Operating conditions forecasting for monitoring and control of electric power systems

Воропай, Н.И.; Glazunova, Anna; Kurbatsky, Victor; Sidorov, Denis; Spiryaev, Vadim; Tomin, Nikita

doi:10.1109/isgteurope.2010.5638934

Cited by 4 publications

(6 citation statements)

References 7 publications

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“…Forecasting the voltage magnitude is necessary because during the state estimation procedure current state is estimated with some delay, while monitoring and control problems require some advance estimation of system state ("to control is to foresee") [3].…”

Section: Problem Statementmentioning

confidence: 99%

Hybrid Model for Short-Term Forecasting in ElectricPower System

Kutbatsky¹,

Sidorov²,

Tomin³

et al. 2011

IJMLC

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The novel hybrid model for forecasting of time series characterized by nonlinear, nonstationary behavior is presented. Experimental studies on synthetic and real data are carried out and discussed. The application of developed approach for forecasting of price situation in the electricity market and for forecasting of the parameters of the expected operation conditions of the electric power system has demonstrated the accuracy improvement using the most common metrics. The system is examined using the benchmark data records from Australian national electricity market.

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Section: Problem Statementmentioning

confidence: 99%

Hybrid Model for Short-Term Forecasting in ElectricPower System

Kutbatsky¹,

Sidorov²,

Tomin³

et al. 2011

IJMLC

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show abstract

“…The increasing penetration of renewable energy has led to increasing dependency on the volatile nature of renewable energy sources. Because of this, changes in a power system's parameters often feature a sharply variable non-stationary behavior, which limits the efficiency of modern technologies for forecasting time series [23,29].…”

Section: Introductionmentioning

confidence: 99%

“…It is worth noting that power systems parameters often feature sharply variable non-stationary behaviour, which limits the efficiency of the stated technologies for forecasting of time series. Studies have shown that hybrid approaches, that is a combination of different intelligent techniques, have great potential and are worth pursuing [29]. Previously, several hybrid forecasting approaches have been proposed, including ARIMA-ANN models [1], Fuzzy-ANN-based models [14], Wavelet-ANN-based models [26], Fuzzyexpert system-based models [4] among others [6].…”

Section: Introductionmentioning

confidence: 99%

Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning

Kurbatsky¹,

Tomin²,

Spiryaev³

et al. 2014

Preprint

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A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression.Apart from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

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“…Sophisticated operation conditions of large scale EPS need a powerful instrument to study dynamic characteristics in real time which includes a system of wide-scale monitoring that enhances control efficiency of normal and emergency EPS conditions, and forecasting the EPS parameters, first of all frequency of the electrical network, voltage magnitude and the active power flows [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Such a forecast can prevent a sharp abrupt change of control inputs at random nature of the frequency change caused by various malfunctions or failures of technical means of control systems, inaccuracy of input data, etc. It is highly desirable to have a tool for forecasting the voltage magnitude because during the state estimation procedure the current state is estimated with some delay, while monitoring and control problems require some advance estimation of system state ("to control is to foresee") [15].…”

Section: Introductionmentioning

confidence: 99%

The hybrid model based on Hilbert-Huang Transform and neural networks for forecasting of short-term operation conditions of power system

Kurbatsky

Sidorov

Spiryaev

et al. 2011

2011 IEEE Trondheim PowerTech

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The paper addresses the conventional approaches to the short-term forecasting of nonstationary processes in complex power systems using the methodology of artificial neural networks (ANNs). In many practical cases the application of different ANNs can provide a satisfactory forecast. But data preprocessing and analysis can significantly improve the forecast. In this paper the Hilbert-Huang Transform (HHT) is used as one of the most promising tools in this area. Here we focus on HHT since this transform underlies the proposed two-stage intelligent approach to short-term forecasting of nonstationary processes.

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Operating conditions forecasting for monitoring and control of electric power systems

Cited by 4 publications

References 7 publications

Hybrid Model for Short-Term Forecasting in ElectricPower System

Hybrid Model for Short-Term Forecasting in ElectricPower System

Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning

The hybrid model based on Hilbert-Huang Transform and neural networks for forecasting of short-term operation conditions of power system

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