Neural network method based on PMU data for voltage stability assessment and visualization

Innah, Herbert; Hiyama, Takashi

doi:10.1109/tencon.2011.6129225

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…The ANN based method proposed in [36] may not be suitable for varying loading conditions [37]. A new technique for voltage stability assessment based on the PMUs using ANN has been addressed [38] where one can visualize the difference of voltage phase angles. This method provides the level of stress and proximity to voltage stability limit in the system.…”

Section: Voltage Stability Monitoring and Control Using Pmu Technmentioning

confidence: 99%

On-line voltage stability monitoring and control in smart grid — A survey

Sahu

Verma

2015

2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON)

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Most of the work on voltage stability assessment of power systems has concentrated on offline studies. With advancement in Phasor Measurement Unit (PMU) technology, it is quite possible to assess voltage stability margin of real time systems in smart grid. This paper has addressed on different works going on in the area of voltage stability monitoring and control using PMU technology. The report in this paper has elaborated on different methodologies which have been proposed in literature for optimal placement of PMUs for control of voltage stability margin in smart grid. The paper sets new scope for promising researchers for the best utilization of PMUs in online voltage stability monitoring and control in smart grid architecture.

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Section: Voltage Stability Monitoring and Control Using Pmu Technmentioning

confidence: 99%

On-line voltage stability monitoring and control in smart grid — A survey

Sahu

Verma

2015

2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON)

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“…9. Specially, 3% is used in (7) and 0.5 is used in (8), which are found to work well after test. A too wide search range leads to an imprecise estimat ion result.…”

Section: A Program Flow Chart For Estimation Of Relative Marginmentioning

confidence: 99%

Relationships Exploration for Real-time Static Voltage Stability Prediction of Power Systems

Liu¹,

Tian²,

Qiu³

et al. 2015

Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

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Abstract-An approach based on data mining and relationships exploration is presented for estimating the static voltage stability relative margin of power systems. The data set is created based on simulations by the software PSS /E. The input variables selected for estimation are corresponding to the relationships highly ranked by MIC and PPMCC. These relationships are also shown and some of them are explained from the perspective of power system operation. If the measured values of these variables are obtained from wide area measurement system (WAMS ), the relative margin can be estimated in real time since its relationships with these variables are explored. The approach is tested on a 39-bus system provided by PS S /E and illustrative results indicate the scheme is accurate and effective. Keywords-Maximal information coefficient (MIC);

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“…Artificial neural networks (ANNs) are used in many computational problems of electrical power systems [29][30][31]. In [32], the authors described the use of a radial basic function (RBF) of an ANN to calculate signal frequency by means of minimizing the entire phasor error; and in [33], to calculate the synchrophasor parameters. In [34,35], the authors used ANNs to identify phasor parameters, while in [36], ANNs were used to calculate the synchrophasor parameters only by considering the static functions.…”

Section: Introductionmentioning

confidence: 99%

Application of an Artificial Neural Network for Measurements of Synchrophasor Indicators in the Power System

2021

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Dynamic phenomena in electric power systems require fast and accurate algorithms for processing signals. The processing results include synchrophasor parameters, e.g., varying amplitude, phase or frequency of sinusoidal voltage or current signals. This paper presents a novel estimation method of synchrophasor parameters that comply with the requirements of IEEE/IEC standards. The authors analyzed an algorithm for measuring the phasor magnitude by means of a selected artificial neural network (ANN), an algorithm for estimating the phasor phase and frequency that makes use of the zero-crossing method. The original components of the presented approach are: the method of the synchrophasor magnitude estimation by means of a suitably trained and applied radial basic function (RBF); the idea of using two algorithms operating simultaneously to estimate the synchrophasor magnitude, phase and frequency that apply identical calculation methods are different in that the first one filters the input signal using the FIR filter and the second one operates without any filter; and the algorithm calculating corrections of the phase shift between the input and output signal and the algorithm calculating corrections of the magnitude estimation. The error results obtained from the applied algorithms were compared with those of the quadrature filter method and the ones presented in literature, as well as with the permissible values of the errors. In all cases, these results were lower than the permissible values and at least equal to the values found in the literature.

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Neural network method based on PMU data for voltage stability assessment and visualization

Cited by 7 publications

References 7 publications

On-line voltage stability monitoring and control in smart grid — A survey

On-line voltage stability monitoring and control in smart grid — A survey

Relationships Exploration for Real-time Static Voltage Stability Prediction of Power Systems

Application of an Artificial Neural Network for Measurements of Synchrophasor Indicators in the Power System

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