Eduardo N. Asada

Identifying multiple interacting bad data in power system state estimation

This paper presents an intelligent search strategy for the conforming bad data errors identification in the generalized power system state estimation, by using the tabu search meta heuristic. The main objective is to detect critical errors involving both analog and topology errors. These errors are represented by conforming errors, whose nature affects measurements that actually do not present bad data and also the conventional bad data identification strategies based on the normalized residual methods.

Penalty-Based Nonlinear Solver for Optimal Reactive Power Dispatch With Discrete Controls

The optimal reactive dispatch problem is a nonlinear programming problem containing continuous and discrete control variables. Owing to the difficulty caused by discrete variables, this problem is usually solved assuming all variables as continuous variables, therefore the original discrete variables are rounded off to the closest discrete value. This approach may provide solutions far from optimal or even unfeasible solutions. This paper presents an efficient handling of discrete variables by penalty function so that the problem becomes continuous and differentiable. Simulations with the IEEE test systems were performed showing the efficiency of the proposed approach.

Power System Observability Analysis Based on Gram Matrix and Minimum Norm Solution

This paper presents a numerical method for observability analysis and restoration in power system state estimation based on Gram matrix factorization. A method to identify observable islands based on minimum norm solutions is also presented. The method has the advantage of being easy to implement because all information used for the new formulation can be extracted or adapted from operations that are present in conventional state estimation. The observability analysis and restoration are performed in a single step in which measurements and pseudo-measurements are processed. If the system is nonobservable, minimum norm solutions obtained with nonredundant measurements are used to identify observable islands and, in the sequel, with the results of Gram matrix factorization, a set of nonredundant injection measurements that restore the global observability of the system is obtained. This approach is an alternative to the classical observability analysis and results in methods that are robust and suitable to be used in real-time applications. Numerical examples to show the performance of the methods are presented.

On the Use of Gram Matrix in Observability Analysis

This letter presents some notes on the use of the gram matrix in observability analysis. This matrix is constructed considering the rows of the measurement Jacobian matrix as vectors, and it can be employed in observability analysis and restoration methods. The determination of nonredundant pseudo-measurements (normally injections pseudo-measurements) for merging observable islands into an observable (single) system is carried out analyzing the pivots of the gram matrix. The gram matrix can also be used to verify local redundancy, which is important in measurement system planning. Some numerical examples are used to illustrate these features. Others features of the gram matrix are under study.

Regularized Least Squares Power System State Estimation

Summary form only given. In this paper, a new formulation for power system state estimation is proposed. The formulation is based on the regularized least squares method, which can deal with ill-posed problems. In this approach, the mathematical unfeasibility which results from the lack of measurements is eliminated. A test procedure based on the variance of estimated linearized active power flow is proposed to identify the observable islands. Some systems of the technical literature are used to show the effectiveness of the proposed method.

A branch-and-bound algorithm for the multi-stage transmission expansion planning

This work presents a branch-and-bound algorithm to solve the multi-stage transmission expansion planning problem. The well known transportation model is employed, nevertheless the algorithm can be extended to hybrid models or to more complex ones such as the DC model. Tests with a realistic power system were carried out in order to show the performance of the algorithm for the expansion plan executed for different time frames.

Identifying critical sets in state estimation using Gram matrix

This paper presents a numerical algorithm for the identification of critical measurements and critical sets in power system state estimation. The proposed algorithm is based on the use of Gram matrix constructed considering the rows of the measurement Jacobian matrix as vectors. This paper shows some features of the Gram matrix that can be useful in the optimal planning of metering systems for power system state estimation. Numerical examples with a 6-bus system and also with IEEE-14 system are used for testing the proposed algorithm.

Dynamic improvement of induction generators connected to distribution systems using a DSTATCOM

The usage of distributed generation and devices based on power electronics have significantly increased in electric power distribution systems. In this context, induction generators have received more attention. However, it is known that such machines draw very large reactive currents during fault occurrence, which depresses the network voltage further and can lead to voltage instability. A solution for this problem is to employ local dynamic reactive power compensation. Therefore, in this work the behavior of a DSTATCOM to improve the voltage stability performance of distribution systems with induction generators is investigated based on three-phase non-linear dynamic simulations. Two control strategies for a DSTATCOM are analyzed: voltage and power factor control. In such studies, a DSTATCOM is simulated through a model based on controllable three-phase voltage sources, which has shown to be suitable for stability studies. Test results have indicated that a DSTATCOM with voltage control mode can improve the voltage stability margins.

A framework for classification of non-linear loads in smart grids using Artificial Neural Networks and Multi-Agent Systems

This paper proposes a general framework that uses the Artificial Neural Networks (ANNs) as a classification tool of nonlinear loads in a simulated smart grid environment by using Multi-Agent Systems (MAS). The increasing of communication and computation infrastructure on devices installed on modern power distribution systems allows new automated and coordinated control actions. This is mainly due to the ability to manage and process information and deploy actions in real-time mode. One important measurement tool is the smart meter, which will be present with all customers. Besides the measurement function, it has the communication feature and also some computational processing capability. Considering this base structure, the objective is to present methods to classify/identify nonlinear loads based only on current or voltage profiles measured by smart meters in this distributed computing environment. In this work, the MAS will manage the data and the tasks related to the classification and the ANN will perform the classification, both tools have been developed in JADE/JAVA and Matlab environment, respectively. Test case using 4000 input signals distributed in eight classes corresponding to nonlinear medical electromedical loads have been used and 98.7% of the samples have been identified correctly. HighlightsA framework for classification of non-linear loads in smart grids is proposed.The framework uses multi-agent system to provide a communication infrastructure.Artificial neural network was utilised as classification tool.Two methods were presented and compared in terms of cost and sensitivity to faults.Test case was defined using electrical loads collected from a hospital environment.

Effects of load imbalance and system asymmetry on three-phase state estimation

This paper focuses on the effects of unbalanced loads over the three-phase state estimation and the problems related to the convergence, specially with observable islands. A simple strategy is proposed to deal with convergence problems of a three-phase state estimator in observable islands with unbalanced loads