High-impedance fault detection (HIFD) is crucial in an effectively grounded distribution system because of the potential threat of fire and electric shock. HIFD has been extensively researched for more than 30 years, and a harmonic component in zero-sequence current is typically used in detection algorithms since waveform distortion is often produced by arc flash. For a stable HIF, it would be invalid or has lower sensitivity because of the limited harmonic content in zero-sequence current waveform. To overcome this problem, in this paper, a novel detection algorithm is proposed for detecting waveform distortion in the time domain. First, for the analysis of arc flash, the solid dielectric electrical breakdown theory is verified to be more suitable than theories based on the heat accumulation theory, and a nonlinear arc model that can be used for analyzing arc flash is then proposed. Furthermore, a novel HIFD algorithm based on the identification of nonlinear voltage-current characteristic profiles (VCCPs) is proposed, with the focus on the quenching and restrike dynamic process of an arc flash. The high performance of the algorithm is verified by performing various simulations and examining field data. An HIFD prototype using the proposed algorithm was developed, and it showed excellent performance in real-time digital simulator tests. The VCCP based disturbance detection approach could also be popularized in the applications of relay protection, harmonic source location and early fault alarming in smart grid.

For Arc High-Impedance Faults (AHIFs) in Active Distribution Networks (ADNs) with neutral grounding via Petersen coil, both nonlinear characteristics caused by arc and the influence of zero-input response have been not considered in previous research. Quantitative analysis methods based on the threshold setting comparison is mostly adopted in current field applications, which cannot balance the detection reliability and sensitivity simultaneously. In response to this problem, a higher precision nonlinear AHIF equivalent model is established based on the logarithmic arc model. State equations describing the occurrences of AHIFs are solved by using piecewise linear fitting technique, and analytical expressions of zero-sequence voltage and currents are present in paper. Furthermore, the regular volt-ampere characteristic dynamic trajectory between the zero-sequence voltage and current in characteristic frequency band is analyzed, and a tangent-inverse method is proposed to extract the direction of dynamic trajectory, then an effective fault detection algorithm is designed. The validity of the proposed nonlinear equivalent model and detection algorithm are verified by various cases’ simulations and field test data.

When a single-phase-to-ground (SPG) fault occurs in a distribution network, the current is weak, and the fault conditions are complex; the existing fault detection technologies do not show reliable performance. Unlike the existing detection methods, we introduced a fundamental component shift and multiple-transient-feature fusion method in this paper. To remove the fundamental component (FC) that contained in the transient zero-sequence current (TZSC), Hilbert transform was used to calculate the analytic signal of TZSC. Then, the shift factor was introduced to remove the FC such that all the transient features can be kept. Second, three typical transient features indexes were calculated. Then, the multiple evidence estimation method was used to fuse the transient features indexes. Finally, selected the feeder with the maximum fault degree as the faulty feeder. To verify the accuracy and extensive applicability of the proposed method, developed the sample radial distribution network model with different fault conditions, arc fault model, and the modified IEEE-34-node test system with power electronics generator and wind energy conversion systems, the proposed method shows good performance.

When a high-impedance fault (HIF) occurs in active distribution networks, the features of the arc current are weak and can be easily confused with the features of the capacitor switching current or load switching current. Meanwhile, fault currents are easily affected by the harmonics and noises of distributed generators, which may result in low detection accuracy for existing HIF detection methods. To accurately detect high-impedance arc faults, an HIF detection method based on the arc changing tendency and nonlinear least squares (NLSs) are proposed. In the proposed method, variational mode decomposition and windowed fast Fourier transform are first employed to extract current amplitudes. Then, NLSs method is introduced to quantitatively estimate the changing tendency of the current amplitudes. Finally, an HIF detection criterion is developed based on the sign of the coefficient in the nonlinear function. Both simulation results and field test results confirm the effectiveness and applicability of the proposed HIF detection method. The inspiration for the proposed method is drawn from the physical characteristics during the initial stage of HIFs. These characteristics remain unaffected by network operation conditions. Comparative analysis reveals that the proposed method offers higher detection accuracy and reliability compared to existing conventional methods.

A prototype vision chip has been designed that incorporates a 20 × 64 array of processing elements on a 31 μm pitch. Each processor element includes 14 bits of digital memory in addition to seven analogue registers. Digital operands include NOR and NOT with operations of diffusion, subtraction, inversion and squaring available in the analogue domain. The cells of the array can be configured as an asynchronous propagation network allowing operations such as flood filling to occur with times of ~1 μs across the array. Exploiting this feature allows the chip to recognise the difference between closed and open shapes at 30,000 frames per second. The chip is fabricated in 0.18 μm CMOS technology.