As a result of the increase in penetration of inverter-based generation such as wind and solar, the dynamics of the grid are being modified. These modifications may threaten the stability of the power system since the dynamics of these devices are completely different from those of rotating generators. Protection schemes need to evolve with the changes in the grid to successfully deliver their objectives of maintaining safe and reliable grid operations. This paper explores the theory of traveling waves and how they can be used to enable fast protection mechanisms. It surveys a list of signal processing methods to extract information on power system signals following a disturbance. The paper also presents a literature review of traveling wave-based protection methods at the transmission and distribution levels of the grid and for AC and DC configurations. The paper then discusses simulations tools to help design and implement protection schemes. A discussion of the anticipated evolution of protection mechanisms with the challenges facing the grid is also presented.

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A Survey of Traveling Wave Protection Schemes in Electric Power Systems

An efficient fault diagnostic approach for active distribution networks considering adaptive detection thresholds

To meet the requirements of quickness and accuracy for fault location in smart distribution networks with high penetration of distributed generations, we propose an integrated fault section location method based on multiple timescales. On the millisecond timescale, we present a distributed fault section location method based on fault current polarity value comparison (PVC) using the first half-cycle fault current signal after the fault inception. On the cycle timescale, we implement a distributed fault section location method based on fault current waveform similarity comparison (WSC) using the first one-cycle fault current signal after the fault inception. On the second timescale, we adopt a fault section location method based on a genetic algorithm (GA) using the steady-state fault current signal. Three timescale fault section location methods thus coordinate with each other through constructed reliability functions of the fault section location methods based on PVC and WSC. The simulation results show that the multi-timescale-based fault section location method achieves a fast and accurate fault section location.

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Multi-Timescale-Based Fault Section Location in Distribution Networks

Vibration signal analysis is an efficient online transformer fault diagnosis method for improving the stability and safety of power systems. Operation in harsh interference environments and the lack of fault samples are the most challenging aspects of transformer fault diagnosis. High-precision performance is difficult to achieve when using conventional fault diagnosis methods. Thus, this study proposes a transformer fault diagnosis method based on the adaptive transfer learning of a two-stream densely connected residual shrinkage network over vibration signals. First, novel time-frequency analysis methods (i.e., Synchrosqueezed Wavelet Transform and Synchrosqueezed Generalized S-transform) are proposed to convert vibration signals into different images, effectively expanding the samples and extracting effective features of signals. Second, a Two-stream Densely Connected Residual Shrinkage (TSDen2NetRS) network is presented to achieve a high accuracy fault diagnosis under different working conditions. Furthermore, the Residual Shrinkage layer (RS layer) is applied as a nonlinear transformation layer to the deep learning framework to remove unimportant features and enhance anti-interference performance. Lastly, an adaptive transfer learning algorithm that can automatically select the source data set by using the domain measurement method is proposed. This algorithm accelerates the training of the deep learning network and improves accuracy when the number of samples is small. Vibration experiments of transformers are conducted under different operating conditions, and their results show the effectiveness and robustness of the proposed method.

Adaptive Transfer Learning Based on a Two-Stream Densely Connected Residual Shrinkage Network for Transformer Fault Diagnosis over Vibration Signals

Aiming at the problem of the inaccurate fault location in high-permeability active distribution network, a novel method of fault location for active distribution network based on improved variational mode decomposition (IVMD) and S-transform is proposed in this paper Firstly, the judgment method of instantaneous frequency is adopted to determine the value of decomposition layers $k$  Meanwhile the algorithm of variational mode decomposition (VMD) is utilized to decompose the fault signal of traveling wave Then, the kurtosis criterion is employed to select the appropriate intrisic mode functions (IMFS) for S-transform in order to obtain the S-matrix with higher resolution The high-frequency components in the matrix are extracted to determine the arrival time for the head of the fault traveling wave Finally, the fault distance can be calculated by the special distance formula without considering the velocity of the traveling wave The simulations show that the proposed method can accurately measure the fault distance for the transmission lines in high permeability active distribution network, and the measurement error is limited within 200m, which verifies the accuracy and effectiveness of the proposed algorithm

/pdf/novel-fault-location-for-high-permeability-active-2rnv9ej606.pdf

Shasha Xie

Papers

Novel Fault Location for High Permeability Active Distribution Networks Based on Improved VMD and S-transform

The pattern recognition of multisource partial discharge in transformers based on parallel feature domain