM E 1354.0 using a cone calorimeter.Results suggest 30% of ATH assists in improving physical tracking/erosion resistance of pristine silicone elastomer rubber by impeding development of leakage current and a great reduction in maximum average temperatures on the surface of RTV2.Further improvement in performance of RTV2 is achieved through introduction of 1%of GN and 5% of GF as seen in RTV4.Moreover,30% of ATH reduces heat release rate and smoke production rate,and this trend is improved with the introduction of GN/GF.RTV4 has pop up as the most promising silicone rubber composite with excellent electrical tracking,erosion,and flame resistance performance relative to its counterparts in this study.

A Novel Location Method for Interline Power Flow Controllers Based on Entropy Theory

As one of the new generation flexible AC transmission systems(FACTS)devices,the interline power flow controller(IPFC)has the significant advantage of simultaneously regulating the power flow of multiple lines.Nevertheless,how to choose the appropriate location for the IPFC converters has not been discussed thoroughly.To solve this problem,this paper proposes a novel location method for IPFC using entropy theory.To clarify IPFC’s impact on system power flow,its operation mechanism and control strategies of different types of serial converters are discussed.Subsequently,to clarify the system power flow characteristic suitable for device location analysis,the entropy concept is introduced.In this process,the power flow distribution entropy index is used as an optimization index.Using this index as a foundation,the power flow transfer entropy index is also generated and proposed for the IPFC location determination study.Finally,electromechanical electromagnetic hybrid simulations based on ADPSS are implemented for validation.These are tested in a practical power grid with over 800 nodes.A modular multilevel converter(MMC)-based IPFC electromagnetic model is also established for precise verification.The results show that the proposed method can quickly and efficiently complete optimized IPFC location and support IPFC to determine an optimal adjustment in the N-1 fault cases.

Data-driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Reactive power optimization of distribution networks is traditionally addressed by physical model based methods,which often lead to locally optimal solutions and require heavy online inference time consumption.To improve the quality of the solution and reduce the inference time burden,this paper proposes a new graph attention networks based method to directly map the complex nonlinear relationship between graphs(topology and power loads)and reactive power scheduling schemes of distribution networks,from a data-driven perspective.The graph attention network is tailored specifically to this problem and incorporates several innovative features such as a self-loop in the adjacency matrix,a customized loss function,and the use of max-pooling layers.Additionally,a rulebased strategy is proposed to adjust infeasible solutions that violate constraints.Simulation results on multiple distribution networks demonstrate that the proposed method outperforms other machine learning based methods in terms of the solution quality and robustness to varying load conditions.Moreover,its online inference time is significantly faster than traditional physical model based methods,particularly for large-scale distribution networks.

Adaptive Harmonic Virtual Impedance Control for Improving Voltage Quality of Microgrids

The effects of nonlinear loads on voltage quality represent an emerging concern for islanded microgrids.Existing research works have mainly focused on harmonic power sharing among multiple inverters,which ignores the diversity of different inverters to mitigate harmonics from nonlinear loads.As a result,the voltage quality of microgrids cannot be effectively improved.To address this issue,this study proposes an adaptive harmonic virtual impedance(HVI)control for improving voltage quality of microgrids.Based on the premise that no inverter is overloaded,the main objective of the proposed control is to maximize harmonic power absorption by shaping the lowest output impedances of inverters.To achieve this,the proposed control is utilized to adjust the HVI of each inverter based on its operation conditions.In addition,the evaluation based on Monte Carlo harmonic power flow is designed to assess the performance of the proposed control in practice.Finally,comparative studies and control-in-the-loop experiments are conducted.

Convex Hull Based Economic Operating Region for Power Grids Considering Uncertainties of Renewable Energy Sources

The increasing integration of renewable energy sources(RESs)presents significant challenges for the safe and economical operation of power grids.Addressing the critical need to assess the effect of RES uncertainties on optimal scheduling schemes(OSSs),this paper introduces a convex hull based economic operating region(CH-EOR)for power grids.The CHEOR is mathematically defined to delineate the impact of RES uncertainties on power grid operations.We propose a novel approach for generating the CH-EOR,enhanced by a big-M preprocessing method to improve the computational efficiency.Performed on four test systems,the proposed big-M preprocessing method demonstrates notable advancements:a reduction in average operating costs by over 10%compared with the box-constrained operating region(BC-OR)derived from robust optimization.Furthermore,the CH-EOR occupies less than 11.79%of the generators'adjustable region(GAR).Most significantly,after applying the proposed big-M preprocessing method,the computational efficiency is improved over 17 times compared with the traditional big-M method.