Line Outage Identification Research Articles

A Secure Satellite-Edge Computing Framework for Collaborative Line Outage Identification in Smart Grid

A Secure Satellite-Edge Computing Framework for Collaborative Line Outage Identification in Smart Grid

Distribution Grid Line Outage Identification With Unknown Pattern and Performance Guarantee

Distribution Grid Line Outage Identification With Unknown Pattern and Performance Guarantee

Wide-Area Line Outage Monitoring by Sparse Phasor Measurements

Timely identification of transmission line outages is key to situational awareness and preventing the propagation of disturbed system conditions. Although a line may get disconnected from both ends simultaneously, sequential tripping of the opposite ends of the line is more common regardless of whether the outage is planned or administered by protective relays. This paper proposes a method for line outage monitoring and identifying the sequence of events that a line is undergoing before getting disconnected from both ends. Using the bus impedance matrix, a transfer function is derived to relate the line outage to the variations of phasors collected in the control center. A closed-form solution is put forward to identify the disconnected line based on the weighted sum of squared residuals concept. The proposed method does not require the network to be fully observable, nor does it count on the reception of any fixed set of measurements. As opposed to existing solutions, the method does not rely on power flow derivations, a feature that highly reduces its decision time. High speed, along with the robustness against partial communication network failures and losses of the time synchronization signal, makes the method suitable for real-time applications. Extensive simulations conducted on the IEEE 39-bus and 118-bus test systems demonstrate the superior performance of the proposed method compared to existing ones.

Quick line outage identification in urban distribution grids via smart meters

The growing integration of distributed energy resources (DERs) in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors. With large-scale DER penetration in distribution grids, traditional outage detection methods, which rely on customers report and smart meters' “last gasp” signals, will have poor performance, because renewable generators and storage and the mesh structure in urban distribution grids can continue supplying power after line outages. To address these challenges, we propose a data-driven outage monitoring approach based on the stochastic time series analysis with a theoretical guarantee. Specifically, we prove via power flow analysis that dependency of time-series voltage measurements exhibits significant statistical changes after line outages. This makes the theory on optimal change-point detection suitable to identify line outages. However, existing change point detection methods require post-outage voltage distribution, which are unknown in distribution systems. Therefore, we design a maximum likelihood estimator to directly learn distribution pa-rameters from voltage data. We prove the estimated parameters-based detection also achieves optimal performance, making it extremely useful for fast distribution grid outage identifications. Furthermore, since smart meters have been widely installed in distribution grids and advanced infrastructure (e.g., PMU) has not widely been available, our approach only requires voltage magnitude for quick outage identification. Simulation results show highly accurate outage identification in eight distribution grids with 17 configurations with and without DERs using smart meter data.

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.

Line outage identification using comparison of bus power mismatch considering PMU outage

In this study, a novel algorithm for line outage identification is developed utilising the PMU provided phasor angles. For various outage cases, the simulated bus power mismatches (SBPMs) using bus susceptance matrix of the full network are to be stored. As soon as actual outage occurs, the bus power mismatches are computed using PMU provided pre outage and post outage phasor angles. Additionally, optimal PMU placement issue is also addressed to ensure the network observability in case of PMU outage. Moreover, random Gaussian noise with zero mean and standard deviation (SD) from 1% to 5% is incorporated in the developed model. Simulation was carried out on standard IEEE 5 bus, 14 bus, 30 bus, 57 bus, and 118 bus systems to check the viability and applicability of the proposed methodology. The obtained test results are found to be competitive with the existing methods.

Line outage identification using comparison of bus power mismatch considering PMU outage

In this study, a novel algorithm for line outage identification is developed utilising the PMU provided phasor angles. For various outage cases, the simulated bus power mismatches (SBPMs) using bus susceptance matrix of the full network are to be stored. As soon as actual outage occurs, the bus power mismatches are computed using PMU provided pre outage and post outage phasor angles. Additionally, optimal PMU placement issue is also addressed to ensure the network observability in case of PMU outage. Moreover, random Gaussian noise with zero mean and standard deviation (SD) from 1% to 5% is incorporated in the developed model. Simulation was carried out on standard IEEE 5 bus, 14 bus, 30 bus, 57 bus, and 118 bus systems to check the viability and applicability of the proposed methodology. The obtained test results are found to be competitive with the existing methods.

Distribution system state estimation with elegant PMU placements using a novel metaheuristic

In recent days, the expansion of the distribution network followed by network complexity and contingencies are noted as major issues in electric power utilities. At this juncture, there is not even a distinct constituent for handling such disturbances that occurs due to natural hazards which in turn causes a difficult situation for decision making tracked by redundant state estimation. Therefore, effective monitoring in distributed networks with phasor measuring unit (PMU) involving accurate placements and identification of numerous line outages is presented. Here, a technique using ant lion optimisation (ALO) through elite methodology (EALO) for recognising accurate PMU locations considering cascaded line outages is projected. To evaluate the effectiveness of the projected method standard IEEE systems, practical Indian utility system and polish large scale bus networks are instigated. The numerical result cares about the exact state of distribution topology in a way for improving the performance of the smart grid.

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.

AMI-Enabled Distribution Network Line Outage Identification via Multi-Label SVM

This letter proposes an effective data mining method for identifying distribution network line outages by leveraging data collected through Advanced Metering Infrastructure (AMI). The line outage identification method is developed based on a Multi-Label Support Vector Machine (ML-SVM) classification scheme that utilizes the status of customers’ smart meters as input data and accordingly identifies the outage/operational status of distribution lines. The $\text{F}_{\beta }$ -score is proposed to validate the performance of the classifier through numerical simulations.

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.

PMU Placement for Line Outage Identification via Multinomial Logistic Regression

We consider the problem of identifying a single line outage in a power grid by using data from phasor measurement units (PMUs). When a line outage occurs, the voltage phasor of each bus node changes in response to the change in network topology. Each individual line outage has a distinctive signature, and a multinomial logistic regression classifier can be trained to distinguish between these signatures reliably. We consider first the ideal case in which PMUs are attached to every bus. We then describe techniques from regularized optimization for placing PMUs selectively on a subset of buses, with the subset being chosen to allow discrimination between as many outage events as possible. Experimental results with synthetic 24-h demand profile data generated for IEEE 14-, 30-, 57-, and 118-bus systems are presented.

An insight to the performance of estimation of distribution algorithm for multiple line outage identification

Abstract Realtime information relating to line outages has significant importance to pre-empt against the power system blackouts. Realtime information can be obtained by using phasor measurement units (PMUs) facilitating the realtime synchronized observations of voltage and current phasors at buses being monitored. Different optimization formulations including but not limited to linear, integer, stochastic, mixed integer and NP hard combinatorial optimization have been used to manipulate these phasor measurements for the detection of line outages. Single and double line outages can be addressed using combinatorial optimization but these are infeasible to apply for the detection of multiple line outages as the increased number of lines increases computational complexity. To alleviate the exponentially increased complexities of these combinatorial optimization problems, while investigating for multiple line outage, evolutionary, Estimation of Distribution Algorithm is used. This method gives near optimal solution in which computational complexity and time is reduced efficiently. In this paper we scrutinize the use of phasor angle measurements to detect multiple power line outages. The proposed EDA is compared with binary particle swarm optimization (BPSO) algorithm, adaptive BPSO and genetic algorithm (GA) in terms of line outage detection performance, fitness convergence w.r.t. iterations and time consumption. The simulation results depict that the proposed EDA outperforms the other state of the art algorithms.

Sparse Sensing Platform for Line-Outage Identification in Multiarea Power Systems

The outage of power lines, which causes unscheduled topology change, needs to be identified as fast as possible so that appropriate remedial actions are carried out accordingly. In this paper, a novel method for phasor measurement units (PMU) placement for identification of line outages in power grids is proposed. The proposed method is inspired by compressed sensing theory and is formulated as a linear binary integer programming problem that determines optimal number and locations of PMUs that should be installed to assure observability of the monitoring system. In the proposed monitoring system, only a subset of buses of the network needs to be monitored by PMUs, which significantly reduces the implementation cost. The proposed sparse sensing platform is applicable for large-scale multiarea transmission lines as the observability of the monitoring system is guaranteed by only using the data collected from PMUs inside the area of ownership of the associated independent system operator (ISO). This characteristic of the proposed monitoring system makes it attractive for application in large-scale transmission systems with different ISOs that are not willing/reluctant to share data.

A new multiple line outage identification formulation using a sparse vector recovery technique

This paper develops new methods to estimate the locations of transmission line outages in transmission networks. Two transmission outage models are developed based on DC and AC power flow equations and injected power changes due to line outages. The models are then generalized to include other transmission lines in addition to the outaged lines to provide a full representation of transmission networks. Three methods are developed based on the DC and AC models: Method_1 uses the DC power flow model; Method_2 uses the AC power flow model with zero line resistance; Method_3 uses the AC power flow model with line losses taken into account. A regularized least square based on ℓ1-norm minimization is developed to solve the models and estimate the locations of outages. Simulation results of the IEEE 39-, 57- and 118-bus systems verify the effectiveness of the developed methods in detecting line outage locations.

Multiple line outages identification: A customized quantum inspired approach

Accurate and prompt identification of power line outages plays an important role in the reliability and robustness of smart power grids. Line outage identification problem has been traditionally formulated as a combinatorial optimization problem and requires an exhaustive search to find an optimal solution. However, this suffers from huge computational complexity as the size of the search space grows exponentially with the number of line outages. This work proposes a computationally efficient optimization technique based on Quantum Inspired Evolutionary Algorithm (QIEA) for the identification of multiple power line outages in smart power grids. In addition to the classical QIEA, a customized version of QIEA is also proposed and its effectiveness is validated against various IEEE bus systems with multiple line outages scenarios. The simulation results demonstrate the validity and accuracy of the proposed solution.

Efficient Location Identification of Multiple Line Outages With Limited PMUs in Smart Grids

The efficient location identification of multiple line outages is critical to not only cascading failure elimination but also repair cost reduction. Most of existing methods, however, fail to handle location identification well. This failure typically occurs because the methods cannot overcome two challenges: the very limited number of phasor measurement units (PMUs) and the high computational complexity. This paper presents an efficient algorithm inspired by the ambiguity group theory to identify the locations of line outages with limited PMUs. Using 14-, 57-, 118-, 300-, and 2383-bus systems, our experimental study demonstrates that the proposed technique successfully identifies the most likely multiple line outages while attaining a 500 × speedup when compared to the method of exhaustive search.

Location Identification of Power Line Outages Using PMU Measurements With Bad Data

The use of phasor angle measurements provided by phasor measurement units (PMUs) in fault detection is regarded as a promising method in identifying locations of power line outages. However, communication errors or system malfunctions may introduce errors to the measurements and thus yield bad data. Most of the existing methods on line outage identification fail to consider such error. This paper develops a framework for identifying multiple power line outages based on the PMUs' measurements in the presence of bad data. In particular, we design an algorithm to identify locations of line outage and recover the faulty measurements simultaneously. The proposed algorithm does not require any prior information on the number of line outages and the noise variance. Case studies carried out on test systems of different sizes validate the effectiveness and efficiency of the proposed approach.

Efficient Identification Method for Power Line Outages in the Smart Power Grid

This paper considers the use of phasor angle measurements provided by phasor measurement units to identify multiple power line outages. The problem of power line outage identification has traditionally been formulated as a combinatorial optimization problem, the optimal solution of which can be found through an exhaustive search. However, the size of the search space grows exponentially with the number of outages and may thus pose a potential problem for the practical implementation of an exhaustive search, especially when multiple power line outages are considered in a power system. To reduce the complexity while improving outage detection performance, we propose a novel global stochastic optimization technique based on cross-entropy optimization, which has been proven to be a powerful tool for many combinatorial optimization problems, to identify multiple line outages. To validate the effectiveness of the proposed approach, the algorithm is tested using IEEE 118- and 300-bus systems, as well as a Polish 2736-bus system. Simulation results demonstrate that the percentage of correctly identified line outages achieved by the proposed method outperforms those obtained by existing sparse signal recovery algorithms.

External system line outage identification using phasor measurement units

This paper investigates the problem of line outage detection in an area external to the operating area boundaries. The so-called “internal system” is assumed to be fully observable and the base case topology of the external system is assumed to be known. A limited number of phasor measurements are also assumed to be available from the external system. The paper takes advantage of the equivalent representation of a line outage by a pair of injections at the line's terminal buses and formulates the outage detection problem as an integer programming problem. Results are verified by simulating various external line outage scenarios using the IEEE 118-bus test system.