Intellignet Detection of Power Transformer Fault Using Feature Driven Machine Learning Method

Skin disease is a very vulnerable and severe issue in today's world. Skin disorder classification is crucial for diagnosis. Several new data mining algorithms have been developed to classify and interpret medical images. The functionality of the K-Nearest Neighbors (KNN) Random Forest (RF) Algorithm is described in this article, along with an analysis of its results. Furthermore, this study demonstrates a high-performing approach that saves both effort and money. The proposed model is designed based on KNN and the Random Forest algorithm. The patient can use this model to classify his skin disease as a primary detection, and the doctor also can ensure his judgment by using this proposed model. Traditional skin disease diagnosis is an expensive and time-consuming procedure. This paper's proposed classification model will identify ten different skin diseases. The Random Forest algorithm has a testing accuracy of 94.22 percent, and K-Nearest Neighbors (KNN) has a testing accuracy of 95.23 percent. The KNN algorithm has an F1 Score of 95.98 percent, whereas the Random Forest (RF) algorithm has an F1 Score of 95.94 percent. It can be increased by expanding the dataset and more feature extraction. This approach may benefit individuals with skin illness who are looking to save money and time as well as avoid skin cancer by identifying cancer at an early stage.

This paper proposes a new algorithm for the protection of power transformer depending on the rate of rise of differential currents. In which, all the winding currents of all the phases of power transformer are sensed through current transformer and based on the phasor difference between currents, differential currents of all phases are computed. Based on the value of differential current, abnormal condition is detected and rate of rise of differential currents (RRDC) is calculated. Further peak of the sum of derivative of RRDC function (SDRRDC) of all three phases is calculated and which is used to distinguish between external disturbances (magnetic inrush) and internal faults of transformer. For the validation of proposed protection algorithm, internal faults (symmetrical and asymmetrical) like line-to-ground (LG), line-to-line (LL), line-to-line-to-ground (LLG), line-to-line-to-line (LLL) and line-to-line-to-line-ground (LLLG), magnetic inrush as external disturbance and energisation of faulted transformer events are considered. Several test cases are generated, simulated and verified using PSCAD/EMTDC and MATALB software packages. The proposed scheme precisely discriminates between magnetic inrush and internal faults condition with less response time than the conventional biased differential current protection scheme.

Training in the mining industry received considerable attention since the Federal Mine Safety and Health Act of 1977 mandating thorough health and safety training. The response to a large extent has been one in which individual facilities developed their own training “programs” rather than sharing in the development of appropriate programs or using another's proven materials. With the basic achievement of health and safety training requirements, the industry has turned its attention to training to improve job performance; here again, independent and relatively informal approaches have been developed. Woodward Associates, Inc., San Diego, has developed, under contract to the U.S. Bureau of Mines, structured equipment operator training programs which teach how to (1) operate the machine and (2) perform the task. The WAI Six-Element approach provides the structure to guide the learning process to (1) ensure exposure to information, (2) promote knowledge and skill acquisition, (3) aid transfer of knowledge and skills to new situations, and (4) assess knowledge and skill acquisition and application. Because of the potential severity of accidents in the mining (and construction) industries, particular attention has been directed to helping future operators to cope with not only normal but also with the abnormal (machine malfunction) and emergency (machine failure) conditions which tend to result in accidents. The Six-Element approach has taken this concern and addressed it through (1) classroom instruction in the cues and procedures for handling abnormal and emergency conditions, and (2) structured on-machine training which employs a truck as task trainer and an OBSAC (On-Board Simulator of Abnormal Conditions) which induces apparent malfunctions, and actual system degrades to allow trainees to experience abnormal and emergency situations under safe conditions and under an instructor's watchful eye. This paper discusses the purpose, benefit, and challenges of introducing and using structured operator training in the mining industry. An evaluation of the experience related to the Haulage Truck Training Program will be discussed. Applications related to training operators of other mobile surface mining machines in normal and abnormal operating conditions will also be discussed.

Power transformers are of the most critical and expensive equipment in the power system industry. Meanwhile, they are exposed to a variety of faulty and abnormal operating conditions. Therefore, to prevent extension of these events they are protected by different protective schemes. Among the various types of the transformer faults, detecting turn-to-turn faults is the most challenging one for the protection systems. Industrial experiences and standard documents reveal that the existing protection systems include severe shortcomings, and still more efforts are required to offer a reliable method for detecting such faults. To this end, this article puts forward the proposal of a novel turn-to-turn fault detection method to satisfy the basic protection requirements of dependability, security, and speed. This method properly detects the low-level turn-to-turn faults while is stable during system transients and external faults. Performance of the proposed method is evaluated through various experimental and simulation studies. The obtained results verify superior operation of the proposed method under different faults and conditions.

The aim of this paper is to develop an effective classification approach based on Random Forest (RF) algorithm. Three fruits; i.e., apples, Strawberry, and oranges were analysed and several features were extracted based on the fruits' shape, colour characteristics as well as Scale Invariant Feature Transform (SIFT). A preprocessing stages using image processing to prepare the fruit images dataset to reduce their color index is presented. The fruit image features is then extracted. Finally, the fruit classification process is adopted using random forests (RF), which is a recently developed machine learning algorithm. A regular digital camera was used to acquire the images, and all manipulations were performed in a MATLAB environment. Experiments were tested and evaluated using a series of experiments with 178 fruit images. It shows that Random Forest (RF) based algorithm provides better accuracy compared to the other well know machine learning techniques such as K-Nearest Neighborhood (K-NN) and Support Vector Machine (SVM) algorithms. Moreover, the system is capable of automatically recognize the fruit name with a high degree of accuracy.

Human Gait refers to motion accomplished through the movement of hand limbs. Gait analysis is a precise investigation of the human walking pattern using sensors attached to the body for recording body movements during activities like walking on a flat surface, treadmill and running. This paper addresses the analysis of human gait based on performance using various classification techniques involving Decision Tree, Random Forest and KNN algorithms. The paper highlights the comparison of these classification models based on various parameters using the RapidMiner Studio tool. The comparison is based on performance metrics and Receiver Operating Characteristic (ROC). The results show that the Random Forest algorithm performs better in classifying normal and abnormal gait.

The monitoring method for erosion resistance of metals and protective coatings under cavitation exposure in abnormal operating conditions is proposed. This method extends the scope of the standard ASTM G32-10 Standard Test Method for Cavitation Erosion Using Vibratory Apparatus. The specialized ultrasonic device has been developed for the study of cavitation destruction of materials and coatings at high pressures, temperatures up to 1000 °C and in liquid media with various properties (including aggressive ones) for implementing the monitoring method. The control system based on continuous monitoring of electric parameters of piezoelectric vibratory system was used to providing the necessary vibration amplitude in the process of cavitation under all changes of liquids parameters. The control system (based on changes monitoring of liquid active resistance) was used for equal efficiency of cavitation process on the surface. Practical recommendations for testing of cavitation erosion of metals and protective coatings in abnormal conditions are given.

Waste facilities deal with dust, odours, and harmful gases. Designing an effective ventilation system for e-waste recycling facilities presents unique challenges due to the complexity of waste materials and potential emissions of hazardous air pollutants. This paper explores key considerations in designing an effective ventilation solution for e-waste facilities featured in an industrial showcase project in NSW, Australia. The facility comprises a Batch Rotary Pyrolysis (BRP) plant area to process waste electrical and electronic equipment, generating a flammable syngas during thermal treatment. Under abnormal conditions, this syngas may be released into the building, posing a fire hazard if ignited by unprotected electrical equipment near BRP machines or attached gas burners. To mitigate this risk, ventilation serves as the first line of defence against the accumulation of explosive gas concentrations, in alignment with AS1482-2013 standards. This study employs Computational Fluid Dynamics (CFD) analysis integrated with the site-specific wind rose to evaluate ventilation effectiveness, creating a detailed 3D model of the proposed facility, its surrounding structures, and natural ventilation components. A localised 10m-height reference wind rose was developed using The Air Pollution Model (TAPM) and CALMET diagnostic meteorological modelling software to provide accurate wind conditions for the project site. The CFD analysis delivers detailed insights, including airspeed distributions and predicted air exchange rates, highlighting hotspot areas with inadequate ventilation. Furthermore, the model facilitates comparative evaluations against regulatory standards by simulating concentrations of key gases (CO, SO₂, and NO₂) under abnormal gas leak conditions, ensuring that both safety and compliance are thoroughly upheld. The study offers comprehensive design input to refine natural ventilation systems, optimise opening sizes, and integrate mechanical ventilation where natural airflow is insufficient due to site orientation. This research demonstrates that an effective ventilation strategy for e-waste facilities can be developed when CFD modelling is incorporated early in the concept design stage. The proposed tool also has multiple downstream applications, including air quality and odour examination, thermal comfort assessment, fire risk evaluation, and smoke dispersion analysis.

Power transformer is a critical and expensive asset in electric transmission and distribution networks. It is essential to monitor the health condition of all power transformer fleet in such networks to avoid unwanted outages. The health index (HI) is a quick and efficient way to assess the condition of power transformers based on multi-criteria. While Power transformer HI method has been well presented in the literature, not much attention was given to handle the uncertainty and reliability of this method due to unavailability of used data. Therefore, this paper aims to tackle this issue through employing Artificial Intelligence (AI)-based techniques to reveal the health condition of power transformers with high accuracy and at the same time handling data uncertainty. The proposed HI approach assesses the power transformer insulation system based on oil quality, dissolved gas analysis (DGA), and paper condition. In this regard, collected data from 504, 150-kV transformers are used to establish the proposed AI-models. Seven AI algorithms including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Artificial Neural Network (ANN), Adaptive Boosting (AdaBoost), and Decision Tree are investigated. A performance comparison of the proposed AI-based HI models is carried out using the scoring-weighting-based HI method as the reference. Results show that RF model provides the best performance in predicting power transformer HI with an accuracy of 97.3%.

Development of a PDA model for water distribution systems using harmony search algorithm

Conventional second harmonic current restriction approach is an adopted method which has been implemented to transformer differential protection schemes. Because of unreliable nature of this approach in discrimination between inrush current, internal faults and external faults and due to the saturation of current transformers, we present new features of the differential protection scheme. This helps to determine the feasibility and applicability of the proposed approach in differential protective scheme for power transformer. The Current Transformer Saturation (CTS) phenomenon issue and its revelation is one of the most significant problems in power system protection particularly in the differential protection. In this paper, the saturation phenomenon and its effects on the differential protection are analyzed. Prevalent algorithms in digital signal processing which are implemented in power system cannot correctly recognize this phenomenon. Mal-operation of differential protection relay takes place when saturation in CT is appeared. This paper presents a Third-Difference Algorithm (TDA) for CTS recognition. In order to make comparison between the presented approach and prevalent digital protective algorithm, an identical case study is simulated. Simulation results also show that the TDA is both satisfactory and consistent with expectation. Keywords: Current transformer, Saturation phenomenon, Third-difference Algorithm, Internal and external faults.

Context. The William Herschel Telescope Enhanced Area Velocity Explorer (WEAVE) is a new, massively multiplexing spectrograph that allows us to collect about one thousand spectra over a 3 square degree field in one observation. The WEAVE Stellar Population Survey (WEAVE-StePS) in the next 5 years will exploit this new instrument to obtain high-S/N spectra for a magnitude-limited (IAB = 20.5) sample of ∼25 000 galaxies at moderate redshifts (z ≥ 0.3), providing insights into galaxy evolution in this as yet unexplored redshift range. Aims. We aim to test novel techniques for retrieving the key physical parameters of galaxies from WEAVE-StePS spectra using both photometric and spectroscopic (spectral indices) information for a range of noise levels and redshift values. Methods. We simulated ∼105 000 galaxy spectra assuming star formation histories with an exponentially declining star formation rate, covering a wide range of ages, stellar metallicities, specific star formation rates (sSFRs), and dust extinction values. We considered three redshifts (i.e. z = 0.3, 0.55, and 0.7), covering the redshift range that WEAVE-StePS will observe. We then evaluated the ability of the random forest and K-nearest neighbour algorithms to correctly predict the average age, metallicity, sSFR, dust attenuation, and time since the bulk of formation, assuming no measurement errors. We also checked how much the predictive ability deteriorates for different noise levels, with S/NI,obs = 10, 20, and 30, and at different redshifts. Finally, the retrieved sSFR was used to classify galaxies as part of the blue cloud, green valley, or red sequence. Results. We find that both the random forest and K-nearest neighbour algorithms accurately estimate the mass-weighted ages, u-band-weighted ages, and metallicities with low bias. The dispersion varies from 0.08–0.16 dex for age and 0.11–0.25 dex for metallicity, depending on the redshift and noise level. For dust attenuation, we find a similarly low bias and dispersion. For the sSFR, we find a very good constraining power for star-forming galaxies, log sSFR ≳ −11, where the bias is ∼0.01 dex and the dispersion is ∼0.10 dex. However, for more quiescent galaxies, with log sSFR ≲ −11, we find a higher bias, ranging from 0.61 to 0.86 dex, and a higher dispersion, ∼0.4 dex, depending on the noise level and redshift. In general, we find that the random forest algorithm outperforms the K-nearest neighbours. Finally, we find that the classification of galaxies as members of the green valley is successful across the different redshifts and S/Ns. Conclusions. We demonstrate that machine learning algorithms can accurately estimate the physical parameters of simulated galaxies for a WEAVE-StePS-like dataset, even at relatively low S/NI, obs = 10 per Å spectra with available ancillary photometric information. A more traditional approach, Bayesian inference, yields comparable results. The main advantage of using a machine learning algorithm is that, once trained, it requires considerably less time than other methods.

This research aims to estimate the delivery time and energy cost of e-scooter vehicles for distributing mail or packages and to show the usage efficiency of e-scooter sharing services in postal service delivery in Turkey. The machine learning (ML) methods used to implement the prediction of delivery time and energy cost as output variables include random forest (RF), gradient boosting (GB), k-nearest neighbour (kNN), and neural network (NN) algorithms. Fifteen input variables under demographic, environmental, geographical, time, and meta-features are utilised in the ML algorithms. The correlation coefficient (R2) values of RF, GB, NN, and kNN algorithms were computed for delivery time as 0.816, 0.845, 0.821, and 0.786, respectively. The GB algorithm, which has a high R2 and the slightest margin of error, exhibited the best prediction performance for delivery time and energy cost. Regarding delivery time, the GB algorithm’s MSE, RMSE, and MAE values were calculated as 149.32, 12.22, and 6.08, respectively. The R2 values of RF, GB, NN, and kNN algorithms were computed for energy cost as 0.917, 0.953, 0.400, and 0.365, respectively. The MSE, RMSE, and MAE values of the GB algorithm were calculated as 0.001, 0.019, and 0.009, respectively. The average energy cost to complete a package or mail delivery process with e-scooter vehicles is calculated as 0.125 TL, and the required time is approximately computed as 11.21 min. The scientific innovation of the study shows that e-scooter delivery vehicles are better for the environment, cost, and energy than traditional delivery vehicles. At the same time, using e-scooters as the preferred way to deliver packages or mail has shown how well the delivery service works. Because of this, the results of this study will help in the development of ways to make the use of e-scooters in delivery service even more efficient.

Techniques and tools meant to aid fire management activities in the Cerrado, such as accurately determining the fuel load and composition spatially and temporally, are pretty scarce. The need to obtain fuel information for more efficient management in a considerably heterogeneous, biodiverse, and fire-dependent environment requires a constant search for improved remote sensing techniques for determining fuel characteristics. This study presents the following objectives: (1) to assess the use of data from Landsat 8 OLI images to estimate the fine surface fuel load of the Cerrado during the dry season by adjusting multiple linear regression equations, (2) to estimate the fuel load through random forest and k-nearest neighbor (k-NN) algorithms in comparison to regression analyses, and (3) to evaluate the importance of predictor variables from satellite images. Therefore, 64 sampling units were collected, and the pixel values associated with the field plots were extracted in a 3 × 3-pixel window surrounding the reference pixel. For multiple linear regression analyses, the R2 values ranged from 0.63 to 0.78, while the R2 values of the models fitted using the random forest algorithm ranged from 0.52 to 0.83 and the R2 values of those fitted using the k-NN algorithm ranged from 0.30 to 0.68. The estimates made through multiple linear regression analyses showed better results for the equations adjusted for the beginning of the dry season (May and June). Adopting the random forest algorithm resulted in improvements in the statistical metrics of evaluation of the fuel load estimates for the Cerrado grassland relative to multiple linear regression analyses. The variable fraction-soil (FS) exerted the most significant effect on surface fuel load estimates, followed by the vegetation indices NDII, GVMI, DER56, NBR, and MSI, all of which use near-infrared and short-wave infrared channels in their calculations.

In the current scenario, plant disease detection is seeking attention from many agricultural scientists. Plant diseases are deeply influenced by the weather conditions, and each disease has its individual weather requirements. The changes in weather parameters such as humidity, temperature, wind speed, etc., can cause many diseases in tomato plants. In the current empirical study, we have taken specific disease powdery mildew whose fungus is named as Leveillula Taurica which belongs to Leotiomycetes class, and it is responsible for the occurrence of this specific disease in tomatoes. In this research, three weather-based prediction models have been developed using k-nearest neighbor (kNN), decision tree (DT), and random forest (RF) algorithm for powdery mildew disease prediction in tomatoes at an early stage. Results indicate that the proposed model, based on RF algorithm, shows the best accuracy of 93.24% for tomato powdery mildew disease (TPMD) dataset. A real-time version of the proposed model can be used by the agricultural experts to take preventive measures in the most sensitive areas that are prone to powdery mildew disease based on the weather conditions. Hence, timely intervention would help in reducing the loss in productivity of tomato crops which will further benefit the global economy, agricultural production, and the food industry.