Modularity clustering based detection of coherent groups of generators with generator integrity indices

Znidi, Faycal; Davarikia, Hamzeh; Iqbal, Kamran

doi:10.1109/pesgm.2017.8273835

Cited by 25 publications

(12 citation statements)

References 9 publications

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“…Normally, the correlation coefficient (CC) varies between -1.0 and +1.0 and measures the intensity and direction of the linear relationship that exists among the two quantities. About the power network, the variables represent the coherency of two different buses where a value of +1.0 indicates there is an ideal positive association among the two buses [28][29][30]. In order, to evaluate the degree of coherency between two different buses, the PPCC among bus i and j is formed as follows:…”

Section: Coherency Decttion Base On Pearson Product-mentioning

confidence: 99%

Coherency Detection and Network Partitioning based on Hierarchical DBSCAN

Znidi

Davarikia

Arani

et al. 2020

2020 IEEE Texas Power and Energy Conference (TPEC)

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After a sudden disturbance, the energy balance of generators is disturbed, and the power outputs of synchronous generators vary as their rotor angles shift from their equilibrium points. This trend essentially presents the versatile response of each machine to the disturbance. Because of this change, the phase angle of the bus also differs. Hence, the versatile response of each machine can be assessed by the phase angles change at the buses close to the synchronous generator. This paper introduces a new methodology for discovering the degree of coherency among buses using the correlation index of the voltage angle between each pair of buses and use the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to partition the network into islands. The proposed approach also provides the network integrity indices (connectivity, splitting, and separation) for studying the dynamic nature of the power network system. The approach is assessed on an IEEE-39 test system with a fully dynamic model. The simulation results presented in this paper demonstrate the efficiency of the proposed approach.

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Section: Coherency Decttion Base On Pearson Product-mentioning

confidence: 99%

Coherency Detection and Network Partitioning based on Hierarchical DBSCAN

Znidi

Davarikia

Arani

et al. 2020

2020 IEEE Texas Power and Energy Conference (TPEC)

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“…where |𝐸 | is the magnitude of the voltage behind the subtransient reactance in the synchronous generator, 𝐵 is the susceptance between the element 𝑖 and 𝑗 in the reduced system, and 𝛿 is the difference between the rotor angle of machines 𝑖 and 𝑗 [12][13][14]. A small change in powers at generator 𝑖 and 𝑗, causing a small change in angular separation between generators 𝑖 and 𝑗 .…”

Section: A Synchronization Power Coefficient As Coherency Index Betwe...mentioning

confidence: 99%

Constraint Clustering Based Islanding Scheme For Power Networks

Davarikia

Znidi

Barati

2020

2020 IEEE Texas Power and Energy Conference (TPEC)

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Controlled islanding, which splits the whole power system into islands, is an effective strategy against rolling blackout during severe disturbances. Finding the islanding solutions in a real-time manner is complicated because of the combinatorial explosion of the solution space occurs for a large power network. In this work, a computationally efficient controlled islanding algorithm is proposed that uses constrained spectral clustering while addressing the generator coherency problem. The objective function used in this controlled islanding algorithm is the minimal power-flow disruption. The sole constraint applied to this solution is related to generator coherency. An undirected edge-weighted graph is created based on absolute values of apparent power flow and constraints related to transmission line availability and coherent generator groups are included by altering the edge weights of the graph and using a subspace projection. Spectral clustering is then applied to the constrained solution subspace to find the islanding solution. The methodology is tested on an IEEE-39 test system with a fully dynamic model. Simulation results demonstrate the efficacy of our approach.

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“…) 𝑼] 𝑠. 𝑡. 𝑼𝑼 𝑇 = 𝑰 (15) where 𝛼 is the weighting parameter that balances the trade-off linking the two terms in the objective function. We may note that this is identical trace minimization problem as introduced in ( 13), but with a "modified" Laplacian given as:…”

Section: 𝑖=1mentioning

confidence: 99%

“…While there is an in-depth treatment of individual topics such as generator coherency, optimization, and the active and reactive power graph-based models in the literature, there is a dearth of information regarding these multiple topics in a single model [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Multi-layer spectral clustering approach to intentional islanding in bulk power systems

Znidi¹,

Davarikia²,

Iqbal³

et al. 2019

J. Mod. Power Syst. Clean Energy

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Intentional controlled islanding (ICI) is a final resort for preventing a cascading failure and catastrophic power system blackouts. This paper proposes a controlled islanding algorithm that uses spectral clustering over multilayer graphs to find a suitable islanding solution. The multicriteria objective function used in this controlled islanding algorithm involves the correlation coefficients between bus frequency components and minimal active and reactive power flow disruption. Similar to the previous studies, the algorithm is applied in two stages. In the first stage, groups of coherent buses are identified with the help of modularity clustering using correlation coefficients between bus frequency components. In the second stage, the ICI solution with minimum active and reactive power flow disruption and satisfying bus coherency is determined by grouping all nodes using spectral clustering on the multi-layer graph. Simulation studies on the IEEE 39-bus test system demonstrate the effectiveness of the method in determining an islanding solution in real time while addressing the generator coherency problem.

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Modularity clustering based detection of coherent groups of generators with generator integrity indices

Cited by 25 publications

References 9 publications

Coherency Detection and Network Partitioning based on Hierarchical DBSCAN

Coherency Detection and Network Partitioning based on Hierarchical DBSCAN

Constraint Clustering Based Islanding Scheme For Power Networks

Multi-layer spectral clustering approach to intentional islanding in bulk power systems

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