Fault Analysis, Detection and Estimation for a Microgrid via <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si124.gif" overflow="scroll"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>/<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si126.gif" overflow="scroll"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math

Morato, Marcelo Menezes; Regner, Daniel Juchem; Mendes, Paulo Renato da Costa; Normey‐Rico, Julio E.; Bordons, Carlos

doi:10.1016/j.ijepes.2018.09.018

Cited by 49 publications

(14 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to observability assumption for the pair (A, C) one can choose L c to make (A − L c C) stable. By assigning (14), condition (11) will be fulfilled. To show this, assume that there exists a nonzero equilibrium for (7), tha is:…”

Section: Theoremmentioning

confidence: 99%

“…As a result, in many applications requiring the whole state variable vector, it is inevitable to use an observer to construct the state variable from the measured output. 1 Observers may be used for observer-based feedback control, [4][5][6][7][8] fault detection [9][10][11][12][13][14] and parameter estimation. 1 Unknown or partially known input, 3,10 robust, 7,15 optimal 2,15 and functional 16 observers have been investigated in the literature for linear 16 as well as descriptor, 3,16 delayed, 9 time varying, 2 linear parameter varying 13,14 and nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

“…1 Observers may be used for observer-based feedback control, [4][5][6][7][8] fault detection [9][10][11][12][13][14] and parameter estimation. 1 Unknown or partially known input, 3,10 robust, 7,15 optimal 2,15 and functional 16 observers have been investigated in the literature for linear 16 as well as descriptor, 3,16 delayed, 9 time varying, 2 linear parameter varying 13,14 and nonlinear systems. 1,[4][5][6][7][8]11,12 The literature of linear observers for linear systems is very mature.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Luenberger‐typecubic observers for state estimation of linear systems

Pasand¹

2020

Adaptive Control & Signal

View full text Add to dashboard Cite

Summary This article introduces a new nonlinear observer for state estimation of linear time invariant systems. The proposed observer contains a (nonlinear) cubic term to enhance observer response. Unlike previously proposed observers, the cubic observer has nonlinear estimation error dynamics. Convergence criteria, performance advantages, and observer‐based feedback control with cubic observers are addressed. Simulation examples demonstrating performance improvement compared with linear observers, are included.

show abstract

Section: Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Luenberger‐typecubic observers for state estimation of linear systems

Pasand¹

2020

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…It is worth highlighting that, although the majority of the results found in the literature consider the case of additive faults, some recent work has proposed a switched observer formulation to estimate actuator multiplicative faults in discrete-time convex systems [135]. Successful applications of observer-based fault estimation, either using high-fidelity simulations or through experimental validation, can be found in the areas of aviation [165,166], bioreactors [12], distillation columns [169], automotive suspension systems [170] and renewable microgrids [171,172].…”

Section: Observer-based Fault Estimationmentioning

confidence: 99%

A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems

2019

View full text Add to dashboard Cite

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).

show abstract

“…The implementation of CBM systems for IMs [6] can reduce these risks, with the goal of detecting machine problems prior to failure [7], and can also help to optimize the schedule of maintenance stops, with the goal of reducing the production losses during the stop. From a broader point of view, CBM systems for IMs can be integrated in maintenance systems for electrical installations, along with CBMs for inverters [8], generators [9], transformers [10][11][12][13], power systems [14], transmission lines [15,16] or microgrids [17,18].…”

Section: Introductionmentioning

confidence: 99%

Induction machine model with space harmonics for fault diagnosis based on the convolution theorem

Sapena-Bañó

Martínez-Román

Puche-Panadero

et al. 2018

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

Condition based maintenance systems (CBM) of induction machines (IMs) require fast and accurate models that can reproduce the fault related harmonics generated by different kinds of faults, in order to help in developing new diagnostic algorithms for detecting the faults at an early stage, to analyse the physical interactions between simultaneous faults of different types, or to train expert systems that can supervise and identify these faults in an autonomous way. To achieve these goals, such models must take into account the space harmonics of the air gap magnetomotive force (MMF) generated by the machine windings under fault conditions, due to the complex interactions between spatial and time harmonics in a faulty machine. One of the most common faults in induction machines is the rotor eccentricity, which can cause significant radial forces and, in extreme cases, produce destructive rotor-stator rub. But the development of a fast, analytic model of the eccentric IM is a challenging task, due to the nonuniformity of the air gap. In this paper, a new method is proposed to obtain such a fast model. This method, which is theoretically justified, enables a fast calculation of the self and mutual inductances of the stator and rotor phases for every rotor position. The proposed method is validated first using a finite elements method (FEM) model, and then, through an experimental test-bed using commercial induction motors with a forced mixed eccentricity fault.

show abstract

Fault Analysis, Detection and Estimation for a Microgrid via H2/H∞

Cited by 49 publications

References 39 publications

Luenberger‐typecubic observers for state estimation of linear systems

Luenberger‐typecubic observers for state estimation of linear systems

A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems

Induction machine model with space harmonics for fault diagnosis based on the convolution theorem

Contact Info

Product

Resources

About