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Details

  • Name

    Shabnam Pesteh
  • Cluster

    Power and Energy
  • Role

    Research Assistant
  • Since

    05th July 2017
001
Publications

2020

Favorable properties of Interior Point Method and Generalized Correntropy in power system State Estimation

Authors
Pesteh, S; Moayyed, H; Miranda, V;

Publication
Electric Power Systems Research

Abstract
The paper provides the theoretical proof of earlier published experimental evidence of the favorable properties of a new method for State Estimation – the Generalized Correntropy Interior Point method (GCIP). The model uses a kernel estimate of the Generalized Correntropy of the error distribution as objective function, adopting Generalized Gaussian kernels. The problem is addressed by solving a constrained non-linear optimization program to maximize the similarity between states and estimated values. Solution space is searched through a special setting of a primal-dual Interior Point Method. This paper offers mathematical proof of key issues: first, that there is a theoretical shape parameter value for the kernel functions such that the feasible solution region is strictly convex, thus guaranteeing that any local solution is global or uniquely defined. Second, that a transformed system of measurement equations assures an even distribution of leverage points in the factor space of multiple regression, allowing the treatment of leverage points in a natural way. In addition, the estimated residual of GCIP model is not necessarily zero for critical (non-redundant) measurements. Finally, that the normalized residuals of critical sets are not necessarily equal in the new model, making the identification of bad data possible in these cases. © 2019 Elsevier B.V.

2019

A new interior point solver with generalized correntropy for multiple gross error suppression in state estimation

Authors
Pesteh, S; Moayyed, H; Miranda, V; Pereira, J; Freitas, V; Simoes Costa, AS; London Jr, JBA;

Publication
Electric Power Systems Research

Abstract
This paper provides an answer to the problem of State Estimation (SE) with multiple simultaneous gross errors, based on Generalized Error Correntropy instead of Least Squares and on an interior point method algorithm instead of the conventional Gauss–Newton algorithm. The paper describes the mathematical model behind the new SE cost function and the construction of a suitable solver and presents illustrative numerical cases. The performance of SE with the data set contaminated with up to five simultaneous gross errors is assessed with confusion matrices, identifying false and missed detections. The superiority of the new method over the classical Largest Normalized Residual Test is confirmed at a 99% confidence level in a battery of tests. Its ability to address cases where gross errors fall on critical measurements, critical sets or leverage points is also confirmed at the same level of confidence. © 2019 Elsevier B.V.

2019

Impact of different central path neighborhoods on gross error identification in State Estimation with generalized correntropy interior point method

Authors
Moayyed, H; Pesteh, S; Miranda, V; Pereira, J;

Publication
2019 International Conference on Smart Energy Systems and Technologies (SEST)

Abstract

2016

The IMBPC HVAC system: A complete MBPC solution for existing HVAC systems

Authors
Ruano, AE; Pesteh, S; Silva, S; Duarte, H; Mestre, G; Ferreira, PM; Khosravani, HR; Horta, R;

Publication
ENERGY AND BUILDINGS

Abstract
This paper introduces the Intelligent MBPC (IMBPC) HVAC system, a complete solution to enable Model Based Predictive Control (MBPC) of existing HVAC installations in a building. The IMPBC HVAC minimizes the economic cost needed to maintain controlled rooms in thermal comfort during the periods of occupation. The hardware and software components of the IMBPC system are described, with a focus on the MBPC algorithm employed. The installation of IMBPC HVAC solution in a University building is described, and the results obtained in terms of economical savings and thermal comfort obtained are compared with standard, temperature regulated control.

2015

An Intelligent Weather Station

Authors
Mestre, G; Ruano, A; Duarte, H; Silva, S; Khosravani, H; Pesteh, S; Ferreira, PM; Horta, R;

Publication
SENSORS

Abstract
Accurate measurements of global solar radiation, atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors (NEN) algorithm and artificial neural network (ANN) models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead.