An Intelligent Dynamic Security Assessment Framework for Power Systems With Wind Power

Xu, Yan; Zhang, Dong; Xu, Zhao; Meng, Ke; Wong, Kit Po

doi:10.1109/tii.2012.2206396

Cited by 90 publications

(37 citation statements)

References 27 publications

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“…( ) = (7) The number of PMUs required for complete observability (while considering zero injection buses) for the IEEE-118 bus system is 29. They were placed on buses 3,8,11,12,17,20,23,28,34,37,40,45,49,52,56,62,65,72,75,77,80,85,86,91,94,102,105,110, and 114.…”

Section: =1mentioning

confidence: 99%

Application of Machine Learning for Online Dynamic Security Assessment in Presence of System Variability and Additive Instrumentation Errors

Nath

Biswas

Pal

2019

2019 North American Power Symposium (NAPS)

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Large-scale blackouts that have occurred in the past few decades have necessitated the need to do extensive research in the field of grid security assessment. With the aid of synchrophasor technology, which uses phasor measurement unit (PMU) data, dynamic security assessment (DSA) can be performed online. However, existing applications of DSA are challenged by variability in system conditions and unaccounted for measurement errors. To overcome these challenges, this research develops a DSA scheme to provide security prediction in real-time for load profiles of different seasons in presence of realistic errors in the PMU measurements. The major contributions of this paper are: (1) develop a DSA scheme based on PMU data, (2) consider seasonal load profiles, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA with and without erroneous measurements. The performance of this approach is tested on the IEEE-118 bus system. Comparative analysis of the accuracies of the ML algorithms under different operating scenarios highlights the importance of considering realistic errors and variability in system conditions while creating a DSA scheme.

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Section: =1mentioning

confidence: 99%

Application of Machine Learning for Online Dynamic Security Assessment in Presence of System Variability and Additive Instrumentation Errors

Nath

Biswas

Pal

2019

2019 North American Power Symposium (NAPS)

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“…After confirming the first-order stability of the original data's logarithmic sequence, the initial data are disintegrated into a deterministic trend, a cyclic component, and a random impact effect according to Equations (3), (8) and (9).…”

Section: The Proposed Multi-stage Intelligent Algorithm For Predictinmentioning

confidence: 99%

Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm

Zhao

Guo

2018

Sustainability

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Abstract:As the most efficient renewable energy source for generating electricity in a modern electricity network, wind power has the potential to realize sustainable energy supply. However, owing to its random and intermittent instincts, a high permeability of wind power into a power network demands accurate and effective wind energy prediction models. This study proposes a multi-stage intelligent algorithm for wind electric power prediction, which combines the Beveridge-Nelson (B-N) decomposition approach, the Least Square Support Vector Machine (LSSVM), and a newly proposed intelligent optimization approach called the Grasshopper Optimization Algorithm (GOA). For data preprocessing, the B-N decomposition approach was employed to disintegrate the hourly wind electric power data into a deterministic trend, a cyclic term, and a random component. Then, the LSSVM optimized by the GOA (denoted GOA-LSSVM) was applied to forecast the future 168 h of the deterministic trend, the cyclic term, and the stochastic component, respectively. Finally, the future hourly wind electric power values can be obtained by multiplying the forecasted values of these three trends. Through comparing the forecasting performance of this proposed method with the LSSVM, the LSSVM optimized by the Fruit-fly Optimization Algorithm (FOA-LSSVM), and the LSSVM optimized by Particle Swarm Optimization (PSO-LSSVM), it is verified that the established multi-stage approach is superior to other models and can increase the precision of wind electric power prediction effectively.

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“…In the Province of Alberta, Canada, in particular, the installed capacity reached 1087 MW in late 2012, and is expected to grow to 2388 MW by 2016 [4]. Despite the environmental benefits of wind power [5], it has an intermittent nature [6], which could affect power systems security [7] and reliability [8].…”

Section: Introductionmentioning

confidence: 99%

Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm

Chitsaz

Amjady

Zareipour

2015

Energy Conversion and Management

210

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An Intelligent Dynamic Security Assessment Framework for Power Systems With Wind Power

Cited by 90 publications

References 27 publications

Application of Machine Learning for Online Dynamic Security Assessment in Presence of System Variability and Additive Instrumentation Errors

Application of Machine Learning for Online Dynamic Security Assessment in Presence of System Variability and Additive Instrumentation Errors

Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm

Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm

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