A Novel Event Detection Method Using PMU Data With High Precision

Cui, Mingjian; Wang, Jianhui; Tan, Jin; Florita, Anthony; Zhang, Yingchen

doi:10.1109/tpwrs.2018.2859323

Cited by 74 publications

(35 citation statements)

References 28 publications

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“…In this system, there exist the system load, automatic voltage regulators (AVRs), and synchronous generators, which have been developed in [23]. The synchrophasor datasets can be gathered from the system PMU devices that have been widely installed in the Wide Area Management Systems (WAMS) [24], [25]. The synchrophasor dataset variables that can be monitored by PMU devices are formulated as:…”

Section: B Synchrophasor Spatiotemporal Data Preprocessingmentioning

confidence: 99%

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Zhang

2020

IEEE Access

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Deep learning techniques have been widely used for power system operations as a very hot topic in recent years. This paper proposed a spatiotemporal deep learning-based low frequency oscillation mode estimation method using synchrophasor data. The proposed deep learning method consists of the graph convolutional network (GCN) and the long short-term memory (LSTM). A graph network is used to model the power network in which the edges represent the connection relationships between system buses. Specifically, the GCN method is used to capture the topological structure of the power networks to obtain the spatial dependence. The LSTM model is used to capture the dynamic change of electrical variables that can be monitored by PMU devices through synchrophasor data to obtain the temporal dependence. Eventually, the proposed GCN-LSTM model is used to capture the spatiotemporal patterns and features of the system-wide synchrophasor data. To validate the effectiveness of the proposed method, we evaluate it on two classic IEEE simulation platform, i.e., IEEE 39-bus system and IEEE 118-bus system, by comparing with the Nonlinear Autoregressive Neural Network with External Input (NARX), the Gated Recurrent Unit (GRU) network, and single LSTM methods. It is demonstrated that the proposed GCN-LSTM method can achieve the best estimation results for different simulation platforms.

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Section: B Synchrophasor Spatiotemporal Data Preprocessingmentioning

confidence: 99%

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Zhang

2020

IEEE Access

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“…Another important aspect about building the dataset is how to define the beginning and end of each data window. This is usually taken care by an event detection algorithm [34]. In this paper, since all the data is simulated, each data window is comprised of the outputs of one specific type of switch action's simulation.…”

Section: Case Studiesmentioning

confidence: 99%

Deep‐learning‐based power distribution network switch action identification leveraging dynamic features of distributed energy resources

Duan

Stewart

2019

IET gener. transm. distrib.

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“…The roles of synchronized phasor measurement units (PMU) in power systems monitoring, control, and protection are prominent and constantly developing [1][2][3], This enables the operator to take advantage of wide area monitoring, protection and control (WAMPAC) [4]. Some of the applications of phasor measurement devices include event detection [5], normal [6] and fault observabilities [7], state estimation [8], and postcontingency analysis [9]. Since PMUs can measure voltage phasor of buses as well as current phasors of branches connected to the buses, it is feasible to observe the network with optimal number of devices.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis‐based optimal PMU placement for fault observability

Mohammadi

Mehraeen

Nazaripouya

2020

IET Generation Trans & Dist

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This paper proposes an algorithm that finds the optimal sets of phasor measurement units (PMUs) to achieve a fault observable system, while it addresses the multi-estimation issue. The optimal PMU placement (OPP) problem here is to find a set of PMU locations with minimum number of members that enables fault observability in a system and satisfies a defined minimum sensitivity requirement in measurements. The proposed algorithm generalizes the impedance method in fault analysis and optimizes PMU utilization to maintain a required minimum sensitivity in each set of measurements, given the required fault detection accuracy. Also, the set of measurements is unique and distinctive for each fault scenario, preventing multi-estimation. A fault is referred to as a set of affected faulty line, fault location, and fault impedance. A sensitivity analysis is performed and sensitivity indices are derived to evaluate measurements quality to detect changes in fault line, location, or resistance. The algorithm is executed on IEEE 7-bus, 14-bus, and 30-bus test systems. Subsequently, artificial neural networks (ANN) are employed to build fault locators through offline training. ANN use an optimal PMU set obtained by the proposed algorithm to uniquely map between the corresponding measurements set and the faults.

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A Novel Event Detection Method Using PMU Data With High Precision

Cited by 74 publications

References 28 publications

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Deep‐learning‐based power distribution network switch action identification leveraging dynamic features of distributed energy resources

Sensitivity analysis‐based optimal PMU placement for fault observability

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