Almas Shintemirov

Power Transformer Fault Classification Based on Dissolved Gas Analysis by Implementing Bootstrap and Genetic Programming

This paper presents an intelligent fault classification approach to power transformer dissolved gas analysis (DGA), dealing with highly versatile or noise-corrupted data. Bootstrap and genetic programming (GP) are implemented to improve the interpretation accuracy for DGA of power transformers. Bootstrap preprocessing is utilized to approximately equalize the sample numbers for different fault classes to improve subsequent fault classification with GP feature extraction. GP is applied to establish classification features for each class based on the collected gas data. The features extracted with GP are then used as the inputs to artificial neural network (ANN), support vector machine (SVM) and K-nearest neighbor ( KNN) classifiers for fault classification. The classification accuracies of the combined GP-ANN, GP-SVM, and GP-KNN classifiers are compared with the ones derived from ANN, SVM, and KNN classifiers, respectively. The test results indicate that the developed preprocessing approach can significantly improve the diagnosis accuracies for power transformer fault classification.

Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers

This paper presents a novel association rule mining (ARM)-based dissolved gas analysis (DGA) approach to fault diagnosis (FD) of power transformers. In the development of the ARM-based DGA approach, an attribute selection method and a continuous datum attribute discretization method are used for choosing user-interested ARM attributes from a DGA data set, i.e. the items that are employed to extract association rules. The given DGA data set is composed of two parts, i.e. training and test DGA data sets. An ARM algorithm namely Apriori-Total From Partial is proposed for generating an association rule set (ARS) from the training DGA data set. Afterwards, an ARS simplification method and a rule fitness evaluation method are utilized to select useful rules from the ARS and assign a fitness value to each of the useful rules, respectively. Based upon the useful association rules, a transformer FD classifier is developed, in which an optimal rule selection method is employed for selecting the most accurate rule from the classifier for diagnosing a test DGA record. For comparison purposes, five widely used FD methods are also tested with the same training and test data sets in experiments. Results show that the proposed ARM-based DGA approach is capable of generating a number of meaningful association rules, which can also cover the empirical rules defined in industry standards. Moreover, a higher FD accuracy can be achieved with the association rule-based FD classifier, compared with that derived by the other methods.

Transformer Core Parameter Identification Using Frequency Response Analysis

We present a novel model-based approach for parameter identification of a laminated core, such as magnetic permeability and electrical conductivity, of power transformers on the basis of frequency response analysis (FRA) measurements. The method establishes a transformer core model using the duality principle between magnetic and electrical circuits for parameter identification with genetic algorithms. We use reference input impedance frequency responses, calculated by a well-known lumped parameter model of a three-phase transformer and finite-element computations, to analyze identification accuracy of the method. The results verify the ability of the approach to accurately identify the core lamination parameters with respect to the reference values. The approach can be used for parameter identification of a demagnetized core with known geometrical parameters when the core lamination samples are unavailable for experimental tests. The approach can also be employed for transformer core modeling and FRA result interpretation at low frequencies.

A Hybrid Winding Model of Disc-Type Power Transformers for Frequency Response Analysis

The paper presents a hybrid model of disc-type power transformer winding for frequency response analysis (FRA) based on traveling wave and multiconductor transmission line (MTL) theories. Each disc of a winding is described by traveling wave equations, which are connected to each other in a form of MTL matrix model. This significantly reduces the order of the model with respect to previously established MTL models of transformer winding. The model is applied to frequency response simulation of two single-phase transformers. The simulations are compared with the experimental data and calculated results using lumped parameter and MTL models reported in other publications. It is shown that the model can be used for FRA result interpretation in an extended range of frequencies up to several mega Hertz and resonance analysis under very fast transient overvoltages (VFTOs).

Detection of minor winding deformation fault in high frequency range for power transformer

This paper presents a simplified distributed parameter model for minor winding deformation fault analysis of power transformers on the basis of frequency response analysis (FRA). The FRA data of an experimental transformer is employed as a reference trace, which are compared with the simulations of the simplified distributed parameter model concerning minor winding deformation faults. In order to perform quantitative analysis when a deformation fault occurs, three statistical indicators are used to analyze the FRA simulation data. It is suggested in the results that minor winding deformation faults can be detected at the frequency range above 1 MHz.

Semi-anthropomorphic 3D printed multigrasp hand for industrial and service robots

This paper presents the preliminary prototype design and implementation of the Nazarbayev University (NU) Hand, a new semi-anthropomorphic multigrasp robotic hand. The hand is designed to be an end effector for industrial and service robots. The main objective is to develop a low-cost, low-weight and easily manufacturable robotic hand with a sensor module allowing acquisition of data for autonomous intelligent object manipulation. 3D printing technologies were extensively used in the implementation of the hand. Specifically, the structure of the hand is printed using a 3D printer as a complete assembly voiding the need of using fasteners and bearings for the assembly of the hand and decreasing the total weight. The hand also incorporates a sensor module containing a LIDAR, digital camera and non-contact infrared temperature sensor for intelligent automation. As an alternative to teach pendants for the industrial manipulators, a teaching glove was developed, which acts as the primary human machine interface between the user and the NU Hand. The paper presents an extensive performance characterization of the robotic hand including finger forces, weight, audible noise level during operation and sensor data acquisition.

Numerical Optimal Control of a Spherical Parallel Manipulator Based on Unique Kinematic Solutions

This paper presents a framework for generating optimal motor trajectories for a spherical parallel manipulator (SPM) with revolute joints, actuated by servomotors with default internal position control settings. The proposed framework consists of three phases. First, an approach to obtain unique forward kinematics is introduced, in order to relate the angular positions of the servomotors to the orientation of the SPM top mobile platform. Then, a configuration space for the SPM is defined by using numerical procedures, in order to guarantee the absence of singularities and of collisions between links during the motion of the manipulator. Finally, reference trajectories of the servomotors are defined via convex optimization. These trajectories determine an optimal evolution of the SPM motion based on the configuration space and original servomotor dynamics. The proposed strategy is experimentally demonstrated on a prototype of Agile Wrist SPM with three servomotors.

An approach for obtaining unique kinematic solutions of a spherical parallel manipulator

In this paper an approach for obtaining unique solutions to forward and inverse kinematics of a spherical parallel manipulator (SPM) system with revolute joints is proposed. Kinematic analysis of a general SPM with revolute joints is revisited and the proposed approach is formulated in the form of easy-to-follow algorithms that are described in detail. A graphical verification method using SPM computer-aided-design (CAD) models is presented together with numerical and experimental examples that confirm the correctness of the proposed approach. It is expected that this approach can be applied to SPMs with different geometries and can be useful in designing real-time control systems of SPMs.

An underactuated adaptive 3D printed robotic gripper

This paper presents the preliminary work on prototype design and analysis of a 3D printed three-fingered underactuated robotic gripper with a breakaway clutch mechanism. An underactuated mechanical design, kinematics of grippers finger and a breakaway clutch mechanism are described in details. These elements provide passive adaptive object grasping and relatively high load carrying capacity of the gripper. The robotic gripper was prototyped using 3D printing technology and off-the-shelf components. Simulation and experimental results of grasping performance characterization of the robotic gripper are also presented.

Hybrid model predictive control for optimal energy management of a smart house

This paper describes the modeling and control of heat and electricity flows in a smart house equipped with a solar heating system, PV panels, and lead-acid batteries for energy storage. The goal is to minimize electricity costs, making best use of renewable sources of heat and electricity. The system model is obtained via system identification from experimental data as a discrete-time hybrid system to capture the main thermal and electrical dynamics, the on-off activation of pumps, heating coil, the connection to the grid, and various operating constraints, including logic constraints and limits on system variables. Based on the obtained model, we derive a hybrid model predictive control (MPC) strategy. The controller is able to track the desired temperature and minimize costs for consuming electricity from the grid, while respecting all the prescribed constraints. Simulation results testify the effectiveness and feasibility of the approach.