Local Frequency-Based Estimation of the Rate of Change of Frequency of the Center of Inertia

Azizi, Sadegh; Sun, Mingyu; Liu, Gaoyuan; Terzija, Vladimir

doi:10.1109/tpwrs.2020.3014818

Cited by 30 publications

(12 citation statements)

References 11 publications

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“…However, since there are thousands of buses in a large-scale power system, the frequency of every bus cannot be evaluated for frequency stability evaluation. Therefore, system frequency based on the center of inertia (CoI) is used to assess the frequency stability of the power system [22].…”

Section: A Analysis Of Frequency Responsementioning

confidence: 99%

Frequency Stability Enhancement of Low-Inertia Large-Scale Power System Based on Grey Wolf Optimization

et al. 2022

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The high penetration of converter-based distributed generations (DGs) to power system can give rise to the lack of rotational inertia while replacing the conventional synchronous generators (SGs), which provide the primary frequency reserve (PFR) in power systems. As the result, the decrease in PFR aggravates the frequency stability. To overcome this problem, the droop coefficients of governors in the remaining conventional SGs must be re-determined newly and properly. This paper proposes a new solution based on the grey wolf optimization (GWO) method to optimally select the droop coefficients of SG governors in the low-inertia large-scale power system due to the high penetration of renewables. The proposed solution is very effective for reducing the computational effort significantly, and it is able to recover not only the steady-state but also the transient frequency stability. To verify the effectiveness of proposed optimization solution based on the GWO method, several case studies are carried out on the practical Korea electric power system with the penetration of wind power plants of 4 GW.INDEX TERMS Computational effort, distributed generations, droop coefficients, frequency stability, grey wolf optimization, large-scale power system.

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Section: A Analysis Of Frequency Responsementioning

confidence: 99%

Frequency Stability Enhancement of Low-Inertia Large-Scale Power System Based on Grey Wolf Optimization

et al. 2022

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“…The frequency of each generator oscillates around the center of system inertia, and when the system stabilizes, the frequency of each generator will eventually approach to the center of system inertia. When the emergency controls are applied, the center of inertia (COI) frequency is usually used to represent the global state of system frequency [2], [5], [8]. In addition, most load shedding schemes use COI as the index.…”

Section: A Power System Center Of Inertia Frequencymentioning

confidence: 99%

“…In [7], an analytical frequency nadir prediction model is proposed to predict frequency nadir and time when it reaches. In [8], the rate of change of frequency of the center of inertia is estimated using only local frequency.…”

Section: Introductionmentioning

confidence: 99%

A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control

Xie

Sun

2021

IEEE Access

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Fast and accurate prediction and control of power system dynamic frequency after disturbance is essential to enhance power system stability. Machine learning methods have great potential in harnessing data for online application with accurate predictions. This paper proposes a two-stage novel transfer and deep learning-based method to predict power system dynamic frequency after disturbance and provide optimal event-based load shedding strategy to maintain system frequency. The proposed deep learning model combines convolutional neural network (CNN) and long short-term memory (LSTM) network to harness both spatial and temporal measurements in the input data, through a four-dimensional (4-D) tensor input construction process including, 1) capture system network topology information and critical measurements from different time intervals; 2) compute a multi-dimensional electric distance matrix and reduce to a 2-D plane which can describe the system nodal distribution; 3) construct 3-D tensors based on state variables at different sample times; and 4) integrate into 4-D tensor inputs. Moreover, a transfer learning process is employed to overcome the challenge of insufficient data and operating condition changes in real power systems for new prediction tasks. Simulation results in IEEE 118-bus system verify that the CNN-LSTM method not only greatly improves the timeliness of online frequency control, but also presents good accuracy and effectiveness. Test cases in the New England 39-bus system and the South Carolina 500-bus system validate that the transfer learning process can provide accurate results even with insufficient training data.

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“…The local frequency curve intersects with the CoI frequency curve when the second derivative of the former becomes zero. A rigorous mathematical proof of the theory for a two-source system can also be found in [24]. Let f n and t n denote the frequency and time instant of the n-th inflection point on the local frequency curve.…”

Section: A Local Estimation Of the Coi Rocofmentioning

confidence: 99%

Underfrequency Load Shedding Using Locally Estimated RoCoF of the Center of Inertia

Sun

Liu

Popov

et al. 2021

IEEE Trans. Power Syst.

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The conventional Under Frequency Load Shedding (UFLS) scheme could result in unacceptably low frequency nadirs or overshedding in power systems with volatile inertia. This paper proposes a novel UFLS scheme for modern power systems whose inertia may vary in a wide range due to high penetration of renewable energy sources (RESs). The proposed scheme estimates the rate of change of frequency (RoCoF) of the center of inertia (CoI), and consequently, the loss of generation (LoG) size, using local frequency measurements only. An innovative inflection point detector technique is presented to remove the effect of local frequency oscillations. This enables fast and accurate LoG size calculation, thereby more effective load shedding. The proposed UFLS scheme also accounts for the effect of the inertia change resulting from LoG events. The performance of the proposed scheme is validated by conducting extensive dynamic simulations on the IEEE 39-bus test system using Real Time Digital Simulator (RTDS). Simulation results confirm that the proposed UFLS scheme outperforms the conventional UFLS scheme in terms of both arresting frequency deviations and the amount of load shed. Index Terms--Center of inertia (CoI), Underfrequency load shedding (UFLS), Rate of change of frequency (RoCoF).

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Local Frequency-Based Estimation of the Rate of Change of Frequency of the Center of Inertia

Abstract: This is a repository copy of Local Frequency-Based Estimation of the Rate of Change of Frequency of the Center of Inertia.

Cited by 30 publications

References 11 publications

Frequency Stability Enhancement of Low-Inertia Large-Scale Power System Based on Grey Wolf Optimization

Frequency Stability Enhancement of Low-Inertia Large-Scale Power System Based on Grey Wolf Optimization

A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control

Underfrequency Load Shedding Using Locally Estimated RoCoF of the Center of Inertia

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