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... the standard set, an element either belongs to or does not belong to a set; so the range of the standard set is , which can be used to solve a two-valued problem. In contrast to the standard set, the fuzzy set enables the description of concepts where the boundary is not explicit. It concerns not only whether an element belongs to the set but also to what degree it belongs to. The range of a fuzzy set is [0, 1]. The comparisons of the standard sets and fuzzy sets are shown in Table 5 [12]. The fuzzy logic analysis consists of three parts: fuzzification, fuzzy inference and defuzzification. Fuzzification is the process of transforming crisp input values into grades of membership for linguistic terms of fuzzy sets. The membership function is used to associate a grade to each linguistic term. A chosen fuzzy inference system (FIS) is responsible for drawing conclusions from the knowledge- based fuzzy rule set of If (X is A) then (Y is B) linguistic statements. Defuzzification then converts the fuzzy output values back into crisp output actions [13]. There are several methods for calculating the output set representative value. The main ones are: defuzzification based on the sets gravity center and the maximum average methods. The neural network technique is used to recognize and classify complex fault patterns without much knowledge about the process, the used trials or the fault patterns themselves. A neural network consists of many simple neurons which are connected with each other. The principal neural networks that we will use for the classification are: Multi-Layer Perceptron (MLP): Is a network organized in layers. A layer is a uniform neurons group without connection with each other and makes a transformation vector. The architecture of the MLP is composed of an input layer, a variable number of hidden layers and by an output layer which is fully connected with them. In particular, as outlined in Figure 2, a three-level fully connected network, using a sigmoid output function, has been considered because it is known that this number of levels allows building decision regions of any ...
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Citations
... The part that can be explored further is the use of soluble gas data analysis to improve the accuracy of diagnosis. Research [12] describes artificial intelligence techniques to classify DGA with power transformers. The approaches used include fuzzy logic, neural network (NN), and support vector machine (SVM). ...
... The oil in the transformer has an electrically insulating mineral, which functions as a heat transfer medium. When electrical and thermal disturbances occur, the molecules decompose and release gases [12,16,17]. Some gases are trapped in the oil, but some are soluble. ...
Fault detection in the incipient stage is necessary to avoid hazardous operating conditions and reduce outage rates in transformers. Fault-detected dissolved gas analysis is widely used to detect incipient faults in oil-immersed transformers. This paper proposes fault diagnosis transformers using an artificial neural network based on classification techniques. Data on the condition of transformer oil is assessed for dissolved gas analysis to measure the dissolved gas concentration in the transformer oil. This type of disturbance can affect the gas concentration in the transformer oil. Fault diagnosis is implemented, and fault reference is provided. The result of the NN method is more accurate than the Tree and Random Forest method, with CA and AUC values 0.800 and 0.913. This classification approach is expected to help fault diagnostics in power transformers.
... Dioxide is least influential. Researchers in these works [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] were using a small dataset of samples ranging from 100 to 400 observation instances. The work done by [12] shows what CO and CO2 have an influence in the performance of the whereas neural networks are robust to noise but weak in processing high dimensional data. ...
... can be seen in [11][12][13][14][15][16][17][18][19][20][21][22][23][60][61][62] ...
... By finding the optimal hyperplane that creates a large margin between the classes, it minimizes the generalisation error. The SVM has been applied by different researchers for condition monitoring [9,[23][24][25][26][27][28][29][30][64][65] ...
Dissolved Gas-in-oil analysis (DGA) is used to monitor the condition of bushings on large power transformers. There are different techniques used in determining the conditions from the data collected, but in this work the Artificial Intelligence techniques are investigated. This work investigates which gases in DGA are related to each other and which ones are important for making decisions. When the related and crucial gases are determined, the other gases are discarded thereby reducing the number of attributes in DGA. Hence a further investigation is done to see how these new datasets influence the performance of the classifiers used to classify the DGA of full attributes. The classifiers used in these experiments were Backpropagation Neural Networks (BPNN) and Support Vector Machines (SVM) whereas the Principal Component Analysis (PCA), Rough Set (RS), Incremental Granular Ranking (GR++) and Decision Trees (DT) were used to reduce the attributes of the dataset. The parameters used when training the BPNN and SVM classifiers are kept fixed to create a controlled test environment when investigating the effects of reducing the number of gases. This work further introduced a new classifier that can handle high dimension dataset and noisy dataset, Rough Neural Network (RNN).
... However, it is inconvenient and time-consuming for industrial applications due to the complex analytical process. Hence, artificial intelligence techniques have been proposed to develop more accurate diagnostic tools based on DGA data [22]. In [23][24][25][26][27][28][29][30][31][32], some artificial intelligence techniques such as fuzzy logic, artificial neural network and support vector machines have been introduced for fault classification with nearly equal performance without determination of problem severity. ...
This research focuses on problem identification due to faults in power transformers during operation by using dissolved gas analysis such as key gas, IEC ratio, Duval triangle techniques, and fuzzy logic approaches. Then, the condition of the power transformer is evaluated in terms of the percentage of failure index and internal fault determination. Fuzzy logic with the key gas approach was used to calculate the failure index and identify problems inside the power transformer. At the same time, the IEC three-gas ratio and Duval triangle are subsequently applied to confirm the problems in different failure types covering all possibilities inside the power transformer. After that, the fuzzy logic system was applied and validated with DGA results of 244 transformers as reference cases with satisfactory accuracy. Two transformers were evaluated and practically confirmed by the investigation results of an un-tanked power transformer. Finally, the DGA results of a total of 224 transformers were further evaluated by the fuzzy logic system. This fuzzy logic is a smart, accurate tool for automatically identifying faults occurring within transformers. Finally, the recommendation of maintenance strategy and time interval is proposed for effective planning to minimize the catastrophic damage, which could occur with the power transformer and its network.
... This fluid is also used for heat transfer by convection cooling [12,13]. When an electrical or thermal fault occurs in the transformer, the oil molecules adjacent to the fault location decompose and release some gases [14]. Some of these gases are dissolved in the oil and some remain free. ...
One of the most important tools for condition monitoring is the gas chromatography test of transformer oil, which is known as dissolved gas analysis (DGA). In this research, the DGA results of > 3000 power transformers operating in Iran's power grid were carefully studied and from among them, the results related to transformers suspicious of being faulty were used to validate the fault detection accuracy of the presented fuzzy inference system (FIS). In most of the previously published papers, the detection and isolation of transformer faults has been based on one or two of the following parameters: absolute concentrations of free and dissolved gases in transformer oil, total dissolved combustible gases, total combustible gases, ratios of some gases to each other, and the rates of gas increase. However, in this research, most of these parameters have been used for fault detection and isolation, according to the IEC 60599 standards. Also, no attempt has been previously made to detect the decomposition of insulation papers of transformers; but the presented FIS is able to detect this fault as well. The overall performance accuracy of the presented system is F1 = 91.2%, which seems to be a suitable value.
... Second, they require a search through a large pool of rules for a correct decision [17,26]. In [27], various AI techniques were present, such as fuzzy logic, ANNs, and SVM classifiers for faults classification of power transformers; it was shown that the SVM classifier method has better performance in diagnosis accuracy than the other AI methods. ...
This article presents an intelligent diagnosis and classification method for power transformer fault classification based on dissolved gas analysis: the support vector machine. It is a powerful algorithm for classification of faults that needs a limited set of small sampling data, a case of applications with non-linear behavior, and a high number of parameters; however, appropriate model parameters must be determined carefully. The selection of parameters has a direct effect on the machine's classification accuracy. In this study, a multi-layer support vector machine classifier is optimized by a grid search method and three heuristic approaches: (1) genetic, (2) differential evolution, and (3) particle swarm optimization algorithms. The performance analysis of the support vector machine hybridized with these optimization methods is demonstrated using the same classification set. The employed structure has five support vector machine layers, each of which uses a Gaussian kernel function due to its advantages of needing one parameter for optimization and providing excellent classification ability for non-linear data. The proposed approach gives highly accurate performance for diagnosis of power transformers. The support vector machine optimized with the particle swarm optimization algorithm has the best accuracy and requires less computational time compared to the other methods.
... Several methods have been devised for using Artificial Intelligence (AI) and Soft Computing (SC) for more advanced and accurate diagnosis of transformers [4,17]. In 2012, Souahlia et al. used fuzzy logic, Support Vector Machine (SVM) and Neural Networks (NN) for fault diagnosis in the transformers [18]. Way back in 1997, Huang et al. showed the use of fuzzy logic for diagnosing the faults in the transformer [22]. ...
... The concentration of all the gases present in the transformer used for the experiment is shown in Fig. 7. We have taken 80 different fault samples that are gathered from different sources and publications [7,9,18]. Fig. 8 shows the associated faults that are present in the transformer that are classified according to the standard IEEE C57-104. ...
Transformers are the vital parts of an electrical grid system. A faulty transformer can destabilize the electrical supply along with the other devices of the transmission system. Due to its significant role in the system, a transformer has to be free from faults and irregularities. Dissolved Gas-in-oil Analysis (DGA) is a method that helps in diagnosing the faults present in an electrical transformer. This paper proposes a hybrid system based on Genetic Neural Computing (GNC) for analyzing and interpreting the data derived from the concentration of the dissolved gases. It is further analyzed and clustered into four subsets according to the standard C57.104 defined by IEEE using genetic algorithm (GA). The clustered data is fed to the neural network that is used to predict the different types of faults present in the transformers. The hybrid system generates the necessary decision rules to assist the system’s operator in identifying the exact fault in the transformer and its fault status. This analysis would then be helpful in performing the required maintenance check and plan for repairs.
... The gas concentrations, generation rates, specific gas ratios, and the total combustible gas are important parameters for interpreting the result of DGA. To facilitate the procedure of power transformer fault classification, algorithms like Modified Differential Evolution [3], Multiclass SVM [18], Fast algorithm [22], Self adaptive RBF NN [25], Artificial Neural Network [20], K Nearest Neighbor [26], Support Vector Machine [19] and Radial Basis Function [27] have been presented in literature. Presently, the conventional ratio methods, statistical schemes and Artificial Intelligence (AI) methods are the major interpreting approaches for power transformer fault analysis. ...
... The Radial basis kernel function is defined as, (17) where, σ -standard deviation, xattribute value, ylabel value [26]. Only the best kernel function yields minimum error and highest classification accuracy. ...
Reliable operations of power transformers are necessary for effective transmission and distribution of power supply. During normal functions of the power transformer, distinct types of faults occurs due to insulation failure, oil aging products, overheating of windings, etc., affect the continuity of power supply thus leading to serious economic losses. To avoid interruptions in the power supply, various software fault diagnosis approaches are developed to detect faults in the power transformer and eliminate the impacts. SVM and SVM-SMO are the software fault diagnostic techniques developed in this paper for the continuous monitoring and analysis of faults in the power transformer. The SVM algorithm is faster, conceptually simple and easy to implement with better scaling properties for few training samples. The performances of SVM for large training samples are complex, subtle and difficult to implement. In order to obtain better fault diagnosis of large training data, SVM is optimized with SMO technique to achieve high interpretation accuracy in fault analysis of power transformer. The proposed methods use Dissolved Gas-in-oil Analysis (DGA) data set obtained from 500 KV main transformers of Pingguo Substation in South China Electric Power Company. DGA is an important tool for diagnosis and detection of incipient faults in the power transformers. The Gas Chromatograph (GC) is one of the traditional methods of DGA, utilized to choose the most appropriate gas signatures dissolved in transformer oil to detect types of faults in the transformer. The simulations are carried out in MATLAB software with an Intel core 3 processor with speed of 3 GHZ and 2 GB RAM PC. The results obtained by optimized SVM and SVM-SMO are compared with the existing SVM classification techniques. The test results indicate that the SVM-SMO approach significantly improve the classification accuracy and computational time for power transformer fault classification.
A new method of dissolved gas analysis is proposed to improve the accuracy of transformer fault diagnosis. The slime mold optimized support vector machine (SMA‐SVM), and logarithmic arctangent transform (LOG‐ACT) are combined. On the one hand, the better global optimization performance of SMA is used to optimize SVM parameters to solve the difficulty of SVM parameter selection. On the other hand, corresponding transformations are carried out for different features: the logarithmic(LOG) transformation is carried out for the original DGA data to retain the order of magnitude information. The arctangent (ACT) transformation is carried out for the ratio features to improve the data structure. Therefore, the combination of data transformation and optimization model can improve the accuracy of diagnosis from two aspects of data structure and classification algorithm. The performance of the proposed method was compared with IEC three ratio method, artificial neural network, optimized artificial neural network, GA‐SVM, and PSO‐SVM. Experimental results using published data show that the proposed method can significantly improve the accuracy of transformer fault diagnosis. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
