Nikita Tomin

Intelligent Coordinated Operation and Emergency Control in Electric Power Systems

Nowadays electric power industry is undergoing radical transformations. Therefore it is necessary to enhance the efficiency of electric power system operation and emergency control. The paper presents the main features of the advanced system for monitoring and forecasting of operating conditions and control of electric power systems. Current state estimation through the integration of traditional information and artificial intelligence technologies is presented. Short-term forecasting of operating conditions by advanced information technologies is discussed. The technique for adaptation of fuzzy logic PSS is suggested. Coordinated emergency control of load and FACTS devices is studied. PMU application to control transients by FACTS devices is discussed.

On the neural network approach for forecasting of nonstationary time series on the basis of the Hilbert-Huang transform

The two-stage adaptive approach for time series forecasting is proposed. The first stage involves the decomposition of the initial time series into basis functions and application to them of the Hilbert transform. At the second stage the obtained functions and their instantaneous amplitudes are used as input variables of neural network forecasting. The efficiency of the developed approach is displayed in real time series in the electric power problem of forecasting the sharply variable implementations of active power flows.

Optimal Training of Artificial Neural Networks to Forecast Power System State Variables

The problem of forecasting state variables of electric power system is studied. The paper suggests data-driven adaptive approach based on hybrid-genetic algorithm which combines the advantages of genetic algorithm and simulated annealing algorithm. The proposed method has two stages. At the first stage the input signal is decomposed into orthogonal basis functions based on the Hilbert-Huang transform. The genetic algorithm and simulated annealing algorithm are applied to optimal training of the artificial neural network and support vector machine at the second stage. The results of applying the developed approach for the short-term forecasts of active power flows in the electric networks are presented. The best efficiency of proposed approach is demonstrated on real retrospective data of active power flow forecast using the hybrid-genetic support vector machine algorithm.

A random forest-based approach for voltage security monitoring in a power system

Voltage collapse is a critical problem that impacts power system operational security. Timely and accurate assessment of voltage security is necessary to detect alarm states in order to prevent a large-scale blackout. This paper presents an on-line voltage security assessment scheme using periodically updated random forest-based decision trees. We demonstrated the proposed method on the modified 53-bus IEEE power system. Results are presented and discussed.

Application of two stages adaptive neural network approach for short-term forecast of electric power systems

The paper presents the two-stapes adaptive approach for short-term forecast of parameters of expected operating conditions. The first stage involves decomposition of the time series into intrinsic modal functions and subsequent application of the Hilbert transform. During the second stage the computed modal functions and amplitudes are employed as input functions for artificial neural networks. Their optimal combinations is constructed using methods of simulated annealing and neural-genetic input selection approach. The efficiency of developed approach is demonstrated on real time the problem of forecasting power flow and voltage level.

A hybrid artificial neural network for voltage security evaluation in a power system

A majority of recent large-scale blackouts have been the consequence of instabilities characterized by sudden voltage collapse phenomena. This paper presents a method for voltage instability monitoring in a power system with a hybrid artificial neural network which consist of a multilayer perceptron and the Kohonen neural network. The proposed method has a couple of the following functions: the Kohonen network is used to classify the system operating state; the Kohonen output patterns are used as inputs to train of a multilayer perceptron for identification of alarm states that are dangerous for the system security. The approach is targeting a blackout prevention scheme; given that the blackout signal is captured before it can collapse the power system. The proposed method is realized in R and demonstrated the modified IEEE One Area RTS-96 power system.

Preventive and emergency control of intelligent power systems

The adaptive emergency control concept is based on realization of a tradeoff between preventive and emergency control by combining preventive and emergency actions. The paper proposes to complement the existing control strategies by predictive control strategy - adaptive emergency control, which implies identification of the emergency, until the emergency has occurred. In this paper presented the smart predictive voltage stability assessment (VSA) for the adaptive emergency control techniques on the basis of the artificial neural network methodology. The main idea of the smart predictive VSA here is a creation of a neural network model, based on a self-organized Kohonen map SOM that will be able to perform monitoring and prediction of emergency condition. Simulation results are obtained by the proposed scheme for different power system networks to assess the security level of the network.

Preventing Large-Scale Emergencies in Modern Power Systems : AI Approach

In recent years, due to liberalization, power systems are being operated closer and closer to their limits. At the same time, they have increased in size and complexity. Both factors increase the risk of major power outages and blackouts. In emergency and abnormal conditions, a power system operator has to deal with large amounts of data. However, due to emotional and psychological stress, an operator may not be able to respond to critical conditions adequately and make correct decisions promptly. Mistakes can damage very expensive power system equipment or worse lead to major emergencies and catastrophic situations. Intelligent systems can play an important role by alarming the operator and suggesting the necessary actions to be taken to deal with a given emergency. This paper outlines some experience obtained at the University of Tasmania, Australia, Energy Systems Institute, Russia and TU-Dortmund University, Germany in developing intelligent systems for preventing large-scale emergencies and blackouts in modern power systems.

Smart Technologies in Emergency Control of Russia's Unified Energy System

The objective trends in electric power systems (EPSs) call for prompter and more adequate response of control systems. New smart measurement, communication and control tools, information and computer technologies can be used to improve EPS controllability. The distinctive features of the Unified Energy System (UES) of Russia are discussed and the current emergency control system is presented in the paper. A modern approach to monitoring, forecasting and control is suggested. Some artificial intelligence applications for development of emergency control in the UES of Russia are presented.

Electricity prices neural networks forecast using the Hilbert-Huang transform

The problem of forecasting of electicity prices is addressed in terms of joint approach employing the general regression artificial neural network and empirical mode decomposition approaches (EMD) which is part of Hilbert-Huang transform. The application of developed approach to day-ahead hourly time series has demonstrated the whole accuracy increase as well as peaks prediction.