Estimation of power system inertia using particle swarm optimization

Zografos, Dimitrios; Ghandhari, Mehrdad; Paridari, Kaveh

doi:10.1109/isap.2017.8071383

Cited by 16 publications

(16 citation statements)

References 13 publications

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“…Table 2 presents the results of the inertia estimation for this case study. The results obtained demonstrate that there was concordance and that the results were better in comparison with the results reported in [24,25,37], where the error in the estimation was between 0.22% and 41.11%. On the other hand, [11] reported an estimation error of 16%.…”

Section: Analysis and Comparison Of The Resultssupporting

confidence: 74%

“…For this case, it was a small disturbance. Nevertheless, the results are still good in comparison with the results reported in the literature, whose values were between 0.22 %, 41.11% [24,25,37], and 16%, reported in [11].…”

Section: Load Incrementsupporting

confidence: 79%

“…In [24], the inertia estimation was based on an approximation of the total power change after a disturbance has occurred. This methodology used the average frequency of the system and the first instant after the application of the disturbance to estimate the inertia.…”

Section: Methods For Inertia Estimationmentioning

confidence: 99%

“…The aforementioned methodologies were based on the swing equation and transients events, like the ROCOF, and some of them used off-line measurements, such as the power system frequency, which led to a low inertia estimation accuracy [25]. On the other hand, the method presented in [24] provided low inertia estimation error by employing particle swarm optimization, but the accuracy of this method depended on the load power change model and a large number of parameters such as currents, load impedance, power change measurements after the disturbance, and the frequency average of the system. Table 1 shows the differences between the main methodologies proposed in the literature for the inertia estimation and the methodology used in this research.…”

Section: Methods For Inertia Estimationmentioning

confidence: 99%

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Inertia Estimation of Wind Power Plants Based on the Swing Equation and Phasor Measurement Units

et al. 2018

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High penetration of wind power plants may have an adverse impact on power systems’ stability by reducing the inertia, and problems like frequency stability could appear due to total inertia in the system being reduced, making the power system more vulnerable to disturbances. However, most recent grid codes include an emulation inertia requirement for wind power plants, because modern wind turbines are capable of providing virtual inertia through power electronic converter controls to improve frequency stability issues. Because of this, it is necessary that the inertia estimation analyze and quantify the impact of the inertia reduction in power systems. In this paper, an implementation of a methodology for the inertia estimation of wind power plants is presented. It is evaluated through synchrophasor measurements obtained from a Real-Time Digital Simulator (RTDS) implementation, using industrial Phasor Measurement Units (PMUs). This methodology is based on the swing equation. Furthermore, a comparison of the results obtained between two professional tools RSCAD and DIgSILENT PowerFactory is performed, in order to evaluate the accuracy and the robustness of the methodology. This methodology is applied for the inertia estimation of an equivalent of the southeast zone of the Mexican power system, where there is a large-scale penetration of wind power plants. The results demonstrate that this methodology can be applied in real power systems using PMUs.

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Section: Analysis and Comparison Of The Resultssupporting

confidence: 74%

Section: Load Incrementsupporting

confidence: 79%

Section: Methods For Inertia Estimationmentioning

confidence: 99%

Section: Methods For Inertia Estimationmentioning

confidence: 99%

See 2 more Smart Citations

Inertia Estimation of Wind Power Plants Based on the Swing Equation and Phasor Measurement Units

et al. 2018

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“…The existing literature on power system parameter identification mainly centers on system inertia [11], and it can be further divided into offline and online identification methods. The offline identification method is typically based on the historical operation data after large power disturbances, such as generator outages [15], [16], which can help to reduce the influences from the dead-band of the governor and noise in the PMU data. However, offline identification cannot deliver the real-time identification result.…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation Control Considering Uncertainties of the Key Parameters in the Frequency Response Model

Liu¹,

Hu²,

Mujeeb³

2021

Preprint

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The highly fluctuated renewable generations and electric vehicles have undergone tremendous growth in recent years. The majority of them are connected to the grid via power electronic devices, resulting in wide variation ranges for several key parameters in the frequency response model (FRM) such as system inertia and load damping factor. In this paper, an automatic generation control (AGC) method considering the uncertainties of these key parameters is proposed. First, the historical power system operation data following large power disturbances are used to identify the FRM key parameters offline. Second, the offline identification results and the normal operation data prior to the occurrence of the disturbance are used to train the online probability estimation model of the FRM key parameters. Third, the online estimation results of the FRM key parameters are used as the input, and the model predictive-based AGC signal optimization method is developed based on distributionally robust optimization (DRO) technology. Case studies conducted on the IEEE 118-Bus System show that the proposed AGC method outperforms the widely utilized PI-based control method in terms of performance and efficiency.<br>

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Estimation of inertia constant in Turkish power system and its analysis during real system disturbances

Yesil¹,

Irmak

2021

Int Trans Electr Energ Syst

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The power system inertia based on the kinetic energy stored in synchronously connected rotating machines has a crucial effect on the frequency stability. As long as the use of renewables is increased, the share of conventional generators in electrical energy generation is decreased. Thus, the existing rotational inertia in the power system is reducing day by day. Therefore, planning the daily generation schedule according to a realistic inertia estimation has become a vital requirement for modern power systems. In this context, this article deals with the change and the effect of the inertia in a power system under different seasonal conditions. Firstly, the inertia constant of Turkish transmission grid is estimated hourly from January to October 2019 by using real system data in order to reveal how the inertia changes under different generation plans. For this purpose, the aggregated model approach in which a total inertia constant is estimated for whole power grid by using inertia constant of each generation unit and the actual generated power of resources is used. Then, six major system disturbances occurred in 2019 are analyzed by utilizing real frequency data measured by phasor measurement unit (PMU) devices. Considering the share of renewable energy in the realized generation at the time of these disturbances, rate of change of frequency (RoCoF) analysis, and inertia calculations are made. As the main contributions, the article clearly illustrates how the inertia contributes to the frequency stability during system failures under different generation conditions. Furthermore, a realistic inertia study is presented, which has never been carried out for Turkish transmission grid. Moreover, real system data obtained from PMU devices during the real disturbances is used instead of using standard bus systems.List of Symbols and Abbreviations: df/dt, Time derivative of the power system frequency; f start , Frequency measured when the disturbance is occurred (Hz); f extreme , The lowest frequency during the disturbance (Hz); t start , Time of f start (s); t extreme , Times of f extreme (s); J, Inertia moment of the generator (kg.m 2 ); ω n , Rated mechanical angular velocity (rad/s); S b , Total rated power of the system (VA); S con , Total generation of conventional power plants (VA); S n , Rated power (VA); S ni , Rated power of generator i (VA); S res , Sum of the generation of renewables (VA); H agg , Aggregated system inertia constant; H i , Inertia constant of turbine-generator i ; H con , inertia constant of conventional power plants; H sys , System inertia constant; E k,sys , Kinetic energy stored in rotating masses of the system; f d , Frequency during the disturbance (Hz); f i , Frequency of generator i (Hz); f m , Actual frequency of the system (Hz); f m * , Derivative of f m (Hz); f n , Nominal frequency (Hz); Δf, Change in frequency; P m , Mechanical power of turbinegenerator i (W); P e , Electrical power of generator i (W); P loss , Generation loss occurred due to the disturbance (W); P int , Power ex...

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Estimation of power system inertia using particle swarm optimization

Cited by 16 publications

References 13 publications

Inertia Estimation of Wind Power Plants Based on the Swing Equation and Phasor Measurement Units

Inertia Estimation of Wind Power Plants Based on the Swing Equation and Phasor Measurement Units

Automatic Generation Control Considering Uncertainties of the Key Parameters in the Frequency Response Model

Estimation of inertia constant in Turkish power system and its analysis during real system disturbances

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