Online synchronous generator out-of-step prediction by ellipse fitting on acceleration power – Speed deviation curve

Sobbouhi, Ali Reza; Vahedi, Abolfazl

doi:10.1016/j.ijepes.2020.105965

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…in which the transient instability was detected earlier by 165 ms. Also, in studies [29] and [30], limited simulations have been represented that only analyze the status of the SG9. As can be observed, the early detection time of them has been obtained 491.11 and 491.87 ms on average, respectively.…”

Section: B 10-generator New England System Case Studymentioning

confidence: 99%

Real-Time Transient Instability Detection in the Power System With High DFIG-Wind Turbine Penetration via Transient Energy

Shabani

Kalantar

Hajizadeh

2022

IEEE Systems Journal

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By increasing the penetration of wind power in the modern power systems, to investigate transient stability is of special importance. This article presents an innovative index based on the concept of the potential energy boundary surface along with kinetic energy, which aims to detect the transient instability of a power system comprising doubly fed induction generator (DFIG)-based wind farms. Accordingly, transient instability detection (TID) is performed without directly calculating the unstable equilibrium point and thus the computational load is decreased. Since the proposed approach requires only the postfault data that can be easily measured by the phasor measurement units, it is suitable for real-time applications. Therefore, the proposed approach can be applied as a general tool to any power system with any change in topology and operating conditions. Moreover, considering that the current-balance form is the preferred industry model for the implementation of transient stability simulation, the network equations in the current-balance form are extracted for grid-connected DFIG and synchronous generators. Different scenarios are simulated in the Western System Coordinating Council 3-machine, 9-bus system, and the 10-generator New England system. To validate the new index for TID, simulation results are compared with the transient stability index and out-of-step distance relay. The obtained results validate the correctness and effectiveness of the presented new index.

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Section: B 10-generator New England System Case Studymentioning

confidence: 99%

Real-Time Transient Instability Detection in the Power System With High DFIG-Wind Turbine Penetration via Transient Energy

Shabani

Kalantar

Hajizadeh

2022

IEEE Systems Journal

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“…The method in [38] is based on centre of inertia (COI) reference, therefore, it is necessary to gather the rotor angles from all of the generator terminals and calculate the COI angle at each time step. In [12–39] the performance of the methods has been evaluated only for the severely disturbed generator. These methods are based on the assumption that the severely disturbed generator has been already identified.…”

Section: Proposed Stability Status Prediction Schemementioning

confidence: 99%

“…The prediction stability accuracy in [12,15,16,38,39] and [45] has been evaluated based on the limited cases, e.g. in [39] only based on 36 cases and in [16] only based on 2 cases, while the accuracy and reliability of the proposed prediction method in this paper have been evaluated by applying 7091 cases.…”

Section: Prediction Accuracymentioning

confidence: 99%

“…In contrast to the proposed scheme in this paper, critical stable cases (the cases that a fault is cleared at critical clearing time) are erroneously predicted as unstable cases in [12] and [39], and they need to identify the critical generator for predicting the stability status. Also the stability status of anti‐accelerating cases cannot be predicted by [12] and [39], whereas, both anti‐accelerating and accelerating cases can be predicted by the proposed scheme in this paper.…”

Section: Comparisonmentioning

confidence: 99%

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An agent‐based fast concurrent prediction scheme for transient and short‐term voltage instability

Lashgari

Shahrtash

2021

IET Generation Trans & Dist

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A novel agent-based online prediction method is presented in this paper, to predict the status of both transient and short-term voltage (STV) stability, against fault occurrence. In the proposed method, the trajectories of the relative frequency deviation (ΔF) and the power imbalance (ΔP) are estimated by employing the third-degree polynomial curve fitting method. By tracking the estimated trajectories on ΔF-ΔP plane and checking some simple defined rules, an early prediction of both transient and STV instability is achieved in an organized multi-agent system (MAS). In order to evaluate the performance of the proposed algorithm, the method has been tested on IEEE 39-bus system, IEEE 118-bus system and IEEE Nordic test system. Based on the obtained results, the proposed algorithm has an overall accuracy of 99.5% under symmetrical and asymmetrical faults, PMU measurement error, different operating points and topological changes.

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“…The deep learning technique employed uses the measurements of the voltage phasor collected across the system for training the online model for stability prediction. [22] present an algorithm for online out-of-step prediction using the acceleration power and rotor speed deviation of the generator by fitting ellipses. A unique measurement and simulation-based mixed method for TSA and emergency control scheme is discussed in [23].…”

Section: Introductionmentioning

confidence: 99%

Synchrophasor-Based Online Transient Stability Assessment Using Regression Models

Chandrashekhar

Srivani²

2022

ECTI-EEC

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An online post-fault transient stability assessment method is proposed in this study using synchrophasor or PMU measurements. Initially, a post-fault multimachine system is converted into a suitable one machine infinite bus (OMIB) system using the single machine equivalent (SIME) method. Thus, the - trajectory obtained through the OMIB system enabled a normalized transient stability index to be calculated offline. By using synchrophasor measurements before and during the fault as inputs, the regression model can be trained offline to predict the normalized stability margins. Following a fault, the synchrophasor measurements are used as input to this trained model for online stability margin prediction. If the predicted margin is negative, then the post-fault power system is indicated to be unstable. Alternatively, positive values for the predicted margin identify the system as stable. The proposed assessment method is verified using the New England (NE) 39 bus test system. The results obtained are then compared with offline simulations.

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Online synchronous generator out-of-step prediction by ellipse fitting on acceleration power – Speed deviation curve

Cited by 10 publications

References 17 publications

Real-Time Transient Instability Detection in the Power System With High DFIG-Wind Turbine Penetration via Transient Energy

Real-Time Transient Instability Detection in the Power System With High DFIG-Wind Turbine Penetration via Transient Energy

An agent‐based fast concurrent prediction scheme for transient and short‐term voltage instability

Synchrophasor-Based Online Transient Stability Assessment Using Regression Models

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