| 1. |
- Axelberg, P.G.V., et al.
(författare)
-
Support vector machine for classification of voltage disturbances
- 2007
-
Ingår i: IEEE Transactions on Power Delivery. - : IEEE. - 0885-8977 .- 1937-4208. ; 22:3, s. 1297-1303
-
Tidskriftsartikel (refereegranskat)abstract
- The support vector machine (SVM) is a powerful method for statistical classification of data used in a number of different applications. However, the usefulness of the method in a commercial available system is very much dependent on whether the SVM classifier can be pretrained from a factory since it is not realistic that the SVM classifier must be trained by the customers themselves before it can be used. This paper proposes a novel SVM classification system for voltage disturbances. The performance of the proposed SVM classifier is investigated when the voltage disturbance data used for training and testing originated from different sources. The data used in the experiments were obtained from both real disturbances recorded in two different power networks and from synthetic data. The experimental results shown high accuracy in classification with training data from one power network and unseen testing data from another. High accuracy was also achieved when the SVM classifier was trained on data from a real power network and test data originated from synthetic data. A lower accuracy resulted when the SVM classifier was trained on synthetic data and test data originated from the power network.
|
|
| 2. |
- Axelberg, P., et al.
(författare)
-
Trace of Flicker Sources by using the Quantity of Flicker power.
- 2008
-
Ingår i: IEEE Transactions on Power Delivery. - : IEEE. - 0885-8977 .- 1937-4208. ; 23:1, s. 465-471
-
Tidskriftsartikel (refereegranskat)abstract
- Industries that produce flicker are often placed close to each other and connected to the same power grid system. This implies that the measured flicker level at the point of common coupling (PCC) is a result of contribution from a number of different flicker sources. In a mitigation process it is essential to know which one of the flicker sources is the dominant one. We propose a method to determine the flicker propagations and trace the flicker sources by using flicker power measurements. Flicker power is considered as a quantity containing both sign and magnitude. The sign determines if a flicker source is placed downstream or upstream with respect to a given monitoring point and the magnitude is used to determine the propagation of flicker power throughout the power network and to trace the dominant flicker source. This paper covers the theoretical background of flicker power and describes a novel method for calculation of flicker power that can be implemented in a power network analyzer. Also conducted simulations and a field test based on the proposed method will be described in the paper.
|
|
| 3. |
- Bollen, Math, et al.
(författare)
-
Classification of Underlying Causes of Power Quality Disturbances: Deterministic versus Statistical Methods
- 2007
-
Ingår i: Eurasip Journal on Applied Signal Processing. - : Springer Science and Business Media LLC. - 1110-8657 .- 1687-0433 .- 1687-6172 .- 1687-6180. ; , s. 17 pages (Article ID 79747)-
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents the two main types of classification methods for power quality disturbances based on underlying causes: deterministic classification, giving an expert system as an example, and statistical classification, with support vector machines as an example. An expert system is suitable when one has limited amount of data and sufficient power system expert knowledge, however its application requires a set of threshold values. Statistical methods are suitable when large amount of data is available for training. Two important issues to guarantee the effectiveness of a classifier, data segmentation and featureextraction, are discussed. Segmentation of a sequence of data recording is pre-processing to partition the datainto segments each representing a duration containing either an event or transition between two events. Extraction of features is applied to each segment individually. Some useful features and their effectiveness are then discussed. Some experimental results are included for demonstrating theeffectiveness of both systems. Finally, conclusions are given together with the discussion of some future research directions.
|
|
| 4. |
|
|
| 5. |
- Gantasala, Sudhakar, et al.
(författare)
-
Aeroelastic simulations of wind turbine using 13 DOF rigid beam model
- 2016
-
Ingår i: Open archives of the 16th International Symposium on Transport Phenomena and Dynamics of Rotating Machinery. - : Symposia on Rotating Machinery.
-
Konferensbidrag (refereegranskat)abstract
- The vibration behavior of wind turbine substructures is mainly dominated by their first few vibration modes because wind turbines operate at low rotational speeds. In this study, 13 degrees of freedom (DOF) model of a wind turbine is derived considering fundamental vibration modes of the tower and blades which are modelled as rigid beams with torsional springs attached at their root. Linear equations of motion (EOM) governing the structural behavior of wind turbines are derived by assuming small amplitude vibrations. This model is used to study the coupling between the structural and aerodynamic behavior of NREL 5 MWmodel wind turbine. Aeroelastic natural frequencies of the current model are compared with the results obtained from the finite element model of this wind turbine. Quasi-steady aerodynamic loads are calculated considering wind velocity changes due to height and tower shadow effects. In this study, vibration responses are simulated at various wind velocities. The derived 13 DOF simplified model of the wind turbine enables to simulate the influence ofchange in parameters and operating conditions on vibration behavior with less computational effort. Besides that, the results of the simplified models can be interpreted with much ease.
|
|
| 6. |
- Gantasala, Sudhakar, et al.
(författare)
-
Identification of ice mass accumulated on wind turbine blades using its natural frequencies
- 2018
-
Ingår i: Wind Engineering. - : Sage Publications. - 0309-524X .- 2048-402X. ; 42:1, s. 66-84
-
Tidskriftsartikel (refereegranskat)abstract
- This work demonstrates a technique to identify information about the ice mass accumulation on wind turbine blades using its natural frequencies, and these frequencies reduce differently depending on the spatial distribution of ice mass along the blade length. An explicit relation to the natural frequencies of a 1-kW wind turbine blade is defined in terms of the location and quantity of ice mass using experimental modal analyses. An artificial neural network model is trained with a data set (natural frequencies and ice masses) generated using that explicit relation. After training, this artificial neural network model is given an input of natural frequencies of the iced blade (identified from experimental modal analysis) corresponding to 18 test cases, and it identified ice masses’ location and quantity with a weighted average percentage error value of 17.53%. The proposed technique is also demonstrated on the NREL 5-MW wind turbine blade data.
|
|
| 7. |
- Gantasala, Sudhakar, et al.
(författare)
-
Influence of Icing on the Modal Behavior of Wind Turbine Blades
- 2016
-
Ingår i: Energies. - : MDPI AG. - 1996-1073. ; 9:11
-
Tidskriftsartikel (refereegranskat)abstract
- Wind turbines installed in cold climate sites accumulate ice on their structures. Icing of the rotor blades reduces turbine power output and increases loads, vibrations, noise, and safety risks due to the potential ice throw. Ice accumulation increases the mass distribution of the blade, while changes in the aerofoil shapes affect its aerodynamic behavior. Thus, the structural and aerodynamic changes due to icing affect the modal behavior of wind turbine blades. In this study, aeroelastic equations of the wind turbine blade vibrations are derived to analyze modal behavior of the Tjaereborg 2 MW wind turbine blade with ice. Structural vibrations of the blade are coupled with a Beddoes-Leishman unsteady attached flow aerodynamics model and the resulting aeroelastic equations are analyzed using the finite element method (FEM). A linearly increasing ice mass distribution is considered from the blade root to half-length and thereafter constant ice mass distribution to the blade tip, as defined by Germanischer Lloyd (GL) for the certification of wind turbines. Both structural and aerodynamic properties of the iced blades are evaluated and used to determine their influence on aeroelastic natural frequencies and damping factors. Blade natural frequencies reduce with ice mass and the amount of reduction in frequencies depends on how the ice mass is distributed along the blade length; but the reduction in damping factors depends on the ice shape. The variations in the natural frequencies of the iced blades with wind velocities are negligible; however, the damping factors change with wind velocity and become negative at some wind velocities. This study shows that the aerodynamic changes in the iced blade can cause violent vibrations within the operating wind velocity range of this turbine.
|
|
| 8. |
- Gantasala, Sudhakar, et al.
(författare)
-
Investigating How an Artificial Neural Network Model Can Be Used to Detect Added Mass on a Non-Rotating Beam Using Its Natural Frequencies : A Possible Application for Wind Turbine Blade Ice Detection
- 2017
-
Ingår i: Energies. - : MDPI. - 1996-1073. ; 10:2
-
Tidskriftsartikel (refereegranskat)abstract
- Structures vibrate with their natural frequencies when disturbed from their equilibrium position. These frequencies reduce when an additional mass accumulates on their structures, like ice accumulation on wind turbines installed in cold climate sites. The added mass has two features: the location and quantity of mass. Natural frequencies of the structure reduce differently depending on these two features of the added mass. In this work, a technique based on an artificial neural network (ANN) model is proposed to identify added mass by training the neural network with a dataset of natural frequencies of the structure calculated using different quantities of the added mass at different locations on the structure. The proposed method is demonstrated on a non-rotating beam model fixed at one end. The length of the beam is divided into three zones in which different added masses are considered, and its natural frequencies are calculated using a finite element model of the beam. ANN is trained with this dataset of natural frequencies of the beam as an input and corresponding added masses used in the calculations as an output. ANN approximates the non-linear relationship between these inputs and outputs. An experimental setup of the cantilever beam is fabricated, and experimental modal analysis is carried out considering a few added masses on the beam. The frequencies estimated in the experiments are given as an input to the trained ANN model, and the identified masses are compared against the actual masses used in the experiments. These masses are identified with an error that varies with the location and the quantity of added mass. The reason for these errors can be attributed to the unaccounted stiffness variation in the beam model due to the added mass while generating the dataset for training the neural network. Therefore, the added masses are roughly estimated. At the end of the paper, an application of the current technique for detecting ice mass on a wind turbine blade is studied. A neural network model is designed and trained with a dataset of natural frequencies calculated using the finite element model of the blade considering different ice masses. The trained network model is tested to identify ice masses in four test cases that considers random mass distributions along the blade. The neural network model is able to roughly estimate ice masses, and the error reduces with increasing ice mass on the blade.
|
|
| 9. |
- Hooshmand, Saleh, et al.
(författare)
-
All-cellulose nanocomposite fibers produced by melt spinning cellulose acetate butyrate and cellulose nanocrystals
- 2014
-
Ingår i: Cellulose. - : Kluwer Academic Publishers. - 0969-0239 .- 1572-882X. ; 21:4, s. 2665-2678
-
Tidskriftsartikel (refereegranskat)abstract
- Bio-based continuous fibers were prepared by melt spinning cellulose acetate butyrate (CAB), cellulose nanocrystals (CNC) and triethyl citrate. A CNC organo-gel dispersion technique was used and the prepared materials (2 and 10 wt% CNC) were melt spun using a twin-screw micro-compounder and drawn to a ratio of 1.5. The microscopy studies showed that the addition of CNC in CAB resulted in defect-free and smooth fiber surfaces. An addition of 10 wt% CNC enhanced the storage modulus and increased the tensile strength and Young’s modulus. Fiber drawing improved the mechanical properties further. In addition, a micromechanical model of the composite material was used to estimate the stiffness and showed that theoretical values were exceeded for the lower concentration of CNC but not reached for the higher concentration. In conclusion, this dispersion technique combined with melt spinning can be used to produce all-cellulose nanocomposites fibers and that both the increase in CNC volume fraction and the fiber drawing increased the mechanical performance.
|
|
| 10. |
- Hooshmand, Saleh, et al.
(författare)
-
Electroconductive composite fibers by melt spinning of polypropylene/polyamide/carbon nanotubes
- 2011
-
Ingår i: Synthetic metals. - : Elsevier. - 0379-6779 .- 1879-3290. ; 161:15-16, s. 1731-1737
-
Tidskriftsartikel (refereegranskat)abstract
- In this study, the blends of polypropylene/polyamide with carbon nanotubes (CNTs) have been prepared and melt spun to as-spun and drawn fibers. Thermal analysis showed that increasing the polyamide content, decreased the degree of crystallinity in the blends. Characterization of fibers showed that both conductivity and tensile strength have been improved by increasing the amount of polyamide in the blends as well as the melt blending temperature; furthermore, the morphology, electrical and mechanical properties of the blends were significantly influenced by adding 1 phr compatibilizer to the blend. The comparison between as-spun fibers and drawn fibers proved that although mechanical properties were improved after drawing, the electrical conductivity was decreased from the order of E−02 to E−06 (S/cm), due to applied draw-ratio of three.
|
|
