Probabilistic Power Flow for Power Quality Assessment of Islanded Microgrid

Chagas, Elton Amorim; Rodrigues, A.B.; Silva, Maria G. da

doi:10.1109/pmaps.2018.8440473

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Since the wind stream applied to the wind turbine have impacts on both node voltage and branch power of microgrid, so the prediction of microgrid power flow is much needed to provide information for the energy management system of microgrid [31]. Probabilistic power flow (PPF) can be used to obtain the probability distributions of microgrid variables, that are functions of load variation, and generation uncertainties (load forecast error, generator failure and stochastic of renewable energy sources) [31][32][33][34][35][36]. Probabilistic measurements based on PPF are more reliable than deterministic values [35], because of the instantaneous…”

Section: Bivariate Wdf Application To Power Flowmentioning

confidence: 99%

“…Probabilistic measurements based on PPF are more reliable than deterministic values [35], because of the instantaneous variability of loads on microgrid busbar, and randomness of renewable resources. The variability in the output power of a wind turbine is due to the randomness behaviour of wind speed [31–37]. Equation (43) presents the relationship between wind speed and the captured power of a selected wind turbine [34, 37–39].…”

Section: Bivariate Wdf Application To Power Flowmentioning

confidence: 99%

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Wind speed estimation based on a novel multivariate Weibull distribution

Salim

Dorrah

Hassan

2019

IET Renewable Power Generation

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Good practice in smart grids is networked sensors topologies which give the ability to fuse sensors to obtain a new feature based on sensors' complementarity. Moreover, to get a prediction based on other sensors, possibly in other areas, in case of sensor failure. This ability may be achieved by correlating sensor data based on their historical observations. This study addresses the problem of stochastic multivariate modelling within the framework of cyber-physical systems. Weibull distribution function (WDF) was found to be the best probability distribution function to fit wind variations. The main idea of this study is to propose a generalised multivariate stochastic model to describe the joint probability between multi-stochastic variables. A novel multivariate WDF for wind modelling is proposed to estimate wind speed in one location based on wind speed data on other location(s). The suggested approach is applied to three cases to demonstrate the efficacy of the developed technique. It is pointed out that this approach could be generalised for the multivariate WDF of wind modelling situations. Finally, it is shown that there are existing structures resemblances between the wind Weibull versus normal distribution models arising from the fact WDF reflects a natural phenomenon with some restrictions. Nomenclature A ω area which rotor blades cover (m 2) C p power coefficient C scale factor f v Weibull distribution function (WDF) F v cumulative distribution function f r j frequency of wind speed in a histogram K shape factor M number of locations that have wind data µ mean value N number of wind data n j number of wind points that fall inside the bin characterised by the wind speed ρ correlation factor ρ 1 correlation factor between variables 1 and 2 ρ 2 correlation factor between variables 1 and 3 ρ 3 correlation factor between variables 2 and 3 ρ ω air density σ standard deviation Σ covariance matrix v series wind speed v i wind speed X variable in Gaussian function IET Renew. Power Gener.

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Section: Bivariate Wdf Application To Power Flowmentioning

confidence: 99%

Section: Bivariate Wdf Application To Power Flowmentioning

confidence: 99%

Wind speed estimation based on a novel multivariate Weibull distribution

Salim

Dorrah

Hassan

2019

IET Renewable Power Generation

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“…The existing power quality evaluation methods mainly include analytic hierarchy process, [7][8][9][10][11] entropy method, [12][13][14] and methods based on probability statistics. 15,16 In power quality assessment, AHP (analytic hierarchy process) is commonly applied, in which the importance of different power quality indicators is sorted to form a judgment matrix in AHP, which can be determined the weight vector. [7][8][9][10][11] However, the traditional AHP cannot accurately identify the specific reasons for the inconsistency of the matrix when the conditions of the matrix consistency test are not met.…”

Section: Introductionmentioning

confidence: 99%

“…The existing power quality evaluation methods mainly include analytic hierarchy process, 7–11 entropy method, 12–14 and methods based on probability statistics. 15,16…”

Section: Introductionmentioning

confidence: 99%

Comprehensive power quality evaluation method of microgrid with dynamic weighting based on CRITIC

Shi

Jiang

et al. 2021

Measurement and Control

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The power quality assessment provides a reference for power quality management and control of microgrid operation. In terms of reflecting the correlation of power quality indexes and the dynamic changes of microgrid operating conditions, the traditional power quality assessment methods need to be improved. A power quality comprehensive evaluation based on CRITIC and dynamic coefficient is proposed in this paper. In this method, the objective weight of power quality indicators in single node is determined by using the intensity of conflict and contrast firstly. For the node weight calculation, the dynamic coefficient is proposed to reflect the different influence degree of node with different connected load. The proposed method in this paper can reflect both the internal characteristic of data sequence and the relationship between different data sequences. In addition, it also can reflect the dynamic changes of microgrid. Finally, an example is used to verify the feasibility of the proposed method.

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