Power Grid Frequency Prediction Using ANN Considering the Stochasticity of Wind Power

Kaur, Simarjit; Agrawal, Sunil; Verma, Yajvender Pal

doi:10.1109/cicn.2013.71

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Compared to previously existing forecasts of the power grid frequency [9], [12], [14], [15], we make three key contributions: First, we introduce the daily profile as a relevant and system-specific null model. Secondly, we improve the statistical evaluation of the WNN predictor by increasing the amount of training and test data from one month [15] to multiple years.…”

Section: Discussionmentioning

confidence: 99%

“…With the increasing popularity of machine learning tech- VOLUME 4, 2016 niques [11], there are many tools available to forecast time series, such as the power grid frequency. Recent studies used artificial neural networks (ANN) [12] to predict hourly frequency time series in India based on features such as wind power generation and power demand. Other authors [9] used a linear state space model and uncertain basis function to predict US frequency time series for up to one second, while a Bayesian network was used to predict the frequency time series for up to 3 minutes [13].…”

Section: Introductionmentioning

confidence: 99%

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Predictability of Power Grid Frequency

2020

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The power grid frequency is the central observable in power system control, as it measures the balance of electrical supply and demand. A reliable frequency forecast can facilitate rapid control actions and may thus greatly improve power system stability. Here, we develop a weighted-nearestneighbour (WNN) predictor to investigate how predictable the frequency trajectories are. Our forecasts for up to one hour are more precise than averaged daily profiles and could increase the efficiency of frequency control actions. Furthermore, we gain an increased understanding of the specific properties of different synchronous areas by interpreting the optimal prediction parameters (number of nearest neighbours, the prediction horizon, etc.) in terms of the physical system. Finally, prediction errors indicate the occurrence of exceptional external perturbations. Overall, we provide a diagnostics tool and an accurate predictor of the power grid frequency time series, allowing better understanding of the underlying dynamics.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictability of Power Grid Frequency

2020

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“…On account of the lack of the availability of the accurate weather information, their effect on frequency prediction could not be considered. [2] Based on the future time block generation frequency data, we can link the same with the real time power generating utility server where we can integrate actual power, frequency and suggested generation data on line. The opportunity utilization for maximizing earnings can be done in accordance with the predicted frequency and we can be prepared in advance for resource mobilization of meeting the opportunity utilization by over /under injection of the power in the grid.…”

Section: Resultsmentioning

confidence: 99%

“…Besides this, it also depends on frequency value in previous blocks. Considering these all-crucial parameter numerous forecasting techniques evolved [2], [3] considering stochasticity of the frequency.…”

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confidence: 99%

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Study of the Short-Term Generation Block Frequency Forecasting of a Power Generation Utility Using Artificial Neural Network

Singh¹,

Payasi²,

Sinha³

2017

IJRET

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Frequency forecasting a very vital aspect in power system operation and control. For In traditional approaches, frequency prediction algorithms prepared based on pattern matching of the previous frequencies, but here in this paper an algorithms is prepared which will include the various other non linear parameters also such as actual power generated, load demand in term of scheduled power generation allocated to particular utility. This deficit/surplus power is taken as one of the key factor for forecasting the frequency to a fair degree of accuracy. ANN's ability to provide most accurate forecasting results with smaller processing time is utilized here in this paper.In this work, we will study short-term frequency prediction for a thermal power generating station station with the help of ANN. We will utilize the past two year data of plant's suggested generation, declared capacity and frequency for each block of 15 minutes to train the Artificial Neural Network. Keywords: ABT, Unscheduled Interchange, NRLDC Artificial Neural Network, Load Dispatch --------------------------------------------------------------------***---------------------------------------------------------------------- BACKGROUNDFrequency forecasting in power system is quite tedious job as nature of frequency is stochastic. It varies with declared capacity of the station, grid network frequency demand, surplus/deficit power in system. Besides this, it also depends on frequency value in previous blocks. Considering these all-crucial parameter numerous forecasting techniques evolved [2], [3] considering stochasticity of the frequency.In conventional methods, such as time series, regression theory model, Kalman Filter and Spectral Expansion technique considered frequency values of the previous times with known per day or per session basis periodicities. Authentic frequency forecasting is not viable with these methods, as they do not adequately reassemble non-linearity between the frequency and various other parameters associated with it.Apart from this, these approaches demonstrate larger processing time, sluggish performance and incorrect forecasting due to rapidly season change. Artificial Intelligence (AI) based approaches such as ANN, Genetic Algorithams, Experts Systems and fuzzy logic have shown their excellence over the conventional methods of forecasting. The Artificial Neural Network learns from experience and easily embodied to adopt the non-linear relationship between frequency and other factors associated with historical data. Hence, with application of the ABT for enforcing charges on power drawl that is sensitive to the system frequency, power utility operator focused on designing predicting generation block frequency for earning UI charges and scheduling of the generators accordingly.

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Generation and Reserve Scheduling Under Frequency Linked Pricing Regime

Verma

Sharma

2022

Lecture Notes in Electrical Engineering

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Power Grid Frequency Prediction Using ANN Considering the Stochasticity of Wind Power

Cited by 6 publications

References 12 publications

Predictability of Power Grid Frequency

Predictability of Power Grid Frequency

Study of the Short-Term Generation Block Frequency Forecasting of a Power Generation Utility Using Artificial Neural Network

Generation and Reserve Scheduling Under Frequency Linked Pricing Regime

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