Wide-Area Phase-Angle Measurements for Islanding Detection—An Adaptive Nonlinear Approach

Liu, Xueqin; Kennedy, Jason; Laverty, David; Morrow, D. John; McLoone, Séamus

doi:10.1109/tpwrd.2016.2518019

Cited by 36 publications

(35 citation statements)

References 35 publications

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“…In this context, controlled islanding, as the last resort for preventing cascading outages, has received ever-increasing attention through splitting the system into several sustainable islands in the past few years [15,16]. By monitoring of power system operating conditions, controlled islanding is to seek an optimal islanding solution quickly, so as to prevent faults from spreading and thereby leading to a system-wide blackout [17]. In essence, it is a typical non-deterministic polynomial-time hard (NP-hard) combinatorial optimization problem [1], and the so-called "combination explosion" in solution space significantly exacerbates the problem-solving difficulties with the increase of system size.…”

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confidence: 99%

Controlled Islanding for a Hybrid AC/DC Grid With VSC-HVDC Using Semi-Supervised Spectral Clustering

2019

IEEE Access

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As the last resort of emergency control, controlled islanding is an effective means of preventing fault-propagation and a system-wide blackout. However, conventional AC transmission lines are unavailable to be employed for power exchange between these islands. To make full use of the DC power modulation capability of VSC-HVDC links, a new controlled islanding model is put forward for an AC/VSC-HVDC hybrid grid to minimize the composite power-flow disruption, in which the DC-terminals belonging to a VSC-HVDC link are placed in different islands. To solve this model, a semi-supervised spectral clustering-based approach is proposed by transforming the problem into a weighted undirected graph segmentation problem. The novelty of our work is to find an optimal islanding solution in real time such that the power exchanges between islands are implemented via a VSC-HVDC link to reduce the generation-load imbalance. The simulation results on the IEEE 39-bus system and a real-world system verify the effectiveness and superiority of the proposed approach.

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confidence: 99%

Controlled Islanding for a Hybrid AC/DC Grid With VSC-HVDC Using Semi-Supervised Spectral Clustering

2019

IEEE Access

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“…The result is to provide new ways to develop systematic tools to identify coherency groups and global stability indexes. By combining algorithms in an innovative way as demonstrated in this brief, it is possible to create more sophisticated and robust PCA variants such as the one proposed in the literature for only coherency identification [14,16].…”

Section: Comparisons With Other Multivariate Clustering Methodsmentioning

confidence: 99%

“…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%

“…Among the most successful multivariate methods for coherency identification are those that can be applied directly to time-series, features or objects such as the principal component analysis (PCA) [8,14], which rely on orthogonal transformations in order to find linear correlations between variables of the dataset. Comparatively, singular value decomposition (SVD) yields a matrix decomposition, which allows extracting the most representative data information using the singular value information [15].…”

Section: Introductionmentioning

confidence: 99%

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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

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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.

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“…A passive method based on measuring the frequency-dependent impedance at an inverter terminal was employed in [20]. A remote method exploiting the phasor measurement unit to collect the related information of an island was employed in [21]. Additionally, the control strategy during islanding operation was also explored in some literature.…”

Section: Introductionmentioning

confidence: 99%

Compatibility Issues with Irregular Current Injection Islanding Detection Methods and a Solution

et al. 2019

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Islanding detection methods, based on injecting high-/low-frequency currents or negative sequence fundamental frequency currents and observing the resultant responses, are collectively referred to as irregular current injection methods in this paper. In multi-distributed generation (DG) operation, if there is no restriction to the phase of injected irregular currents, the currents at the same frequency may cancel each other out, and then their convergent current may be too small to cause a detectable response, for which reason islanding detection will be severely affected. Accordingly, this paper raises a compatibility issue, which requires the phase difference between any two injected irregular currents to be within a certain interval. In response to this issue, a solution is proposed. According to this solution, the terminal voltage of DG units is referenced to conduct irregular currents injection, and only certain high-frequency currents are used as injected currents. If this solution is adopted by as many manufacturers as possible, the effect and reliability of such methods will be greatly improved.

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Wide-Area Phase-Angle Measurements for Islanding Detection—An Adaptive Nonlinear Approach

Cited by 36 publications

References 35 publications

Controlled Islanding for a Hybrid AC/DC Grid With VSC-HVDC Using Semi-Supervised Spectral Clustering

Controlled Islanding for a Hybrid AC/DC Grid With VSC-HVDC Using Semi-Supervised Spectral Clustering

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

Compatibility Issues with Irregular Current Injection Islanding Detection Methods and a Solution

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