Timely and accurate fault location techniques can reduce power outages and improve power supply reliability. This paper proposes a new fault location method for active distribution networks (ADNs) which utilizes limited synchronized measurements (PMUs). The proposed method formulates the fault location problem as the estimation of a block-sparse fault injection current signal which can inherently reveal fault types and locations. A modified block-sparse Bayesian learning (BSBL) algorithm is utilized to estimate this block-sparse signal by exploiting its both block structure and intra-block correlation. In addition, the proposed method provides a strategy for the optimal placement of much fewer PMUs that will not yield a complete observable system. The proposed method is able to pinpoint the fault position accurately, and is applicable to both balanced and unbalanced ADNs. The effectiveness of the proposed method is examined on the IEEE 123-node distribution networks with DGs and the results are discussed thoroughly.

In this paper, a distance relay-based wide-area backup protection (WABP) algorithm for transmission lines is presented. The proposed WABP collects the statuses of zone 2 and zone 3 distance relays in local substation and immediate neighboring substations. When a fault has occurred, the WABP decides whether the fault is on the protected transmission line or somewhere else. Then, it determines the appropriate time delay of tripping and acts in coordination with conventional distance relays. With the proposed WABP equipped, the setting of conventional distance relays can be simplified. The requirement of coordination between the conventional relays can be loosened and the difficulty of setting calculation can be reduced. Simulation results show that the proposed WABP can coordinate with conventional distance relays set by different methods, and it can correctly detect faults and help to reduce blackout area with fault tolerance.

To recognize errors in the power equipment defect records in real time, we propose an error recognition method based on knowledge graph technology. According to the characteristics of power equipment...

The effective identifications of important nodes in power grid are foundations of differentiated management and stable operation of power grid. However, the current studies on this field are not thorough enough. This paper applies the model based on co-citation (MBCC)-hypertext induced topic selection (HITS) algorithm used in web page ranking to power grid and proposes an index to determine node importance of power grid from the perspectives of inflow and outflow power. MBCC-HITS algorithm is modified considering load, power source, and line flow comprehensively for the differences between Internet and power grid in this paper. Then a method for evaluating node importance is proposed using the modified algorithm. Lastly, the rationality and validity of the proposed method are verified by comparisons with other methods in case studies of IEEE 14-bus and 118-bus systems.

The rapid proliferation of distributed generation (DG) in active distribution networks (ADNs) increases the power system modeling complexity, which requires more efficient numerical methods to analyze dynamic interactions among DGs in ADN. This paper proposes a reduced-order dynamic phasor (DP) modeling method which is applied to large-scale ADNs with DGs for reducing the modeling scale and allowing larger time steps in ADN analyses. First, a unified state-space DP model, referred to as full-order DP model for linear circuits, is developed on the abc-axis to provide a comprehensive technique for interfacing DGs with other electric devices in large ADNs. The model order reduction (MOR) is applied to this full-order model to reduce the computation burden for large-scale ADNs. The paper develops a state-space DP model on positive and negative sequence synchronous rotating reference frames for three-phase DGs, and a stationary frame for single-phase DGs. The accuracy and the efficiency of the proposed models are verified using the modified IEEE 123-node test feeder and IEEE European low voltage test feeder with DGs.

To detect the categories and positions of various transformer components in inspection images automatically, this paper proposes a transformer component detection model with high detection accuracy, based on the structure of Faster R-CNN. In consideration of the significant difference in component sizes, double feature maps are used to adapt to the size change, by adjusting two weights dynamically according to the object size. Moreover, different from the detection of ordinary objects, there is abundant useful information contained in the relative positions between components. Thus, the relative position features are defined and introduced to the refinement of the detection results. Then, the training process and detection process are proposed specifically for the improved model. Finally, an experiment is given to compare the accuracy and efficiency of the improved model and the original Faster R-CNN, along with other object detection models. Results show that the improved model has an obvious advantage in accuracy, and the efficiency is significantly higher than that of manual detection, which suggests that the model is suitable for practical engineering applications.