Online synchronous generator out‐of‐step prediction by electrical power curve fitting

Sobbouhi, Ali Reza; Vahedi, Abolfazl

doi:10.1049/iet-gtd.2018.5938

Cited by 13 publications

(12 citation statements)

References 24 publications

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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%

“…Various methods have been proposed in the literature for predicting or detecting transient or STV instability. In [12], first, the trend of generator output power has been predicted using sinusoidal curve fitting and then the transient stability has been evaluated by employing time‐domain equal area criterion. In [13], based on the conversion of the multi‐machine system into an equivalent one‐machine‐infinite‐bus system and predicting the rotor speed trajectory of the simplified system by acquiring online sequential learning machine method, transient instability has been predicted.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Proposed Stability Status Prediction Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…In an AC power system, during normal operation all synchronous generators operate in synchronism, that is, all generators in the grid must have the same frequency and voltage phase sequence. Occurrence of extreme contingencies in a power system can lead to voltage, frequency or angular instabilities, which may result in either under/over-frequency situations or may lead to loss-of-synchronism between zones within the power grid or between interconnected systems [1,2]. If such loss-ofsynchronism occurs, it is imperative that the asynchronous areas be isolated before synchronous generators (SGs) are damaged or before a cascading outage and shutdown of larger areas of the power grid occurs.…”

Section: Introductionmentioning

confidence: 99%

Out‐of‐step detection of synchronous generators using dual computational techniques based on correlation and instantaneous powers

Mahmoud

Malik

2022

IET Generation Trans & Dist

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To maintain synchronous operation of interconnected systems, it is necessary to keep balance between power generation and consumption. Faults may result in large oscillations in power flows and loss‐of‐synchronism between one generator and the rest of the power system. A correlation coefficient is a useful statistic that can be used to detect voltage, frequency or angular instabilities, and to determine an asynchronous process. Hence, it is a smart technique to predict the out‐of‐step event following fault situations. In this paper, the technique based on the auto/cross‐correlation and instantaneous powers is presented to identify sudden disturbances of various electrical signals in the case of asynchronous operation. To validate the method performance, a power network with real parameters is simulated on Alternative Transient Program (ATP) platform, and the algorithm is implemented in MATLAB software. Simulation studies reveal that the proposed scheme is able to detect the out‐of‐step conditions upon which the relay issues a trip signal, yet remains inactive under normal synchronized operating conditions. Thus, the out‐of‐step is declared before the second pole slipping occurs, and an instability time can be accurately assessed. Moreover, it can develop novel quadrilateral operating characteristics to discriminate between the synchronous and asynchronous operation of the AC generator.

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“…The challenges of non-linearity and the rapid development of the electromechanical transient process make it difficult to predict the system transient stability quickly and accurately after the fault has been cleared [6]. In recent years, many large-scale power system blackouts have occurred worldwide, which makes fast-online transient stability assessments and emergency control more urgent [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Traditional transient stability assessment (TSA) methods include a time-domain method based on (1) electromechanical transient simulations [11][12][13], and (2) direct methods based on Lyapunov's stability theory [14,15], transient energy function (TEF) related approaches [2,16], equal area criterion (EAC) related approaches [9,17], and so on. Time-domain simulation with detailed dynamic component models is more accurate and can be used as a standard for testing other TSA methods.…”

Section: Introductionmentioning

confidence: 99%

An Improved Power System Transient Stability Prediction Model Based on mRMR Feature Selection and WTA Ensemble Learning

Liu

Sun

et al. 2020

Applied Sciences

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Fast online transient stability assessment (TSA) is very important to maintain the stable operation of power systems. However, the existing transient stability assessment methods suffer the drawbacks of unsatisfactory prediction accuracy, difficult applicability, or a heavy computational burden. In light of this, an improved high accuracy power system transient stability prediction model is proposed, based on min-redundancy and max-relevance (mRMR) feature selection and winner take all (WTA) ensemble learning. Firstly, the contributions of four different series of raw sampled data from all of the three-time stages, namely the pre-fault, during-fault and post-fault, to transient stability are compared. The new feature of generator electromagnetic power is introduced and compared with three conventional types of input features, through a support vector machine (SVM) classifier. Furthermore, the two types of most contributive input features are obtained by the mRMR feature selection method. Finally, the prediction results of the electromagnetic power of generators and the voltage amplitude of buses are combined using the WTA ensemble learning method, and an improved transient stability prediction model with higher accuracy for unstable samples is obtained, whose overall prediction accuracy would not decrease either. The real-time data collected by wide area monitoring systems (WAMS) can be fed into this model for fast online transient stability prediction; the results can also provide a basis for the future emergency control decision-making of power systems.

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Online synchronous generator out‐of‐step prediction by electrical power curve fitting

Cited by 13 publications

References 24 publications

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

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

Out‐of‐step detection of synchronous generators using dual computational techniques based on correlation and instantaneous powers

An Improved Power System Transient Stability Prediction Model Based on mRMR Feature Selection and WTA Ensemble Learning

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