Probabilistic assessment of power system transient stability incorporating SMES

The application of energy storage (ES) in power system is limited due to the high cost of the ES device, which exponentially increases with its capacity. This paper is to improve the saturation-dependent stability of the power system equipped with the energy storage based damping controller (ESDC), and hence, reduce the required size of the ES. The phenomenon that the capacity of ES is smaller than the required value produced by the ESDC, is modeled as actuator saturation using the saturation function. The proposed method is to design an anti-windup compensator (AWC), which in the event of saturation, produces a signal based on the output difference between the ESDC and saturated ES and then augment the signal to the ESDC to alleviate the adverse effect of saturation. The AWC is designed with the reduced-order model of power system and linear matrix inequality. Detailed design procedure is introduced. Case studies based on a modified 4-machine 2-area power system and 10-machine New England power system are carried out to demonstrate the effectiveness of the AWC design method.

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“…Hence in this paper, a modified 4-machine 11-bus system from our previous work [22] is used as the first test case, as shown in Fig. 3.…”

Section: A System Description and Esdc Designmentioning

confidence: 99%

Design of Anti-Windup Compensator for Energy Storage-Based Damping Controller to Enhance Power System Stability

Fang

Yao

Chen

et al. 2014

IEEE Trans. Power Syst.

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“…For instance, [22] presented an analytical approach to determine PTS for online applications. [23]- [24] used Monte-Carlo Simulation (MCS) approach to present a stochastic-based approach to assess the PTS index. [15] proposed the inclusion of probabilistic considerations in transient stability investigation of a multimachine practical power system.…”

Section: Prediction Of Probabilistic Transient Stability Using Suppor...mentioning

confidence: 99%

Prediction of Probabilistic Transient Stability Using Support Vector Machine

Shahzad

2022

TEEE

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Transient stability assessment is an integral part of dynamic security assessment of power systems. Traditional methods of transient stability assessment, such as time domain simulation approach and direct methods, are appropriate for offline studies and thus, cannot be applied for online transient stability prediction, which is a major requirement in modern power systems. This motivated the requirement to apply an artificial intelligence-based approach. In this regard, supervised machine learning is beneficial for predicting transient stability status, in the presence of uncertainties. Therefore, this paper examines the application of a binary support vector machine-based supervised machine learning, for predicting the transient stability status of a power system, considering uncertainties of various factors, such as load, faulted line, fault type, fault location and fault clearing time. The support vector machine is trained using a Gaussian Radial Basis function kernel and its hyperparameters are optimized using Bayesian optimization. Results obtained for the IEEE 14-bus test system demonstrated that the proposed method offers a fast technique for probabilistic transient stability status prediction, with an excellent accuracy. DIgSILENT PowerFactory and MATLAB was utilized for transient stability time-domain simulations (for obtaining training data for support vector machine) and for applying support vector machine, respectively.

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“…Theresa and Mariesa [17] developed an approach to analyse the effect of random load and generation variations on the transient stability of a structure-preserved power system based on the well-known energy function method. Fang et al [ 18] conducted the probabilistic evaluation of power system transient stability incorporating wind farm of DFIG type and superconductivity magnetic energy storage based on the Monte Carlo simulation and Bayesian classification. The stochastic factors involving the disturbance sequence and wind speed were taken into account during the analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Fang et al . [18] conducted the probabilistic evaluation of power system transient stability incorporating wind farm of DFIG type and superconductivity magnetic energy storage based on the Monte Carlo simulation and Bayesian classification. The stochastic factors involving the disturbance sequence and wind speed were taken into account during the analysis.…”

Section: Introductionmentioning

confidence: 99%

Effects of wind generation intermittency and volatility on power system transient stability

Shi

Sun

Yao

et al. 2014

IET Renewable Power Generation

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The transient stability analysis incorporating wind power intermittency and volatility is studied in this study. The wind turbines with doubly fed induction generator (DFIG) and direct-driven permanent magnet synchronous generator (PMSG) are considered, respectively, during analysis. In modelling of DFIG and PMSG, the default GE wind turbine model and the Western Electricity Coordinating Council generic wind turbine model are employed, respectively. Based on the Jensen model, which is applied to describe the wake effect in a wind farm located on flat terrain, an equivalent simplified model of a wind farm is established regarding wind farm layout. On the basis of the built wind farm model, the Monte Carlo simulation technique combined with two evaluation indices, namely angle-based margin index and critical clearing time (CCT) is applied on the IEEE 10-generator-39-bus test system and a real-sized China-Jiangxi power grid as a benchmark to exploit and explore the effects of wind power intermittency and volatility on power system transient stability. The frequency distributions of transient stability evaluation index, CCT, probability of system transient instability and the range of wind speeds causing system transient instability are simulated. In addition, the influence of wake effect on transient stability is discussed as well.

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Probabilistic assessment of power system transient stability incorporating SMES

Cited by 16 publications

References 15 publications

Design of Anti-Windup Compensator for Energy Storage-Based Damping Controller to Enhance Power System Stability

Design of Anti-Windup Compensator for Energy Storage-Based Damping Controller to Enhance Power System Stability

Prediction of Probabilistic Transient Stability Using Support Vector Machine

Effects of wind generation intermittency and volatility on power system transient stability

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