This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control(LFC)systems integrated with Battery Energy Storage Systems(BESS).As renewable energy sources gain greater pene...This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control(LFC)systems integrated with Battery Energy Storage Systems(BESS).As renewable energy sources gain greater penetration,power grids are becoming increasingly vulnerable to cyber threats,potentially leading to frequency instability and widespread disruptions.We model two significant attack vectors:load-altering attacks(LAAs)and false data injection attacks(FDIAs)that corrupt frequency measurements.These are analyzed for their impact on grid frequency stability in both linear and nonlinear LFC models,incorporating generation rate constraints and nonlinear loads.A coordinated attack strategy is presented,combining LAAs and FDIAs to achieve stealthiness by concealing frequency deviations from system operators,thereby maximizing disruption while evading traditional detection.To counteract these threats,we propose an Unknown Input Observer(UIO)-based detection framework for linear and nonlinear LFCs.The UIO is designed using linear matrix inequalities(LMIs)to estimate system states while isolating unknown attack inputs,enabling attack detection through monitoring measurement residuals against a predefined threshold.For mitigation,we leverage BESS capabilities with two adaptive strategies:dynamic mitigation for dynamic LAAs,which tunes BESS parameters to enhance the system’s stability margin and accelerate convergence to equilibrium;and staticmitigation for static LAAs and FDIAs.Simulations show that the UIO achieves high detection accuracy,with residuals exceeding thresholds promptly under coordinated attacks,even in nonlinear models.Mitigation strategies reduce frequency deviations by up to 80%compared to unmitigated cases,restoring stability within seconds.展开更多
Load frequency control(LFC)is a critical function to balance the power consumption and generation.Thegrid frequency is a crucial indicator for maintaining balance.However,the widely used information and communication ...Load frequency control(LFC)is a critical function to balance the power consumption and generation.Thegrid frequency is a crucial indicator for maintaining balance.However,the widely used information and communication infrastructure for LFC increases the risk of being attacked by malicious actors.The dynamic load altering attack(DLAA)is a typical attack that can destabilize the power system,causing the grid frequency to deviate fromits nominal value.Therefore,in this paper,we mathematically analyze the impact of DLAA on the stability of the grid frequency and propose the network parameter regulation(NPR)to mitigate the impact.To begin with,the dynamic LFC model is constructed by highlighting the importance of the network parameter.Then,we model the DLAA and analyze its impact on LFC using the theory of second-order dynamic systems.Finally,we model the NPR and prove its effect in mitigating the DLAA.Besides,we construct a least-effort NPR considering its infrastructure cost and aim to reduce the operation cost.Finally,we carry out extensive simulations to demonstrate the impact of the DLAA and evaluate the mitigation performance of NPR.The proposed cost-benefit NPR approach can not only mitigate the impact of DLAA with 100%and also save 41.18$/MWh in terms of the operation cost.展开更多
This paper addresses the complexity of wake control in large-scale wind farms by proposing a partitioning control algorithm utilizing the FLORIDyn(FLOW Redirection and Induction Dynamics)dynamic wake model.First,the i...This paper addresses the complexity of wake control in large-scale wind farms by proposing a partitioning control algorithm utilizing the FLORIDyn(FLOW Redirection and Induction Dynamics)dynamic wake model.First,the impact of wakes on turbine effective wind speed is analyzed,leading to a quantitative method for assessing wake interactions.Based on these interactions,a partitioning method divides the wind farm into smaller,computationally manageable zones.Subsequently,a heuristic control algorithm is developed for yaw optimization within each partition,reducing the overall computational burden associated with multi-turbine optimization.The algorithm’s effectiveness is evaluated through case studies on 11-turbine and 28-turbine wind farms,demonstrating power generation increases of 9.78%and 1.78%,respectively,compared to baseline operation.The primary innovation lies in coupling the higher-fidelity dynamic FLORIDyn wake model with a graph-based partitioning strategy and a computationally efficient heuristic optimization,enabling scalable and accurate yaw control for large wind farms,overcoming limitations associated with simplified models or centralized optimization approaches.展开更多
This study presents an emergency control method for sub-synchronous oscillations in wind power gridconnected systems based on transfer learning,addressing the issue of insufficient generalization ability of traditiona...This study presents an emergency control method for sub-synchronous oscillations in wind power gridconnected systems based on transfer learning,addressing the issue of insufficient generalization ability of traditional methods in complex real-world scenarios.By combining deep reinforcement learning with a transfer learning framework,cross-scenario knowledge transfer is achieved,significantly enhancing the adaptability of the control strategy.First,a sub-synchronous oscillation emergency control model for the wind power grid integration system is constructed under fixed scenarios based on deep reinforcement learning.A reward evaluation system based on the active power oscillation pattern of the system is proposed,introducing penalty functions for the number of machine-shedding rounds and the number of machines shed.This avoids the economic losses and grid security risks caused by the excessive one-time shedding of wind turbines.Furthermore,transfer learning is introduced into model training to enhance the model’s generalization capability in dealing with complex scenarios of actual wind power grid integration systems.By introducing the Maximum Mean Discrepancy(MMD)algorithm to calculate the distribution differences between source data and target data,the online decision-making reliability of the emergency control model is improved.Finally,the effectiveness of the proposed emergency control method for multi-scenario sub-synchronous oscillation in wind power grid integration systems based on transfer learning is analyzed using the New England 39-bus system.展开更多
To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control s...To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control strategy is proposed for peak shaving sce-narios.First,considering the difference between peak and valley loads and the operating costs of EVs,a peak shaving model for EVs is constructed.Second,the design of IGEO has improved the global exploration and local development capabilities of the golden eagle optimizer(GEO)algorithm.Subsequently,IGEO is used to solve the peak shaving model and obtain the overall EV grid connected charging and discharging instructions.Next,using the k-means algorithm,EVs are dynamically divided into priority charging groups,backup groups,and priority discharging groups based on SOC differences.Finally,a dual layer power distribution scheme for EVs is designed.The upper layer determines the charging and discharging sequences and instructions for the three groups of EVs,whereas the lower layer allocates the charging and discharging instructions for each group to each EV.The proposed strategy was simulated and verified,and the results showed that the designed IGEO had faster optimization speed and higher optimization accuracy.The pro-posed EV grouping control strategy effectively reduces the peak-valley difference in the power grid,reduces the operational life loss of EVs,and maintains a better SOC balance for EVs.展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
The increasing intelligence of power systems is transforming distribution networks into Cyber-Physical Distribution Systems(CPDS).While enabling advanced functionalities,the tight interdependence between cyber and phy...The increasing intelligence of power systems is transforming distribution networks into Cyber-Physical Distribution Systems(CPDS).While enabling advanced functionalities,the tight interdependence between cyber and physical layers introduces significant security challenges and amplifies operational risks.To address these critical issues,this paper proposes a comprehensive risk assessment framework that explicitly incorporates the physical dependence of information systems.A Bayesian attack graph is employed to quantitatively evaluate the likelihood of successful cyber attacks.By analyzing the critical scenario of fault current path misjudgment,we define novel system-level and node-level risk coupling indices to preciselymeasure the cascading impacts across cyber and physical domains.Furthermore,an attack-responsive power recovery optimization model is established,integrating DistFlowbased physical constraints and sophisticated modeling of information-dependent interference.To enhance resilience against varying attack scenarios,a defense resource allocation model is constructed,where the complex Mixed-Integer Nonlinear Programming(MINLP)problem is efficiently linearized into a Mixed-Integer Linear Programming(MILP)formulation.Finally,to mitigate the impact of targeted attacks,the optimal deployment of terminal defense resources is determined using a Stackelberg game-theoretic approach,aiming to minimize overall system risk.The robustness and effectiveness of the proposed integrated framework are rigorously validated through extensive simulations under diverse attack intensities and defense resource constraints.展开更多
As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation c...As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation capability and promoting renewable integration.However,evaluating the adjustable capability of such hybrid stations while considering security constraints remains a major challenge.This paper first analyzes the adjustable capabilities of all the resources within such a station based on the power-energy boundary(PEB)model.Then,an optimal formulation is proposed to obtain the adjusted parameters of the aggregate feasible region(AFR)model,which embeds low-dimensional linear models within high-dimensional linear models to improve the accuracy.To solve this formulation,it is transformed using duality theory and an alternating optimization algorithm is designed to obtain the solution.Finally,a multi-station adjustable capability aggregation method considering security constraints is introduced.Simulation results verify that the proposed method effectively reduces infeasible regions and improves smoothness of aggregated boundaries,providing an accurate and practical tool for flexibility evaluation in PSCSs and offering guidance for aggregators and system planners.展开更多
This study investigates the distinct impacts of eastern Pacific(EP)and central Pacific(CP)El Niño events on winter shortwave solar radiation(SSR)in southern China,revealing different spatial distributions and und...This study investigates the distinct impacts of eastern Pacific(EP)and central Pacific(CP)El Niño events on winter shortwave solar radiation(SSR)in southern China,revealing different spatial distributions and underlying mechanisms.The results show that,during the developing winter of EP El Niño,significant SSR reductions occur in southwestern China and the east coast of southern China due to a strong,zonally extended Northwest Pacific anticyclone that transports moisture from the tropical Northwest Pacific and North Indian Ocean,while the northeast of southern China experiences a weak increase in SSR.In contrast,during the developing winter of CP El Niño,SSR decreases in the east of southern China with a significant decrease in the lower basin of the Yangtze River but an increase in the west of southern China with a remarkable increase in eastern Yunnan.The pronounced east-west dipole pattern in SSR anomalies is driven by a meridionally elongated Northwest Pacific anticyclone,which enhances northward moisture transport to the east of southern China while leaving western areas drier.Further research reveals that distinct moisture anomalies during the developing winter of EP and CP events result in divergent SSR distributions across southern China,primarily through modulating the total cloud cover.These findings highlight the critical need to differentiate between El Niño types when predicting medium and long-term variability of radiation in southern China.展开更多
An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale...An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10^(−4) p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach.展开更多
This paper presents a programmable frequency scan algorithm based on harmonic balance.The core idea involves treating systems under perturbation as nonlinear time-periodic(NTP)systems.Steady-state harmonics are first ...This paper presents a programmable frequency scan algorithm based on harmonic balance.The core idea involves treating systems under perturbation as nonlinear time-periodic(NTP)systems.Steady-state harmonics are first solved via Newton-Raphson iteration through a set of nonlinear equations,and then input-output variables are selected to estimate the linear transfer function of the original NTP system without perturbations.The applications and insights of the proposed algorithm are discussed,particularly in guiding existing frequency scan algorithms,which are restricted by time-domain signal generation or measurement.This improvement is achieved through linear stability analysis of NTP systems with perturbations.展开更多
In the dispersed computing environment driven by intelligent networks,intrusion detection faces significant challenges.This paper proposes a multilayer decentralized federated learning framework based on mean field ga...In the dispersed computing environment driven by intelligent networks,intrusion detection faces significant challenges.This paper proposes a multilayer decentralized federated learning framework based on mean field game theory(MFG-DFL).The framework organizes networked computing points(NCPs)into a three-layer collaborative architecture,and innovatively introduces MFG theory to model the complex dynamic interactions,which among large-scale NCPs as a game between a representative NCP and the mean field.By solving the coupled HJB and FPK equations,we design a dynamic incentive mechanism to fairly quantify and reward NCP contributions,thus aligning individual rationality with the global objectives of the system.The simulation results on the CICIoT2023 data set demonstrate the outstanding performance of the proposed framework.Specifically,it achieves an intrusion detection accuracy of 81.09%in highly non-IID scenarios,showcasing a well-balanced trade-off between computational efficiency and performance enhancement.展开更多
A decision feedback equalization(DFE)algorithm is proposed by simplifying Volterra structure.The simplification principle and process of the proposed Volterra-based equalization algorithm are presented.With the suppor...A decision feedback equalization(DFE)algorithm is proposed by simplifying Volterra structure.The simplification principle and process of the proposed Volterra-based equalization algorithm are presented.With the support of this algorithm,the signal damage for four-level pulse amplitude modulation signal(PAM-4)is compensated,which is caused by device bandwidth limitation and dispersion during transmission in C-band intensity modulation direct detection(IM-DD)fiber system.Experiments have been carried out to demonstrate that PAM-4 signals can transmit over 2 km in standard single-mode fiber(SSMF)based on a 30 GHz Mach-Zehnder modulator(MZM).The bit error rate(BER)can reach the threshold of hard decision-forward error correction(HD-FEC)(BER=3.8×10-3)and its sensitivity is reduced by 2 d Bm compared with traditional feedforward equalization(FFE).Meanwhile,the algorithm complexity is greatly reduced by 55%,which provides an effective theoretical support for the commercial application of the algorithm.展开更多
The dispatching for monthly generation plan is to manage the congestion considering the security constrains of the power grid, where the monthly generation plan is the result of vary monthly power exchange, including ...The dispatching for monthly generation plan is to manage the congestion considering the security constrains of the power grid, where the monthly generation plan is the result of vary monthly power exchange, including long-term power contract, power exchange among provinces and generation constitution exchanges. The application of monthly security constrained dispatching is with significant meaning for the security and stability of power grid. This paper brings forward the purpose and contents of security dispatching and introduces the working procedure and mathematic models. At last, the practical example of the Anhui Province power grid is introduced to explain the models.展开更多
The multi-agent theory is introduced and applied in the way to strike the control amount of emergency control according to stability margin, based on which an emergency control strategy of the power system is presente...The multi-agent theory is introduced and applied in the way to strike the control amount of emergency control according to stability margin, based on which an emergency control strategy of the power system is presented. The multi-agent control structure which is put forward in this article has three layers: system agent, areal agent and local agents. System agent sends controlling execution signal to the load-local agent according to the position and the amount of load shedding upload from areal agent;The areal agent judges whether the power system is stable by monitoring and analyzing the maximum relative power angle. In the condition of instability, determines the position of load-shedding, and the optimal amount of load-shedding according to the stability margin based on the corrected transient energy function, upload control amount to system agent;local-generator agent is mainly used for real-time monitoring the power angle of generator sets and uploading it to the areal agency, local-loads agent control load by receiving the control signal from system agent. Simulations on IEEE39 system show that the proposed control strategy improves the system stability.展开更多
This paper made a research on the Intelligent Optimization Operating Modeling of Pumped Storage Power Station in Hunan Power Grid. First it introduces the characteristics of Hunan power grid and analysis the practical...This paper made a research on the Intelligent Optimization Operating Modeling of Pumped Storage Power Station in Hunan Power Grid. First it introduces the characteristics of Hunan power grid and analysis the practical requirement of dispatching. Then it brings forward the intelligent optimization model and set up running model for pumped storage power station of Hei Mi-feng. At last, it introduces the application of pumped storage power station in Hunan power grid and proves the effectiveness of the optimization models.展开更多
The integration of renewable energy sources(RESs)with inverter interfaces has fundamentally reshaped power system dynamics,challenging traditional stability analysis frameworks designed for synchronous generator-domin...The integration of renewable energy sources(RESs)with inverter interfaces has fundamentally reshaped power system dynamics,challenging traditional stability analysis frameworks designed for synchronous generator-dominated grids.Conventional classifica-tions,which decouple voltage,frequency,and rotor angle stability,fail to address the emerging strong voltage‒angle coupling effects caused by RES dynamics.This coupling introduces complex oscillation modes and undermines system robustness,neces-sitating novel stability assessment tools.Recent studies focus on eigenvalue distributions and damping redistribution but lack quantitative criteria and interpretative clarity for coupled stability.This work proposes a transient energy-based framework to resolve these gaps.By decomposing transient energy into subsystem-dissipated components and coupling-induced energy exchange,the method establishes stability criteria compatible with a broad variety of inverter-interfaced devices while offering an intuitive energy-based interpretation for engineers.The coupling strength is also quantified by defining the relative coupling strength index,which is directly related to the transient energy interpretation of the coupled stability.Angle‒voltage coupling may induce instability by injecting transient energy into the system,even if the individual phase angle and voltage dynamics themselves are stable.The main contributions include a systematic stability evaluation framework and an energy decomposition approach that bridges theoretical analysis with practical applicability,addressing the urgent need for tools for managing modern power system evolving stability challenges.展开更多
The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often sim...The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often simplify reliability considerations or fail to holistically integrate them with economic and technical constraints.This paper introduces a novel,two-stage optimization framework for offshore wind farm(OWF)ECS planning that systematically incorporates reliability.The first stage employs Mixed-Integer Linear Programming(MILP)to determine an optimal radial network topology,considering linearized reliability approximations and geographical constraints.The second stage enhances this design by strategically placing tie-lines using a Mixed-Integer Quadratically Constrained Program(MIQCP).This stage leverages a dynamic-aware adaptation of Multi-Source Multi-Terminal Network Reliability(MSMT-NR)assessment,with its inherent nonlinear equations successfully transformed into a solvable MIQCP form for loopy networks.A benchmark case study demonstrates the framework’s efficacy,illustrating how increasing the emphasis on reliability leads to more distributed and interconnected network topologies,effectively balancing investment costs against enhanced system resilience.展开更多
With the development of integrated power and gas distribution systems(IPGS)incorporating renewable energy sources(RESs),coordinating the restoration processes of the power distribution system(PS)and the gas distributi...With the development of integrated power and gas distribution systems(IPGS)incorporating renewable energy sources(RESs),coordinating the restoration processes of the power distribution system(PS)and the gas distribution system(GS)by utilizing the benefits of RESs enhances service restoration.In this context,this paper proposes a coordinated service restoration framework that considers the uncertainty in RESs and the bi-directional restoration interactions between the PS and GS.Additionally,a coordinated service restoration model is developed considering the two systems’interdependency and the GS’s dynamic characteristics.The objective is to maximize the system resilience index while adhering to operational,dynamic,restoration logic,and interdependency constraints.A method for managing uncertainties in RES output is employed,and convexification techniques are applied to address the nonlinear constraints arising from the physical laws of the IPGS,thereby reducing solution complexity.As a result,the service restoration optimization problem of the IPGS can be formulated as a computationally tractable mixed-integer second-order cone programming problem.The effectiveness and superiority of the proposed framework are demonstrated through numerical simulations conducted on the interdependent IEEE 13-bus PS and 9-node GS.The comparative results show that the proposed framework improves the system resilience index by at least 65.07%compared to traditional methods.展开更多
Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to...Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to mitigate these losses by redirecting wakes away from downstream turbines.However,the effectiveness of yaw-based AWC is highly dependent on the accuracy of the underlying wake prediction models,which often require site-specific adjustments to reflect local atmospheric conditions and turbine characteristics.This paper presents an integrated,data-driven framework tomaximize wind farmpower output.Themethodology consists of three key stages.First,a practical simulation-assisted matching method is developed to estimate the True North Alignment(TNA)of each turbine using historical Supervisory Control and Data Acquisition(SCADA)data,resolving a common source of operational uncertainty.Second,key wake expansion parameters of the Floris engineering wake model are calibrated using site-specific SCADA power data,tailoring the model to the JibeiWind Farm in China.Finally,using this calibrated model,the derivative-free solver NOMAD is employed to determine the optimal yaw angle settings for an 11-turbine cluster under various wind conditions.Simulation studies,based on real operational scenarios,demonstrate the effectiveness of the proposed framework.The optimized yaw control strategies achieved total power output gains of up to 5.4%compared to the baseline zero-yaw operation under specific wake-inducing conditions.Crucially,the analysis reveals that using the site-specific calibrated model for optimization yields substantially better results than using a model with generic parameters,providing an additional power gain of up to 1.43%in tested scenarios.These findings underscore the critical importance of TNA estimation and site-specific model calibration for developing effective AWC strategies.The proposed integrated approach provides a robust and practical workflow for designing and pre-validating yaw control settings,offering a valuable tool for enhancing the economic performance of wind farms.展开更多
基金supported by the Natural Science Foundation of China No.62303126the project Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control(LFC)systems integrated with Battery Energy Storage Systems(BESS).As renewable energy sources gain greater penetration,power grids are becoming increasingly vulnerable to cyber threats,potentially leading to frequency instability and widespread disruptions.We model two significant attack vectors:load-altering attacks(LAAs)and false data injection attacks(FDIAs)that corrupt frequency measurements.These are analyzed for their impact on grid frequency stability in both linear and nonlinear LFC models,incorporating generation rate constraints and nonlinear loads.A coordinated attack strategy is presented,combining LAAs and FDIAs to achieve stealthiness by concealing frequency deviations from system operators,thereby maximizing disruption while evading traditional detection.To counteract these threats,we propose an Unknown Input Observer(UIO)-based detection framework for linear and nonlinear LFCs.The UIO is designed using linear matrix inequalities(LMIs)to estimate system states while isolating unknown attack inputs,enabling attack detection through monitoring measurement residuals against a predefined threshold.For mitigation,we leverage BESS capabilities with two adaptive strategies:dynamic mitigation for dynamic LAAs,which tunes BESS parameters to enhance the system’s stability margin and accelerate convergence to equilibrium;and staticmitigation for static LAAs and FDIAs.Simulations show that the UIO achieves high detection accuracy,with residuals exceeding thresholds promptly under coordinated attacks,even in nonlinear models.Mitigation strategies reduce frequency deviations by up to 80%compared to unmitigated cases,restoring stability within seconds.
基金supported by the project Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘Load frequency control(LFC)is a critical function to balance the power consumption and generation.Thegrid frequency is a crucial indicator for maintaining balance.However,the widely used information and communication infrastructure for LFC increases the risk of being attacked by malicious actors.The dynamic load altering attack(DLAA)is a typical attack that can destabilize the power system,causing the grid frequency to deviate fromits nominal value.Therefore,in this paper,we mathematically analyze the impact of DLAA on the stability of the grid frequency and propose the network parameter regulation(NPR)to mitigate the impact.To begin with,the dynamic LFC model is constructed by highlighting the importance of the network parameter.Then,we model the DLAA and analyze its impact on LFC using the theory of second-order dynamic systems.Finally,we model the NPR and prove its effect in mitigating the DLAA.Besides,we construct a least-effort NPR considering its infrastructure cost and aim to reduce the operation cost.Finally,we carry out extensive simulations to demonstrate the impact of the DLAA and evaluate the mitigation performance of NPR.The proposed cost-benefit NPR approach can not only mitigate the impact of DLAA with 100%and also save 41.18$/MWh in terms of the operation cost.
基金supported by the Science and Technology Project of China South Power Grid Co.,Ltd.under Grant No.036000KK52222044(GDKJXM20222430).
文摘This paper addresses the complexity of wake control in large-scale wind farms by proposing a partitioning control algorithm utilizing the FLORIDyn(FLOW Redirection and Induction Dynamics)dynamic wake model.First,the impact of wakes on turbine effective wind speed is analyzed,leading to a quantitative method for assessing wake interactions.Based on these interactions,a partitioning method divides the wind farm into smaller,computationally manageable zones.Subsequently,a heuristic control algorithm is developed for yaw optimization within each partition,reducing the overall computational burden associated with multi-turbine optimization.The algorithm’s effectiveness is evaluated through case studies on 11-turbine and 28-turbine wind farms,demonstrating power generation increases of 9.78%and 1.78%,respectively,compared to baseline operation.The primary innovation lies in coupling the higher-fidelity dynamic FLORIDyn wake model with a graph-based partitioning strategy and a computationally efficient heuristic optimization,enabling scalable and accurate yaw control for large wind farms,overcoming limitations associated with simplified models or centralized optimization approaches.
基金funded by Sponsorship of Science and Technology Project of State Grid Xinjiang Electric Power Co.,Ltd.,grant number SGXJ0000TKJS2400168.
文摘This study presents an emergency control method for sub-synchronous oscillations in wind power gridconnected systems based on transfer learning,addressing the issue of insufficient generalization ability of traditional methods in complex real-world scenarios.By combining deep reinforcement learning with a transfer learning framework,cross-scenario knowledge transfer is achieved,significantly enhancing the adaptability of the control strategy.First,a sub-synchronous oscillation emergency control model for the wind power grid integration system is constructed under fixed scenarios based on deep reinforcement learning.A reward evaluation system based on the active power oscillation pattern of the system is proposed,introducing penalty functions for the number of machine-shedding rounds and the number of machines shed.This avoids the economic losses and grid security risks caused by the excessive one-time shedding of wind turbines.Furthermore,transfer learning is introduced into model training to enhance the model’s generalization capability in dealing with complex scenarios of actual wind power grid integration systems.By introducing the Maximum Mean Discrepancy(MMD)algorithm to calculate the distribution differences between source data and target data,the online decision-making reliability of the emergency control model is improved.Finally,the effectiveness of the proposed emergency control method for multi-scenario sub-synchronous oscillation in wind power grid integration systems based on transfer learning is analyzed using the New England 39-bus system.
基金supported by the National Natural Science Foundation of China(52077078)China Southern Power Grid Company Limited 036000KK52220004(GDKJXM20220147).
文摘To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control strategy is proposed for peak shaving sce-narios.First,considering the difference between peak and valley loads and the operating costs of EVs,a peak shaving model for EVs is constructed.Second,the design of IGEO has improved the global exploration and local development capabilities of the golden eagle optimizer(GEO)algorithm.Subsequently,IGEO is used to solve the peak shaving model and obtain the overall EV grid connected charging and discharging instructions.Next,using the k-means algorithm,EVs are dynamically divided into priority charging groups,backup groups,and priority discharging groups based on SOC differences.Finally,a dual layer power distribution scheme for EVs is designed.The upper layer determines the charging and discharging sequences and instructions for the three groups of EVs,whereas the lower layer allocates the charging and discharging instructions for each group to each EV.The proposed strategy was simulated and verified,and the results showed that the designed IGEO had faster optimization speed and higher optimization accuracy.The pro-posed EV grouping control strategy effectively reduces the peak-valley difference in the power grid,reduces the operational life loss of EVs,and maintains a better SOC balance for EVs.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金supported by China Southern Power Grid Company Limited(066500KK52222006).
文摘The increasing intelligence of power systems is transforming distribution networks into Cyber-Physical Distribution Systems(CPDS).While enabling advanced functionalities,the tight interdependence between cyber and physical layers introduces significant security challenges and amplifies operational risks.To address these critical issues,this paper proposes a comprehensive risk assessment framework that explicitly incorporates the physical dependence of information systems.A Bayesian attack graph is employed to quantitatively evaluate the likelihood of successful cyber attacks.By analyzing the critical scenario of fault current path misjudgment,we define novel system-level and node-level risk coupling indices to preciselymeasure the cascading impacts across cyber and physical domains.Furthermore,an attack-responsive power recovery optimization model is established,integrating DistFlowbased physical constraints and sophisticated modeling of information-dependent interference.To enhance resilience against varying attack scenarios,a defense resource allocation model is constructed,where the complex Mixed-Integer Nonlinear Programming(MINLP)problem is efficiently linearized into a Mixed-Integer Linear Programming(MILP)formulation.Finally,to mitigate the impact of targeted attacks,the optimal deployment of terminal defense resources is determined using a Stackelberg game-theoretic approach,aiming to minimize overall system risk.The robustness and effectiveness of the proposed integrated framework are rigorously validated through extensive simulations under diverse attack intensities and defense resource constraints.
基金supported by Science and Technology Project of China Southern Power Grid Company(036000KK52222007(GDKJXM20222121)).
文摘As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation capability and promoting renewable integration.However,evaluating the adjustable capability of such hybrid stations while considering security constraints remains a major challenge.This paper first analyzes the adjustable capabilities of all the resources within such a station based on the power-energy boundary(PEB)model.Then,an optimal formulation is proposed to obtain the adjusted parameters of the aggregate feasible region(AFR)model,which embeds low-dimensional linear models within high-dimensional linear models to improve the accuracy.To solve this formulation,it is transformed using duality theory and an alternating optimization algorithm is designed to obtain the solution.Finally,a multi-station adjustable capability aggregation method considering security constraints is introduced.Simulation results verify that the proposed method effectively reduces infeasible regions and improves smoothness of aggregated boundaries,providing an accurate and practical tool for flexibility evaluation in PSCSs and offering guidance for aggregators and system planners.
基金funded by a Project from China Southern Power Grid Company Ltd.(Nos.ZBKJXM20232481 and ZBKJXM20232482)。
文摘This study investigates the distinct impacts of eastern Pacific(EP)and central Pacific(CP)El Niño events on winter shortwave solar radiation(SSR)in southern China,revealing different spatial distributions and underlying mechanisms.The results show that,during the developing winter of EP El Niño,significant SSR reductions occur in southwestern China and the east coast of southern China due to a strong,zonally extended Northwest Pacific anticyclone that transports moisture from the tropical Northwest Pacific and North Indian Ocean,while the northeast of southern China experiences a weak increase in SSR.In contrast,during the developing winter of CP El Niño,SSR decreases in the east of southern China with a significant decrease in the lower basin of the Yangtze River but an increase in the west of southern China with a remarkable increase in eastern Yunnan.The pronounced east-west dipole pattern in SSR anomalies is driven by a meridionally elongated Northwest Pacific anticyclone,which enhances northward moisture transport to the east of southern China while leaving western areas drier.Further research reveals that distinct moisture anomalies during the developing winter of EP and CP events result in divergent SSR distributions across southern China,primarily through modulating the total cloud cover.These findings highlight the critical need to differentiate between El Niño types when predicting medium and long-term variability of radiation in southern China.
基金supported by Yunnan Power Grid Co.,Ltd.Science and Technology Project:Research and application of key technologies for graphical-based power grid accident reconstruction and simulation(YNKJXM20240333).
文摘An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10^(−4) p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach.
基金supported by China Southern Power Grid Corporation(036000KC23090005(GDKJXM20231027)).
文摘This paper presents a programmable frequency scan algorithm based on harmonic balance.The core idea involves treating systems under perturbation as nonlinear time-periodic(NTP)systems.Steady-state harmonics are first solved via Newton-Raphson iteration through a set of nonlinear equations,and then input-output variables are selected to estimate the linear transfer function of the original NTP system without perturbations.The applications and insights of the proposed algorithm are discussed,particularly in guiding existing frequency scan algorithms,which are restricted by time-domain signal generation or measurement.This improvement is achieved through linear stability analysis of NTP systems with perturbations.
基金National Science Foundation Project of China(62436004,62372317)National Key Research and Development Program of China(2023YFC3331702)。
文摘In the dispersed computing environment driven by intelligent networks,intrusion detection faces significant challenges.This paper proposes a multilayer decentralized federated learning framework based on mean field game theory(MFG-DFL).The framework organizes networked computing points(NCPs)into a three-layer collaborative architecture,and innovatively introduces MFG theory to model the complex dynamic interactions,which among large-scale NCPs as a game between a representative NCP and the mean field.By solving the coupled HJB and FPK equations,we design a dynamic incentive mechanism to fairly quantify and reward NCP contributions,thus aligning individual rationality with the global objectives of the system.The simulation results on the CICIoT2023 data set demonstrate the outstanding performance of the proposed framework.Specifically,it achieves an intrusion detection accuracy of 81.09%in highly non-IID scenarios,showcasing a well-balanced trade-off between computational efficiency and performance enhancement.
基金supported by the China Southern Power Grid Science and Technology Project,Research on 230 MHz Iot Access Architecture for Deep Coverage of Power Edge Services and R&D of Key Communication Devices(No.036000KK52180036).
文摘A decision feedback equalization(DFE)algorithm is proposed by simplifying Volterra structure.The simplification principle and process of the proposed Volterra-based equalization algorithm are presented.With the support of this algorithm,the signal damage for four-level pulse amplitude modulation signal(PAM-4)is compensated,which is caused by device bandwidth limitation and dispersion during transmission in C-band intensity modulation direct detection(IM-DD)fiber system.Experiments have been carried out to demonstrate that PAM-4 signals can transmit over 2 km in standard single-mode fiber(SSMF)based on a 30 GHz Mach-Zehnder modulator(MZM).The bit error rate(BER)can reach the threshold of hard decision-forward error correction(HD-FEC)(BER=3.8×10-3)and its sensitivity is reduced by 2 d Bm compared with traditional feedforward equalization(FFE).Meanwhile,the algorithm complexity is greatly reduced by 55%,which provides an effective theoretical support for the commercial application of the algorithm.
文摘The dispatching for monthly generation plan is to manage the congestion considering the security constrains of the power grid, where the monthly generation plan is the result of vary monthly power exchange, including long-term power contract, power exchange among provinces and generation constitution exchanges. The application of monthly security constrained dispatching is with significant meaning for the security and stability of power grid. This paper brings forward the purpose and contents of security dispatching and introduces the working procedure and mathematic models. At last, the practical example of the Anhui Province power grid is introduced to explain the models.
文摘The multi-agent theory is introduced and applied in the way to strike the control amount of emergency control according to stability margin, based on which an emergency control strategy of the power system is presented. The multi-agent control structure which is put forward in this article has three layers: system agent, areal agent and local agents. System agent sends controlling execution signal to the load-local agent according to the position and the amount of load shedding upload from areal agent;The areal agent judges whether the power system is stable by monitoring and analyzing the maximum relative power angle. In the condition of instability, determines the position of load-shedding, and the optimal amount of load-shedding according to the stability margin based on the corrected transient energy function, upload control amount to system agent;local-generator agent is mainly used for real-time monitoring the power angle of generator sets and uploading it to the areal agency, local-loads agent control load by receiving the control signal from system agent. Simulations on IEEE39 system show that the proposed control strategy improves the system stability.
文摘This paper made a research on the Intelligent Optimization Operating Modeling of Pumped Storage Power Station in Hunan Power Grid. First it introduces the characteristics of Hunan power grid and analysis the practical requirement of dispatching. Then it brings forward the intelligent optimization model and set up running model for pumped storage power station of Hei Mi-feng. At last, it introduces the application of pumped storage power station in Hunan power grid and proves the effectiveness of the optimization models.
基金supported by the Science and Technology Project of China Southern Power Grid Co.,Ltd under Grant 036000KC23090004(GDKJXM20231026).
文摘The integration of renewable energy sources(RESs)with inverter interfaces has fundamentally reshaped power system dynamics,challenging traditional stability analysis frameworks designed for synchronous generator-dominated grids.Conventional classifica-tions,which decouple voltage,frequency,and rotor angle stability,fail to address the emerging strong voltage‒angle coupling effects caused by RES dynamics.This coupling introduces complex oscillation modes and undermines system robustness,neces-sitating novel stability assessment tools.Recent studies focus on eigenvalue distributions and damping redistribution but lack quantitative criteria and interpretative clarity for coupled stability.This work proposes a transient energy-based framework to resolve these gaps.By decomposing transient energy into subsystem-dissipated components and coupling-induced energy exchange,the method establishes stability criteria compatible with a broad variety of inverter-interfaced devices while offering an intuitive energy-based interpretation for engineers.The coupling strength is also quantified by defining the relative coupling strength index,which is directly related to the transient energy interpretation of the coupled stability.Angle‒voltage coupling may induce instability by injecting transient energy into the system,even if the individual phase angle and voltage dynamics themselves are stable.The main contributions include a systematic stability evaluation framework and an energy decomposition approach that bridges theoretical analysis with practical applicability,addressing the urgent need for tools for managing modern power system evolving stability challenges.
基金supported by the Science and Technology Project of China South Power Grid Co.,Ltd.,Grant Nos.036000KK52222044,GDKJXM20222430。
文摘The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often simplify reliability considerations or fail to holistically integrate them with economic and technical constraints.This paper introduces a novel,two-stage optimization framework for offshore wind farm(OWF)ECS planning that systematically incorporates reliability.The first stage employs Mixed-Integer Linear Programming(MILP)to determine an optimal radial network topology,considering linearized reliability approximations and geographical constraints.The second stage enhances this design by strategically placing tie-lines using a Mixed-Integer Quadratically Constrained Program(MIQCP).This stage leverages a dynamic-aware adaptation of Multi-Source Multi-Terminal Network Reliability(MSMT-NR)assessment,with its inherent nonlinear equations successfully transformed into a solvable MIQCP form for loopy networks.A benchmark case study demonstrates the framework’s efficacy,illustrating how increasing the emphasis on reliability leads to more distributed and interconnected network topologies,effectively balancing investment costs against enhanced system resilience.
基金funded by the Science and Technology Project of State Grid Shanxi Electric Power Company(5205E0230001).
文摘With the development of integrated power and gas distribution systems(IPGS)incorporating renewable energy sources(RESs),coordinating the restoration processes of the power distribution system(PS)and the gas distribution system(GS)by utilizing the benefits of RESs enhances service restoration.In this context,this paper proposes a coordinated service restoration framework that considers the uncertainty in RESs and the bi-directional restoration interactions between the PS and GS.Additionally,a coordinated service restoration model is developed considering the two systems’interdependency and the GS’s dynamic characteristics.The objective is to maximize the system resilience index while adhering to operational,dynamic,restoration logic,and interdependency constraints.A method for managing uncertainties in RES output is employed,and convexification techniques are applied to address the nonlinear constraints arising from the physical laws of the IPGS,thereby reducing solution complexity.As a result,the service restoration optimization problem of the IPGS can be formulated as a computationally tractable mixed-integer second-order cone programming problem.The effectiveness and superiority of the proposed framework are demonstrated through numerical simulations conducted on the interdependent IEEE 13-bus PS and 9-node GS.The comparative results show that the proposed framework improves the system resilience index by at least 65.07%compared to traditional methods.
基金the Science and Technology Project of China South Power Grid Co., Ltd. under Grant No. 036000KK52222044 (GDKJXM20222430).
文摘Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to mitigate these losses by redirecting wakes away from downstream turbines.However,the effectiveness of yaw-based AWC is highly dependent on the accuracy of the underlying wake prediction models,which often require site-specific adjustments to reflect local atmospheric conditions and turbine characteristics.This paper presents an integrated,data-driven framework tomaximize wind farmpower output.Themethodology consists of three key stages.First,a practical simulation-assisted matching method is developed to estimate the True North Alignment(TNA)of each turbine using historical Supervisory Control and Data Acquisition(SCADA)data,resolving a common source of operational uncertainty.Second,key wake expansion parameters of the Floris engineering wake model are calibrated using site-specific SCADA power data,tailoring the model to the JibeiWind Farm in China.Finally,using this calibrated model,the derivative-free solver NOMAD is employed to determine the optimal yaw angle settings for an 11-turbine cluster under various wind conditions.Simulation studies,based on real operational scenarios,demonstrate the effectiveness of the proposed framework.The optimized yaw control strategies achieved total power output gains of up to 5.4%compared to the baseline zero-yaw operation under specific wake-inducing conditions.Crucially,the analysis reveals that using the site-specific calibrated model for optimization yields substantially better results than using a model with generic parameters,providing an additional power gain of up to 1.43%in tested scenarios.These findings underscore the critical importance of TNA estimation and site-specific model calibration for developing effective AWC strategies.The proposed integrated approach provides a robust and practical workflow for designing and pre-validating yaw control settings,offering a valuable tool for enhancing the economic performance of wind farms.
