Applications of Machine Learning Strategy for Wireless Power Transfer and Identification

The objective of my research is to propose and demonstrate Machine Learning (ML) applications of wireless power transfer and identification technology. Several works describe the implementation of a ML strategy based on 1) the use of Neural Networks (NN) for real-time range-adaptive automatic impedance matching of Wireless Power Transfer (WPT) applications, 2) the Naive Bayes algorithm for the prediction of the drone’s position, thus enhancing the WPT efficiency, and 3) the Support Vector Machine (SVM) classification strategy for read/interrogation enhancement in chipless RFID applications. The ML approach for the effective prediction of the optimal parameters of the tunable matching network, and classification range-adaptive transmitter coils (Tx) is introduced, aiming to achieve an effective automatic impedance matching over a wide range of relative distances. A novel WPT system consisting of a tunable matching circuit and 3 Tx coils which have different radius controlled by trained NN models is characterized. A proof-ofconcept WPT platform which allows the accurate prediction of the drone’s position based on the flight data utilizing ML classification using the Naive Bayes algorithm is also given. A ML-based approach for classification and of detection tag IDs has been presented, which can perform effective transponder readings for a wide variety of ranges and contexts, while providing high tag-ID detection accuracy. A SVM algorithm was trained using measurement data, and its accuracy was tested and characterized as a function of the included training data. In summary, this research sets a precedent, opening the door to a rich and wide area of research for the implementation of ML methods for the enhancement of WPT and chipless RFID applications.Ph.D

A deep learning approach to improve the control of dynamic wireless power transfer systems

In this paper, an innovative approach for the fast estimation of the mutual inductance between transmitting and receiving coils for Dynamic Wireless Power Transfer Systems (DWPTSs) is implemented. To this end, a Convolutional Neural Network (CNN) is used; an image representing the geometry of two coils that are partially misaligned is the input of the CNN, while the output is the corresponding inductance value. Finite Element Analyses are used for the computation of the inductance values needed for CNN training. This way, thanks to a fast and accurate inductance estimated by the CNN, it is possible to properly manage the power converter devoted to charge the battery, avoiding the wind up of its controller when it attempts to transfer power in poor coupling conditions

Implementation of a neural network-based electromyographic control system for a printed robotic hand

3D printing has revolutionized the manufacturing process reducing costs and time, but only when combined with robotics and electronics, this structures could develop their full potential. In order to improve the available printable hand designs, a control system based on electromyographic (EMG) signals has been implemented, so that different movement patterns can be recognized and replicated in the bionic hand in real time. This control system has been developed in Matlab/ Simulink comprising EMG signal acquisition, feature extraction, dimensionality reduction and pattern recognition through a trained neural-network. Pattern recognition depends on the features used, their dimensions and the time spent in signal processing. Finding balance between this execution time and the input features of the neural network is a crucial step for an optimal classification.Ingeniería Biomédic

Acoustic power distribution techniques for wireless sensor networks

Recent advancements in wireless power transfer technologies can solve several residual problems concerning the maintenance of wireless sensor networks. Among these, air-based acoustic systems are still less exploited, with considerable potential for powering sensor nodes. This thesis aims to understand the significant parameters for acoustic power transfer in air, comprehend the losses, and quantify the limitations in terms of distance, alignment, frequency, and power transfer efficiency. This research outlines the basic concepts and equations overlooking sound wave propagation, system losses, and safety regulations to understand the prospects and limitations of acoustic power transfer. First, a theoretical model was established to define the diffraction and attenuation losses in the system. Different off-the-shelf transducers were experimentally investigated, showing that the FUS-40E transducer is most appropriate for this work. Subsequently, different load-matching techniques are analysed to identify the optimum method to deliver power. The analytical results were experimentally validated, and complex impedance matching increased the bandwidth from 1.5 to 4 and the power transfer efficiency from 0.02% to 0.43%. Subsequently, a detailed 3D profiling of the acoustic system in the far-field region was provided, analysing the receiver sensitivity to disturbances in separation distance, receiver orientation and alignment. The measured effects of misalignment between the transducers are provided as a design graph, correlating the output power as a function of separation distance, offset, loading methods and operating frequency. Finally, a two-stage wireless power network is designed, where energy packets are inductively delivered to a cluster of nodes by a recharge vehicle and later acoustically distributed to devices within the cluster. A novel dynamic recharge scheduling algorithm that combines weighted genetic clustering with nearest neighbour search is developed to jointly minimise vehicle travel distance and power transfer losses. The efficacy and performance of the algorithm are evaluated in simulation using experimentally derived traces that presented 90% throughput for large, dense networks.Open Acces

Artificial neural networks and their applications to intelligent fault diagnosis of power transmission lines

Over the past thirty years, the idea of computing based on models inspired by human brains and biological neural networks emerged. Artificial neural networks play an important role in the field of machine learning and hold the key to the success of performing many intelligent tasks by machines. They are used in various applications such as pattern recognition, data classification, stock market prediction, aerospace, weather forecasting, control systems, intelligent automation, robotics, and healthcare. Their architectures generally consist of an input layer, multiple hidden layers, and one output layer. They can be implemented on software or hardware. Nowadays, various structures with various names exist for artificial neural networks, each of which has its own particular applications. Those used types in this study include feedforward neural networks, convolutional neural networks, and general regression neural networks. Increasing the number of layers in artificial neural networks as needed for large datasets, implies increased computational expenses. Therefore, besides these basic structures in deep learning, some advanced techniques are proposed to overcome the drawbacks of original structures in deep learning such as transfer learning, federated learning, and reinforcement learning. Furthermore, implementing artificial neural networks in hardware gives scientists and engineers the chance to perform high-dimensional and big data-related tasks because it removes the constraints of memory access time defined as the von Neuman bottleneck. Accordingly, analog and digital circuits are used for artificial neural network implementations without using general-purpose CPUs. In this study, the problem of fault detection, identification, and location estimation of transmission lines is studied and various deep learning approaches are implemented and designed as solutions. This research work focuses on the transmission lines’ datasets, their faults, and the importance of identification, detection, and location estimation of them. It also includes a comprehensive review of the previous studies to perform these three tasks. The application of various artificial neural networks such as feedforward neural networks, convolutional neural networks, and general regression neural networks for identification, detection, and location estimation of transmission line datasets are also discussed in this study. Some advanced methods based on artificial neural networks are taken into account in this thesis such as the transfer learning technique. These methodologies are designed and applied on transmission line datasets to enable the scientist and engineers with using fewer data points for the training purpose and wasting less time on the training step. This work also proposes a transfer learning-based technique for distinguishing faulty and non-faulty insulators in transmission line images. Besides, an effective design for an activation function of the artificial neural networks is proposed in this thesis. Using hyperbolic tangent as an activation function in artificial neural networks has several benefits including inclusiveness and high accuracy

Smart process monitoring of machining operations

The following thesis explores the possibilities to applying artificial intelligence techniques in the field of sensory monitoring in the manufacturing sector. There are several case studies considered in the research activity. The first case studies see the implementation of supervised and unsupervised neural networks to monitoring the condition of a grinding wheel. The monitoring systems have acoustic emission sensors and a piezoelectric sensor capable to measuring electromechanical impedance. The other case study is the use of the bees' algorithm to determine the wear of a tool during the cutting operations of a steel cylinder. A script permits this operation. The script converts the images into a numerical matrix and allows the bees to correctly detect tool wear

Modelling of a protective scheme for AC 330 kV transmission line in Nigeria

Transmission lines play a vital role in the reliable and efficient delivery of electrical power over long distances, and these lines are affected by faults that occur due to lightning strikes, equipment failures, human, animal or vegetation interference, environmental factors, ageing equipment, voltage sag or grid faults adverse effects on the line. Therefore, protecting these transmission lines becomes crucial with the increasing demand for electricity and the need to ensure grid stability. The modelling process involves the development of a comprehensive protection scheme utilising modern technologies and advanced algorithms. The protection scheme encompasses various elements, including fault detection, fault classification, fault location, and fault clearance. It incorporates intelligent devices, such as protective relays and communication systems, to enable rapid and accurate fault identification and isolation. First, a 330 kV, 500 km three-phase Delta transmission line is modelled using MATLAB/SIMULINK. A section of the Delta network in Delta State Nigeria was used since the entire Nigeria 330 kV network is large. Faulty current and voltage data were generated for training using the CatBoost, 93340 data sizes comprising fault data from three-phase current and voltage extracted from the Delta transmission line model in Nigeria were designed, and twelve fault conditions were used. The CatBoost classifier was employed to classify the faults after different machine language algorithm was used to train the same data with other parameters. The trainer achieved the best accuracy of 99.54%, with an error of 0.46%, at 748 iterations out of 1000 compared to GBoost, XBoost and other classification techniques. Second, the Artificial Neural Network technique was used to train this data, and an accuracy of 100% was attained for fault detection and about 99.5% for fault localisation at different distances with 0.0017 microseconds of detection and an average error of 0% to 0.5%. This model performs better than Support Vector Machine and Principal Component Analysis with a higher fault detection time. The effect of noise signal on the ANN model was studied, and the discrete wavelet technique was used to de-noise the signal for better performance and to enhance the model’s accuracy during transient. Third, the wavelet transforms as a data extraction model to detect the threshold value of current and voltage and the coordination time for the backup relay to trip if the primary relay does not operate or clear the fault on time. The difference between the proposed model and the model without the threshold value was analysed. The simulated result shows that the trip time of the two relays demonstrates a fast and precise trip time of 60% to 99.87% compared to other techniques used without the threshold values. The proposed model can eliminate the trial-and-error in programming the instantaneous overcurrent relay setting for optimal performance. Fourth, the PSO-PID controller algorithm was used to moderate the load frequency of the transmission network. Due to the instability between the generation and distribution, there is always a switch in the stability of the transmission or load frequency; therefore, the PSO-PID algorithm was used to stabilise the Delta power station as a pilot survey from the Nigerian transmission network. Also, a hybrid system with five types of generation and two load centres was used in this model. It has been shown that the proposed control algorithm is effective and improves system performance significantly. As a result, the suggested PSO-PID controller is recommended for producing high-quality, dependable electricity. Moreover, the PSO-PID algorithm produces 0.00 seconds settling time and 0.0005757 ITAE. It’s essential to carefully consider potential drawbacks like complexity and computational overhead, sensitivity to algorithm parameters, potential parameter convergence and limited interpretability and assess their impact on the specific LFC application before implementing a PSO-PID controller in a power system. When implemented with the model in this research, the Delta transmission line network will reduce the excessive fault that occurs in the transmission line and improve the energy efficiency of the entire network when replicated with the Nigerian network. Generally, for the effective design and implementation of the protection scheme of the 330 kV transmission line, the fault must be detected and classified, and the exact location of the fault must be ascertained before the relay protection and load frequency control will be applied for effective fault management and control system

Artificial Intelligence-Based Methods for Power System Security Assessment with Limited Dataset

This thesis concerns the relationship between the load, load model, and power system stability. It investigates the possibility of developing a dynamic load model to represent the power system load characteristic during system faults when the power system operates at a high percentage of the power generation from wind farms, solar power, and vehicle-to-grid technology. Additionally, with artificial intelligence supporting the seamless integration of an increasingly distributed and multi-directional power system to unlock the vast potential of renewables, new approaches are proposed to improve the training performance for the applications of artificial neural networks in non-intrusive load monitoring and dynamic security assessment. An improved hybrid load model is proposed to represent the load characteristics in the above power system operation. Genetic algorithms and the multi-curve identification method are applied to determine the parameters of the load model, aiming to minimize the error between the estimated and measured values. The results indicate that the proposed hybrid load model has a reasonably low fitting error to represent the load dynamics. In addition, new approaches are proposed to tackle the challenges posed by limited data when training artificial neural networks (ANNs) for their application in power systems. The knowledge transfer approach is utilized to support the ANN training to generate synthetic data for non-intrusive load monitoring. The results indicate that this approach improves the issue of mode collapse and reduces the need for lengthy training iterations, making the ANN effective for generating synthetic data from limited data. Moreover, the knowledge transfer approach also supports ANN training with limited data for dynamic security assessment. Kernel principal component analysis is employed to eliminate the dimensionality reduction step. The results indicate an improvement in the training performance

Artificial Intelligence-Based Methods for Power System Security Assessment with Limited Dataset

This thesis concerns the relationship between the load, load model, and power system stability. It investigates the possibility of developing a dynamic load model to represent the power system load characteristic during system faults when the power system operates at a high percentage of the power generation from wind farms, solar power, and vehicle-to-grid technology. Additionally, with artificial intelligence supporting the seamless integration of an increasingly distributed and multi-directional power system to unlock the vast potential of renewables, new approaches are proposed to improve the training performance for the applications of artificial neural networks in non-intrusive load monitoring and dynamic security assessment. An improved hybrid load model is proposed to represent the load characteristics in the above power system operation. Genetic algorithms and the multi-curve identification method are applied to determine the parameters of the load model, aiming to minimize the error between the estimated and measured values. The results indicate that the proposed hybrid load model has a reasonably low fitting error to represent the load dynamics. In addition, new approaches are proposed to tackle the challenges posed by limited data when training artificial neural networks (ANNs) for their application in power systems. The knowledge transfer approach is utilized to support the ANN training to generate synthetic data for non-intrusive load monitoring. The results indicate that this approach improves the issue of mode collapse and reduces the need for lengthy training iterations, making the ANN effective for generating synthetic data from limited data. Moreover, the knowledge transfer approach also supports ANN training with limited data for dynamic security assessment. Kernel principal component analysis is employed to eliminate the dimensionality reduction step. The results indicate an improvement in the training performance