Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Artificial Intelligence (AI) techniques are considered the most advanced approaches for diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional approach widely adopted for diagnosing incipient faults in power transformers. The IEC-599 standard Ratio Method is an accurate method that evaluates the DGA. All the classical approaches have limitations because they cannot diagnose all faults accurately. Precisely diagnosing defects in power transformers is a significant challenge due to their extensive quantity and dispersed placement within the power network. To deal with this concern and to improve the reliability and precision of fault diagnosis, different Artificial Intelligence techniques are presented. In this manuscript, an artificial neural network (ANN) is implemented to enhance the accuracy of the Rogers Ratio Method. On the other hand, it should be noted that the complexity of an ANN demands a large amount of storage and computing power. In order to address this issue, an optimization technique is implemented with the objective of maximizing the accuracy and minimizing the architectural complexity of an ANN. All the procedures are simulated using the MATLAB R2023a software. Firstly, the authors choose the most effective classification model by automatically training five classifiers in the Classification Learner app (CLA). After selecting the artificial neural network (ANN) as the sufficient classification model, we trained 30 ANNs with different parameters and determined the 5 models with the best accuracy. We then tested these five ANNs using the Experiment Manager app and ultimately selected the ANN with the best performance. The network structure is determined to consist of three layers, taking into consideration both diagnostic accuracy and computing efficiency. Ultimately, a (100-50-5) layered ANN was selected to optimize its hyperparameters. As a result, following the implementation of the optimization techniques, the suggested ANN exhibited a high level of accuracy, up to 90.7%. The conclusion of the proposed model indicates that the optimization of hyperparameters and the increase in the number of data samples enhance the accuracy while minimizing the complexity of the ANN. The optimized ANN is simulated and tested in MATLAB R2023a—Deep Network Designer, resulting in an accuracy of almost 90%. Moreover, compared to the Rogers Ratio Method, which exhibits an accuracy rate of just 63.3%, this approach successfully addresses the constraints associated with the conventional Rogers Ratio Method. So, the ANN has evolved a supremacy diagnostic method in the realm of power transformer fault diagnosis

A Natural Language Generation Algorithm for Greek by Using Hole Semantics and a Systemic Grammatical Formalism

This work is about the progress of previous related work based on an experiment to improve the intelligence of robotic systems, with the aim of achieving more linguistic communication capabilities between humans and robots. In this paper, the authors attempt an algorithmic approach to natural language generation through hole semantics and by applying the OMAS-III computational model as a grammatical formalism. In the original work, a technical language is used, while in the later works, this has been replaced by a limited Greek natural language dictionary. This particular effort was made to give the evolving system the ability to ask questions as well as the authors developed an initial dialogue system using these techniques. The results show that the use of these techniques the authors apply can give us a more sophisticated dialogue system in the future

A Review of the Potential for the Recovery of Wind Turbine Blade Waste Materials

A successful circular economy can only exist when it relies solely on renewable energy sources. The adoption of resilient business models and the consequent redesign of legislation on all sectors are essential to ensure sustainable economic growth. Wind energy can offer clean and renewable energy with a low environmental impact. Nevertheless, waste in end of life composite materials resulting from wind turbines is a problem that needs to be addressed. Composite materials are commonly used in wind turbines due to their excellent mechanical properties, matched by low weight. Notably, the recycling technologies of such materials is limited. Material flows and estimations of end of life materials are of great importance and will convince stakeholders that markets for recycling composites are viable investments

Development of Seasonal ARIMA Models for Traffic Noise Forecasting

In this paper, a time series analysis approach is adopted to monitor and predict a traffic noise levels dataset, measured in a site of Messina, Italy. In general, acoustical noise shows a high prediction complexity, since its slope is strongly related to the variability of the sources and to intrinsic randomness. In the analysed site the predominant source is road traffic, that has a periodic and non-stationary behaviour. The study of the time evolution of this hazardous agent is very useful to assess the impact to human health and activities. The time series models adopted in this paper are of the stochastic seasonal ARIMA class; these types of model are based on the strong periodicity registered in the acoustical equivalent levels. The observed periodicity is related to the highly variability of urban traffic in the different days of the week. Three different seasonal ARIMA models are proposed and calibrated on a rich dataset of 800 sound level measurements. The predictive capabilities of these techniques are encouraging. The implemented models show a good forecasting performances in terms of low residuals, i.e. difference between observed and estimated noise values. The residuals are analysed by means of statistical indexes, plots and tests

Development of Seasonal ARIMA Models for Traffic Noise Forecasting

In this paper, a time series analysis approach is adopted to monitor and predict a traffic noise levels dataset, measured in a site of Messina, Italy. In general, acoustical noise shows a high prediction complexity, since its slope is strongly related to the variability of the sources and to intrinsic randomness. In the analysed site the predominant source is road traffic, that has a periodic and non-stationary behaviour. The study of the time evolution of this hazardous agent is very useful to assess the impact to human health and activities. The time series models adopted in this paper are of the stochastic seasonal ARIMA class; these types of model are based on the strong periodicity registered in the acoustical equivalent levels. The observed periodicity is related to the highly variability of urban traffic in the different days of the week. Three different seasonal ARIMA models are proposed and calibrated on a rich dataset of 800 sound level measurements. The predictive capabilities of these techniques are encouraging. The implemented models show a good forecasting performances in terms of low residuals, i.e. difference between observed and estimated noise values. The residuals are analysed by means of statistical indexes, plots and tests

Design of a Supraharmonic Monitoring System Based on an FPGA Device

During the last few decades, the poor quality of produced electric power is a key factor that has affected the operation of critical electrical infrastructure such as high-voltage equipment. This type of equipment exhibits multiple different failures, which originate from the poor electric power quality. This phenomenon is basically due to the utilization of high-frequency switching devices that operate over modern electrical generation systems, such as PV inverters. The conduction of significant values of electric currents at high frequencies in the range of 2 to 150 kHz can be destructive for electrical and electronic equipment and should be measured. However, the measuring devices that have the ability of analyzing a signal in the frequency domain present the ability of analyzing up to 2.5 kHz–3 kHz, which are frequencies too low in comparison to the high switching frequencies that inverters, for example, work. Electric currents at 16 kHz were successfully measured on an 8 kWp roof PV generator. This paper presents a fast-developed modern measuring system, using a field programmable gate array, aiming to detect electric currents at high frequencies, with a capability for working up to 150 kHz. The system was tested in the laboratory, and the results are satisfactory

Impact of Lithium Battery Recycling and Second-Life Application on Minimizing Environmental Waste

In the prospect of greener transportation means and global emission limitations for the protection of the environment, the electric vehicles’ market share is constantly increasing. It is expected that 32% of new vehicles sold in 2030 will be pure electric or plug-in hybrids. As all electric vehicles utilize lithium batteries to power the powertrain, the need for rare earth materials, like lithium or nickel, exceeds the planet’s ability to provide the required capacities. Additionally, even though lithium-ion batteries provide high energy density, they have some disadvantages like a limited range and durability at high-temperature operation. This issue can be improved greatly with the implementation of a hybrid energy storage system consisting of batteries and ultracapacitors. In this paper, the power efficiency of this storage system will be analyzed. Finally, when the cells reach below a specific capacity threshold, they can be removed from the vehicle to be installed in renewable energy plants for storing surplus energy production. Therefore, environmental waste is minimized while simultaneously assisting grid power demands, before being recycled to recover a portion of the rare metals used

Brushed DC Motor Drives for Industrial and Automobile Applications with Emphasis on Control Techniques: A Comprehensive Review

The current paper presents an inclusive survey about the AC to DC and DC to DC converters for brushed DC Motor Drives. An essential number of different AC to DC and DC to DC topologies and control techniques, applied on the brushed DC motor drives are presented. This extensive literature review exposes advantages, disadvantages and limitations besides giving the basic operating principles of various topologies and control techniques

Condition Assessment of Power Transformers through DGA Measurements Evaluation Using Adaptive Algorithms and Deep Learning

Condition assessment for critical infrastructure is a key factor for the wellbeing of the modern human. Especially for the electricity network, specific components such as oil-immersed power transformers need to be monitored for their operating condition. Classic approaches for the condition assessment of oil-immersed power transformers have been proposed in the past, such as the dissolved gases analysis and their respective concentration measurements for insulating oils. However, these approaches cannot always correctly (and in many cases not at all) classify the problems in power transformers. In the last two decades, novel approaches are implemented so as to address this problem, including artificial intelligence with neural networks being one form of algorithm. This paper focuses on the implementation of an adaptive number of layers and neural networks, aiming to increase the accuracy of the operating condition of oil-immersed power transformers. This paper also compares the use of various activation functions and different transfer functions other than the neural network implemented. The comparison incorporates the accuracy and total structure size of the neural network