Power Grid Graph Research Articles

Locating the source of forced oscillations on the basis of Lyapunov modal analysis

Based on the recently proposed Lyapunov modal analysis framework, this study proposes a method for locating the source of forced oscillation in power system when its frequency is close to the natural oscillation of the system. This method combines the selective modal analysis and spectral decomposition of Lyapunov functions, which characterize the energy of voltage perturbation. The proposed modal indicators characterize the energy of voltage disturbances accumulated from the forced oscillation in the nodes. Based on the corresponding spectral decomposition, the part corresponding to the resonance between the forced oscillation and the natural oscillation is subtracted from this energy. “Non-resonant” part of the index calculated in each node of the network allows visualizing the profile of “non-resonant” energy on the power grid graph. Based on the obtained profile it is possible to solve the inverse problem and determine the location of the source of forced oscillations. In a test experiment with IEEE 68 bus system, forced oscillations at a frequency close to the system’s natural oscillation were introduced in different nodes of the system. Profiles of the “non-resonant” part of the energy are obtained on the network graph, from which the location of the source of the forced oscillation can be determined. The profile of the proposed modal indicator on the network graph can be obtained either from a known model of the system or from the measured data.

Accurate querying of frequent subgraphs in power grid graph data

With the development of information technology, the amount of power grid topology data has gradually increased. Therefore, accurate querying of this data has become particularly important. Several researchers have chosen different indexing methods in the filtering stage to obtain more optimized query results because currently there is no uniform and efficient indexing mechanism that achieves good query results. In the traditional algorithm, the hash table for index storage is prone to “collision” problems, which decrease the index construction efficiency. Aiming at the problem of quick index entry, based on the construction of frequent subgraph indexes, a method of serialized storage optimization based on multiple hash tables is proposed. This method mainly uses the exploration sequence to make the keywords evenly distributed; it avoids conflicts of the stored procedure and performs a quick search of the index. The proposed algorithm mainly adopts the “filter-verify” mechanism; in the filtering stage, the index is first established offline, and then the frequent subgraphs are found using the “contains logic” rule to obtain the candidate set. Experimental results show that this method can reduce the time and scale of candidate set generation and improve query efficiency.

A Distributed Spatiotemporal Contingency Analysis for the Lebanese Power Grid

We address a topological vulnerability analysis of the Lebanese power grid subject to random and cascading failures. Using an Apache Spark implementation that maps the topology of the grid to a complex network, we begin by developing a local structural understanding of the Lebanese power grid that reveals a certain level of decentralization via numerous connected components. Our Apache Spark implementation simulates the random and cascading sequences of events by which energy centers in Lebanon can be exposed and are at risk. The implementation is based on the bulk-synchronous parallel model and maintains optimal work, linear communication time, and a constant number of synchronization barriers. We complement this paper with a spatial understanding of the exposed hotspots. Our results reveal that failures in the power grid are spatially long-range correlated and correlations decay with distance. In a couple of attack scenarios, our Spark implementation achieves significant speedup on 16 cores for a graph with about $9\times 10^{5}$ nodes. Scalability toward 32 nodes improves when experimenting with replicas of the power grid graph which are double and quadruple the original size. This renders this paper suitable to larger networks at many vital levels beyond the power grid.

Design and Implementation of Power Grid Graph Data Management Platform Based on Distributed Storage

With the construction of smart grids, the number of power device has increased rapidly, and various types of services in power grid management have been interconnected and refined. This has made how to store and effectively manage the massive data generated in power grid information management has become a bottleneck. This paper designs and implements a power grid graph data management platform based on distributed database HBase. Experiments on power grid topology data show that the graph data management platform proposed in this paper only takes nearly 80% of the time based on the graph database Neo4j when the scale is large enough, which can realize the storage and analysis of large-scale power grid topology data, and meet the needs of power grid informationization.

Two-stage controlled islanding strategy based on Stoer-Wagner and improved Dinic algorithms

The controlled islanding for the power system is an effective method to deal with the emergent situations caused by large disturbances. The size of the solution space would increase exponentially as the scale of the power grid increases. The goal of our controlled islanding strategy is to divide the system into several islands quickly. Meanwhile, the generator coherency and the power-flow disruption have to be taken into consideration carefully. This paper proposed a two-stage fast islanding strategy for large power networks, which is on the basis of large power grid graph theories. In the first stage, the Stoer-Wagner algorithm is employed to obtain the grouping cluster of coherent generators in the dynamic undirected liaison graph. In the second stage, the improved Dinic max-flow method is proposed to search the optimal splitting boundary so as to acquire the minimum power flow impact. Our two-stage islanding strategy does not need to reduce the whole power network. Simulations on IEEE 118-bus and 162-bus power systems showed that the proposed strategy can acquire high quality solutions effectively and efficiently.

Statistical Structure Learning to Ensure Data Integrity in Smart Grid

Robust control and management of the grid relies on accurate data. Both phasor measurement units and remote terminal units are prone to false data injection attacks. Thus, it is crucial to have a mechanism for fast and accurate detection of tampered data—both for preventing attacks that may lead to blackouts, and for routine monitoring and control of current and future grids. We propose a decentralized false data injection detection scheme based on the Markov graph of the bus phase angles. We utilize the conditional covariance test CMIT to learn the structure of the grid. Using the dc power flow model, we show that, under normal circumstances, the Markov graph of the voltage angles is consistent with the power grid graph. Therefore, a discrepancy between the calculated Markov graph and learned structure should trigger the alarm. Our method can detect the most recent stealthy deception attack on the power grid that assumes knowledge of the bus-branch model of the system and is capable of deceiving the state estimator; hence damaging power network control, monitoring, demand response, and pricing scheme. Specifically, under the stealthy deception attack, the Markov graph of phase angles changes. In addition to detecting a state of attack, our method can detect the set of attacked nodes. To the best of our knowledge, our remedy is the first to comprehensively detect this sophisticated attack and it does not need additional hardware. Moreover, it is successful no matter the size of the attacked subset. Simulation of various power networks confirms our claims.