Online TTC Estimation Using Nonparametric Analytics Considering Wind Power Integration

Liu, Youbo; Zhao, Junbo; Xu, Lixiong; Liu, Tingjian; Qiu, Gao; Liu, Junyong

doi:10.1109/tpwrs.2018.2867953

Cited by 45 publications

(27 citation statements)

References 27 publications

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“…Reference [18] leverages twin convolutional support vector machine to investigate the size and type of independent transient stability margin. To compute the total transfer capability (TTC) of the interconnected system, the authors in [19] propose a real-time measurementbased TTC estimator. The group Lasso regression is utilized for the offline training process.…”

Section: B Previous Work On Dsamentioning

confidence: 99%

An Integrated Online Dynamic Security Assessment System for Improved Situational Awareness and Economic Operation

Diao

Zhang

et al. 2019

IEEE Access

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The ever-increasing penetration of centralized and distributed renewable energy, power electronics-based transmission equipment and loads, advanced protection and control systems, storage devices and new power market rules all contribute to the growing dynamics and stochastic behaviors being observed in today's grid operation. Understanding operational risks and providing prompt control actions are of great importance to ensure secure and economic operation of a bulk power system. In this paper, a novel integrated online dynamic security assessment system (DSAS) is developed that intakes real-time EMS snapshots combining both bus/branch and node/breaker network models, performs dynamic contingency analysis under various conditions, calculates real-time transfer limits, and provides online control suggestions to mitigate operational risks. Several unique and innovative features are developed to address practical challenges, including: (1) real-time stitching of power flow information from both node/breaker and bus/branch models covering different geographical regions of interest; (2) online corrections and enhancements to power flow and dynamic models; (3) equipment-name-based modelling approaches for complex contingencies and control systems; and (5) distributed computing capabilities for significant computational speed enhancement. The developed DSAS has been deployed in the control center of State Grid Jiangsu Electric Power Company with hundreds of HVAC and 8 (U)HVDC transmission lines, which has been running reliably since Sept. 2018, achieving satisfactory performance in improving situational awareness and economic operation of the power grid. INDEX TERMS Co-simulation, distributed computing, dynamic security assessment, EMS, node/breaker model, transient stability, user defined model.

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Section: B Previous Work On Dsamentioning

confidence: 99%

An Integrated Online Dynamic Security Assessment System for Improved Situational Awareness and Economic Operation

Diao

Zhang

et al. 2019

IEEE Access

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“…It is widely recognised that the ATC calculation should accommodate reasonable uncertainties in the system conditions to guarantee flexible and reliable system operations [2]. In a power system with a significant proportion of wind power generation, where the principle of addressing uncertainty attracts more attention, probabilistic ATC calculation is considered to be more promising than deterministic methods [7][8][9][10]. Monte Carlo simulations (MCS) are widely used for assessing probabilistic ATC [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic available transfer capability assessment in power systems with wind power integration

Sun¹,

Tian

Rao³

et al. 2020

IET Renewable Power Generation

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Extending current deterministic tools to incorporate significant stochastic wind power is becoming an important as well as challenging task for present-day power system decision-making. This paper proposes a novel probabilistic assessment method to assess the available transfer capability (ATC). Usually, a large number of ATC evaluations is needed to obtain accurate results using time-consuming Monte Carlo simulations (MCS). To alleviate the computation burden of probabilistic ATC, a statistically-equivalent surrogate model for the ATC solution is constructed by introducing canonical low-rank approximation (LRA). By implementing LRA for the base case and a set of enumerated contingencies, the uncertainties of wind power generation and load, as well as transmission equipment outages, are addressed in an efficient way. With the proposed method, the probability of ATC is characterised, and the most influential uncertain factors are identified, which helps to determine a suitable ATC level. The effectiveness of the proposed method is validated via case studies with a modified IEEE 118-bus system.

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“…Actually, when the power system of interest is of hundreds of buses or even more, the aforementioned methods such as multiple linear regression or ridge regression can hardly work, due to the well-known ''curse of dimensionality'' phenomenon [15] known to data scientists and statisticians. In order to overcome this problem, in this paper, we apply a sparse machine learning algorithm called the Lasso (least absolute shrinkage and selection operator), which was widely applied to power system community, such as power system transient stability problems [16], [17], total transfer capability online estimation [18], and voltage stability margin online monitoring [19]. This paper examines this approach in assessing, understanding, and predicting the small-signal behavior of large interconnected power systems and estimates the direct relationship between the mode damping and the system operation point.…”

Section: Introductionmentioning

confidence: 99%

Power System Oscillation Mode Prediction Based on the Lasso Method

Pawlak

et al. 2020

IEEE Access

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This paper utilizes modern statistical and machine learning methodology to predict the oscillation mode of interest in complex power engineering systems. The damping ratio of the electromechanical oscillation mode is formulated as a function of the power of the generators and loads as well as bus voltage magnitudes in the entire power system. The celebrated Lasso algorithm is implemented to solve this high-dimension modeling problem. By the nature of the L 1 design, the Lasso algorithm can automatically render a sparse solution, and by eliminating redundant features, it provides desirable prediction power. The resultant model processes a simple structure, and it is easily interpretable. The precision of our sparse modeling framework is demonstrated in the context of an IEEE 50-Generator 145-Bus power network and an online learning framework for the power system oscillation mode prediction is also provided.

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Online TTC Estimation Using Nonparametric Analytics Considering Wind Power Integration

Cited by 45 publications

References 27 publications

An Integrated Online Dynamic Security Assessment System for Improved Situational Awareness and Economic Operation

An Integrated Online Dynamic Security Assessment System for Improved Situational Awareness and Economic Operation

Probabilistic available transfer capability assessment in power systems with wind power integration

Power System Oscillation Mode Prediction Based on the Lasso Method

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