192 research outputs found

    Fault diagnosis in multi-machine power systems using the Derivative-free nonlinear Kalman Filter

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    In this paper a new approach to parametric change detection and failure diagnosis for interconnected power units is proposed. The method is based on a new nonlinear filtering scheme under the name Derivative-free nonlinear Kalman Filter and on statistical processing of the obtained state estimates, according to the properties of the statistical distribution. To apply this fault diagnosis method, first it is shown that the dynamic model of the distributed interconnected power generators is a differentially flat one. Next, by exploiting differential flatness properties a change of variables (diffeomorphism) is applied to the power system, which enables also to solve the associated state estimation (filtering) problem. Additionally, statistical processing is performed for the obtained residuals, that is for the differences between the state vector of the monitored power system and the state vector provided by the aforementioned filter when the latter makes use of a fault-free model. It is shown, that the suitably weighted square of the residuals’ vector follows the statistical distribution. This property allows to use confidence intervals and to define thresholds that demonstrate whether the distributed power system functions as its fault-free model or whether parametric changes have taken place in it and thus a fault indication should be given. It is also shown that the proposed statistical criterion enables fault isolation to be performed, that is to find out the specific power generators within the distributed power system which have exhibited a failure. The efficiency of the proposed filtering method for condition monitoring in distributed power systems is confirmed through simulation experiments

    DFIG Based Wind Turbine System For Clemson Micro-grid

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    As an important part of the smart grid, the micro-grid interfaces with distributed energy sources, loads and control devices. A doubly fed induction generator (DFIG) based wind turbine (WT) is the main power source of the presented project. The DFIG system is connected to the three phase AC grid via back-to-back power converter and an LCL filter. Decoupled q-d control strategies are investigated for the DFIG system. Matlab/Simulink results will show the performance of the proposed system. Hardware validation results are also presented and discussed. As a rapidly increasing research interest area the dc micro-grid has been extensively investigated. A topology is proposed to connect the DFIG based WT system to a dc link using a diode bridge and a three phase power converter. The rotor side of the DFIG is connected to the dc link through a converter while the stator is connecting to a three phase diode bridge with the dc side connected to a dc link. The control method is developed to regulate the stator frequency and the d-q axis voltage of the diode bridge to operate the DFIG at a desired stator frequency and generate the required power. Undesired harmonics in the three phase system will lead to excessive THD, a decrease the power quality and an increase the power loss of the system. An novel methods to compensate the current harmonics by controlling the power converter of the DFIG system is also proposed. With the DFIG connected to the three phase AC gird, the focus has been put into a scenario: a nonlinear load connected to the same node of the DFIG point of common coupling (PCC) to the gird, to draw the harmonics to the system. In the proposed dc link system, the diode bridge will introduce harmonics to the stator current of the DFIG. In both cases, the selected low-order harmonics are detected and calculated by a multiple reference frame estimator. The control methods of how to regulate the harmonics are developed for both the grid-side converter and the rotor-side converter based on multiple reference frame theory. A hybrid state observer for speed-sensorless motor drives of induction machines is also proposed. The hybrid observer comprises of a Luenberger observer and a sliding mode observer. For a conventional induction motor with shorted rotor, the stator currents and rotor flux linkages are estimating following a Luenberger observer. While, for a DFIG the similar approach will apply to the stator currents and rotor currents. The rotor speed is estimated using a sliding mode observer. The combination of two observers takes advantage of both approaches. The Luenberger observer is easy to realize and the computational burden is small. The sliding mode observer is known for its robustness with respect to model parameter errors and it will also provide a fast convergence rate. The chattering of the sliding mode observer is addressed by applying a boundary layer

    A Review on Direct Power Control of Pulsewidth Modulation Converters

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    Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work

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    This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community

    Sliding Mode Based Dynamic State Estimation for Synchronous Generators in Power Systems

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    This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record This letter deals with the design of a robust sliding mode observer for dynamic state estimation applied to synchronous generators in power systems. Assuming only the frequency deviation of the generator is measured via phasor measurement units, we use a robust sliding mode estimation technique to dynamically reconstruct the rotor angle and the transient voltage. The adopted estimation technique is insensitive to matched bounded uncertainties affecting the dynamics of the synchronous generator. A stability analysis and tuning rules for the observer are also provided. Numerical simulations confirm the validity of the approach

    A survey of differential flatness-based control applied to renewable energy sources

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    Conference ProceedingsThis paper presents an overview of various methods used to minimize the fluctuating impacts of power generated from renewable energy sources. Several sources are considered in the study (biomass, wind, solar, hydro and geothermal). Different control methods applied to their control are cited, alongside some previous applications. Hence, it further elaborates on the adoptive control principles, of which includes; Load ballast control, dummy load control, proportional integral and derivative (PID) control, proportional integral (PI) control, pulse-width modulation (PWM) control, buck converter control, boost converter control, pitch angle control, valve control, the rate of river flow at turbine, bidirectional diffuser-augmented control and differential flatnessbased controller. These control operations in renewable energy power generation are mainly based on a steady-state linear control approach. However, the flatness based control principle has the ability to resolve the complex control problem of renewable energy systems while exploiting their linear properties. Using their flatness properties, feedback control is easily achieved which allows for optimal/steady output of the system components. This review paper highlights the benefits that range from better control techniques for renewable energy systems to established robust grid (or standalone generations) connections that can bring immense benefits to their operation and maintenance costs

    Prädiktive Regelung und Finite-Set-Beobachter für Windgeneratoren mit variabler Drehgeschwindigkeit

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    This dissertation presents several model predictive control (MPC) techniques and finite-position-set observers (FPSOs) for permanent-magnet synchronous generators and doubly-fed induction generators in variable-speed wind turbines. The proposed FPSOs are novel ones and based on the concept of finite-control-set MPC. Then, the problems of the MPC techniques like sensitivity to variations of the model parameters and others are investigated and solved in this work.Die vorliegende Dissertation stellt mehrere unterschiedliche Verfahren der modellprädiktiven Regelung (MPC) und so genannte Finite-Position-Set-Beobachter (FPSO) sowohl für Synchrongeneratoren mit Permanentmagneterregung als auch für doppelt gespeiste Asynchrongeneratoren in Windkraftanlagen mit variabler Drehzahl vor und untersucht diese. Für die Beobachter (FPSO) wird ein neuartiger Ansatz vorgestellt, der auf dem Konzept der Finite-Control-Set-MPC basiert. Außerdem werden typische Eigenschaften der MPC wie beispielsweise die Anfälligkeit gegenüber Parameterschwankungen untersucht und kompensiert

    Flux observer algorithms for direct torque control of brushless doubly-fed reluctance machines

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    Direct Torque Control (DTC) has been extensively researched and applied to most AC machines during the last two decades. Its first application to the Brushless Doubly-Fed Reluctance Machine (BDFRM), a promising cost-effective candidate for drive and generator systems with limited variable speed ranges (such as large pumps or wind turbines), has only been reported a few years ago. However, the original DTC scheme has experienced flux estimation problems and compromised performance under the maximum torque per inverter ampere (MTPIA) conditions. This deficiency at low current and torque levels may be overcome and much higher accuracy achieved by alternative estimation approaches discussed in this paper using Kalman Filter (KF) and/or Sliding Mode Observer (SMO). Computer simulations accounting for real-time constraints (e.g. measurement noise, transducer DC offset etc.) have produced realistic results similar to those one would expect from an experimental setup

    Power system dynamic state estimation: motivations, definitions, methodologies, and future work

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    This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community
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