Methodology for Optimal Deployment of Corrective Control Measures to Ensure Transient Stability of Uncertain Power Systems

Morales, Juan D.; Milanović, Jovica V.

doi:10.1109/tpwrs.2020.3035113

Cited by 9 publications

(11 citation statements)

References 35 publications

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“…In order to obtain the database of harmonic measurements suitable for ANN training, a modified IEEE 68-bus New England Test System-New York Power System (NETS-NYPS) test network (shown in Fig. 1) was simulated using Monte Carlo based probabilistic approach considering various system operation conditions caused by uncertainties [20,21]. It is a 230 kV transmission system which consists of 68 buses in five geographical areas.…”

Section: A Test Networkmentioning

confidence: 99%

“…The probabilistic scaling factors utilised to represent the operating uncertainties of RES and load demand are updated every 10-min interval, in accordance with the time step required by standard IEEE519 [25]. The probabilistic output/loading scaling factors of wind, PV plans and loads are assumed to be sampled by following Weibull distribution (φ=11.1, k=2.2), Beta distribution (α=13.7, β=1.3) and Normal distribution (μ=1, σ=0.033), respectively [21].…”

Section: B Modelling Of Uncertaintiesmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic Harmonic Estimation in Uncertain Transmission Networks Using Sequential ANNs

Zhao

Milanović

2022

2022 20th International Conference on Harmonics &Amp; Quality of Power (ICHQP)

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In the face of transition towards the decarbonisation, increasing penetration level of power electronic interfaced renewable connections such as wind farms and PV plants are constantly influencing the uncertainties of transmission network and leading to additional uncontrolled harmonic power flows. These potential harmonic distortion issues could result in significant financial losses. To address this problem, the estimation of harmonic propagation through transmission network with increasing penetration of nonlinear loads, power electronics based renewable generation and control devices is becoming increasingly important. This paper proposes a comprehensive framework of applying sequential artificial neural network (ANN) techniques to estimate individual order harmonic distortions and total harmonic distortions (THD) at unmonitored buses in large uncertain transmission networks based on offline measurements and simulations. This study will contribute to facilitate the standard compliance, reduce the extent of the monitor installation, accelerate the assessment of harmonic performance and mitigation studies, as well as contribute to the forecast of potential harmonic issues in large transmission system.

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Section: A Test Networkmentioning

confidence: 99%

Section: B Modelling Of Uncertaintiesmentioning

confidence: 99%

Probabilistic Harmonic Estimation in Uncertain Transmission Networks Using Sequential ANNs

Zhao

Milanović

2022

2022 20th International Conference on Harmonics &Amp; Quality of Power (ICHQP)

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“…For loads, wind, and PV plans, the probabilistic output/loading scaling factors are assumed to be sampled by following Normal distribution (μ=1, σ=0.033). Weibull distribution (φ=11.1, k=2.2) and Beta distribution (α=13.7, β=1.3), respectively [14]. In transmission network, harmonic distortions are mainly injected by PE interfaced wind, PV, and load demands.…”

Section: Probablistic Modelling Of Uncertainties and Harmonicmentioning

confidence: 99%

“…The test network is modelled and simulated in the DigSILENT/PowerFactory environment [12], using Monte Carlo based probabilistic approach for considering operating uncertainties caused by PE interfaced generation and nonliner loads [13,14]. The sequential ANNs was implemented in MATLAB.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Probabilistic Harmonic Estimation in Sparsely Monitored Transmission Networks

Zhao

Milanović

Rodríguez-Pajarón³

et al. 2022

2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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Accurate estimation of harmonics in uncertain, power electronics interfaced large transmission networks without installation of excessive number of power quality monitors can significantly improve and facilitate the probabilistic harmonic propagation studies. Traditional harmonic state estimation methods have been widely studied but are still very challenging in practical application due to the requirement of a large number of synchronized monitoring devices and real-time operational structure of the network. Based on a preliminary study that demonstrates the effectiveness of sequential artificial neural networks (ANNs) in the probabilistic harmonic estimation in uncertain transmission networks, this paper presents further comprehensive accuracy assessment in terms of different types/numbers of harmonic measurements, different stop errors to optimise training time and limited numbers of installed power quality monitors due to realistic reasons. It has been demonstrated that the sequential ANNs is sufficiently accurate and applicable in estimating harmonics in uncertain transmission network, thus contributing to facilitate the identification of potential harmonic issues, benchmarking, standard compliance and the deployment of appropriate harmonic propagation and mitigation solutions.

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“…The generators are considered out of step when the rotor angle deviations exceed the pre-specified limits following faults [5]. Therefore, the critical fault clearing time (CCT) is considered as accurate indicator of the system transient stability [6], [7]. Artificial intelligent and statistical analysis methods were applied to reduce the TSA computation time using phasor measurement units (PMUs) [8].…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of power system transient instability incorporating renewable energy sources

Hoballah

Elsayed²,

Otaibi³

et al. 2022

IJECE

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Transient stability affected by renewable energy sources integration due to reductions of system inertia and uncertainties associated with the expected generation. The ability to manage relation between the available big data and transient stability assessment (TSA) enables fast and accurate monitoring of TSA to prepare the required actions for secure operation. This work aims to build a predictive model using Gaussian process regression for online TSA utilizing selected features. The critical fault clearing time (CCT) is used as TSA index. The selected features map the system dynamics to reduce the burden of data collection and the computation time. The required data were collected offline from power flow calculations at different operating conditions. Therefore, CCT was calculated using electromagnetic transientsimulation at each operating point by applying self-clearance three phase short circuit at prespecified locations. The features selection was implemented using the neighborhood component analysis, the Minimum Redundancy Maximum Relevance algorithm, and K-means clustering algorithm. The vulnerability of selected features tends to result great variation on the best features from the three methods. Hybrid collection of the best common features was used to enhance the TSA by refining the final selected features. The proposed model was investigated over 66-bus system.

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Methodology for Optimal Deployment of Corrective Control Measures to Ensure Transient Stability of Uncertain Power Systems

Cited by 9 publications

References 35 publications

Probabilistic Harmonic Estimation in Uncertain Transmission Networks Using Sequential ANNs

Probabilistic Harmonic Estimation in Uncertain Transmission Networks Using Sequential ANNs

Accuracy of Probabilistic Harmonic Estimation in Sparsely Monitored Transmission Networks

Risk assessment of power system transient instability incorporating renewable energy sources

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