A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification

Abstract

Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem. This is because the number of hypotheses grows exponentially with the network size. A new "Learning-to-Infer" method is developed for efficient inference of every line status in the network. Optimizing the line outage detector is transformed to and solved as a discriminative learning problem based on Monte Carlo samples generated with power flow simulations. A major advantage of the developed Learningto-Infer method is that the labeled data used for training can be generated in an arbitrarily large amount rapidly and at very little cost. As a result, the power of offline training is fully exploited to learn very complex classifiers for effective real-time multiline outage identification. The proposed methods are evaluated in the IEEE 30, 118 and 300 bus systems. Excellent performance in identifying multi-line outages in real time is achieved with a reasonably small amount of data.

Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

“…Machine learning has found numerous applications in power systems. It has been used for designing demand response programs [88], consumer behavior modeling [89], fault location detection [90,91] and protection [92], cybersecurity [93], electricity price forecasting [94], demand prediction [95], renewable energy generation forecasting [96,97], transient stability assessment [98], voltage control [79,99], bad data detection [100], energy theft detection [101], grid topology identification [102], outage identification [103], microgrid energy management [104], emergency management [105], power flow estimation [106], optimal power flow prediction [107], unit commitment [108], state estimation [109], reliability management [110], event classification [111], power fluctuation identification [112], energy disaggregation [113], and power quality disturbance classification [114]. However, most of the presented works use a centralized learning framework, and, despite these accomplishments, research on distributed learning architectures in power systems remains very limited.…”

Estimation of the Admittance Matrix in Power Systems Under Laplacian and Physical Constraints

“…In particular, topology identification and the estimation of the topologies of distribution grids have been discussed in [33]- [38]. Other works explored the detection of topological changes and the identification of edge disconnections in electrical networks using hypothesis testing methods (see [39], [40] and [41], [42], respectively). The methods in [43]- [46] can reveal part of the grid information, such as the grid connectivity and the eigenvectors of the topology matrix.…”

Application of Physics-Informed Machine Learning Techniques for Power Grid Parameter Estimation

“…With growing interest in machine learning algorithms and the availability of fine-grained data due to PMU deployment, data-driven algorithms have been used for power system topology identification in transmission [4,5] and distribution networks [6,7]. More recently, data-driven algorithms using PMU measurements have also been applied in novel applications such as line-outage identification [8,9] and power grid security [10,11]. Algorithms based on steady-state measurements are applicable when the measurement time window is sufficiently large.…”