Irina Kolosok

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.

Bad data detection at decomposition of state estimation problem

Bad data detection is the most important problem in electric power system (EPS) state estimation. The paper addresses the algorithm of bad data detection by the test equation method at decomposition of state estimation problem. The decomposition algorithm includes structural and functional decomposition of the problem. The structural decomposition is carried out by dividing the calculated scheme into subsystems with respect to voltage levels. The functional decomposition is performed in accordance with the problems solved during the state estimation process: bad data detection, state estimation on the basis of the quadratic and robust criteria. This makes it possible to increase the efficiency of bad data detection and, hence, the accuracy of estimates; organize flexible choice of the method for solving one or another state estimation problem for each subsystem, accelerate the process of measurement processing and, hence, reduce the time of the entire scheme state estimation.

Monitoring of EPS operation by the state estimation methods

State estimation is the most important procedure providing the electric power system control with reliable quality information. The role of state estimation problem is getting more pronounced with transition to a qualitatively new level of technologies and control of power systems based on the wide-area monitoring and control systems (WAMS/WACS). Currently the electric power system (EPS) state estimation methods are developed on the basis of combined application of conventional measurements from SCADA and phasor measurement that are provided by PMU. Practical application of PMUs reveals a number of factors that affect the correctness of their measurements. Therefore, the need arises to verify the PMU measurements. The paper suggests the approaches to validation of PMU measurements on the basis of test equation technique. Consideration is given to the modification of state estimation algorithm by combining the SCADA and PMU measurements.

PMU placement on the basis of SCADA measurements for fast load flow calculation in electric power systems

The paper considers the problem of PMU placement in such a way that the volume of initial information based on the SCADA and PMU measurements is sufficient to determine all the state vector components for load flow calculations without iterations. The PMU number in this case should be minimal. The problem of PMU placement is solved by the simulated annealing method.

Test equations for validation of critical measurements and critical sets at power system state estimation

The paper presents new approaches to determination and validation of critical measurements and critical sets. These approaches are based on the use of test equations for the problems of state estimation. The concept of test equations is outlined, the methods for a priori data analysis using test equations are presented, the possible application of dynamic algorithms and artificial neural network (ANN) to search for bad data is considered. The idea of neural network validation is based on the capability of the learned ANN to restore an ideal image by its distorted copy. The efficiency of the suggested approaches has been confirmed by the results of experimental studies.

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.

A Multi-Agent Approach to Electric Power Systems

Electric power systems are rather complicated objects for modeling, investigation and control because of many elements and complex structure. Comprehensive multi-functional software is necessary to study multi-dimensional systems of the kind. The problems of current state estimation should be solved for monitoring of electric power system operation conditions. Emergency control actions are required to improve stability of electric power systems. A multi-agent approach can be used to solve such complex problems of electric power systems. The chapter deals with the following important areas of modeling, investigation and control of large electric power systems: • Effective construction of comprehensive software by using the multi-agent approach; • Decomposition of state estimation problem for large electric power system by using phasor measurement units and the multi-agent approach; • Multi-agent approach to coordination of emergency control devices against voltage collapse. The effectiveness of multi-agent approach for solving the above problems is illustrated by test examples.

Decomposition algorithm for power system state estimation by the test equation technique and its implementation on the basis of multi-agent approach

State estimation in the modern power systems of large dimensionality is a sophisticated problem which is accompanied by the difficulties related to inhomogeneity of the calculated schemes, a large volume of information to be processed and the requirement for high-speed software. Distributed data processing in decomposition of the state estimation problem is an efficient method of tackling the difficulties. The paper addresses decomposition algorithm of state estimation by the test equation technique. The algorithm is based on the structural and functional decomposition of state estimation problem. The structural decomposition suggests dividing the calculated schemes into subsystems. A two-level algorithm is proposed to divide the calculated network into subsystems. Application of the test equation technique which makes it possible to fix the values of measured variables and set zero variances for them, as well as placement of Phasor Measurement Units (PMU) at boundary nodes make it possible to essentially simplify the procedure of coordinating the solutions obtained for separate subsystems. The functional decomposition is performed in accordance with the problems solved within state estimation: a priory detection of bad data, state estimation on the basis of quadratic and robust criteria. The multiagent system proposed to implement the suggested state estimation algorithm is described. The example of calculating the estimates while dividing the calculated scheme into subsystems with PMU placed at boundary nodes is presented.

The test equation method for linear state estimation based on PMU data

The advent of next generation devices for synchronized phasor measurements (voltage and current values in lines) - PMU - makes it possible to realize linear algorithms of state estimation. The paper suggests the development of the test equation technique for linear state estimation of power system facilities that are monitored on the basis of PMU measurements. New algorithms for the construction of the test equations on the basis of PMU measurements are presented. The algorithms are based on the exclusion of unmeasured variables or state vector components from the electrical circuit equations written in the rectangular coordinates. By virtue of linear nature of these equations the obtained test equations are also linear. The paper presents the algorithms for bad data detection in PMU measurements as well as the algorithms for calculation of estimates, using linear test equations which make it possible to obtain a solution within one iteration. Performance of the algorithms was tested on a test network equipped with PMUs. The developed algorithms allow us to make local state estimation of individual power system facilities (power plants, substations, network sections) that are monitored online on the basis of PMU measurements.

Intelligent control and protection in the Russian electric power system

Abstract The chapter presents the following contributions: (1) a general overview of an intelligent electric power system with an active and adaptive network (IESAAN) as the Russian vision of a smart grid, its technological platform, and control system; (2) state estimation (SE) techniques as informational support of the IESAAN control including SE with phasor measurements use, FACTS modeling in SE applications, dynamic SE, and cyber-physical security issues of SE; (3) intelligent operation and smart emergency protection in Russia including requirements for new protection systems; a novel system of monitoring, forecasting, and control of electric power system (EPS); artificial intelligence applications in EPS such as a forecast of state variables based on dynamic SE and hybrid data-driven approaches; a total transfer capability estimation method; an automatic decision tree-based system for online voltage security control; a multiagent coordination of emergency control devices; and an intelligent system for preventing large-scale emergencies; (4) a description of smart grid territorial clusters in the interconnected power systems of Russia.