Research On Power Cable Fault Diagnosis And Location System Based On Cloud-side Collaboration

The development of science and technology has promoted the intelligence and diversification of electrical equipment,providing convenience for people’s better lives while making the power supply system more complex.Cables are the link for transmitting electric energy throughout the entire power system,which is crucial for stable operation of the power system.However,cables inevitably experience various problems during operation that can cause malfunctions.If not dealt with in a timely manner,it can bring immeasurable losses to production and life.Therefore,timely and effective diagnosis of cable faults and locating fault points to quickly solve problems is significant for ensuring power supply safety.This article proposes a cloud-edge collaborative power cable fault diagnosis and location system to address the problems of low efficiency,low accuracy,and low realtime performance caused by the unclear characteristics of current power cable faults.The system includes an edge perception layer,a network transmission layer,a cloud computing layer,and an application management layer.The edge perception layer is used for collecting and preprocessing current fault signals;the network transmission layer uses heterogeneous fusion interactive networks to upload signal preprocessing results to the cloud computing layer;the cloud computing layer analyzes and processes uploaded data to complete power cable fault diagnosis and location;the application management layer displays analysis results for maintenance personnel to provide repair decisions.This article proposes a fault diagnosis method for power cable based on the EEMD fusion BAS-CNN-Attention network,which is aimed at the problem of complex and varied application scenarios of power cable and large amount of noise interference in current signals.The method first uses the EEMD method to decompose the current signal,then selects IMF components containing fault information through correlation coefficient screening.Finally,feature information is input into a CNN network optimized by BAS algorithm,and Attention mechanism is used to focus on more significant features to reduce feature dimensionality and enhance network discriminability for accurate classification diagnosis.Compared with traditional time-domain and frequency-domain analysis methods,this method has higher robustness and accuracy,effectively extracts feature information from power cable fault signals,thus achieving reliability and accuracy in fault diagnosis.To address the problems of long time consumption and low efficiency in existing power cable fault location methods,an improved delay estimation algorithm is proposed to estimate the time difference between receiving fault signals at adjacent monitoring nodes for fast location.This designed system was applied at Taoyuan Coal Mine substation high-voltage cables under Huainan Mining Group undergoing testing showing that this new design achieves precise monitoring goals meeting expected design targets safeguarding safe/stable operation of electrical systems having theoretical research significance/engineering practical value.Figure [53] Table [12] Reference [81]...