Recognition of post-contingency dynamic vulnerability regions: Towards smart grids

Int. Trans. Electr. Energ. Syst.

et al. 2014

Summary This paper proposes a data‐mining‐based approach for predicting the power system post‐contingency vulnerability status in real time. To this aim, the system dynamic vulnerability regions (DVRs) are first determined by applying singular value decomposition, by means of empirical orthogonal functions (EOFs), to a post‐contingency data base obtained from phasor measurement units (PMUs) adequately located throughout the system. In this way, the obtained pattern vectors (EOF scores) allow mapping the DVRs within the coordinate system formed by the set of EOFs, which permits revealing the main patterns immersed in the collected PMU signals. The database along with the DVRs enable the definition, training, and identification of a support vector classifier (SVC) which is employed to predict the post‐contingency vulnerability status as regards three short‐term stability phenomena, that is: transient stability, short‐term voltage stability, and short‐term frequency stability (TVFS). Enhanced procedures for feature extraction and selection as well as heuristic optimization‐based parameter identification are proposed to ensure a robust performance of the SVC. Numerical results, obtained by implementation of the proposed approach on two different size test power systems, demonstrate the methodology viewpoint and effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.

Section: Post-contingency Dvrsmentioning

confidence: 99%

Data-mining-based approach for predicting the power system post-contingency dynamic vulnerability status

Cepeda¹,

Rueda

Int. Trans. Electr. Energ. Syst.

et al. 2014

“…8, are analyzed. These voltage angles are determined via Monte Carlo (MC) simulations as detailed in [1].…”

Section: Post-contingency Dynamic Signalmentioning

confidence: 99%

“…In this context, bulk power systems are facing an increased risk of major threats to system reliability and security, caused by critical perturbations that eventually might push them to partial or total blackouts [1], [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Benefits of empirical orthogonal functions in pattern recognition applied to vulnerability assessment

Cepeda¹,

2014 IEEE PES Transmission &Amp; Distribution Conference and Exposition - Latin America (PES T&D-La)

2014

One of the main challenges of power system vulnerability assessment is to handle adequate tools capable of analyzing huge volumes of data in real time. For instance, phasor measurement units (PMUs) offer lots of dynamic time series data regarding phasor electric signals. This dynamic data can be analyzed via signal processing tools, such as Fourier Transform, in order to obtain patterns that alert about possible system stress. However, due to the specific dynamic behavior of postcontingency electric signals, they do not exactly correspond to pure periodic signals. Thus, applying classical Fourier-related transforms might not allow obtaining relevant patterns from these types of signals. In this connection, the present paper proposes to use a well-proven time series data mining technique, called empirical orthogonal functions (EOFs), instead of Fourierrelated transforms. EOFs better adapt to the peculiar shape of the post-contingency system variables, which allows getting a better pattern recognition, and so a better vulnerability assessment. With the aim of showing the benefits of EOF, a fair comparison of this data mining tool with Discrete Fourier Transform (DFT) is presented in this paper via three different signal examples. IndexTerms-Electric power systems, vulnerability assessment, data mining, empirical orthogonal functions (EOF), Discrete Fourier Transform (DFT).

“…While much work has been directed towards the development of methods for assessing the three causes of vulnerability regarding stability issues [8]- [10], the possible overloads have often been treated as negligible in vulnerability assessment tasks. However, sometimes high electric postcontingency currents might provoke overloads which could increase the system vulnerability problem [11].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic-based overload estimation for real-time smart grid vulnerability assessment

Cepeda

Ramírez

2012 Sixth IEEE/PES Transmission and Distribution: Latin America Conference and Exposition (T&D-La)

2012

Self Cite

In recent years, important efforts to improve monitoring, protection and control of power systems have been explored. In this connection, several novel approaches for assessing vulnerability in real time have been developed. However, most of the work is commonly focused on tackling stability phenomena, while the possible overloads are often treated as negligible in real-time power system security. But sometimes, high electric post-contingency currents might provoke overloads which could increase the system vulnerability problem. This paper presents a novel method for assessing the possibility of fast post-contingency overloads using Statistical Distribution Factors (SDFs) that allow computing an Overload Index (OVI) in real time. First, Monte Carlo-based contingency analysis is performed to iteratively calculate ac Distribution Factors (ac-DFs). After, SDFs are defined by the mean and standard deviation of ac-DFs. These SDFs are then used together with principal component analysis (PCA) and support vector machine classifier (SVM-C) in order to structure a table-based real-time post-contingency overload estimation algorithm, which allow computing OVIs depending on the actual operating state and the type of contingency. The proposal is tested on the IEEE New England 39-bus test system. Results show the feasibility of the methodology in alerting about fast possible overloads. Index Terms--Monte Carlo, overload, principal component analysis, security, smart grid, support vector machine, vulnerability assessment.