Dynamic Constraint-Driven Event-Triggered Control of Strict-Feedback Systems Without Max/Min Values on Irregular Constraints

This work proposes an event-triggered adaptive control approach for a class of uncertain nonlinear systems under irregular constraints.Unlike the constraints considered in most existing papers,here the external irregular constraints are considered and a constraints switching mechanism(CSM)is introduced to circumvent the difficulties arising from irregular output constraints.Based on the CSM,a new class of generalized barrier functions are constructed,which allows the control results to be independent of the maximum and minimum values(MMVs)of constraints and thus extends the existing results.Finally,we proposed a novel dynamic constraint-driven event-triggered strategy(DCDETS),under which the stress on signal transmission is reduced greatly and no constraints are violated by making a dynamic trade-off among system state,external constraints,and inter-execution intervals.It is proved that the system output is driven to close to the reference trajectory and the semi-global stability is guaranteed under the proposed control scheme,regardless of the external irregular output constraints.Simulation also verifies the effectiveness and benefits of the proposed method.

Wave field structure and power coupling features of blue-core helicon plasma driven by various antenna geometries and frequencies

This paper deals with wave propagation and power coupling in blue-core helicon plasma driven by various antennas and frequencies.It is found that compared to non-blue-core mode,for blue-core mode,the wave can propagate in the core region,and it decays sharply outside the core.The power absorption is lower and steeper in radius for blue-core mode.Regarding the effects of antenna geometry for blue-core mode,it shows that half helix antenna yields the strongest wave field and power absorption,while loop antenna yields the lowest.Moreover,near axis,for antennas with m=+1,the wave field increases with axial distance.In the core region,the wave number approaches to a saturation value at much lower frequency for non-blue-core mode compared to blue-core mode.The total loading resistance is much lower for blue-core mode.These findings are valuable to understanding the physics of blue-core helicon discharge and optimizing the experimental performance of blue-core helicon plasma sources for applications such as space propulsion and material treatment.

Effect of Types and Orders of Electromagnetic Field Finite Element Meshes on Power Communication Harmonic Parameters Calculation Results of Tubular Hydrogenerators

In generator design field,waveform total harmonic distortion(THD)and telephone harmonic factor(THF)are parameters commonly used to measure the impact of generator no-load voltage harmonics on the power communication quality.Tubular hydrogenerators are considered the optimal generator for exploiting low-head,high-flow hydro resources,and they have seen increasingly widespread application in China's power systems recent years.However,owing to the compact and constrained internal space of such generators,their internal magnetic-field harmonics are pronounced.Therefore,accurate calculation of their THD and THF is crucial during the analysis and design stages to ensure the quality of power communication.Especially in the electromagnetic field finite element modeling analysis of such generators,the type and order of the finite element meshes may have a significant impact on the THD and THF calculation results,which warrants in-depth research.To address this,this study takes a real 34 MW large tubular hydrogenerator as an example,and establishes its electromagnetic field finite element model under no-load conditions.Two types of meshes,five mesh densities,and two mesh orders are analyzed to reveal the effect of electromagnetic field finite element mesh types and orders on the calculation results of THD and THF for such generators.

Online Optimization in Power Systems With High Penetration of Renewable Generation:Advances and Prospects

Traditionally,offline optimization of power systems is acceptable due to the largely predictable loads and reliable generation.The increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain,and have redirected attention to online optimization methods.However,online optimization is a broad topic that can be applied in and motivated by different settings,operated on different time scales,and built on different theoretical foundations.This paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques used.In particular,we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain,i.e.,optimization-guided dynamic control,feedback optimization for single-period problems,Lyapunov-based optimization,and online convex optimization techniques for multi-period problems.Lastly,we recommend some potential future directions for online optimization in the power systems domain.

Adaptive Uniform Performance Control of Strict-Feedback Nonlinear Systems With Time-Varying Control Gain

In this paper,we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients,which mainly includes the following steps.Firstly,by introducing several key transformation functions and selecting the initial value of the time-varying scaling function,the symmetric prescribed performance with global and semi-global properties can be handled uniformly,without the need for control re-design.Secondly,to handle the problem of unknown time-varying control coefficient with an unknown sign,we propose an enhanced Nussbaum function(ENF)bearing some unique properties and characteristics,with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer required.Thirdly,by utilizing the core-function information technique,the nonparametric uncertainties in the system are gracefully handled so that no approximator is required.Furthermore,simulation results verify the effectiveness and benefits of the approach.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Assessing Strategy of Power Transformers Insulation State Based on Part-division and Entropy Method

Failure mechanisms of power transformers are complex and uncertain; it is difficult to determine index weights of insulation state. Therefore, it is a challenge to acquire an accurate assessment of insulation state of power transformers. In this paper, an assessing strategy for transformer insulation is proposed base on part-division of transformer and a comprehensive weight determination method. An index system of transformer is established on the basis of part-division of transformer. Each index’s weight is consisted of two parts, the constant weight and the variable weight, which are determined by improved analytic hierarchy process (AHP) and entropy method respectively. Af- ter categorizing insulation state into four levels and standardizing assessing indexes, a Cauchy membership function is forged, and a fuzzy algorithm is employed to simulate the uncertainty of the insulation state. Finally, a confidence criterion is employed to perform part-division based condition assessment of transformer. Case studies reveal that the proposed assessing strategy method is effective, convenient, and practical; with the new strategy, potential failures of transformers can be forecasted and insulation state of transformer parts can also be as- sessed. Furthermore, the assessing results can be used to guide condition-based maintenance.

Numerical Analysis of Magnetic-Shielding Effectiveness for Magnetic Resonant Wireless Power Transfer System

Magnetic radiation phenomena appear inevitably in the magnetic-resonance wireless power transfer （MR-WPT） system, and regarding this problem the magnetic-shielding scheme is applied to improve the electromagnetic performance in engineering. In this study, the shielding effectiveness of a two-coil MR-WPT system for different material shields is analyzed in theory using Moser＇s formula and Schelkunoff＇s formula. On this basis a candidate magnetic-shielding scheme with a double-layer structure is determined, which has better shielding effectiveness and coils coupling coefficient. Finally, some finite element simulation results validate the correctness of the theoretical analysis, and the shielding effectiveness with the double-layer shield in maximum is 30？dB larger than the one with the single-layer case.

Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal

Discharge plasma parameter measurement is a key focus in low-temperature plasma research.Traditional diagnostics often require costly equipment,whereas electro-acoustic signals provide a rich,non-invasive,and less complex source of discharge information.This study harnesses machine learning to decode these signals.It establishes links between electro-acoustic signals and gas discharge parameters,such as power and distance,thus streamlining the prediction process.By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques,the Mel-Frequency Cepstral Coefficients(MFCCs)of the acoustic signals are extracted to construct the predictors.Three machine learning models(Linear Regression,k-Nearest Neighbors,and Random Forest)are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance.All models display impressive performance in prediction precision and fitting abilities.Among them,the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error(MSE=0.00571)and the highest R-squared value(R^(2)=0.93877).The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm,which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.

The characteristics of negative corona discharge and radio interference at different altitudes based on coaxial wire-cylinder gap

The corona discharge from transmission lines in high-altitude areas is more severe than at lower altitudes. The radio interference caused thereby is a key factor to be considered when designing transmission lines. To study the influence of altitude on negative corona characteristics, an experimental platform comprising a movable small corona cage was established: experiments were conducted at four altitudes in the range of 1120-4320 m, and data on the corona current pulse and radio interference level of 0.8-mm diameter fine copper wire under different negative voltages were collected. The experimental results show that the average amplitude, repetition frequency and average current of the corona current pulse increase with increasing altitude. The dispersion of pulse amplitude increases with increase in altitude, while the randomness of the pulse interval decreases continuously. Taking the average current as an intermediate variable,the relationship between radio interference level and altitude is obtained. The result of this research has some significance for understanding the corona discharge characteristics of ultra-highvoltage lines.

Analysis and Modeling of Time Output Characteristics for Distributed Photovoltaic and Energy Storage

Due to the unpredictable output characteristics of distributed photovoltaics,their integration into the grid can lead to voltage fluctuations within the regional power grid.Therefore,the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios.This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic(PV)generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks(TCN)and Long Short-Term Memory(LSTM).To begin with,an analysis of the spatiotemporal distribution patterns of PV generation is conducted,and outlier data is handled using the 3σ rule.Subsequently,a novel approach that combines temporal convolution and LSTM networks is introduced,with TCN extracting spatial features and LSTM capturing temporal features.Finally,a real spatiotemporal dataset from Gansu,China,is established to compare the performance of the proposed network against other models.The results demonstrate that the model presented in this paper exhibits the highest predictive accuracy,with a single-step Mean Absolute Error(MAE)of 1.782 and an average Root Mean Square Error(RMSE)of 3.72 for multi-step predictions.

Multi-area Frequency-constrained Unit Commitment for Power Systems with High Penetration of Renewable Energy Sources and Induction Machine Load

The increasing penetration of renewable energy sources(RESs)brings great challenges to the frequency security of power systems.The traditional frequency-constrained unit commitment(FCUC)analyzes frequency by simplifying the average system frequency and ignoring numerous induction machines(IMs)in load,which may underestimate the risk and increase the operational cost.In this paper,we consider a multiarea frequency response(MAFR)model to capture the frequency dynamics in the unit scheduling problem,in which regional frequency security and the inertia of IM load are modeled with high-dimension differential algebraic equations.A multi-area FCUC(MFCUC)is formulated as mixed-integer nonlinear programming(MINLP)on the basis of the MAFR model.Then,we develop a multi-direction decomposition algorithm to solve the MFCUC efficiently.The original MINLP is decomposed into a master problem and subproblems.The subproblems check the nonlinear frequency dynamics and generate linear optimization cuts for the master problem to improve the frequency security in its optimal solution.Case studies on the modified IEEE 39-bus system and IEEE 118-bus system show a great reduction in operational costs.Moreover,simulation results verify the ability of the proposed MAFR model to reflect regional frequency security and the available inertia of IMs in unit scheduling.

Communication Resources Allocation for Time Delay Reduction of Frequency Regulation Service in High Renewable Penetrated Power System

The high renewable penetrated power system has severe frequency regulation problems.Distributed resources can provide frequency regulation services but are limited by com-munication time delay.This paper proposes a communication resources allocation model to reduce communication time delay in frequency regulation service.Communication device resources and wireless spectrum resources are allocated to distributed resources when they participate in frequency regulation.We reveal impact of communication resources allocation on time delay reduction and frequency regulation performance.Besides,we study communication resources allocation solution in high renewable energy penetrated power systems.We provide a case study based on the HRP-38 system.Results show communication time delay decreases distributed resources'ability to provide frequency regulation service.On the other hand,allocating more communication resources to distributed resources'communica-tion services improves their frequency regulation performance.For power systems with renewable energy penetration above 70%,required communications resources are about five times as many as 30%renewable energy penetrated power systems to keep frequency performance the same.Index Terms-Communication resources allocation,commun-ication time delay,distributed resource,frequency regulation,high renewable energy penetrated power system.

Unreliability Tracing of Power Systems for Identifying the Most Critical Risk Factors Considering Mixed Uncertainties in Wind Power Output

For conventional power systems,the forced outage of components is the major cause of load shedding.Unreliability tracing is utilized to allocate the total system load-shedding risk among individual components in ac-cordance with their different contributions.Therefore,critical components are identified and pertinent measures can be taken to improve system reliability.The integra-tion of wind power introduces additional risk factors into power systems,causing previous unreliability tracing methods to become inapplicable.In this paper,a novel unreliability tracing method is proposed that considers both aleatory and epistemic uncertainties in wind power output and their impacts on power system load-shedding risk.First,modelling methods for wind power output considering aleatory and epistemic uncertainties and component outages are proposed.Then,a variance-based index is proposed to measure the contributions of indi-vidual risk factors to the system load-shedding risk.Fi-nally,a novel unreliability tracing framework is devel-oped to identify the critical factors that affect power sys-tem reliability.Case studies verify the ability of the pro-posed method to accurately allocate load-shedding risk to individual risk factors,thus providing decision support for reliability enhancement.

Pricing Binary Variables of System-wide Constraints for Power System Optimization

With the trend of multiple energies or flexible demand in power systems,binary variables appear in systemwide constraints,which are the foundation of marginal pricing currently in markets.An appropriate pricing method incentivizes compliance of market participants;otherwise,compliance can be incentivized by paying discriminatory uplift payments which jeopardize transparency of markets.This paper proposes two theorems to examine whether the binary variables brought by multiple energies and flexible demand will impact compliance under marginal pricing.The first theorem shows sufficient conditions with which marginal pricing with fixed binary variables incentivizes compliance,while the second theorem shows sufficient conditions to require uplift payments.To improve transparency by reducing uplift payments under cases which fall into the second theorem,this paper further proposes a pricing method by combining 1)designed constraints to price binary variables in system-wide constraints,and 2)convex hull pricing to price binary variables in private constraints.Effectiveness of the proposed theorems and pricing method is verified in an electricity-gas case(consisting of the IEEE 30-bus system and the NGS 10-node system)and the IEEE 118-bus test system.

Consensus problems of first-order dynamic multi-agent systems with multiple time delays

Consensus problems of first-order multi-agent systems with multiple time delays are investigated in this paper. We discuss three cases： 1） continuous, 2） discrete, and 3） a continuous system with a proportional plus derivative controller. In each case, the system contains simultaneous communication and input time delays. Supposing a dynamic multi-agent system with directed topology that contains a globally reachable node, the sufficient convergence condition of the system is discussed with respect to each of the three cases based on the generalized Nyquist criterion and the frequency-domain analysis approach, yielding conclusions that are either less conservative than or agree with previously published results. We know that the convergence condition of the system depends mainly on each agent’s input time delay and the adjacent weights but is independent of the communication delay between agents, whether the system is continuous or discrete. Finally, simulation examples are given to verify the theoretical analysis.

ADMM-based Distributed Algorithm for Economic Dispatch in Power Systems With Both Packet Drops and Communication Delays

By virtue of alternating direction method of multipliers(ADMM), Newton-Raphson method, ratio consensus approach and running sum method, two distributed iterative strategies are presented in this paper to address the economic dispatch problem(EDP) in power systems. Different from most of the existing distributed ED approaches which neglect the effects of packet drops or/and time delays, this paper takes into account both packet drops and time delays which frequently occur in communication networks. Moreover, directed and possibly unbalanced graphs are considered in our algorithms, over which many distributed approaches fail to converge. Furthermore, the proposed schemes can address the EDP with local constraints of generators and nonquadratic convex cost functions, not just quadratic ones required in some existing ED approaches. Both theoretical analyses and simulation studies are provided to demonstrate the effectiveness of the proposed schemes.

Optimal Operation of Integrated Heat and Electricity Systems:A Tightening McCormick Approach

Combined heat and electricity operation with variable mass flow rates promotes flexibility,economy,and sustainability through synergies between electric power systems(EPSs)and district heating systems(DHSs).Such combined operation presents a highly nonlinear and nonconvex optimization problem,mainly due to the bilinear terms in the heat flow model—that is,the product of the mass flow rate and the nodal temperature.Existing methods,such as nonlinear optimization,generalized Benders decomposition,and convex relaxation,still present challenges in achieving a satisfactory performance in terms of solution quality and computational efficiency.To resolve this problem,we herein first reformulate the district heating network model through an equivalent transformation and variable substitution.The reformulated model has only one set of nonconvex constraints with reduced bilinear terms,and the remaining constraints are linear.Such a reformulation not only ensures optimality,but also accelerates the solving process.To relax the remaining bilinear constraints,we then apply McCormick envelopes and obtain an objective lower bound of the reformulated model.To improve the quality of the McCormick relaxation,we employ a piecewise McCormick technique that partitions the domain of one of the variables of the bilinear terms into several disjoint regions in order to derive strengthened lower and upper bounds of the partitioned variables.We propose a heuristic tightening method to further constrict the strengthened bounds derived from the piecewise McCormick technique and recover a nearby feasible solution.Case studies show that,compared with the interior point method and the method implemented in a global bilinear solver,the proposed tightening McCormick method quickly solves the heat–electricity operation problem with an acceptable feasibility check and optimality.

Research on Propagation Characteristics of XLPE Cable Electrical Trees in different Electrode System

The electrical tree discharge channel will be formed at concentrate spot of electric field in solid insulation dielectric,in order to study the difference of electrical tree under different electrical field,the short-cable electrode system with actual XLPE cable was designed,experiments were performed under 12 kV,15 kV,18 kV,21 kV compare to the needle-plate electrode system.Experiment results show that the electrical tree of short-cable electrode system have the same growth trend with the needle-plate electrode system in the growing characteristic,the dense of electrical tree increase with the increase of voltage level,electrical tree of short-cable electrode system growth is slower than the needle-plate electrode system at the same voltage;To get the same shape of electrical tree,the voltage of short-cable electrode system must be higher than needle-plate electrode system,the results show that the semiconductor layer and the copper shield layer outside of XLPE cable have very important affection on the electrical trees degradation.