Power System Contingency Analysis Research Articles

Efficient contingency analysis of power systems using linear power flow with generalized warm-start compensation

Contingency analysis (CA) is fundamental to power system planning and operation. The compensation method and linear power flow (LPF) models are frequently used in CA to boost efficiency. However, the conventional compensation scheme fails to adapt to modern LPF models with novel structures. To bridge this gap, this paper proposes a generalized warm-start compensation algorithm (GWSCA) for LPFs. To efficiently calculate the inversion of a perturbed coefficient matrix in LPF models, a generalized decomposition method is devised based on the matrix inverse lemma and full rank decomposition. A warm-start strategy is developed to improve the computational accuracy by using the precontingency operating point of a power grid. The proposed GWSCA is incorporated with an LPF model based on logarithmic transformation of voltage magnitudes to implement fast and accurate CA. Simulation results show that GWSCA outperforms the conventional algorithms in terms of computational efficiency and accuracy.

An ensemble based data mining model for contingency analysis of power system under STLO

<span lang="EN-US">In a large, interconnected power system, contingency analysis is a useful tool for pinpointing the potential consequences of post-event scenarios on the system's safety. In this work, the Newton-Raphson technique is applied to every single outage of a transmission line to compute the load flows. For the static security classification of the power system, the line voltage stability performance index (LVSI) is used. There are three levels of static security of power system namely: non-critical (the least severe), semi-critically insecure (the next lowest severe), and critical (the next highest severe). The various data mining techniques such as decision trees, bagging-based ensemble methods, and boosting-based ensemble methods were applied to assess the severity of the line under various loading and contingency conditions. Test systems based on the IEEE 30 bus system were used with the proposed machine learning classifiers. The experimental results proved that bagging based ensemble method provided better accuracy compared to the decision tree and the AdaBoost ensemble method for predicting the power system security assessment. The bagging-based ensemble method has a predictive accuracy of 85% and an AUC of 0.94.</span>

Steady-State Security and Power System Contingency Analysis: A Review

Steady-State Security and Power System Contingency Analysis: A Review

Steady state Contingency Analysis of IEEE-39 Bus system Using PSS/E

Nowadays the stable, secure and reliable power system operation is the main objective of anyelectric power utility. The Power system is vulnerable to any contingency i.e. loss of Transmission line,Transformer, Generating unit etc. The power system control engineer should have detail analysis of at leastthe most probable and sever contingencies of the system and accordingly devise the remedial action plan.Thus, Power system contingency analysis and evaluation play vital role in ensuring power system stabilityand reliability. Keeping in view the importance of contingency analysis in power system operation andcontrol, this research focuses on steady state Contingency analysis of IEEE-39 bus system using newtonRaphson method. PSS/E is used for the analysis, different contingencies are evaluated and ranked basedupon the Power flow (Overloading), Voltage and phase angle deviation. Once the ranking is performed, thePV and QV analysis is performed for most severe contingencies. Remedial Actions is proposed for themost severe contingencies and this research could be applied to any real time network.

Hierarchical Clustering-Based Framework for Interconnected Power System Contingency Analysis

This paper investigates a conceptual, theoretical framework for power system contingency analysis based on agglomerative hierarchical clustering. The security and integrity of modern power system networks have received considerable critical attention, and contingency analysis plays a vital role in assessing the adverse effects of losing a single element or more on the integrity of the power system network. However, the number of possible scenarios that should be investigated would be enormous, even for a small network. On the other hand, artificial intelligence (AI) techniques are well known for their remarkable ability to deal with massive data. Rapid developments in AI have led to a renewed interest in its applications in many power system studies over the last decades. Hence, this paper addresses the application of the hierarchical clustering algorithm supported by principal component analysis (PCA) for power system contingency screening and ranking. The study investigates the hierarchy clustering under different clustering numbers and similarity measures. The performance of the developed framework has been evaluated using the IEEE 24-bus test system. The simulation results show the effectiveness of the proposed framework for contingency analysis.

Data Mining and Machine Learning Technique in Contingency Analysis of Power System with UPFC

This paper explains how to predict the severity of the system by connecting with and without a unified power flow controller under different load and n-1 conditions by calculating the LVSI and by the application of data mining and machine learning techniques. A large amount of data will occur during the process of contingency analysis and it is necessary that how to get this to the system value to assess the severity of the system. With the help of data mining and machine learning techniques, analysis of data generated from the simulations for different load conditions are carried out and is used to estimate the severity of the line. In this, the IEEE 30 Bus System is used by calculating the line voltage stability index and the simulation work is carried out by using MATLAB and WEKA software.

Implementation of adaptive neuro fuzzy inference system and back propagation neural network for the appraisal of power system contingency analysis

Power System Security and Contingency analysis is one of the most important tasks in power systems. In operation, contingency analysis assists engineers to operate the power system at a secure and safe operating point where equipment are loaded within their safe operating area (SOA). Power is dispatched to customers with acceptable quality standards. The results of off-line load flow calculations are used to estimate performance indices (PI flow, PI V). MATLAB toolbox was the proposed methodology used for the implementation. The proposed approach for contingency analysis was found to be appropriate for screening and ranking fast voltage and line flow contingencies.

Contingency Analysis and Ranking of Kurdistan Region Power System Using Voltage Performance Index

In this paper, analysis and ranking of single contingency due to the outage of transmission lines for a large scale power system of the Kurdistan Region (KR) are presented. Power System Simulator software (PSS®E33) is used to simulate the Kurdistan Region power system network and perform the contingency analysis for single line outage. This analysis is essential in order to predict and evaluate the voltage stability in case of contingency occurrence to know the most severe case and plan for managing it. All possible transmission line outages of the network are tested individually. After each branch disconnects, load flow analysis are applied by using Newton Raphson method then all bus voltages are recorded, and compared with them before the contingency. Voltage performance index is calculated for all possible contingencies to rank them according to their severity and determine the most severe contingency which is corresponding to the highest value of performance index. Also, the contingencies which cause load loss and amount of this load are observed.

Predicting Failure Cascades in Large Scale Power Systems via the Influence Model Framework

Large blackouts in power grids are often the consequence of uncontrolled failure cascades. The ability to predict the failure cascade process in an efficient and accurate manner is important for power system contingency analysis. In this paper, we propose to apply the influence model for the prediction and screening of failure cascades in large scale DC and AC power networks. Then, the trained influence model is applied to some large power grids with thousands of buses and transmission lines. The prediction performance is evaluated in four different aspects, from the global perspective of the overall failure size to the individual information regarding the link failure time. The results show that under limited training samples, the proposed framework is capable of predicting the failure cascade size with a 7% error rate, the final state of links with a 10% error rate, and the failure time within 1 time unit for both the DC and AC model. One major advantage of the proposed method is that it can reduce the computational time of the failure cascade prediction by a few orders of magnitude with limited compromise in accuracy, as compared to the power flow based contingency analysis. Another important feature of the proposed method is that the trained influence parameters can reveal the critical initial contingencies. This information is very helpful for identifying the worst contingency scenarios for system operators.

Multi-period vulnerability analysis of power grids under multiple outages: An AC-based bilevel optimization approach

This paper describes a methodology for the N-k contingency analysis of bulk power systems. The method encompasses the evaluation of contingencies’ effects on the power system over a range of system demand levels. The proposed model is inherently a multi-period bilevel optimization problem. Unlike the conventional bilevel optimization problems for the N-k contingency analysis, the proposed model considers the effects of reactive power dispatch, losses, and voltage profile. In doing so, the problem is formulated as a multi-period AC-based bilevel mixed-integer nonlinear programming (MINLP) problem. To guarantee the global optimality of the solution, this paper linearizes and then transforms it into a one-level mixed-integer linear programming (MILP) problem using different linearization techniques and the duality theory. The simulation results on the annual load profile of the IEEE Reliability Test System (RTS) verify the effectiveness of the proposed model.

Power System Contingency Analysis by using Voltage and Active Power Performance Index

Power System Contingency Analysis by using Voltage and Active Power Performance Index

Power System NERC Reliability Compliance Study and Violation Mitigation

Power system contingency analysis plays a pivotal role in identifying potential weak spots and ensuring reliable operation of power systems. Voltage stability is a critical aspect of performance evaluation of power system reliability. The North American Electric Reliability Corporation demands utilities to perform compliance studies to make sure that their systems meet the requirements of adequacy and security aspects of reliability. This paper presents contingency studies performed for a real utility system. The voltage stability criteria are defined, and a general software package is used to perform analysis including regular ACCC analysis, PV analysis, QV analysis, voltage sensitivity analysis, and dynamic studies. The analysis can be carried out manually. In order to facilitate analysis and to avoid tedious steps of running the simulation, recording results, and processing and reporting results, software tools are developed to automate all sorts of tasks and steps based on Python language. The studies have demonstrated that the software tools are efficient for carrying out contingency studies. Example results are reported in the paper in order to illustrate typical study results for a real power utility system.

A New Approach for Contingency Analysis Based on Centrality Measures

The contingency analysis of power systems represents a critical part of security monitoring, required to maintain power system reliability. In general, traditional $N-1$ contingency analysis methods simulate a few outages, due to the high computational costs involved. Thus, they may fail to identify some critical contingencies that can lead to cascading failures. This paper proposes a new approach to $N-1$ contingency analysis of electric transmission systems, based on network centrality measures. The proposed method evaluates all possible transmission line outages in a very short computational time, and it requires only topological information. Results are shown for two electric power systems: ITAIPU 11 bus and IEEE 39 bus. Comparisons between the results obtained by the proposed method and traditional ones show the accuracy of the proposed method to identify critical buses and transmission lines, in local and global context, even in absence of electrical information. The proposed method is interesting as a direct and fast tool applied to the pre-analysis process, since topological network behavior is verified. Thus, pre-analysis provides a prior response to the system operator of the points by which the electrical power system analysis should begin, in order to ensure safe operational state of the system.

A Mixed Integer Programming Model for Evaluating the Hidden Probabilities of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math> </inline-formula>-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math> </inline-formula> Line Contingencies in Smart Grids

In contingency analysis of large-scale power systems, the high-order contingencies involving a relatively large number of component failures are traditionally assumed to have a very low occurrence probability. However, this may underestimate the probability of some ${N}$ - ${k}$ contingencies and thus pose risk to the reliable system operations considering that those contingencies would cause severe consequences. In this paper, we investigate the problem of hidden ${N}$ - ${k}$ contingencies and cast the problem of evaluating the hidden probability of an ${N}$ - ${k}$ contingency as a mixed integer linear programming problem. The simulation results on the IEEE 14-bus and 118-bus systems verify the effectiveness of the proposed model.

Exploiting Coarse-Grained Parallelism Using Cloud Computing in Massive Power Flow Computation

We present a novel architecture of parallel contingency analysis that accelerates massive power flow computation using cloud computing. It leverages cloud computing to investigate huge power systems of various and potential contingencies. Contingency analysis is undertaken to assess the impact of failure of power system components; thus, extensive contingency analysis is required to ensure that power systems operate safely and reliably. Since many calculations are required to analyze possible contingencies under various conditions, the computation time of contingency analysis increases tremendously if either the power system is large or cascading outage analysis is needed. We also introduce a task management optimization to minimize load imbalances between computing resources while reducing communication and synchronization overheads. Our experiment shows that the proposed architecture exhibits a performance improvement of up to 35.32× on 256 cores in the contingency analysis of a real power system, i.e., KEPCO2015 (the Korean power system), by using a cloud computing system. According to our analysis of the task execution behaviors, we confirmed that the performance can be enhanced further by employing additional computing resources.

A High-Performance Parallelization and Load-Balancing Approach for Modern Power-Systems

Due to the recent trends of chip-miniaturization, the performance of a single-core is plateauing and hence, improving the performance of serial-execution based legacy code has become challenging. Since the expansion in power system operation continues to increase the number of contingencies to be examined, serial-execution platforms present a crucial bottleneck in analyzing sufficiently large number of contingencies within a reasonably small time for performing power-system stability analysis. This paper presents an approach to parallelize power system contingency analysis over multicore processors using Chapel language. To achieve load-balancing for avoiding wastage of computation resources, the authors use efficient work-stealing scheduling. They discuss the important features of Chapel and design choices which enable us to achieve high performance gains. The approach is evaluated using hundreds of contingencies of a large 13029-bus power system. The authors compare the performance of serial-execution with that obtained using 2, 4, 8 and 16 threads. The simulation results have shown that the approach outperforms a conventional scheduling technique, namely master-slave scheduling and also scales effectively with increasing number of threads. It is believed that the performance gains obtained from the approach would be highly useful for control center operators in analyzing a large number of contingencies and thus taking suitable corrective and preventive action against catastrophic events. Also, the insights gained from these experiments will be useful in several business enterprises.

A flexible framework of line power flow estimation for high-order contingency analysis

Abstract Traditionally, power system contingency analysis involves massive power flow calculations, which are usually based on linear approximation methods, such as dc load flow model or distribution factors based method, for fast speed but with compromised accuracy. Particularly, the accuracy of power flow results can deteriorate with the increase of k given N – k contingency analysis. Consequently, the obtained results may provide misleading information by under estimating the impact of some severe contingencies. In order to effectively implement online N – k contingency analysis, we propose a flexible framework of power flow estimation, where generalized line outage distribution factors ( GLODF s) and ac power flow model are integrated together to formulate a two-stage scheme. At first stage, the Monte Carlo sampling technique is used to generate tables of computing errors for the hybrid ac -( GLODF s/ ac ) method. The achieved error information can then provide useful references in the second stage to select either ac - GLODF s based method or ac power flow model for N – k contingency analysis. Theoretically, the framework can provide significantly enhanced accuracy as well as satisfied efficiency. Finally, comprehensive case studies with the IEEE -118 bus system are given to validate the proposed framework.

PARAGON: an approach for parallelization of power system contingency analysis using Go programming language

SUMMARY With the recent emphasis on N-k contingency analysis (CA) and expansion of power systems, the computational demand of CA has greatly increased to ensure power system security. The serial execution platforms are proving to be insufficient to cater to this demand. This paper presents PARAGON, an efficient approach for parallelization of power system contingency analysis using Go programming language. PARAGON uses Google's Go language to parallelize CA over multiple cores using goroutines. The main contribution of this paper is implementation of work-stealing scheduler on widely used multicore processors using concurrent programming language, which can be easily adopted by power system control centers to accelerate legacy codes. Simulations have been performed using a large power system with hundreds of contingencies, and contingency analysis has been parallelized using 2, 4, 8, 12, and 16 cores. The results show that PARAGON provides large computational advantage over serial execution and also outperforms a conventional scheduling technique, namely master-slave scheduling. Copyright © 2014 John Wiley & Sons, Ltd.

SCALE: A hybrid MPI and multithreading based work stealing approach for massive contingency analysis in power systems

In this paper, we present SCALE, a hybrid message passing interface (MPI) and multithreading based work Stealing approach for massive Contingency AnaLysis in powEr systems. SCALE performs time domain simulation of power systems using efficient numerical algorithms and scales the approach for analyzing a large number of contingencies using MPI. For achieving dynamic load balancing, SCALE uses efficient implementation of the work stealing algorithm. SCALE uses a hybrid MPI and multithreading based implementation, where MPI is used for communication between different nodes (processors) and multithreading is used within each processor to facilitate implementation of non-blocking version of work-stealing algorithm. We have evaluated SCALE on a large, 13,029 bus system using thousands of contingencies. Also, we have compared the dynamic load balancing based scheduling approach of SCALE with a state-of-art scheduling approach, namely master–slave scheduling approach. The results show that SCALE is effective in analyzing a large number of contingencies and scales well to a large number of processors.

Assessment of branch outage contingencies using the continuation method

This paper provides a contribution to the contingency analysis of electric power systems under steady state conditions. An alternative methodology is presented for static contingency analyses that only use continuation methods and thus provides an accurate determination of the loading margin. Rather than starting from the base case operating point, the proposed continuation power flow obtains the post-contingency loading margins starting from the maximum loading and using a bus voltage magnitude as a parameter. The branch selected for the contingency evaluation is parameterised using a scaling factor, which allows its gradual removal and assures the continuation power flow convergence for the cases where the method would diverge for the complete transmission line or transformer removal. The applicability and effectiveness of the proposed methodology have been investigated on IEEE test systems (14, 57 and 118 buses) and compared with the continuation power flow, which obtains the post-contingency loading margin starting from the base case solution. In general, for most of the analysed contingencies, few iterations are necessary to determine the post-contingency maximum loading point. Thus, a significant reduction in the global number of iterations is achieved. Therefore, the proposed methodology can be used as an alternative technique to verify and even to obtain the list of critical contingencies supplied by the electric power systems security analysis function.