M.T. Schilling

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.

Fault location in electrical power systems using intelligent systems techniques

In this work an artificial neural network based methodology is proposed for power systems fault location. Several artificial neural networks are employed, each of them being responsible for detecting faults involving a limited number of components. The proposed methodology is tested using a test system and a real Brazilian system. Indexes based on the Hamming distance are also proposed for feature selection and analysis.

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.

Reporting bulk power system delivery point reliability

This paper is part of an ongoing activity of the IEEE Application of Probability Methods Subcommittee to advance the probabilistic methods used to analyze bulk power system (BPS) reliability. The objective of the Task Force is to develop a set of guidelines for measuring delivery point reliability. This involves reviewing published literature and other documentation of existing procedures, definitions, and indices. This report presents the results of the Task Force efforts to compile a useful set of terms and procedures for consistent reporting of BPS delivery point reliability.

Bibliography on Power Systems Probabilistic Security Analysis 1968-2008

This paper presents a bibliography on power systems probabilistic security, encompassing small signal, transient stability analysis, and a plethora of closely related subjects.

Monte Carlo Simulation and Contingency Enumeration in Bulk Power Systems Reliability Assessment

In reliability assessment of bulk power systems, two methods have been largely studied and used: contingency enumeration and non-sequential Monte Carlo simulation. Both have their wellknown advantages and drawbacks. Contingency enumeration is conceptually simple and usually requires low computational effort. Conversely, Monte Carlo simulation is computationally harder, but much more versatile to model random aspects. This paper depicts some major aspects regarding both methods. It also shows that it is not a matter of choosing the definite and unique technique, but how they can be used in a complementary way. A real power system, based on the Brazilian interconnected electrical system, and the commercial program NH2 are used to illustrate that both methods are feasible to bulk power systems, and can be used in order to achieve complementary results

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.

Generating High Quality Pseudo-Measurements to Keep State Estimation Capabilities

This paper proposes a methodology for providing real-time high quality pseudo-measurements to be used by the state estimation function. A forecasting step is added to the estimation process in which one-step-ahead forecasts, obtained considering recent past state estimation results, are adopted as pseudo-measurements. The model used to make forecasts is based on an artificial neural network. Test results using the IEEE-24 bus test system are presented to illustrate the performance of the proposed methodology.