Enhanced optimal trained hybrid classifiers for aging assessment of power transformer insulation oil

2020 ◽  
Vol 17 (3) ◽  
pp. 407-426
Author(s):  
Harkamal Deep Singh ◽  
Jashandeep Singh
Keyword(s):  
Prediction Models ◽  
Power Transformer ◽  
Electrical Equipment ◽  
Diagnostic Techniques ◽  
Assessment Model ◽  
Content Type ◽  
Lion Algorithm ◽  
Hybrid Classifiers ◽  
Transformer Insulation ◽  
Insulation Oil

Purpose As a result of the deregulations in the power system networks, diverse beneficial operations have been competing to optimize their operational costs and improve the consistency of their electrical infrastructure. Having certain and comprehensive state assessment of the electrical equipment helps the assortment of the suitable maintenance plan. Hence, the insulation condition monitoring and diagnostic techniques for the reliable and economic transformers are necessary to accomplish a comprehensive and proficient transformer condition assessment. Design/methodology/approach The main intent of this paper is to develop a new prediction model for the aging assessment of power transformer insulation oil. The data pertaining to power transformer insulation oil have been already collected using 20 working power transformers of 16-20 MVA operated at various substations in Punjab, India. It includes various parameters associated with the transformer such as breakdown voltage, moisture, resistivity, tan δ, interfacial tension and flashpoint. These data are given as input for predicting the age of the insulation oil. The proposed aging assessment model deploys a hybrid classifier model by merging the neural network (NN) and deep belief network (DBN). As the main contribution of this paper, the training algorithm of both NN and DBN is replaced by the modified lion algorithm (LA) named as a randomly modified lion algorithm (RM-LA) to reduce the error difference between the predicted and actual outcomes. Finally, the comparative analysis of different prediction models with respect to error measures proves the efficiency of the proposed model. Findings For the Transformer 2, root mean square error (RMSE) of the developed RM-LA-NN + DBN was 83.2, 92.5, 40.4, 57.4, 93.9 and 72 per cent improved than NN + DBN, PSO, FF, CSA, PS-CSA and LA-NN + DBN, respectively. Moreover, the RMSE of the suggested RM-LA-NN + DBN was 97.4 per cent superior to DBN + NN, 96.9 per cent superior to PSO, 81.4 per cent superior to FF, 93.2 per cent superior to CSA, 49.6 per cent superior to PS-CSA and 36.6 per cent superior to LA-based NN + DBN, respectively, for the Transformer 13. Originality/value This paper presents a new model for the aging assessment of transformer insulation oil using RM-LA-based DBN + NN. This is the first work uses RM-LA-based optimization for aging assessment in power transformation insulation oil.

A methodology for the optimized design of power transformer insulation system

2018 ◽  
Vol 37 (3) ◽  
pp. 1002-1010 ◽  
Author(s):  
Hugo Rodriguez-Ignacio ◽  
Xose M. Lopez-Fernandez ◽  
Casimiro Álvarez-Mariño
Keyword(s):  
Power Transformer ◽  
Safety Margin ◽  
Analytical Calculation ◽  
Optimized Design ◽  
Insulation System ◽  
Content Type ◽  
Candidate Solution ◽  
Wide Range ◽  
Two Stages ◽  
Transformer Insulation

Purpose The purpose of this paper is to present a methodology based on an optimizer linked with electric finite element method (FEM) for automating the optimized design of power transformer insulation system structures. Design/methodology/approach The proposed methodology combines two stages to obtain the optimized design of transformer insulation system structures. First, an analytical calculation is carried out with the optimizer to search a candidate solution. Then, the candidate solution is numerically checked in detail to validate its consistency. Otherwise, these two steps are iteratively repeated until the optimizer finds a candidate solution according to the objective function. Findings The solutions found applying the proposed methodology reduce the inter-electrode distances compared to those insulation designs referenced in the literature for the same value of safety margin. Originality/value The proposed methodology explores a wide range of insulation system structures in an automated way which is not possible to do with the classical trial-and-error approach based on personal expertise.

scholarly journals DGA Method-Based ANFIS Expert System for Diagnosing Faults and Assessing Quality of Power Transformer Insulation Oil

2015 ◽  
Vol 10 (1) ◽  
pp. 13 ◽  
Author(s):  
Mohd Muhridza Bin Yaacob ◽  
Ahmed Raisan Hussein ◽  
Mohd Fauzi Bin Othman
Keyword(s):  
Expert System ◽  
Power Transformer ◽  
Oil Quality ◽  
Gas Analysis ◽  
Ratio Method ◽  
Economic Losses ◽  
Intelligent Software ◽  
Transformer Insulation ◽  
Insulation Oil

<p class="zhengwen"><span lang="EN-GB">Accurate fault diagnostics and assessment of electrical power transformer insulation oil for lifelong endurance are the key issues addressed in this research. The durability of a transformer is significantly determined by the quality of its insulation oil, which deteriorates over time due to temperature fluctuations and moisture content. Protecting transformers from potential failure through early and precise diagnosis of faults and through efficient assessment of oil quality during the actual conduct of the operation can avoid sizeable economic losses. The ANFIS Expert System that uses intelligent software plays an important role in this regard. The dissolved gas analysis (DGA) in oil is a reliable method for diagnosing faults and assessing insulation oil quality in transformers. The safeguarding teams of transformer power stations often suffer from the occurrence of sudden faults, which result in severe damages and heavy monetary loss. The oil in transformers must be appropriately treated to circumvent such failures. In this research, an ANFIS Expert System was used to diagnose faults and to assess the status and quality of insulation oil in power transformers. A suitable treatment was identified using the Rogers ratio method depending on the DGA in oil. The graphical user interface from the MATLAB environment was used and proven effective for fault diagnosis and oil quality evaluation. The training algorithm is capable of assessing oil quality according to the specifications of the IEEE standard C57-104 and the IEC standard 60599.</span></p>

scholarly journals Aging Assessment of Power Transformer Insulation Oil using Hybrid Meta-Heuristic Trained Artificial Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 7115-7128
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Search Algorithm ◽  
Power Transformer ◽  
Particle Swarm ◽  
Transformer Oil ◽  
Training Algorithm ◽  
Artificial Neural ◽  
Transformer Insulation ◽  
Insulation Oil

In today's economic state, power transformer remains as the most expensive equipment in electrical system, in which insulation oil has been taken a significant role for performing a prominent operation. Since the insulation oil happens to degrade soon due to aging, high temperature and chemical reactions such as the oxidation, the periodic checking of oil followed by its replacement is necessary to stop the unexpected failure of the transformer. Moreover, it will be very advantageous if it happens to implement an automated model for predicting the age of transformer oil from time to time. The main intent of this paper is to develop an age assessment framework of transformer insulation oil using intelligent approaches. Here, diverse parameters associated with the transformer such as Breakdown Voltage (BDV), moisture, resistivity, tan delta, interfacial tension, and flash point is given as input for predicting the age of the insulation oil. These data have been already collected using 20 working power transformers operated at various substations in Punjab, India. In the proposed model, the collected parameters are subjected to a well-performing machine learning algorithm termed as Artificial Neural Network (ANN) in order to predict the age of the insulation oil. As a main contribution, the existing training algorithm in ANN so called as Levenberg–Marquardt (LM) is replaced by a hybrid metaheuritics algorithm. The newly developed hybrid algorithm merges the idea of Crow Search Algorithm (CSA), and Particle Swarm Optimization (PSO), and the new algorithm is termed as Particle Swarm-based Crow Search Algorithm (PS-CSA). The new training algorithm optimizes the weight of ANN using the hybrid CS-PSO updating procedure, in such a way that the difference between the predicted and actual outcome is minimum. Hence, this age prediction of transformer insulation oil will be beneficial for the environs to avoid the drastic condition.

scholarly journals Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2223 ◽  
Author(s):  
Sayed A. Ward ◽  
Adel El-Faraskoury ◽  
Mohamed Badawi ◽  
Shimaa A. Ibrahim ◽  
Karar Mahmoud ◽  
...  
Keyword(s):  
Power Transformer ◽  
Partial Discharge ◽  
Electrical Power ◽  
Diagnostic Techniques ◽  
Gas Analysis ◽  
Economic Losses ◽  
Dissolved Gas ◽  
Electricity Transmission ◽  
Dissolved Gas Analysis ◽  
Precise Interpretation

Power transformers are considered important and expensive items in electrical power networks. In this regard, the early discovery of potential faults in transformers considering datasets collected from diverse sensors can guarantee the continuous operation of electrical systems. Indeed, the discontinuity of these transformers is expensive and can lead to excessive economic losses for the power utilities. Dissolved gas analysis (DGA), as well as partial discharge (PD) tests considering different intelligent sensors for the measurement process, are used as diagnostic techniques for detecting the oil insulation level. This paper includes two parts; the first part is about the integration among the diagnosis results of recognized dissolved gas analysis techniques, in this part, the proposed techniques are classified into four techniques. The integration between the different DGA techniques not only improves the oil fault condition monitoring but also overcomes the individual weakness, and this positive feature is proved by using 532 samples from the Egyptian Electricity Transmission Company (EETC). The second part overview the experimental setup for (66/11.86 kV–40 MVA) power transformer which exists in the Egyptian Electricity Transmission Company (EETC), the first section in this part analyzes the dissolved gases concentricity for many samples, and the second section illustrates the measurement of PD particularly in this case study. The results demonstrate that precise interpretation of oil transformers can be provided to system operators, thanks to the combination of the most appropriate techniques.

Power Transformer Fault Diagnosis Based on Vibration Correlation Analysis

2014 ◽  
Author(s):  
Kaixing Hong ◽  
Hai Huang
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Fundamental Frequency ◽  
Vibration Analysis ◽  
Laboratory Tests ◽  
Power Transformer ◽  
Assessment Model ◽  
Vibration Method ◽  
Proposed Model ◽  
Transformer Fault

In this paper, a condition assessment model using vibration method is presented to diagnose winding structure conditions. The principle of the model is based on the vibration correlation. In the model, the fundamental frequency vibration analysis is used to separate the winding vibration from the tank vibration. Then, a health parameter is proposed through the vibration correlation analysis. During the laboratory tests, the model is validated on a test transformer, and manmade deformations are provoked in a special winding to compare the vibrations under different conditions. The results show that the proposed model has the ability to assess winding conditions.

Building holistic and agile collection development and assessment

2015 ◽  
Vol 16 (1) ◽  
pp. 62-85 ◽  
Author(s):  
Cheri Jeanette Duncan ◽  
Genya Morgan O'Gara
Keyword(s):  
Quantitative Data ◽  
Holistic Approach ◽  
Assessment Model ◽  
Data Sets ◽  
Collection Development ◽  
Content Type ◽  
Qualitative And Quantitative ◽  
Institutional Goals ◽  
Scholarly Output ◽  
Use Of Data

Purpose – The purpose of this paper is to examine the development of a flexible collections assessment rubric comprised of a suite of tools for more consistently and effectively evaluating and expressing a holistic value of library collections to a variety of constituents, from administrators to faculty and students, with particular emphasis to the use of data already being collected at libraries to “take the temperature” of how responsive collections are in supporting institutional goals. Design/methodology/approach – Using a literature review, internal and external conversations, several collections pilot projects, and a variety of other investigative mechanisms, this paper explores methods for creating a more flexible, holistic collection development and assessment model using both qualitative and quantitative data. Findings – The products of scholarship that academic libraries include in their collections are expanding exponentially and range from journals and monographs in all formats, to databases, data sets, digital text and images, streaming media, visualizations and animations. Content is also being shared in new ways and on a variety of platforms. Yet the framework for evaluating this new landscape of scholarly output is in its infancy. So, how do libraries develop and assess collections in a consistent, holistic, yet agile, manner? Libraries must employ a variety of mechanisms to ensure this goal, while remaining flexible in adapting to the shifting collections environment. Originality/value – In so much as the authors are aware, this is the first paper to examine an agile, holistic approach to collections using both qualitative and quantitative data.

Using system dynamics to evaluate renewable electricity development in Malaysia

Kybernetes ◽  
2014 ◽  
Vol 43 (1) ◽  
pp. 24-39 ◽  
Author(s):  
Salman Ahmad ◽  
Razman bin Mat Tahar
Keyword(s):  
System Dynamics ◽  
Fossil Fuels ◽  
Systems Approach ◽  
Capacity Expansion ◽  
Assessment Model ◽  
Flow Diagram ◽  
Content Type ◽  
The Government ◽  
Policy Targets

Purpose – The purpose of this paper is to provide an assessment of Malaysia's renewable capacity target. Malaysia relies heavily on fossil fuels for electricity generation. To diversify the fuel-mix, a technology-specific target has been set by the government in 2010. Considering the complexity in generation expansion, there is a dire need for an assessment model that can evaluate policy in a feedback fashion. The study also aims to expand policy evaluation literature in electricity domain by taking a dynamic systems approach. Design/methodology/approach – System dynamics modelling and simulation approach is used in this study. The model variables, selected from literature, are constituted into casual loop diagram. Later, a stock and flow diagram is developed by integrating planning, construction, operation, and decision making sub-models. The dynamic interactions between the sub-sectors are analysed based on the short-, medium- and long-term policy targets. Findings – Annual capacity constructions fail to achieve short-, medium- and long-term targets. However, the difference in operational capacity and medium- and long-term target are small. In terms of technology, solar photovoltaic (PV) attains the highest level of capacity followed by biomass. Research limitations/implications – While financial calculations are crucial for capacity expansion decisions, currently they are not being modelled; this study primarily focuses on system delays and exogenous components only. Practical implications – A useful model that offers regulators and investors insights on system characteristics and policy targets simultaneously. Originality/value – This paper provides a model for evaluating policy for renewable capacity expansion development in a dynamic context, for Malaysia.

Sign in / Sign up

Export Citation Format

Share Document