Probabilistic approach-based PMU placement for real-time power system vulnerability assessment

Cepeda, Jaime C.; Rueda, José L.; Erlich, I.; Colomé, D. G.

doi:10.1109/isgteurope.2012.6465671

Cited by 25 publications

(26 citation statements)

References 12 publications

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“…Similarly to the previous system, three types of relays have been modelled and included in this system: OSR, VR, and FR. Additionally, six PMUs have already located at buses 2,9,11,17,22, and 28, based on the results presented in [45]. The test system single-line diagram is depicted in Figure 21, which also includes the PMU location.…”

Section: Ieee New England 39-bus Test Systemmentioning

confidence: 99%

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

Cepeda¹,

Rueda

Colomé

et al. 2014

Int. Trans. Electr. Energ. Syst.

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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.

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Section: Ieee New England 39-bus Test Systemmentioning

confidence: 99%

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

Cepeda¹,

Rueda

Colomé

et al. 2014

Int. Trans. Electr. Energ. Syst.

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“…To achieve the complete observability of the given system, total 42 PMUs are required. The optimal locations for these PMUs are 6,8,11,12,14,16,17,19,20,22,23,25,26,29,31,32,34,36,37,41,42,44,45,46,47,48,49,51,52,56,57,58,59, 60, 61, 62, 63, 64, 65, 66, 67, and 68. Placing PMUs on these locations will make the system completely observable at least by two PMUs, even when single-transmission line or PMU is lost.…”

Section: Optimal Pmu Placementsmentioning

confidence: 99%

“…Real-time control of small-signal oscillations is also as important as real-time monitoring of small-signal oscillations. In literature, deterministic and probabilistic approaches for small-signal analysis were reported [17][18][19][20][21][22][23][24]. Robust damping controller using h-infinity, linear matrix inequality techniques have been widely used [17,18] for damping out inter-area oscillations.…”

Section: Introductionmentioning

confidence: 99%

“…In the aforementioned literature [17][18][19][20][21][22], the authors have assumed that PMUs are placed on all generator terminals, which is practically not possible. Few researchers have also applied probabilistic approach for small-signal stability studies [23,24]. In these references, authors have explored Monte Carlo-based probabilistic Eigen analysis technique for various operating points and uncertainties of the power system.…”

Section: Introductionmentioning

confidence: 99%

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Ranking of phasor measurement units based on control strategy for small-signal stability

Sharma

Tyagi

2014

Int. Trans. Electr. Energ. Syst.

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Summary This paper presents a methodology to rank phasor measurement unit (PMU) locations based on small‐signal stability. In the proposed scheme, integer linear programming is utilized to identify the optimal locations of PMUs. These locations are ranked using analytical hierarchy process. Modal analysis is conducted to identify inter‐area modes leading to instability. A control scheme is proposed, to nullify this instability. Based on the control scheme, few critical buses are determined, which are given the highest priorities while ranking. Because a critical bus can be a generator, or tie line or load bus, therefore, three different ranking criteria are utilized. Based on ranking, PMUs are placed in multiple stages such that in the initial stage itself, all critical buses are observed. To check the effectiveness of the proposed scheme, fuzzy and proportional‐integral‐derivative controllers are employed. Proposed methodology is successfully tested on 16‐machine, 68‐bus system. Copyright © 2014 John Wiley & Sons, Ltd.

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“…Clustering approaches are used in the power system research in many applications such as operation and control applications [ 13 , 14 ], generators coherency identification [ 15 , 16 , 17 ], event location [18] , power system vulnerability assessment [19] , controlled islanding [20] and power system restoration [21] . Hierarchical Clustering [22] is applied in this paper to partition the power system network into specific zones that comprising the correlated system buses and identifying the optimal PMU location within each zone based on simple rules.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Clustering based optimal PMU placement for power system fault observability

Eissa

Kassem

2018

Heliyon

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Optimal number and location of phasor measurement units (PMUs) in the power system networks faces challenges for achieving the full network observability during fault conditions. Achieving fault observability approach requires more constraints than normal system observability and consequently suffers from complex analysis and heavy computational burden for the large-scale networks. A new algorithm for determining the optimal PMU placement considering the network fault observability is introduced. The proposed algorithm is achieved through four stages. The first stage is achieved through the network fault simulation to obtain the post fault change in voltage (ΔV) at each bus. Then, the post fault change in voltage (ΔV) is used to build the network connectivity matrix (CM) and forming a new developed Faulted Connectivity Matrix (FCM) that describes the power system topology during the fault conditions. The correlation between the buses is obtained, in the second stage, by applying Pearson correlation coefficient. Hierarchical Clustering technique is given, in the third stage, to cluster the network into coherent zones to find the most correlated buses. Finally, the optimal location of the PMUs is identified within each zone based on simple proposed placement rules. The proposed algorithm is tested under a variety of fault events applied on different standard test systems. The results show the simplicity and the effectiveness of the proposed algorithm.

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Probabilistic approach-based PMU placement for real-time power system vulnerability assessment

Cited by 25 publications

References 12 publications

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

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

Ranking of phasor measurement units based on control strategy for small-signal stability

Hierarchical Clustering based optimal PMU placement for power system fault observability

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