WAMS-Based Coherency Detection for Situational Awareness in Power Systems With Renewables

Liu, Shengyuan; Wen, Fushuan; Ding, Yi; Xue, Yusheng; Zhao, Yuxuan; Yi, Shimin

doi:10.1109/tpwrs.2018.2820066

Cited by 60 publications

(43 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, limited availability of the rotor angle and speed measurements makes the implementation of this moment not applicable in real-world power systems. The method presented in [25] makes use of kernel principal component analysis and affinity propagation (AP) clustering technique to reduce dimensions of the multi-indices distances and to automatically determine the optimal number of coherent groups. However, this method also requires center-of-inertia centered generator rotor angles and speeds; and due to the basic preference parameter method for the AP clustering tends to partition independent (outlier) generators to the nearest group, leading to suboptimal results.…”

Section: A Generator Slow Coherencymentioning

confidence: 99%

“…The main challenge is to partition the generators into robust (over time) and an optimal number of clusters. This paper extends the unsupervised affinity propagation (AP) clustering technique to partition coherent generators into groups [25].…”

Section: G Partition Of Generators Into Coherent Groupsmentioning

confidence: 99%

“…suggests p being the median of the input similarity matrix [34]. In many cases, this approach leads to suboptimal clustering results, since it tends to cluster the outlier nodes (often implied as independent exemplars) to the centrally oriented exemplars [25], [35]. In order to overcome this problem, a novel AP preferences parameter adaptive method is proposed for the identification of exemplary nodes.…”

Section: ) Ap Preferences Parametermentioning

confidence: 99%

See 2 more Smart Citations

Synchronized Measurement Technology Supported Online Generator Slow Coherency Identification and Adaptive Tracking

Naglič

Popov

Meijden

et al. 2020

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

In an electric power system, slow coherency can be applied to identify groups of the generating units, the rotors of which are swinging together against each other at approximately the same oscillatory frequencies of inter-area modes. This serves as a prerequisite-step of several emergency control schemes to identify power system control areas and improve transient stability. In this paper, slow coherent generators are grouped based on the direction and the strength of electromechanical coupling between different generators. The proposed algorithm performs low-pass filtering of generator frequency measurements. It adaptively determines the minimal number of the measurements to be processed in an observation window, and performs data selectivity to prevent mixing of interfering coherency indices. Finally, it adaptively tracks grouping changes of slow coherent generators and determines a finite number of groups for an improved affinity propagation clustering. The proposed algorithm is implemented as an online MATLAB program and verified in real-time using RTDS power system simulator with the integration of actual synchronized measurement technology components as hardware-in-the-loop. The obtained results demonstrate the effectiveness of the proposed algorithm for robust and near real-time identification of grouping changes of slow coherent generators during the quasi-steady-state and electromechanical transient period following a disturbance. where he was involved in modeling SF6 circuit breakers. His current research interests include future power systems, large-scale power system transients, intelligent protection for future power systems, and wide-area monitoring and protection. Prof. Popov is a member of CIGRE. He has actively participated in WG C4.502 and WG A2/C4.39. He was a recipient of the IEEE PES Prize Paper Award and the IEEE Switchgear Committee Award in 2011. He is an Associate Editor of the International Journal of Electric Power and Energy Systems. Mart A. M. M. van der Meijden (M'10) received the M.Sc. degree (cum laude) in electrical engineering from the Eindhoven University of Technology, Eindhoven, The Netherlands, in 1981.He is leading research programs on intelligent electrical power grids, reliable, and large-scale integration of renewable (wind and solar) energy sources in the European electrical power systems and advanced grid concepts. He has more than 30 years of working experience in the field of process automation and the transmission and the distribution of gas, district heating, and electricity. Since 2003, he has been with TenneT TSO, Arnhem, The Netherlands, Europe's first cross-border grid operator for electricity, where he is the Manager of Research and Development/Innovation and was responsible for the development of the TenneT long-term vision on the electrical transmission system. He has been a Full Professor (part-time) with the

show abstract

Section: A Generator Slow Coherencymentioning

confidence: 99%

Section: G Partition Of Generators Into Coherent Groupsmentioning

confidence: 99%

Section: ) Ap Preferences Parametermentioning

confidence: 99%

See 1 more Smart Citation

Synchronized Measurement Technology Supported Online Generator Slow Coherency Identification and Adaptive Tracking

Naglič

Popov

Meijden

et al. 2020

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

show abstract

“…Based on this expression, the proposed method requires around 0.95 s T = 0.25 + 0.1 + 0.60 of computational time to perform the evaluation. With this time, the proposed method can calculate an effective detection of the transient period and monitor the coherency electromechanical oscillatory behaviour of the system in comparison with [14,16]. At the same time, the algorithm meets the technical requirements of the IEEE standards 1547-2003 and IEC 61727, that consider a time response for islanding detection <2 and 3 s, respectively [36,37].…”

Section: Study Case One: Stable Responsementioning

confidence: 99%

Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique

Barocio

Korba

Sattinger³

et al. 2019

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

Identification of coherent generators and the determination of the stability system condition in large interconnected power system is one of the key steps to carry out different control system strategies to avoid a partial or complete blackout of a power system. However, the oscillatory trends, the larger amount data available and the non-linear dynamic behaviour of the frequency measurements often mislead the appropriate knowledge of the actual coherent groups, making wide-area coherency monitoring a challenging task. This paper presents a novel online unsupervised data mining technique to identify coherent groups, to detect the power system disturbance event and determine status stability condition of the system. The innovative part of the proposed approach resides on combining traditional plain algorithms such as singular value decomposition (SVD) and Kmeans for clustering together with new concept based on clustering slopes. The proposed combination provides an added value to other applications relying on similar algorithms available in the literature. To validate the effectiveness of the proposed method, two case studies are presented, where data is extracted from the large and comprehensive initial dynamic model of ENTSO-E and the results compared to other alternative methods available in the literature.

show abstract

“…In [24][25][26], the ordered binary decision diagram (OBDD) method was proposed to satisfy different conditions, including easy synchronization and a good power balance in each island. In [27][28][29], a slow coherency-based method was used to provide islanding cutsets by identifying coherent generator groups first, and then searching for minimal cutsets with minimal PF imbalance in each controlled island [30]. An alternative is PF tracing [31] as it clusters the network into islands in such a way that power flowing in cutset lines is minimized.…”

Section: Introductionmentioning

confidence: 99%

A Three-Stage Procedure for Controlled Islanding to Prevent Wide-Area Blackouts

Shao¹,

Mao²,

Liu³

et al. 2018

Energies

View full text Add to dashboard Cite

Controlled islanding has been proposed as a last resort action to stop blackouts from happening when all standard methods have failed. Successful controlled islanding has to deal with three important issues: when, and where to island, and the evaluation of the dynamic stability in each island after islanding. This paper provides a framework for preventing wide-area blackouts using wide area measurement systems (WAMS), which consists of three stages to execute a successful islanding strategy. Normally, power system collapses and blackouts occur shortly after a cascading outage stage. Using such circumstances, an adapted single machine equivalent (SIME) method was used online to determine transient stability before blackout was imminent, and was then employed to determine when to island based on transient instability. In addition, SIME was adopted to assess the dynamic stability in each island after islanding, and to confirm that the chosen candidate island cutsets were stable before controlled islanding was undertaken. To decide where to island, all possible islanding cutsets were provided using the power flow (PF) tracing method. SIME helped to find the best candidate islanding cutset with the minimal PF imbalance, which is also a transiently stable islanding strategy. In case no possible island cutset existed, corresponding corrective actions such as load shedding and critical generator tripping, were performed in each formed island. Finally, an IEEE 39-bus power system with 10 units was employed to test this framework for a three-stage controlled islanding strategy to prevent imminent blackouts.

show abstract

WAMS-Based Coherency Detection for Situational Awareness in Power Systems With Renewables

Cited by 60 publications

References 38 publications

Synchronized Measurement Technology Supported Online Generator Slow Coherency Identification and Adaptive Tracking

Synchronized Measurement Technology Supported Online Generator Slow Coherency Identification and Adaptive Tracking

Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique

A Three-Stage Procedure for Controlled Islanding to Prevent Wide-Area Blackouts

Contact Info

Product

Resources

About