scholarly journals Development of a Wavelet – ANFIS Based Fault Location and Identification System for Underground Power Cables

2020 ◽  
Vol 9 (11) ◽  
pp. 35-41
Keyword(s):  
Fault Location ◽  
Power Transmission ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Identification System ◽  
Power Cables ◽  
Inference System ◽  
Underground Cables ◽  
Fuzzy Input ◽  
Underground Power Cables

Transmissionlinesare thebackboneof electricalpower systems and other power utilities as they are used for transmission and distribution of power. Power is distributed to the end-user through either overhead cables or underground cables. In the case of underground cables, their propensity to fail in service increases as they age with time. The increase in failure rates and system crashes on older underground power cables now negatively affect system reliability and involve numerous losses. It is therefore easy to realize that the consequences of this trend need to be managed [3]. Identification of the type of defects and their locations along the length ofthe cablesis vital to minimize the operating costs by reducing lengthy and expensive patrolsto locate the faults, and to speed up repairs and restoration of power in the lines. In this study, a method that combines wavelets and neurofuzzy techniques for faultlocation and identificationare proposed. For this methodology a power transmission line model was developed and different fault locations were simulated in MATLAB/SIMULINK, and, as an input to the training and development of the Adaptive Network Fuzzy Inference System (ANFIS), certain selected features of the wavelet transformed signals were used. Fault index obtained from wavelet transformation are used as input variable for fuzzy input block function. Different membership functions were observed within input block function. As per formulation of rules, for membership function, the output value of the defuzzifier component was decoded to give a crisp value of ANFIS output. ANFIS results were compared with actual values. A comparison of the ANFIS output values and the actual values show that the percentage error was less than 1%. Thus, it can be concluded that the wavelet-ANFIS technique is accurate enough to be used in identifying and locating underground power line faults. Which will help in solving this time taking and tedious problem more efficiently and thereby reducing human effort in finding the type of fault and its location.

APLIKASI SISTEM CERDAS UNTUK PREDIKSI ENERGI LISTRIK PEMAKAIAN SENDIRI DI PT INDONESIA POWER SUB UNIT PLTA KABUPATEN WONOGIRI

2017 ◽  
Vol 8 (2) ◽  
pp. 489
Author(s):  
Herliyani Hasanah ◽  
Nurmalitasari Nurmalitasari
Keyword(s):  
Fuzzy Inference System ◽  
Input Data ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy Input

Kebutuhan akan energi listrik menjadi kebutuhan primer nasional. Dalam keberlangsungan proses produksi energi listrik pada pembangkitan – pembangkitan diperlukan energi listrik untuk pemakaian sendiri. Dalam penelitian ini dibangun sebuah aplikasi sistem cerdas untuk memprediksi energi listrik pemakaian sendiri di PT Indonesia Power sub unit PLTA Wonogiri. Pada penelitian ini menggunakan 2 kelompok input, yaitu input FIS (Fuzzy Inference System) dan input pada NN (Neuro Fuzzy). Input data  merupakan data produksi harian energi listrik di PLTA Wonogiri selama kurun waktu 2010 – 2016. Variabel data yang digunakan dalam penelitian ini adalah data produksi listrik untuk pemakaian PLTA Wonogiri adalah energi listrik yang dihasilkan PLTA Wonogiri dengan satuan KwH (f), elevasi muka air waduk dengan satuan meter (a1) dan debit air yang masuk ke turbin dengan satuan /detik (a2).  Output yang diperoleh adalah pusat centroid (m), derajat keanggotaan (mf), bobot (w) dan konsekuen parameter ( c ). Dari hasil pengujian diperoleh keluaran dengan performansi yang optimal pada saat Fuzzy C Means 2 kelas dengan parameter laju pembelajaran 0.4, momentum 0.6 dengan bessar Mean Percentage Error 0.377970875.  Kata kunci:  prediksi, pemakaian sendiri, energi listrik, fuzzy inference system, neuro fuzzy

Penerapan Metode ANFIS untuk Prediksi Nilai Akhir dan Kelulusan Uji Kompetensi

2018 ◽  
Vol 4 (1) ◽  
pp. 21-28
Author(s):  
Rayendra
Keyword(s):  
Word Processing ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Information And Communication ◽  
Anfis Method ◽  
Final Score ◽  
Computer Literate ◽  
Competence Test

To improve the graduation of Computer Literate Certified Professional (CLCP) competence test conducted by Competence Test of Information and Communication Technology (TUK-TIK) needs to be done continuous improvement by increasing try out competency test. Past values of the competency test can be used as modeling to predict the final score and the passing of the competency test. With the modeling can be predicted the passing of competency test participants through try out-try out done so that can be known weakness of candidate competency test from three units of CLCP competence. The modeling used to predict the final score and the passing of this competency test is the Adaptive Neuro Fuzzy Inference System (ANFIS) method. Used 20 past data of competency test participants with 6 criteria as input value from three CLCP competence units namely Word Processing, Spreadsheet, and Presentation. The resulting prediction is accurate enough with MAPE (Mean Absolute Percentage Error) value for each competency unit of 0.31492%, 0.284202%, and 0.267167%

scholarly journals Kajian Adaptive Neuro-Fuzzy Inference System (ANFIS) Dalam Memprediksi Penerimaan Mahasiswa Baru Pada Universitas Buana Perjuangan Karawang

2021 ◽  
Vol 6 (1) ◽  
pp. 44-54
Author(s):  
Tatang Rohana Cucu
Keyword(s):  
Error Rate ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Hybrid Technique ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
New Students ◽  
The Mean

Abstract - The process of admitting new students is an annual routine activity that occurs in a university. This activity is the starting point of the process of searching for prospective new students who meet the criteria expected by the college. One of the colleges that holds new student admissions every year is Buana Perjuangan University, Karawang. There have been several studies that have been conducted on predictions of new students by other researchers, but the results have not been very satisfying, especially problems with the level of accuracy and error. Research on ANFIS studies to predict new students as a solution to the problem of accuracy. This study uses two ANFIS models, namely Backpropagation and Hybrid techniques. The application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model in the predictions of new students at Buana Perjuangan University, Karawang was successful. Based on the results of training, the Backpropagation technique has an error rate of 0.0394 and the Hybrid technique has an error rate of 0.0662. Based on the predictive accuracy value that has been done, the Backpropagation technique has an accuracy of 4.8 for the value of Mean Absolute Deviation (MAD) and 0.156364623 for the value of Mean Absolute Percentage Error (MAPE). Meanwhile, based on the Mean Absolute Deviation (MAD) value, the Backpropagation technique has a value of 0.5 and 0.09516671 for the Mean Absolute Percentage Error (MAPE) value. So it can be concluded that the Hybrid technique has a better level of accuracy than the Backpropation technique in predicting the number of new students at the University of Buana Perjuangan Karawang.   Keywords: ANFIS, Backpropagation, Hybrid, Prediction

scholarly journals Model fusion approach for monthly reservoir inflow forecasting

2016 ◽  
Vol 18 (4) ◽  
pp. 634-650 ◽  
Author(s):  
Yun Bai ◽  
Jingjing Xie ◽  
Xiaoxue Wang ◽  
Chuan Li
Keyword(s):  
Fuzzy Inference ◽  
Moving Average ◽  
Back Propagation ◽  
Small Sample ◽  
Percentage Error ◽  
Inference System ◽  
Model Fusion ◽  
Monthly Scale ◽  
Inflow Forecasting ◽  
Fusion Approach

Considering the complexity of reservoir systems, a model fusion approach is proposed in this paper. According to different inflow information represented, the historical monthly data can be constructed as two time series, namely, yearly-scale series and monthly-scale series. Even grey model (EGM) and adaptive neuro-fuzzy inference system (ANFIS) are adopted for the forecasts at the two scales, respectively. Grey relational analysis (GRA) is subsequently used as a scale-normalized model fusion tool to integrate the two scales' results. The proposed method is evaluated using the data of the Three Gorges reservoir ranging from January 2000 to December 2012. The forecast performances of the individual-scale models are improved substantially by the suggested method. For comparison, two peer models, back-propagation neural network and autoregressive integrated moving average model, are also involved. The results show that, having combined together the small-sample forecast ability of the EGM in the yearly-scale, the nonlinearity of the ANFIS in the monthly-scale, and the grey fusion capability of the GRA, the present approach is more accurate for holistic evaluation than those models in terms of mean absolute percentage error, normalized root-mean-square error, and correlation coefficient criteria, and also for peak inflow forecasting in accordance with peak percent threshold statistics.

Mapping arsenic vulnerability at different spatial scales using statistical and machine learning models 

2021 ◽  
Author(s):  
Sonal Bindal
Keyword(s):  
Spatial Resolution ◽  
Spatial Data ◽  
Fuzzy Inference ◽  
Spatial Scales ◽  
Fuzzy Model ◽  
Fuzzy Regression ◽  
Percentage Error ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Predictive Algorithms

<p>In the recent years, prediction modelling techniques have been widely used for modelling groundwater arsenic contamination. Determining the accuracy, performance and suitability of these different algorithms such as univariate regression (UR), fuzzy model, adaptive fuzzy regression (AFR), logistic regression (LR), adaptive neuro-fuzzy inference system (ANFIS), and hybrid random forest (HRF) models still remains a challenging task. The spatial data which are available at different scales with different cell sizes. In the current study we have tried to optimize the spatial resolution for best performance of the model selecting the best spatial resolution by testing various predictive algorithms. The model’s performance was evaluated based of the values of determination coefficient (R<sup>2</sup>), mean absolute percentage error (MAPE) and root mean square error (RMSE). The outcomes of the study indicate that using 100m × 100m spatial resolution gives best performance in most of the models. The results also state HRF model performs the best than the commonly used ANFIS and LR models.</p>

Biometric Identification System Using Neuro and Fuzzy Computational Approaches

2016 ◽  
pp. 335-366
Author(s):  
Tripti Rani Borah ◽  
Kandarpa Kumar Sarma ◽  
Pranhari Talukdar
Keyword(s):  
Fuzzy System ◽  
Fuzzy Inference ◽  
Biometric Identification ◽  
Identification System ◽  
Inference System ◽  
Computational Approaches ◽  
Neuro Fuzzy ◽  
Actual Design ◽  
Authentication System ◽  
Numeric Data

In all authentication systems, biometric samples are regarded to be the most reliable one. Biometric samples like fingerprint, retina etc. is unique. Most commonly available biometric system prefers these samples as reliable inputs. In a biometric authentication system, the design of decision support system is critical and it determines success or failure. Here, we propose such a system based on neuro and fuzzy system. Neuro systems formulated using Artificial Neural Network learn from numeric data while fuzzy based approaches can track finite variations in the environment. Thus NFS systems formed using ANN and fuzzy system demonstrate adaptive, numeric and qualitative processing based learning. These attributes have motivated the formulation of an adaptive neuro fuzzy inference system which is used as a DSS of a biometric authenticable system. The experimental results show that the system is reliable and can be considered to be a part of an actual design.

Effect of Dry-Zone Formation around Underground Power Cables on Their Ratings

2016 ◽  
pp. 188-210
Keyword(s):  
Thermal Resistivity ◽  
Power Cables ◽  
Zone Formation ◽  
Thermal Failure ◽  
Underground Cables ◽  
Buried Cables ◽  
Cable Properties ◽  
Dry Zone ◽  
Rating Factor ◽  
Underground Power Cables

Current ratings of buried cables are determined by the characteristics of surrounding soils and cable properties as given in IEC 60287-1-3 (1982). In this standard the soil thermal resistivity of the surrounding soil is supposed to be varies from 0.5 oC m/w to 1.2 oC m/w but under loading the heat dissipated from underground power cables increases the soil thermal resistivity and this may leads to cable thermal failure and thermal instability of the soil around the underground cables. For this reason de-rating factors for cable loading taking the dry zone formation into consideration has to be considered during distribution cable network design. Several approaches have been adopted to establish current ratings of buried cables based on constant values of soil thermal conductivities. Mathematical models are suggested by many researches to study the drying out phenomenon around underground power cables. In this chapter de-rating factor for underground power cables taking dry zone formation into account is calculated depending on IEC 60287-1-3 (1982). This chapter also contains an experimental work carried out on different types of soils to investigate the formation of dry zone phenomena under loading by heat source simulates the underground cables.

Dry Band Formation around Underground Cable

2016 ◽  
pp. 68-85
Keyword(s):  
Power Distribution ◽  
Life Time ◽  
Thermal Resistivity ◽  
Power Cables ◽  
Band Formation ◽  
Underground Cables ◽  
Underground Cable ◽  
Parallel Circuits ◽  
Cable Laying ◽  
Underground Power Cables

The current ratings of underground power distribution cables are affected by ambient temperature, cable laying depth, number of cables in parallel circuits, sheath bonding and thermal resistivity of soil. One important factor usually ignored is the formation of dry zones around the underground power cables. Dry zones are usually formed around underground cables when they are loaded due to the migration of soil moisture content. This in turn may cause an abrupt rise in temperature of the cable sheath, leading to thermal damage of cable insulation or reduces the insulation life time.

scholarly journals An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1915
Author(s):  
Shupei Chen ◽  
Zhigang Chen ◽  
Jia Wu ◽  
Kanghuai Liu
Keyword(s):  
Social Networks ◽  
Data Transmission ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Routing Algorithm ◽  
Opportunistic Networks ◽  
Membership Functions ◽  
Inference System ◽  
Fuzzy Input ◽  
Delay Tolerant

In opportunistic networks, the requirement of QoS (quality of service) poses several major challenges to wireless mobile devices with limited cache and energy. This implies that energy and cache space are two significant cornerstones for the structure of a routing algorithm. However, most routing algorithms tackle the issue of limited network resources from the perspective of a deterministic approach, which lacks an adaptive data transmission mechanism. Meanwhile, these methods show a relatively low scalability because they are probably built up based on some special scenarios rather than general ones. To alleviate the problems, this paper proposes an adaptive delay-tolerant routing algorithm (DTCM) utilizing curve-trapezoid Mamdani fuzzy inference system (CMFI) for opportunistic social networks. DTCM evaluates both the remaining energy level and the remaining cache level of relay nodes (two-factor) in opportunistic networks and makes reasonable decisions on data transmission through CMFI. Different from the traditional fuzzy inference system, CMFI determines three levels of membership functions through the trichotomy law and evaluates the fuzzy mapping from two-factor fuzzy input to data transmission by curve-trapezoid membership functions. Our experimental results show that within the error interval of 0.05~0.1, DTCM improves delivery ratio by about 20% and decreases end-to-end delay by approximate 25% as compared with Epidemic, and the network overhead from DTCM is in the middle horizon.

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