Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee

Line outage identification in distribution grids is essential for sustainable grid operation. In this work, we propose a practical yet robust detection approach that utilizes only readily available voltage magnitudes, eliminating the need for costly phase angles or power flow data. Given the sensor data, many existing detection methods based on change-point detection require prior knowledge of outage patterns, which are unknown for real-world outage scenarios. To remove this impractical requirement, we propose a data-driven method to learn the parameters of the post-outage distribution through gradient descent. However, directly using gradient descent presents feasibility issues. To address this, we modify our approach by adding a Bregman divergence constraint to control the trajectory of the parameter updates, which eliminates the feasibility problems. As timely operation is the key nowadays, we prove that the optimal parameters can be learned with convergence guarantees via leveraging the statistical and physical properties of voltage data. We evaluate our approach using many representative distribution grids and real load profiles with 17 outage configurations. The results show that we can detect and localize the outage in a timely manner with only voltage magnitudes and without assuming a prior knowledge of outage patterns.Comment: 12 page

Distribution Grid Line Outage Detection with Privacy Data

Change point detection is important for many real-world applications. While sensor readings enable line outage identification, they bring privacy concerns by allowing an adversary to divulge sensitive information such as household occupancy and economic status. In this paper, to preserve privacy, we develop a decentralized randomizing scheme to ensure no direct exposure of each user's raw data. Brought by the randomizing scheme, the trade-off between privacy gain and degradation of change point detection performance is quantified via studying the differential privacy framework and the Kullback-Leibler divergence. Furthermore, we propose a novel statistic to mitigate the impact of randomness, making our detection procedure both privacy-preserving and have optimal performance. The results of comprehensive experiments show that our proposed framework can effectively find the outage with privacy guarantees.Comment: 5 page

Data-Efficient Minimax Quickest Change Detection with Composite Post-Change Distribution

The problem of quickest change detection is studied, where there is an additional constraint on the cost of observations used before the change point and where the post-change distribution is composite. Minimax formulations are proposed for this problem. It is assumed that the post-change family of distributions has a member which is least favorable in some sense. An algorithm is proposed in which on-off observation control is employed using the least favorable distribution, and a generalized likelihood ratio based approach is used for change detection. Under the additional condition that either the post-change family of distributions is finite, or both the pre- and post-change distributions belong to a one parameter exponential family, it is shown that the proposed algorithm is asymptotically optimal, uniformly for all possible post-change distributions.Comment: Submitted to IEEE Transactions on Info. Theory, Oct 2014. Preliminary version presented at ISIT 2014 at Honolulu, Hawai

Distributed Power-Line Outage Detection Based on Wide Area Measurement System

In modern power grids, the fast and reliable detection of power-line outages is an important functionality, which prevents cascading failures and facilitates an accurate state estimation to monitor the real-time conditions of the grids. However, most of the existing approaches for outage detection suffer from two drawbacks, namely: (i) high computational complexity; and (ii) relying on a centralized means of implementation. The high computational complexity limits the practical usage of outage detection only for the case of single-line or double-line outages. Meanwhile, the centralized means of implementation raises security and privacy issues. Considering these drawbacks, the present paper proposes a distributed framework, which carries out in-network information processing and only shares estimates on boundaries with the neighboring control areas. This novel framework relies on a convex-relaxed formulation of the line outage detection problem and leverages the alternating direction method of multipliers (ADMM) for its distributed solution. The proposed framework invokes a low computational complexity, requiring only linear and simple matrix-vector operations. We also extend this framework to incorporate the sparse property of the measurement matrix and employ the LSQRalgorithm to enable a warm start, which further accelerates the algorithm. Analysis and simulation tests validate the correctness and effectiveness of the proposed approaches