M.B. Do Coutto Filho

An Efficient Dynamic State Estimation Algorithm including Bad Data Processing

This paper presents a comparison between the performance of dynamic and tracking estimators, in power systems operating under quasi-static conditions, concerning their characteristics of forecasting and filtering. From this comparison a new dynamic estimator which incorporates the main advantages of the previous estimators is proposed. Also, a new scheme of detection and identification of bad data properly built for dynamic algorithms is presented. Numerical results showing the performance of the new algorithm under different operational conditions are discussed.

Alarm processing in electrical power systems through a neuro-fuzzy approach

This work presents a methodology that combines the use of artificial neural networks and fuzzy logic for alarm processing and identification of faulted components in electrical power systems. Fuzzy relations are established and form a database employed to train artificial neural networks. The artificial neural networks inputs are alarm patterns, while each output neuron is responsible for estimating the degree of membership of a specific system component into the class of faulted components. The proposed method allows good interpretation of the results, even in the presence of difficult corrupted alarm patterns. Tests are performed with a test system and with part of a real Brazilian system.

Meter placement for power system state estimation using simulated annealing

Power system state estimators usually require a set of redundant measurements, which are appropriately chosen according to the type, number and location of the measurement points in the supervised electric network. Selection of a measurement system aims at attending to requirements such as, observability, reliability and quality of the estimation process, talcing into account the associated monetary costs. This paper presents a metaheuristics-based methodology to solve the problem of meter placement for power system state estimation. The suggested technique ensures the reliability of the system at a low cost. Simulation results covering the application of the proposed methodology are presented and discussed.

Optimal metering systems for monitoring power networks under multiple topological scenarios

Summary form only given. This work presents a methodology for designing optimal metering systems for real-time power system monitoring, taking into account different topologies that the network may experiment. Genetic algorithms are employed to achieve a trade-off between investment costs and reliability of the state estimation process under many different topology scenarios. This is done by formulating a fitness function where the cost of the metering system is minimized, while no critical measurements and/or critical sets are allowed in the optimal solution. An efficient algorithm for the identification of critical measurements and sets (irrespective of state estimation runs) is employed during the evaluation of the fitness function. Simulation results illustrate the performance of the proposed method

Power System Probabilistic Reliability Assessment: Current Procedures in Brazil

This paper gives a detailed account of the current practices used in Brazil for probabilistic reliability assessment of the national power grid, from the adequacy point of view. These procedures are utilized by the Brazilian independent electric system operator and were recently made mandatory by the National Electric Energy Regulatory Agency (ANEEL).

Identifying critical measurements & sets for power system state estimation

Data redundancy is an important prerequisite for state estimation. During system operation, critical redundancy levels can be reached, creating adverse conditions for the state estimation process, especially regarding data validation. In this paper a numerical algorithm for the identification of critical measurements and sets is proposed. Results covering its application to typical power system networks are presented and discussed.

An integrated approach to power system reliability assessment

This paper presents an integrated approach to power system reliability assessment. It gives an updated view of the subject and aims to unify, classify and extend some fundamental ideas and controversial issues which provide theoretical and practical support to these studies. The resulting conceptual framework lays the basis for a useful taxonomy which may be recalled for development or comparison of dissimilar techniques and computational programs which are frequently used in this field of engineering.

Exploring fuzzy relations for alarm processing and fault location in electrical power systems

This work investigates the construction of fuzzy relations for alarm processing and fault location in electrical power systems. Several data aggregation classes are tested and compared. Fuzzy relations are established with the aid of the knowledge on protection devices operation for faults involving different system components. Tests are performed with a 7-bus test system and with part of a real Brazilian power system.

Automatic contingency selection based on a pattern analysis approach

Summary form only given. This paper presents a method for power systems automatic contingency selection and static security evaluation. It is possible to identify potentially harmful contingencies in a very short computational time, being the risk of false alarms and contingency misses. The method is tested for many different operating conditions and simulated with the IEEE 24 bus test system. Classification rates for contingency selection and static security evaluation are also provided.

Hierarchical Dynamic State Estimation in Electric Power Systems

Abstract In many countries, huge power networks are formed-from the interconnection of electric power companies, each one having its own supervisory control center. In order to supervise efficiently such power systems, hierarchical state estimation techniques have to be considered. This paper presents a new algorithm using hierarchical concepts to solve the dynamic state estimation (DSE) problem for large-scale electric power system operating under quasi-static conditions. A two-level calculation is performed to give a satisfactory solution from the computational point of view for the usual steps of the DSE procedure: model parameter estimation, state prediction and state filtering. At the local level, a conventional DSE algorithm is carried out simultaneously and independently for (all subsystems. The coordination of these local estimations is obtained at a national center for supervision and control, which is at the top of operational hierarchy in the interconnected electrical system. Numerical results show the performance of the proposed algorithm under different operational conditions: normal, sudden variation of states and occurrence of bad data.