Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

AUTO-EXTRACTING TECHNIQUE OF DYNAMIC CHAOS FEATURES FOR NONLINEAR TIME SERIES

The main purpose of nonlinear time series analysis is based on the rebuilding theory of phase space, and to study how to transform the response signal to rebuilt phase space in order to extract dynamic feature information, and to provide effective approach for nonlinear signal analysis and fault diagnosis of nonlinear dynamic system. Now, it has already formed an important offset of nonlinear science. But, traditional method cannot extract chaos features automatically, and it needs man＇s participation in the whole process. A new method is put forward, which can implement auto-extracting of chaos features for nonlinear time series. Firstly, to confirm time delay r by autocorrelation method; Secondly, to compute embedded dimension m and correlation dimension D; Thirdly, to compute the maximum Lyapunov index λmax; Finally, to calculate the chaos degree Dch of Poincare map, and the non-circle degree Dnc and non-order degree Dno of quasi-phase orbit. Chaos features extracting has important meaning to fault diagnosis of nonlinear system based on nonlinear chaos features. Examples show validity of the proposed method.

Analysis of Dynamic Characteristics of Crustal Horizontal Deformation in the South Segment of Longmenshan Fault Zone Revealed by GPS Data after the Wenchuan Earthquake

In order to obtain deformation parameters in the south segment of Longmenshan fault zone,Euler datum transformation and the least square collocation for data interpolation and smoothing are used to process GPS displacement time series data in the south segment of Longmenshan fault zone,and the rigid and elastic-plastic block motion model is used to calculate the strain parameters in each subarea. Conjoint analysis of displacement,velocity of each station and strain parameters of each subarea reveals that the influence of the Wenchuan earthquake on the south segment of Longmenshan fault zone increases from southeast to northwest,causing a highest deformation rate 6 times the background value and heightening the influence of the hidden faults on the difference of the earth surface along its two sides,which leads to the seismic risk of the southern segment increasing from north to south. The comparison of seismic risk among subareas based on the tectonic and seismicity background indicates that the most dangerous area is on the southeast of Longmenshan faults,and the background strain accumulation and the promoting effect of the Wenchuan earthquake advanced the occurrence of Lushan earthquake and the sinistral strike-slip on the rupture plane. The Wenchuan earthquake also caused a slight two-year long continuous strain release in the south segment of Xianshuihe fault,but the influence is far less than the effect of the compressive strain caused by the Sichuan-Yunnan block.

Hybrid robust fault detection and isolation of satellite reaction wheel actuators

In this paper,a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults,external disturbances,and parametric uncertainties.The proposed methodology incorporates a residual generation module,including a bank of filters,into an intelligent residual evaluation module.First,residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances.The residual evaluation module is developed based on the suggested series and parallel forms.Further,a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance.A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances,manoeuvres,uncertainties,and noises.The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.