Control method based on DRFNN sliding mode for multifunctional flexible multistate switch

To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation,a control method involving flexible multistate switches(FMSs)is proposed in this study.This approach is based on an improved double-loop recursive fuzzy neural network(DRFNN)sliding mode,which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults.First,an improved DRFNN sliding mode control(SMC)method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system.To improve the robustness of the system,an adaptive parameter-adjustment strategy for the DRFNN is designed,where its dynamic mapping capabilities are leveraged to improve the transient compensation control.Additionally,a quasi-continuous second-order sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability.The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem.A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink.The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis.

High impedance fault detection in distribution network based on S-transform and average singular entropy

When a high impedance fault(HIF)occurs in a distribution network,the detection efficiency of traditional protection devices is strongly limited by the weak fault information.In this study,a method based on S-transform(ST)and average singular entropy(ASE)is proposed to identify HIFs.First,a wavelet packet transform(WPT)was applied to extract the feature frequency band.Thereafter,the ST was investigated in each half cycle.Afterwards,the obtained time-frequency matrix was denoised by singular value decomposition(SVD),followed by the calculation of the ASE index.Finally,an appropriate threshold was selected to detect the HIFs.The advantages of this method are the ability of fine band division,adaptive time-frequency transformation,and quantitative expression of signal complexity.The performance of the proposed method was verified by simulated and field data,and further analysis revealed that it could still achieve good results under different conditions.

Newly designed identifying method for ice thickness on highvoltage transmission lines via machine vision

The icing of transmission lines will bring considerable challenges to the safe operation of the power grid.Therefore,a novel method combines machine vision and machine learning algorithms for identifying the ice thickness on high‐voltage transmission line(HVTL)as proposed herein.First,noise and background interference in the image are filtered,and the grey image is used as input.Then,the algorithms of improved Canny edge detection,Hough transform,improved K‐means clustering,and least‐squares fitting are adopted in turn to locate the edges of conductors.Finally,according to the distance mapping model based on monocular vision,the ice thickness of the conductor is determined by calculating the width difference before and after icing.The experimental results show that the proposed method can accurately locate the edge of the conductor in both field and experimental environments.Moreover,it can ensure ideal effects under different illumination and hardly not be affected by distortion in both horizontal and vertical directions.Besides,the distance mapping model can map the pixel distance to the actual distance with high precision,no matter whether the background is simple or complex,and the calculated ice thickness has only a small deviation compared to the actual value.In addition,the proposed method shows high reliability and effectiveness when various interference such as different backgrounds,uneven icing,height difference changes,conductor movement,contrast changes,and conductor sag occur.