Accurate prediction of damping in large interconnected power systems with the aid of regression analysis

Archer, B.A.; Annakkage, U.D.; Jayasekara, B.; Wijetunge, P.

doi:10.1109/pes.2009.5275684

Cited by 7 publications

(10 citation statements)

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“…Regression analysis methods have been applied to the study of the power system [14] in widespread areas, such as the derivation of an exact transient stability boundary of the power system [15]; contingency severity assessment for power system voltage security studies [16]; applications of transient stability forecasting [17]. In [10] spatial electric load forecasting approaches are discussed comprehensively.…”

Section: Regression Analysismentioning

confidence: 99%

Weather-based interruption prediction in the smart grid utilizing chronological data

Sarwat

Amini

Domijan

et al. 2015

J. Mod. Power Syst. Clean Energy

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This unique study will demonstrate a combined effect of weather parameters on the total number of power distribution interruptions in a region. Based on common weather conditions, a theoretical model can predict interruptions and risk assessment with immediate weather conditions. Using daily and hourly weather data, the created models will predict the number of daily or by-shift interruptions. The weather and environmental conditions to be addressed will include rain, wind, temperature, lightning density, humidity, barometric pressure, snow and ice. Models will be developed to allow broad applications. Statistical and deterministic simulations of the models using the data collected will be conducted by employing existing software, and the results will be used to refine the models. Models developed in this study will be used to predict power interruptions in areas that can be readily monitored, thus validating the models. The application has resulted in defining the predicted number of interruptions in a region with a specific confidence level. Reliability is major concern for every utility. Prediction and timely action to minimize the outage duration improves reliability. Use of this predictor model with existing smart grid self-healing technology is proposed.

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Section: Regression Analysismentioning

confidence: 99%

Weather-based interruption prediction in the smart grid utilizing chronological data

Sarwat

Amini

Domijan

et al. 2015

J. Mod. Power Syst. Clean Energy

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“…In [8], the authors apply regression analysis to estimate the damping under different operating conditions using the results from load flow studies along with results from eigenvalue analysis. Load flow data from simulations are used in [9] along with the application of generalized linear models to find correlations between load flow quantities and damping.…”

Section: Statistical Analysis and Forecasting Of Damping In The Nordimentioning

confidence: 99%

Statistical Analysis and Forecasting of Damping in the Nordic Power System

Sulla

Koivisto

Seppänen

et al. 2015

IEEE Trans. Power Syst.

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This paper presents an application of multiple linear regression (MLR) to extract significant correlations between damping of electromechanical modes and system operating conditions and to forecast future damping values, based on existing day-ahead market forecasts for power flows and generation. The presented analysis uses measurements from the Nordic power system. First, a static MLR model is developed to explain the variability of the damping of the 0.35-Hz inter-area mode in the Nordic system. Together with the static model, a dynamic MLR model is used for forecasting the damping 24 hours ahead, using day-ahead market forecasts. Test results indicate the proposed methods are able to correctly predict about 90% of the low damped operating conditions observed during a year, if day-ahead market forecasts are accurate. These results suggest that the methods could be used to issue early warnings about future operating conditions with low damping.Index Terms-Damping forecast, electromechanical oscillations, multiple linear regression (MLR), phasor measurement unit (PMU) data.

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“…For each base case, active and reactive power at load buses are multiplied by a random factor. The random variation of the loads at node i can be seen as follows [21] [12]:…”

Section: A Database Generationmentioning

confidence: 99%

“…An approach to improve the damping by adjusting the operating point is suggested in [10]. Besides, some methods to predict the damping ratios using multiple linear regression or decision trees are proposed in [11], [12] and [13]. These methods require an offline learning phase but give fast and accurate online results after training.…”

Section: Introductionmentioning

confidence: 99%

Building low-dimensional damping predictors of the power system modes of oscillation

2012

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This paper proposes a method for building damping predictors of the power system modes. This method is able to accurately predict the damping of each mode of oscillation and also to locate the variables, monitored by operators, having the biggest influence on the mode dynamics. The method consists of three steps. First, a database of historical data is set up for each potentially dangerous mode of oscillation. This database contains as inputs a set of operating conditions and as output the corresponding mode damping ratio. Afterwards, the inputoutput pairs "operating conditions -mode damping ratio" are used to build an ensemble of regression trees whose structure is analyzed to identify the most relevant variables. In the last step, a second tree-based regressor is run by considering only as inputs these most relevant variables in order to have a lowdimensional predictor. The usefulness of the predictors to avoid poorly damped operating points is then presented. The approach is tested on a 16-machine power system and gives good results.

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Accurate prediction of damping in large interconnected power systems with the aid of regression analysis

Cited by 7 publications

References 0 publications

Weather-based interruption prediction in the smart grid utilizing chronological data

Weather-based interruption prediction in the smart grid utilizing chronological data

Statistical Analysis and Forecasting of Damping in the Nordic Power System

Building low-dimensional damping predictors of the power system modes of oscillation

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