Aiming at the problems of poor real-time performance and low time-frequency resolution in existing detection algorithms for composite power quality disturbances,a real-time power quality disturbance detection method based on improved adaptive S-transform(IAST)is proposed.A globally adaptive Gaussian window is constructed as the kernel function of the IAST,allowing the effective window length and frequency spectrum to adapt dynamically with the detection frequency.This avoids the need for frequent switching of window parameters to improve time-frequency resolution,thereby reducing algorithm complexity.The window parameters are optimized with the objective of enhancing signal energy concentration,ensuring accurate time-frequency positioning of various types of disturbances.An automatic thresholding method is used to determine the dominant frequency points of the actual disturbance signals,which are then subjected to time-frequency transformation to further improve computational efficiency.Simulation and experimental results show that,compared with existing algorithms for detecting composite power quality disturbances,the proposed method offers superior real-time performance,strong time-frequency resolution,and low computational complexity,making it suitable for accurate real-time detection of complex power quality disturbances.
A wind-PV-thermal-energy storage system(WPTESS)is bundled and transmitted using AC collection,MMC-HVDC transmission,and AC grid connection.Among them,short-circuit faults at the AC sending line will generate new fault characteristics.Firstly,the fault current characteristics are analyzed by combining the fault ride-through control strategies of the WPTESS and MMC-HVDC.When the voltage drops to a range between 0.2 p.u.and 0.9 p.u.,the mechanism by which the fault current phase angle on both sides of the AC sending line is affected by the degree of voltage drop and the upper limit control of the amplitude limiting link is clarified through theoretical derivation.Using phasor analysis,it is revealed that the fault current phase angle on the WPTESS side is less than 75°,and on the MMC-HVDC side is less than 45°.The fault current amplitude characteristics under grounding faults and phase-to-phase faults are analyzed using fault sequence network.Secondly,considering the short-circuit ratio and using Thevenin's equivalent method,the reasons for the reduction of fault current phase angle due to the increase in system short-circuit capacity caused by thermal power integration are explored.In addition,the characteristics of the fault current amplitude of a wind-PV-energy storage system varying proportionally with input power are analyzed.Finally,the correctness of the theoretical analysis is verified through RTDS simulation.
Accurately describing the error characteristics of wind power output prediction is helpful for the rational allocation of system reserve capacity and the optimization of day-ahead scheduling plans.This paper proposes a day-ahead optimization scheduling method for power systems considering wind power ramping reserve requirements.First,based on the wind power ramping segment,the ramping characteristics are extracted,and a two-dimensional interval of ramping amplitude-predicted power is established.The adaptive kernel density estimation method is used to fit the probability distribution of wind power prediction errors.Then,based on the distribution of wind power prediction errors,the system reserve requirements are determined.Looking for minimal comprehensive operating costs of reserve costs and risk costs,a continuous-time day-ahead optimization scheduling model is established.Next,the Bernstein polynomial interpolation solution space transform is adopted to complete model conversion,thereby optimizing reserve capacity,unit combination,and output plans.Finally,a case study verifies that the established wind power prediction error distribution model can accurately describe the stochastic characteristics of wind power.The proposed day-ahead scheduling method can effectively allocate system reserve capacity,ensuring operational safety and economic efficiency.
With the construction of large-capacity long-distance high-voltage direct current transmission projects and the large-scale integration of renewable energy,frequency security of the power system is facing severe challenges.For fast and accurate online assessment of frequency security,a data-driven model based on a metric learning(ML)and generative adversarial network(GAN)is proposed.First,the key frequency security indicators are selected as the outputs of the model,and an input feature set is constructed.Then,the improved Wasserstein generative adversarial network(WGAN)based on the Wasserstein distance metric is used to learn the distribution information of historical operation scenarios of power systems.This generates operational scenarios covering typical modes to build the training sample set.Considering the inapplicability of a single machine learning model to frequency security assessment with complicated operational modes of power systems,a combined assessment model for assessment composed of multiple sub-models is constructed based on metric learning for a kernel regression(MLKR)method.Finally,a simplified Shandong power system example is used to verify the effectiveness of the proposed method.
Long control link delays can lead to negative damping in the impedance range of flexible DC transmission systems based on modular multi platform converters(MMC).These can easily interact with distributed capacitors in the AC system and cause high-frequency oscillations.Although existing active damping suppression strategies can effectively suppress high-frequency oscillations,they will deteriorate the impedance characteristics of the mid frequency range and increase the risk of mid frequency oscillations.To effectively suppress mid-to-high frequency oscillations,this paper proposes a passive damping suppression strategy by paralleling a passive impedance reshaping device at the AC side bus of the MMC.First,an MMC simplified model is derived and its accuracy is verified.Secondly,impedance reshaping device parameters are designed based on the Nyquist stability criterion and minimum power loss constraint to achieve impedance reshaping of the MMC system.Finally,the impedance method is applied to compare the impedance characteristics of the MMC system before and after reshaping,and the correction effect of the impedance reshaping device on the impedance of the MMC system is analyzed.An MMC electromagnetic transient simulation model with a passive impedance reshaping device is built in PSCAD/EMTDC.The results show that this suppression strategy can effectively suppress the oscillation phenomenon of the MMC system in the mid-to-high frequency range,with little impact on the steady-state characteristics of the system,and low fundamental frequency power loss.
