Power transformer fault diagnosis based on MIC feature extraction and INGO-SVM

Accurate faults diagnosis in power transformers is important for utilities to schedule maintenance and minimises the operation cost. Dissolved gas analysis (DGA) is one of the proven and widely accepted tools for incipient fault diagnosis in power transformers. To improve the accuracy and solve the cases that cannot be classified using Rogers’ Ratios, IEC ratios and Duval triangles methods, a novel DGA technique based on Parzen window estimation have been presented in this study. The model uses the concentrations of five combustible hydrocarbon gases: methane, ethane, ethylene, acetylene and hydrogen to compute the probability of transformers fault categories. Performance of the proposed method has been evaluated against different conventional techniques and artificial intelligence‐based approaches such as support vector machines, artificial neural networks, rough sets analysis and extreme learning machines for the same set of transformers. A comparison with other soft computing approaches shows that the proposed method is reliable and effective for incipient fault diagnosis in power transformers.

Fault diagnosis of power transformer based on multi-layer SVM classifier

PurposePower transformers are vital components of an electrical network. A defective transformer can cause instability and blackouts in parts of the network. An accurate classification of different transformer faults results in a relatively accurate fault diagnosis and timely corrective actions. It is possible to increase productivity and reduce costs by using fault detection of power transformers through the analysis of gases dissolved in oil. The proposed technique is a suitable tool to help the utilities and engineers in charge of preventive maintenance by reducing the costs of different fault diagnosis tests for power transformers.Design/methodology/approachIn this paper, the IEC 60599 standard along with clustering and classification methods are used to classify power transformer’s fault types. K-means and Fuzzy C-means clustering methods are used for clustering, and the support vector machine (SVM) method is used for classification of different types of faults in ‎power ‎transformers. The performance of K-means and SVM methods is improved by using the Grasshopper Optimization Algorithm (GOA). The efficiency of the proposed methods is evaluated using real field data of power transformers. The purpose of this study is to propose hybrid methods including K-means-GOA clustering and SVM-GOA classification for accurate fault diagnosis. These methods have been used for the first time in fault diagnosis determination of power transformers through gas analysis. The Silhouette criteria is used in this paper to compare the efficiency of different clustering methods.FindingsSimulation results of the paper are based on the gas chromatography data related to 266 different real power transformers. They show the high accuracy and high-performance speed of intelligent clustering and classification methods compared to conventional ones. This analysis would be helpful in performing the required maintenance check and plan for repairs.Originality/valueThe applicability and efficiency of the proposed hybrid K-means-GOA and SVM-GOA models are verified for transformer fault detection using the experimental diverse data set including 266 set of real field test parameters of power transformers.

Power transformers undertake the task of transforming voltage and transmitting electrical energy. Its operating status is directly connected with the stability and safety of the whole power system, and it is very important to judge the operating conditions of power transformers and diagnose fault types. The use of dissolved gas analysis technology in oil can provide preliminary fault diagnosis for transformers. However, with the increasing demand for fault diagnosis accuracy in modern electrical equipment, relying only on dissolved gas analysis technology in oil cannot satisfy the demands. To lift the transformer fault diagnosis accuracy, this study introduces the K‐means algorithm into the model and constructs a high‐precision and fast convergence diagnosis method and a power transformer fault location recognition model. In the example analysis, kernel functions were selected for training five typical gases to obtain the optimal parameters, and their prediction curves and errors were analyzed. Its diagnostic accuracy is 98.4%, and the error in all five gases is within 1 (uL/L). The average error of the improved support vector machine intelligent algorithm is lower than that of the previous model and other prediction methods. By testing the same sample data, the correctness of this method was verified. The significance of improving support vector machines lies in further improving the performance and applicability of the original support vector machine algorithm, providing a basis for future transformer maintenance and contributing to social development and continuous improvement of economic benefits.

Failure of power transformer is very complex, so that it is difficult to use the mathematical model to describe their faults. In this study, an intelligent diagnostic method based on ant colony-support vector machine (AC-SVM) approach is presented for fault diagnosis of power transformer. The AC-SVM selects kernel function parameter and soft margin constant C penalty parameter of support vector machine (SVM) classifier. The performance of the AC-SVM system proposed in this study is evaluated by cases in China. The test results show that this AC-SVM model is effective to detect failure of power transformer.

Transformer fault diagnosis plays a critical role in ensuring the reliability and safety of power systems. Traditional diagno stic methods often suffer from limited feature extraction capability and low adaptability to complex operating conditions. To address these issues, this paper proposes a transformer fault diagnosis approach based on an improved convolutional neural network (CNN). The proposed model introduces an optimized network structure with enhanced feature extraction layers and an adaptive pooling mechanism to capture both local and global fault characteristics from dissolved gas analysis (DGA) and vibration signal datasets. Experimental results on benchmark datasets demonstrate that the improved CNN achieves higher accuracy and robustness compared with conventional CNN and support vector machine (SVM) methods, especially under noisy and unbalanced data scenarios. The proposed approach provides an effective and scalable solution for real-time transformer fault monitoring, contributing to the advancement of intelligent substations and the reliable operation of modern power grids.

This paper presents genetic-based neural networks (GNNs) for fault diagnosis of power transformers. The GNNs automatically tune the network parameters, connection weights and bias terms of the neural networks, to yield the best model according to the proposed genetic algorithm. Due to the global search capabilities of the genetic algorithm and the highly nonlinear mapping nature of the neural networks, the GNNs can identify complicated relationships among dissolved gas contents in transformer oil and corresponding fault types. The proposed GNNs have been tested on the Taipower Company diagnostic records and compared with the fuzzy logic diagnosis system, artificial neural networks and the conventional method. The test results show that the proposed GNNs improve the diagnosis accuracy and the learning speed of the existing approaches.

The accuracy of machine learning-based arc fault diagnosis methods relies on the quality of the selected features. First, an experimental system is constructed for collecting realistic arc fault current signals, and different multi-domain feature extraction methods are described. Then, the feature relationships are investigated using various correlation methods to reveal the negative impact of weakly relevant or redundant features on the machine learning model. Based on the combination of feature clustering (FC) and maximal information coefficient (MIC), this paper proposes a feature selection (FS) strategy. The strategy searches for cluster centers through FC and merging processes to obtain non-redundant features with the highest representational abilities. The elimination of weakly relevant clusters and the selection of the most relevant features within clusters are performed based on the MIC scores. Subsequently, a valid feature subset with the highest representativeness and relevance is selected to improve the accuracy of arc fault diagnosis. Finally, the proposed method is evaluated against several popular FS strategies using three typical classifiers on different arc fault feature datasets, and the results show that the strategy effectively improves the accuracy of fault diagnosis. In addition, the cross-validation and hardware tests verify the stability, interference immunity, and feasibility of the proposed method for practical applications.

The bidirectional gated recurrent unit (BiGRU) method based on dissolved gas analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings such as the fuzzy boundaries of DGA data, and the BiGRU parameters are difficult to determine. Therefore, this paper proposes a power transformer fault diagnosis method based on landmark isometric mapping (L-Isomap) and Improved Sand Cat Swarm Optimization (ISCSO) to optimize the BiGRU (ISCSO-BiGRU). Firstly, L-Isomap is used to extract features from DGA feature quantities. In addition, ISCSO is further proposed to optimize the BiGRU parameters to build an optimal diagnosis model based on BiGRU. For the ISCSO, four improvement methods are proposed. The traditional sand cat swarm algorithm is improved using logistic chaotic mapping, the water wave dynamic factor, adaptive weighting, and the golden sine strategy. Then, benchmarking functions are used to test the optimization performance of ISCSO and the four algorithms, and the results show that ISCSO has the best optimization accuracy and convergence speed. Finally, the fault diagnosis method based on L-Isomap and ISCSO-BiGRU is obtained. Using the model for fault diagnosis, the example simulation results show that using L-ISOMP to filter and downscale the model inputs can better improve model performance. The results are compared with the SCSO-BiGRU, WOA-BiGRU, GWO-BiGRU, and PSO-BiGRU fault diagnosis models. The results show that the fault diagnosis rate of ISCSO-BiGRU is 94.8%, which is 11.69%, 10.39%, 7.14%, and 5.9% higher than that of PSO-BiGRU, GWO-BiGRU, WOA-BiGRU, and SCSO-BiGRU, respectively, and validate that the proposed method can effectively improve the fault diagnosis performance of transformers.

This study presents a multi-class least square support vector machines (LS-SVM)-based classifier for transformer fault diagnosis. First, the original binary classifier is extended for multi-class classification that is common in fault diagnosis by using combination schemes, that is, the minimal output coding, error correcting output codes, one-against-one and one-against-all schemes. Second, the algorithm of particle swarm optimisation is implemented to select the optimal feature parameters for the multi-class LS-SVM classifiers. Then the effectiveness of the proposed approach is verified on the basis of the experiments on benchmark classification data and real-world transformer data. For comparison purpose, three widely used transformer diagnosis methods such as the IEC criteria, back propagation neural network and standard support vector machines are utilised. The results show the proposed approach has a better performance both in training and testing accuracies.

Power transformers are important parts in power supply systems. The stability, safety and reliability of transformers are especially important for electricity systems. This paper uses dissolved gas value as a feature parameter and creates a power transformer fault detection model by support vector machine classifier. In order to address the difficulty of determining the parameters for support vector machine, an optimization algorithm based on chaos particle swarm is proposed. The approach is not easy to be trapped into local optimum and improves population diversity and particle ergodicity. The experiment results prove that the support vector machine using chaos particle swarm optimization can achieve classification with higher precision and faster speed in power transformer fault diagnosis, compared to the support vector machine using traditional optimization algorithm.

Hyperspectral remote sensing imagery, due to its rich source of spectral information, provides an efficient tool for land cover classifications in complex geographical areas. However, the high-dimensional space of this imagery poses two important challenges in the classification process: the Hughes phenomena and the existence of relevant and redundant features. The robustness of Support Vector Machines (SVM) in high-dimensional space makes them an efficient tool for classifying hyperspectral imagery. However, optimum SVM parameter determination and optimum feature selection are the two optimization issues that strongly effect SVM performance. Traditional optimization algorithms can discover optimum solutions in a limited search space with one local optimum. Nevertheless, in high-dimensional space traditional optimization algorithms usually get trapped in a local optimum, therefore it is necessary to apply meta-heuristic optimization algorithms to obtain near-global optimum solutions. This study evaluates the potential of Ant Colony Optimization (ACO) for determining SVM parameters and selecting features. Results obtained from AVIRIS and ROSIS hyperspectral datasets demonstrate the superior performance of SVM, achieved by simultaneously optimizing SVM parameters and subsets of the input feature. For comparison, the evaluation is also performed by applying it to other meta-heuristic optimization algorithms such as simulated annealing, tabu search, and genetic algorithm. The results demonstrate a better performance of the ACO-based algorithm in regards to improving the classification accuracy and decreasing the size of selected feature subsets.

An integrated framework of input determination for ensemble forecasts of monthly estuarine saltwater intrusion

This paper presents the artificial neural network approach for incipient fault diagnosis of power transformers filled with oil.DGA data from reputed testing unit is obtained to deal with all possible faulty conditions in a power transformer. Well designed artificial neural network having the adaptive features and fast diagnosis capabilities are proposed and testing and training results of DGA samples made available are presented using neural network tool in Matlab 7.10. The diagnosis accuracy obtained during training and testing of samples is better. Programming features are incorporated proposing appropriate preventive maintenance action represented by a type of fault, so that the transformer in service can be saved.

Transformer fault diagnosis is a complex task that includes many possible types of faults and demands special trained personnel. This paper presents an intelligent fault diagnosis method of power transformer based on fuzzy logic and rough set theory. By using a fuzzy logic technique, the continuous attribute values are transformed into the fuzzy values by automatically deriving membership functions from a set of data with similarity clustering. With the concepts of fuzzy similarity relation and fuzzy similarity classes, β-positive region, β-negative region andβ-boundary region of rough-fuzzy approximation space are given. Also, a fuzzy rough set learning algorithm is given for inducing rules from quantitative data. The application to fault diagnosis of transformer shows the proposed algorithm can find more objective and effective diagnostic rules from the quantitative data and has yielded promising results.