Very Short-Term Power System Frequency Forecasting

Yurdakul, Ogun; Eser, Fatih; Sivrikaya, Fikret; Albayrak, Şahin

doi:10.1109/access.2020.3013165

Cited by 20 publications

(3 citation statements)

References 20 publications

(29 reference statements)

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“…Therefore, in this study, the active load, reactive load, and total load amount of the system after the disturbance, as well as the reserve capacity and the total load amount of each generator at the moment and the power shortage value after the disturbance, are chosen as input features. Additionally, an extra feature is selected for the input feature set (Yurdakul et al, 2020).…”

Section: The Selection Of Input Featuresmentioning

confidence: 99%

Frequency prediction of a post-disturbance power system using a hybrid ARIMA and DBN model

Wang,

Li,

Pan

et al. 2024

Front. Energy Res.

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The online prediction of power system dynamic frequency helps to guide the choice of control measures quickly and accurately after a disturbance, and this then ensures the reliable and stable operations of a power system. However, the prediction performance of the traditional single model is not accurate enough, and the prediction method cannot reflect the dynamic mechanism of the power system. To address these challenges, based on the analysis of the mechanism of the dynamic operation of a power system, a dynamic frequency online prediction method using the autoregressive integrated moving average (ARIMA) model and the deep belief network (DBN) is proposed in this paper. First, according to the mechanism of the dynamic operation of a power system, the dynamic frequency can be regarded as having two stages after the disturbance occurs. In the first stage, the frequency changes monotonously in the short term, which is predicted by the ARIMA model. Furthermore, the second stage is an oscillation phase with changing amplitude, which is predicted by the DBN. The calibration process is used to combine the two predicted results. Second, the three metrics including the frequency nadir (fnadir), the quasi-steady state frequency (fss), and the frequency curve obtained through the prediction are analyzed to measure the accuracy of the prediction results. Finally, to verify the accuracy of the proposed model, the IEEE 10-generator 39-bus benchmark system is used for verification.

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Section: The Selection Of Input Featuresmentioning

confidence: 99%

Frequency prediction of a post-disturbance power system using a hybrid ARIMA and DBN model

Wang,

Li,

Pan

et al. 2024

Front. Energy Res.

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“…Therefore, ML has been a popular technique for analyzing power system stability in recent years [ 24 , 25 ]. Various DL methods, such as long short-term memory (LSTM) [ 26 ], convolutional neural networks (CNNs) [ 27 ], and graph neural networks (GNNs) [ 28 ], have been widely applied to power system stability prediction. To the best of the authors’ knowledge, LSTM is good at extracting sequence features [ 29 ], CNNs are good at extracting local features [ 30 ], and GNNs are good at extracting topology structure features [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

Frequency Stability Prediction of Power Systems Using Vision Transformer and Copula Entropy

Liu

Han

et al. 2022

Entropy

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This paper addresses the problem of frequency stability prediction (FSP) following active power disturbances in power systems by proposing a vision transformer (ViT) method that predicts frequency stability in real time. The core idea of the FSP approach employing the ViT is to use the time-series data of power system operations as ViT inputs to perform FSP accurately and quickly so that operators can decide frequency control actions, minimizing the losses caused by incidents. Additionally, due to the high-dimensional and redundant input data of the power system and the O(N2) computational complexity of the transformer, feature selection based on copula entropy (CE) is used to construct image-like data with fixed dimensions from power system operation data and remove redundant information. Moreover, no previous FSP study has taken safety margins into consideration, which may threaten the secure operation of power systems. Therefore, a frequency security index (FSI) is used to form the sample labels, which are categorized as “insecurity”, “relative security”, and “absolute security”. Finally, various case studies are carried out on a modified New England 39-bus system and a modified ACTIVSg500 system for projected 0% to 40% nonsynchronous system penetration levels. The simulation results demonstrate that the proposed method achieves state-of-the-art (SOTA) performance on normal, noisy, and incomplete datasets in comparison with eight machine-learning methods.

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“…A key architectural element of an LSTM block is the memory cell, which, in conjunction with the operation of the gates, spearheads the storage of valuable information. The joint operation of the memory cell and the LSTM gates imparts the capability to capture long-term temporal dependencies to an LSTM block [10].…”

Section: A Long Short-term Memory Modelmentioning

confidence: 99%

Forecasting Daily Primary Three-Hour Net Load Ramps in the CAISO System

Yurdakul

Meyer

Sivrikaya

et al. 2021

2020 52nd North American Power Symposium (NAPS)

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The deepening penetration of variable energy resources creates unprecedented challenges for system operators (SOs). An issue that merits special attention is the precipitous net load ramps, which require SOs to have flexible capacity at their disposal so as to maintain the supply-demand balance at all times. In the judicious procurement and deployment of flexible capacity, a tool that forecasts net load ramps may be of great assistance to SOs. To this end, we propose a methodology to forecast the magnitude and start time of daily primary threehour net load ramps. We perform an extensive analysis so as to identify the factors that influence net load and draw on the identified factors to develop a forecasting methodology that harnesses the long short-term memory model. We demonstrate the effectiveness of the proposed methodology on the CAISO system using comparative assessments with selected benchmarks based on various evaluation metrics.

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Very Short-Term Power System Frequency Forecasting

Cited by 20 publications

References 20 publications

Frequency prediction of a post-disturbance power system using a hybrid ARIMA and DBN model

Frequency prediction of a post-disturbance power system using a hybrid ARIMA and DBN model

Frequency Stability Prediction of Power Systems Using Vision Transformer and Copula Entropy

Forecasting Daily Primary Three-Hour Net Load Ramps in the CAISO System

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