Xiangxiang Wei

Fault Line Selection Method of Small Current to Ground System Based on Atomic Sparse Decomposition and Extreme Learning Machine

This paper proposed a fault line voting selection method based on atomic sparse decomposition (ASD) and extreme learning machine (ELM). Firstly, it adopted ASD algorithm to decompose zero sequence current of every feeder line at first two cycles and selected the first four atoms to construct main component atom library, fundamental atom library, and transient characteristic atom libraries 1 and 2, respectively. And it used information entropy theory to calculate the atom libraries; the measure values of information entropy are got. It constructed four ELM networks to train and test atom sample and then obtained every network accuracy. At last, it combined the ELM network output and confidence degree to vote and then compared the vote number to achieve fault line selection (FLS). Simulation experiment illustrated that the method accuracy is 100%, it is not affected by fault distance and transition resistance, and it has strong ability of antinoise interference.

A Novel Fault Line Selection Method Based on Improved Oscillator System of Power Distribution Network

A novel method of fault line selection based on IOS is presented. Firstly, the IOS is established by using math model, which adopted TZSC signal to replace built-in signal of duffing chaotic oscillator by selecting appropriate parameters. Then, each line’s TZSC decomposed by db10 wavelet packet to get CFB with the maximum energy principle, and CFB was solved by IOS. Finally, maximum chaotic distance and average chaotic distance on the phase trajectory are used to judge fault line. Simulation results show that the proposed method can accurately judge fault line and healthy line in strong noisy background. Besides, the nondetection zones of proposed method are elaborated.

Stepped Fault Line Selection Method Based on Spectral Kurtosis and Relative Energy Entropy of Small Current to Ground System

This paper proposes a stepped selection method based on spectral kurtosis relative energy entropy. Firstly, the length and type of window function are set; then when fault occurs, enter step 1: the polarity of first half-wave extremes is analyzed; if the ratios of extremes between neighboring lines are positive, the bus bar is the fault line, else, the SK relative energy entropies are calculated, and then enter step 2: if the obtained entropy multiple is bigger than the threshold or equal to the threshold, the overhead line of max entropy corresponding is the fault line, if not, enter step 3: the line of max entropy corresponding is the fault line. At last, the applicability of the proposed algorithm is presented, and the comparison results are discussed.

An adaptive fault detection method based on atom sparse and evidence fusion for the small current to ground system

An adaptive fault detection method for the small current to ground system is proposed based on the atom sparse and evidence fusion theory. Firstly, the two cycles of transient zero-sequence current are obtained when occur fault, which is decomposed by the matching pursuit algorithm, and the iterations are set to four, then, according to the correlation analysis, the best three atoms of maximum correlation are chosen and which are sorted by the correlation coefficients. Secondly, we obtained the determine fault measure (DFM) values by calculating the atomic energy entropy, and selected the atomic fault trust (FT) function by revising the DFM. Finally, the FT values are integrated by the D-S (Dempster–Shafer) evidence theory, then the fault comprehensive trust values are gained, the selection results are output. Simulation results show that the method of fault line detection is accurate and reliable.

Faulty Line Detection Method Based on Optimized Bistable System for Distribution Network

The noneffectively neutral grounded distribution network is called small current to ground system (SCGS) in China. When single-phase to ground fault occurs in SCGS, the fault current is weak, and the noise impairs the feature of fault current, both of which make faulty line detection difficult. This paper presents a faulty line detection method for SCGS, based on optimized bistable system. The proposed method consists of two steps: 1) The optimized bistable system, whose potential function parameters are optimized by particle swarm optimization algorithm, is used to extract transient zero-sequence current (TZSC) in strong noise background; 2) the optimized bistable system and cross correlation coefficient are used to propose a faulty line detection criterion, it based on squared distance, which contains the waveform difference and energy of TZSC. Simulation and field experiments prove that the method can detect faulty line exactly with various fault situations, such as different signal-noise ratios, grounding resistances, initial angles, faulty lines and unbalanced load.

Faulty Line Selection Method for Distribution Network Based on Variable Scale Bistable System

Since weak fault signals often lead to the misjudgment and other problems for faulty line selection in small current to ground system, this paper proposes a novel faulty line selection method based on variable scale bistable system (VSBS). Firstly, VSBS is adopted to analyze the transient zero-sequence current (TZSC) with different frequency variety scale ratio and noise intensity, and the results show that VSBS can effectively extract the variation trends of initial stage of TZSC. Secondly, TZSC is input to VSBS for calculation with Runge-Kutta equations, and the output signal is chosen as the characteristic currents. Lastly, correlation coefficients of every line characteristic current are used as the index to a novel faulty line selection criterion. A large number of simulation experiments prove that the proposed method can accurately select the faulty line and extract weak fault signals in the environment with strong noise.