Artificial Intelligence Based Technique for the Classification of Incipient Faults in Power Transformer

2015 ◽  
Vol 785 ◽  
pp. 29-33
Author(s):  
Fathiah Zakaria ◽  
Dalina Johari ◽  
Ismail Musirin
Keyword(s):  
Power System ◽  
Power Transformer ◽  
Optimization Technique ◽  
Gas Analysis ◽  
Optimization Techniques ◽  
Computational Technique ◽  
Ann Model ◽  
Great Ability ◽  
Incipient Faults

Power transformer has been identified as crucial and vital equipment in power system. Any disturbance such as faults will result in immense impact to the whole power system. This paper presents the development of an Evolutionary Programming (EP) – Taguchi Method (TM) – Artificial Neural Network (ANN) based technique for the classification of incipient faults in power transformer using Dissolved Gas Analysis (DGA) method based on historical industrial data. It involved the development of ANN model and embedding TM and EP as the optimization techniques in order to enhance the system accuracy and efficiency. ANN is a powerful computational technique that mimics how human brain process information. It has great ability to learn from experiences and examples, hence greatly suitable for classification, pattern recognition and forecasting purposes. In designing the ANN model, there are parameters which need to be chosen wisely. However, there is no systematic ways and guidelines to select the optimal ANN parameters. It is greatly dependent on the design knowledge and experiences of the experts. The process of finding suitable parameters is become difficult, tedious and time consuming, thus optimization technique is needed to overcome the shortcoming. In this study, TM and EP were employed as the optimization techniques to improve the ANN-based model. The findings obtained from the proposed technique have proved the effectiveness of both TM and EP in optimizing the ANN model. As a result, a reliable EP-TM-ANN based system has been successfully developed that can classify incipient faults in power transformer.

scholarly journals Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique

2021 ◽  
Vol 13 (12) ◽  
pp. 6644
Author(s):  
Ali Selim ◽  
Salah Kamel ◽  
Amal A. Mohamed ◽  
Ehab E. Elattar
Keyword(s):  
Power System ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Optimal Allocation ◽  
Optimization Technique ◽  
Mathematical Formulation ◽  
Optimization Techniques ◽  
Sensitivity Factor ◽  
Hybrid Technique ◽  
Analytical Technique

In recent years, the integration of distributed generators (DGs) in radial distribution systems (RDS) has received considerable attention in power system research. The major purpose of DG integration is to decrease the power losses and improve the voltage profiles that directly lead to improving the overall efficiency of the power system. Therefore, this paper proposes a hybrid optimization technique based on analytical and metaheuristic algorithms for optimal DG allocation in RDS. In the proposed technique, the loss sensitivity factor (LSF) is utilized to reduce the search space of the DG locations, while the analytical technique is used to calculate initial DG sizes based on a mathematical formulation. Then, a metaheuristic sine cosine algorithm (SCA) is applied to identify the optimal DG allocation based on the LSF and analytical techniques instead of using random initialization. To prove the superiority and high performance of the proposed hybrid technique, two standard RDSs, IEEE 33-bus and 69-bus, are considered. Additionally, a comparison between the proposed techniques, standard SCA, and other existing optimization techniques is carried out. The main findings confirmed the enhancement in the convergence of the proposed technique compared with the standard SCA and the ability to allocate multiple DGs in RDS.

scholarly journals Fuzzy Algorithm for Power Transformer Diagnostics

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Nitin K. Dhote ◽  
Jagdish B. Helonde
Keyword(s):  
Gas Chromatography ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Inference System ◽  
Incipient Faults ◽  
The World

Dissolved gas analysis (DGA) of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results fall outside conventional methods codes or when more than one fault exist in the transformer. To overcome these limitations, the fuzzy inference system (FIS) is proposed. Two hundred different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.

scholarly journals Fire-Fly Algorithm Based ANN Classification of Power Transformer Faults

2020 ◽  
Vol 9 (4) ◽  
pp. 1880-1884
Keyword(s):  
Wavelet Transforms ◽  
Performance Metrics ◽  
Feature Vector ◽  
Learning Algorithm ◽  
Power Transformer ◽  
Gas Analysis ◽  
Phase Detection ◽  
Double Line ◽  
Waveform Data

Monitoring and estimating the states of the transformer during faulted phase condition is essential to continuity of supply. Varied techniques are proposed for faulted phase detection to improve condition assessment. In this paper, we propose a novel method to detect and classify power transformer faults using wavelet transform Multi Resolution Analysis (MRA) as feature extracted parameter vector and Fire-Fly Algorithm (FFA) based Artificial Neural network training as classification method. The observed Dissolved Gas Analysis (DGA) waveform data is analyzed with wavelet transforms (WT) to identify abnormalities which is supported by MRA. In MRA, the current, voltage and temperature of winding and oil are decomposed into high and low frequency components. The magnitude of components, signifies the feature vector, gives a detection criteria. After detecting feature vector, dominant coefficients of WT can be used to train the ANN with FFA based learning algorithm. Different types of faults are created on transformer such as Single Line-Ground (SLG), Line-Line (LL), Double Line-Ground LLG, Three phase fault (LLLG) for the analysis using WT and ANN. The detection and classification of the fault signal are executed and examined in different winding location and different fault conditions. Finally, the presented precise model recognizes the faults based on performance metrics with high classification accuracy for various classes.

Minimization of losses and FACTS installation cost using proposed differential gravitational search algorithm optimization technique

2016 ◽  
Vol 23 (2) ◽  
pp. 235-251
Author(s):  
SN Deepa ◽  
J Rizwana
Keyword(s):  
Power System ◽  
Power Flow ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Gravitational Search Algorithm ◽  
Optimal Location ◽  
Optimization Techniques ◽  
System Stability ◽  
Gravitational Search ◽  
Installation Cost

The optimal location of Flexible AC Transmission Systems (FACTS) controllers in a multi-machine power system using proposed differential gravitational search algorithm (DGSA) optimization method is proposed in this paper. The main objective of this paper is to employ DGSA optimization technique to solve optimal power flow problem in the presence of Unified Power Flow controller for improving voltage profile by reducing losses along with the installation cost thereby enhancing the power system stability. A differential operator is incorporated into the gravitational search algorithm for effective search of the better solution. Due to this, the convergence and accuracy will be faster. The IEEE-6 bus, IEEE-14 bus and IEEE-30 bus systems are tested along with three other optimization techniques to validate the effectiveness of this proposed method. This proposed algorithm presents an optimal location of FACTS devices in transmission lines.

Power Transformer Incipient Faults Diagnosis Based on Dissolved Gas Analysis

2016 ◽  
Vol 1 (1) ◽  
pp. 10 ◽  
Author(s):  
Osama E. Gouda ◽  
Saber M. Saleh ◽  
Salah Hamdy El-hoshy
Keyword(s):  
Power Transformer ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Insulation System ◽  
Matlab Program ◽  
Incipient Faults ◽  
Faults Diagnosis ◽  
Incipient Fault ◽  
Paper Insulation ◽  
Incipient Fault Diagnosis

Incipient fault diagnosis of a power transformer is greatly influenced by the condition assessment of its insulation system oil and/or paper insulation. Dissolved gas-in-oil analysis (DGA) is one of the most powerfull techniques for the detection of incipient fault condition within oil-immersed transformers. The transformer data has been analyzed using key gases, Doernenburg, Roger, IEC and Duval triangle techniques. This paper introduce a MATLAB program to help in unification DGA interpretation techniques to investigate the accuracy of these techniques in interpreting the transformer condition and to provide the best suggestion for the type of the fault within the transformer based on fault percentage. It proposes a proper maintenance action based on DGA results which is useful for planning an appropriate maintenance strategy to keep the power transformer in acceptable condition. The evaluation is carried out on DGA data obtained from 352 oil samples has been summarized  into 46 samples that have been collected from a 38 different transformers of different rating and different life span.

scholarly journals FAULT DIAGNOSIS OF POWER TRANSFORMER BASED ON DISSOLVED GAS ANALYSIS AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

2013 ◽  
pp. 298-300
Author(s):  
PALLAVI PATIL ◽  
VIKAL INGLE
Keyword(s):  
Power System ◽  
Power Transformer ◽  
Fuzzy Rule ◽  
Well Being ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Inference System ◽  
Multiple Faults ◽  
Dissolved Gas Analysis ◽  
Neuro Fuzzy

Power Transformers are a vital link in a power system. Well-being of power transformer is very much important to the reliable operation of the power system. Dissolved Gas Analysis (DGA) is one for the effective tool for monitoring the condition of the transformer. To interpret the DGA result multiple techniques are available.IEC codes are developed to diagnose transformer faults. But there are cases of errors and misleading judgment due to borderline and multiple faults. Methods were developed to solve this problem by using fuzzy membership functions to map the IEC codes and heuristic experience to adjust the fuzzy rule. This paper proposes a neuro-fuzzy method to perform self learning and auto rule adjustment for producing best rules.

Genetic-TLBO Algorithm to Solve Non-Linear Complex Problems for Life Quality Improvement

2019 ◽  
Vol 14 ◽  
Author(s):  
Emad Roshandel ◽  
Mojtaba Moattari
Keyword(s):  
Genetic Algorithm ◽  
Power System ◽  
Optimization Technique ◽  
Search Space ◽  
Heuristic Optimization ◽  
Great Ability ◽  
Complex Problems ◽  
Tlbo Algorithm ◽  
Wide Range ◽  
Non Linear

Background: A large number of nature-based optimization methods have been proposed to use as efficient tools in scientific studies. Genetic Algorithm (GA), which operates based on human genetical evolution, has been an outstanding mostly used solver in a wide range of applications. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation. Initialization, selection, crossover, and mutation are the main parts of the GA population-based method which enables GA to have a prominent explorative feature. On the other hand, the Teaching Learning Based Optimization algorithm (TLBO) is of great performance during searching for the optimum solution among individuals. Therefore, it is expected that the combination of both algorithms in a certain logical way improves the optimization time. Objective: The study intends to determine ways of improving the performance of the TLBO algorithm to solve a complex non-linear problem. Power system studies are one of the most complex problems for analysis. Therefore, a powerful heuristic optimization procedure would have a valuable contribution to solving such problems. In addition, the proposed heuristic algorithm will help scientists to apply the technique to their problems. Methodology:: According to the aforementioned explanation, a new efficient optimization approach is proposed which optimizes the parameters of multi-machine power system stabilizers (PSSs). The TLBO algorithm includes two different stages in its main structure, which are aptly called teacher and student stages. The student stage of TLBO is replaced by the genetic algorithm in order to improve the explorative feature of the main TLBO. The PSS parameters are obtained for four PSSs which are connected to four generators. Results: The performance of the proposed stabilizer is compared with other formerly designed stabilizers reported in the literature consisting of multi-band PSSs for two areas four-machine power system. Simulation results demonstrate the effectiveness and robustness of the proposed PSS in damping local and inter-area oscillation modes under various disturbances and confirm its superiority in comparison with other types of PSSs. Conclusion: A search heuristic method like the genetic algorithm can dramatically improve the performance of meta-heuristic optimization technique. In actuality, the TLBO as a meta-heuristic optimization technique suffers from a direct search of random solutions in its student stage. Then, the TLBO relinquishes some parts of search space which may restrict the algorithm to find absolute maximums or minimums. In this condition, the GA with a great ability in searching the whole search space effectively improves the TLBO. According to the obtained results, the proposed algorithm, named Genetic-TLBO, obviates the conventional TLBO flaws successfully.

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