Multiple Line Outages Research Articles

Distributed Finite-Time Observer for Multiple Line Outages Detection in Power Systems

Distributed Finite-Time Observer for Multiple Line Outages Detection in Power Systems

The Most Frequent N-k Line Outages Occur in Motifs That Can Improve Contingency Selection

Multiple line outages that occur together show a variety of spatial patterns in the power transmission network. Some of these spatial patterns form network contingency motifs, which we define as the patterns of multiple outages that occur much more frequently than multiple outages chosen randomly from the network. We show that choosing N-k contingencies from these commonly occurring contingency motifs accounts for most of the probability of multiple initiating line outages. This result is demonstrated using historical outage data for two transmission systems. It enables N-k contingency lists that are much more efficient in accounting for the likely multiple initiating outages than exhaustive listing or random selection. The N-k contingency lists constructed from motifs can improve risk estimation in cascading outage simulations and help to confirm utility contingency selection.

Optimal Sizing of Movable Energy Resources for Enhanced Resilience in Distribution Systems: A Techno-Economic Analysis

This article introduces a techno-economic analysis aimed at identifying the optimal total size of movable energy resources (MERs) to enhance the resilience of electric power supply. The core focus of this approach is to determine the total size of MERs required within the distribution network to expedite restoration after extreme events. Leveraging distribution line fragility curves, the proposed methodology generates numerous line outage scenarios, with scenario reduction techniques employed to minimize computational burden. For each reduced multiple line outage scenario, a systematic reconfiguration of the distribution network, represented as a graph, is executed using tie-switches within the system. To evaluate each locational combination of MERs for a specific number of these resources, the expected load curtailment (ELC) is calculated by summing the load curtailment within microgrids formed due to multiple line outages. This process is repeated for all possible locational combinations of MERs to determine minimal ELC for each MER total size. For every MER total size, the minimal ELCs are determined. Finally, a techno-economic analysis is performed using power outage cost and investment cost of MERs to pinpoint an optimal total size of MERs for the distribution system. To demonstrate the effectiveness of the proposed approach, case studies are conducted on the 33-node and the modified IEEE 123-node distribution test systems.

A graph theory and coalitional game theory-based pre-positioning of movable energy resources for enhanced distribution system resilience

This paper proposes an approach based on graph theory and coalitional game theory for pre-positioning of movable energy resources (MERs) to improve the resilience of the electric power supply. By utilizing the weather forecasting and monitoring data, the proposed approach determines staggering locations of MERs in order to ensure the quickest possible response following an extreme event. The proposed approach starts by generating multiple line outage scenarios based on fragility curves of distribution lines, where the fuzzy k-means method is used to create a set of reduced line outage scenarios. The distribution network is modeled as a graph and distribution network reconfiguration is performed for each reduced line outage scenario. The expected load curtailment (ELC) corresponding to each location is calculated using the amount of curtailed load and probability of each reduced scenario. The optimal route to reach each location and its distance is determined using Dijkstra’s shortest path algorithm. The MER deployment cost function associated to each location is determined based on the ELC and the optimal distance. The MER deployment cost functions are used to determine candidate locations for MER pre-positioning. Finally, the Shapley value, a solution concept of coalitional game theory, is used to determine the sizes of MERs at each candidate location. The proposed approach for pre-positioning of MERs is validated through case studies performed on a 33-node and a modified IEEE 123-node distribution test systems.

Multiple Line Outage Detection for Power Systems Based on Binary Matching Pursuit

Line outage detection plays a key role in black-out prevention and recovery in power systems. Considering the number of outage lines is sparse as compared to total transmission lines in the early line outage stage, compressive sensing theory is applied to study the multiple line outage detection of power systems. By formulating the sparse line outage detection problem in a standard compressive sensing form, a binary matching pursuit algorithm is proposed to solve the problem. A dice coefficient and an atom exclusion strategy are subsequently introduced to improve the detection accuracy of the proposed algorithm. Algorithm complexity has also been analyzed. Simulation results demonstrate that the proposed algorithm not only has more resilience to noise and less sensitivity to the data sampling size, but also shows lower computational burden and higher detection efficiency.

Adaptive Distributed Graph Model for Multiple-Line Outage Identification in Large-Scale Power System

Adaptive Distributed Graph Model for Multiple-Line Outage Identification in Large-Scale Power System

A stochastic distributed control approach for load restoration of networked microgrids with mobile energy storage systems

In response to extreme events, substantial research efforts have focused on developing load restoration strategies for networked microgrids (MGs). However, existing distributed control approaches only consider independent time periods, which cannot capture the time-coupled flexibility of storage units. Additionally, mobile energy storage systems (MESSs) have been deployed for resilience enhancement due to their advantages in mobility and flexibility. However, existing research on networked MGs utilizes simplistic energy management approaches for MG modeling without detailed network structures, which cannot capture the mobility of MESSs. To address these issues, this paper proposes a three-stage stochastic distributed control approach based on rolling optimization to enhance the resilience of networked MGs with MESSs. Specifically, a stochastic linearized OPF is formulated in the first stage to capture the flexibility of MESSs and uncertainties, while a consensus algorithm is utilized in the second stage to calculate the power exchange among MGs. Finally, a detailed non-linear AC OPF algorithm is employed in the third stage to capture technical constraints relating to stability properties towards accurate optimization results. Case studies considering load distinction, multiple line outages, and limited generation resources are developed to demonstrate the effectiveness of the proposed distributed control approach in accurate and timely decision-making.

Multiple Line Outage Detection in Power Systems by Sparse Recovery Using Transient Data

Fast and accurate transmission line outage detection can help the central control unit to respond rapidly to better maintain the security and reliability of power systems. It is especially critical in the situation of multiple line outages which is more likely to trigger cascading failures. In this article, we investigate the problem of multiple line outage detection through sparse representation based on the transient dynamics captured by Phasor Measurement Units (PMUs). An accumulation-based method is proposed to reduce the noise interference. By introducing an adaptive threshold selection rule, a modified sparse signal recovery algorithm is proposed to improve the performance of multiple line outage detection. Furthermore, an event-triggered mechanism is presented to reduce the computation burden. Finally, numerical experiments based on the IEEE Standard Test Bus System are conducted to illustrate the effectiveness of our method.

Machine learning methods for power line outage identification

As Phasor Measurement Units (PMUs) become widely deployed, power systems can take advantage of the large amount of data provided by PMUs and leverage the advances in big data analytics to improve real-time monitoring and diagnosis. In this paper, we develop practical analytics that are not tightly coupled with the power flow analysis and state estimation, as these tasks require detailed and accurate information about the power system. We focus on power line outage identification, and use a machine learning framework to locate line outages. The same framework is used for the prediction of both single line and multiple line outages. We investigate a range of machine learning algorithms and feature extraction methods. The algorithms are designed to capture the essential dynamic characteristics of the power system when the topology change occurs abruptly. The proposed methods use only voltage phasor angles obtained by continuously monitoring the buses. We tested the proposed methods on their prediction performance under different levels of noise and missingness. It is shown that the proposed methods have better tolerance for noisy data and incomplete data when compared to the previous work that involves solving power flow equations or state estimation equations.

A Markovian Influence Graph Formed From Utility Line Outage Data to Mitigate Large Cascades

We use observed transmission line outage data to make a Markovian influence graph that describes the probabili- ties of transitions between generations of cascading line outages. Each generation of a cascade consists of a single line outage or multiple line outages. The new influence graph defines a Markov chain and generalizes previous influence graphs by including multiple line outages as Markov chain states. The generalized influence graph can reproduce the distribution of cascade size in the utility data. In particular, it can estimate the probabilities of small, medium and large cascades. The influence graph has the key advantage of allowing the effect of mitigations to be analyzed and readily tested, which is not available from the observed data. We exploit the asymptotic properties of the Markov chain to find the lines most involved in large cascades and show how upgrades to these critical lines can reduce the probability of large cascades.

Tractable Stochastic Unit Commitment for Large Systems During Predictable Hazards

Many foreseeable natural hazards, including extreme weather events, lead to an outage of multiple transmission lines. Although such outages can be predicted in advance, there is a great deal of uncertainty in these predictions. To appropriately use the failure estimations in power system scheduling, this paper formulates a stochastic unit commitment (SUC) problem with explicit modeling of the predicted outages. The formulated problem, however, is extremely computationally-demanding, as the uncertainty is placed on the binary status of transmission lines. This paper, then, develops a computationally efficient algorithm to solve the formulated SUC for large-scale systems. The algorithm employs generation shift factors to enable rapid calculation of power flows. Additionally, flow canceling transactions are used to model multiple line outages without having to recalculate shift factors. Finally, critical constraints are iteratively detected and added to the problem. This approach substantially reduces the size of the problem, which helps computational tractability. The effectiveness of the developed algorithm is demonstrated through simulation studies on the Texas 2000-bus test system. The algorithm is used to minimize the lost load during a hypothetical hurricane. The results show that the algorithm is computationally tractable and can effectively identify a preventive dispatch, leading to a substantial reduction in power outages.

Multiple Line-Outage Detection in Power System With Load Stochastic Perturbations

In this brief, we consider multiple line-outage detection problem based on dynamic model of the power system subjects to load stochastic perturbations. A novel detection algorithm is proposed using data obtained from phase measurement units (PMUs). In order to reduce the computation burden, the algorithm is further improved by means of introducing the original topology of power system. Moreover, we propose a predictive algorithm to solve the sampling interval problem of data acquisition. Finally, simulation results are given to demonstrate the applicability of the proposed algorithm.

PMU based line outage identification using comparison of current phasor measurement technique

In this paper, a novel algorithm for identification of multiple line outage based on comparison of current phasor measurement (CCPM) technique is presented. A least square norm minimization model has been developed considering bad data due to faulty phasor measurement unit (PMU). In this proposed algorithm, the line current phasors obtained from load flow simulation for several outage cases are stored. On occurrence of actual outage, PMU provided current phasors are compared with the stored simulated current phasors using least square norm minimization approach. Moreover, random Gaussian noise with zero mean and standard deviation from 1% to 5% is introduced in the proposed model to check the feasibility in real power network. The performance of the proposed algorithm is evaluated by introducing two novel indices i.e., estimation accuracy including critical cases (EAICC) and estimation accuracy excluding critical cases (EAECC). Moreover, another new performance index i.e., success rate with repeated trial (SRWRT) is proposed to verify the noise independency of the test results with repeated number of trials. Performance of the algorithm is tested on IEEE 5-bus, 14-bus, 30-bus, 57-bus, and 118-bus systems. Numerical results show the reliability and viability of the proposed methodology for identification of multiple line outages.

A Sensitivity-Based Approach for the Detection of Multiple-Line Outages Using Phasor Measurements

A new approach for identifying multiple line outages in electric power systems by using data obtained from synchrophasor measurements is proposed in this paper. A novelty of the proposal is the application of the sensitivity theory to the system's alternating current power flow model to determine the most likely contingency scenario, with not previous knowledge of the number of line outages. Since the proposal considers both voltage and current synchrophasor measurements, a set of observable lines is defined such that their outages are identified directly from those measurements. In this context, the more observed transmission lines there are, the smaller the search space for identifying potential outages in unobserved transmission lines. On the other hand, the detection problem of multiple-line outages is decomposed into the sequential uncovering of different single-line outages by using the concept of total derivative. The reduction of the search space and the decomposition of the multiple-line outages problem significantly reduce the computational burden of the proposed approach. Finally, numerical results demonstrate that the proposal presents a very satisfactory percentage of success for identifying the correct set of line outages, even when a limited number of PMUs is embedded in the system.

Compressive System Identification for Multiple Line Outage Detection in Smart Grids

Real-time power line outage detection (POD) and localization is an important monitoring task for the modern smart grid. Reliable monitoring of power lines status plays a critical role in the system-wide blackout prevention. In this paper, the main aim is to address the multiple POD problems by exploiting the compressive system identification—a time-efficient approach in a complex network analysis. A typical power network is considered as a single graph, and the mathematical formulation of the POD problem is initialized using the dc power-flow model and graph theory concepts. Next, a sparse representation-based formulation for this problem (POD-SRP) is reported and further improved and generalized in case of multiple large-scale outages. Practical and technical challenges associated with this sparse recovery problem are partially addressed by developing new SRP solvers. Furthermore, a new sparse-based mathematical formulation for POD is introduced and termed as “Binary-POD-SRP,” which specifically deals with two particular issues, namely, the high coherence and the signal dynamic outrange. Finally, the identification performance of the proposed framework is evaluated by a variety of case studies, which are modeled using IEEE standard test-beds. We specifically discuss how the inherent challenges within large-scale multiple-outages can be solved by applying these new techniques and formulations.

Phasor Measuring Unit (PMU) Placement Tested with Multiple Line Outages for Power System Data Communications

Phasor Measuring Unit (PMU) Placement Tested with Multiple Line Outages for Power System Data Communications

Artificial neural network and phasor data‐based islanding detection in smart grid

Islanding is an unusual condition in a power system where the generating station continues to supply the local load after one or multiple transmission line outage. This study develops a new islanding detection technique using the artificial neural network (ANN) classifier, which is provided with synchronised phasor measurements from a nine-bus Western Electricity Coordinating Council power system. An excessive number of data frames are generated in the phasor data concentrator. Before sending these data to the classifier, multiplier-based method (MBM) and Andrews plot-based method (APBM) are applied for dimension reduction and feature extraction. Comparisons are prepared with other dimension reduction algorithms. The accuracy of the classifier has been increased by increasing the number of hidden layers, the best accuracy is observed at a certain level for APBM. Non-detection zone (NDZ) for APBM is also evaluated. It is observed that the classification accuracy, and the detection time change when the neural network is retrained. All the results are compared and analysed statistically. This method can perform faster compared to other existing algorithms with an excellent accuracy and smaller NDZ.

Multiple Power Line Outage Detection in Smart Grids: Probabilistic Bayesian Approach

Efficient power line outage identification is an important step which ensures reliable and smooth operation of smart grids. The problem of multiple line outage detection (MLOD) is formulated as a combinatorial optimization problem and known to be NP-hard. Such a problem is optimally solvable with the help of an exhaustive evaluation of all possible combinations of lines in outage. However, the size of search space is exponential with the number of power lines in the grid, which makes exhaustive search infeasible for practical sized smart grids. A number of published works on MLOD are limited to identify a small, constant number of lines outages, usually known to the algorithm in advanced. This paper applies the Bayesian approach to solve the MLOD problem in linear time. In particular, this paper proposes a low complexity estimation of outage detection algorithm, based on the classical estimation of distribution algorithm. Thanks to an efficient thresholding routine, the proposed solution avoids the premature convergence and is able to identify any arbitrary number (combination) of line outages. The proposed solution is validated against the IEEE-14 and 57 bus systems with several random line outage combinations. Two performance metrics, namely, success generation ratio and percentage improvement have been introduced in this paper, which quantify the accuracy as well as convergence speed of proposed solution. The comparison results demonstrate that the proposed solution is computationally efficient and outperforms a number of classical meta-heuristics.

Block-Wise Compressive Sensing Based Multiple Line Outage Detection for Smart Grid

Smart grids, which have the ability to detect and monitor necessary system parameters and user behaviors, have gradually become the development trend for future power networks. With increasing scale and access of new energy source, SGs become more unstable and vulnerable when changes of network topology occur abruptly. Among different kinds of abrupt changes, unexpected line outages may give rise to significant potential damages to SGs and lead to the very bad user experience. Thus, accurate and rapid detection of line outages turn into one of the important tasks and challenges in SGs. By utilizing the sparse property of line outages, compressive sensing (CS)-based line outage detection schemes have been proposed recently, in which the phase information collected from phasor measurement units is also fully utilized. In this paper, a novel block-wise CS (BW-CS)-based multiple line outage detection scheme is proposed for SGs. Firstly, by exploiting the sparsity of line outages and topology structure of the power grid, a novel reactance model is introduced, which makes the block-wise CS algorithm can be adopted. Then, by modifying one step of the conventional CS algorithm with the redefined element selection, criterion is modified to improve the recovery accuracy. Finally, the proposed scheme is extended into a three-phase power system. The simulation results show that our proposed BW-CS method can achieve superior precision with the similar complexity compared to the traditional methods.

A Spectral Approach for the Efficient Identification of Power Transmission Network Uncontrolled Separation

Uncontrolled network separation (system island formation) is one of the most critical contingencies in power systems. The integrity of the whole power transmission network is a prerequisite for reliable system operation. Therefore, in order to safeguard the system operation and control, it is imperative to quickly identify the topological changes, especially the formation of system islands. In this paper, we developed a spectral clustering-based approach to efficiently detect the existing or the potential network islands. The core of this approach is a graph-algebraic model, which combines the real-power deliverability and topological information of a power transmission network. Based on an improved spectral clustering algorithm, the proposed approach can efficiently identify the critical situations of the network splitting under multiple line outages. This approach has been successfully tested by using the New England 39-bus and 118-bus systems under different scenarios.