scholarly journals Fault Prediction using a Grey-Markov Model from the Dissolved Gases Contents in Transformer Oils

2021 ◽  
Vol 256 ◽  
pp. 01038
Author(s):  
Yang Liu ◽  
Yu Du ◽  
Zhiwu Wang ◽  
Guangming Feng ◽  
Shaowei Rao ◽  
...  
Keyword(s):  
Markov Model ◽  
Measurement Data ◽  
Adaptive Method ◽  
Gas Analysis ◽  
Fault Prediction ◽  
Grey Model ◽  
Forecasting Model ◽  
Time Interval ◽  
Dissolved Gases ◽  
Fibonacci Sequences

A novel method to predict transformer fault by forecasting the variation trend of the dissolved gases content is proposed. After the content of each feature gas, such as hydrogen and methane, is obtained by the proposed forecasting model, the fault type can be diagnosed by the dissolved gas analysis (DGA) technologies. Firstly, the GM (1,1) grey model with unequal time interval is introduced to generate a general forecasting model for each feature gas. The introduced grey model with unequal time interval will enforce no constrain on the historical measurement data. Consequently, the time intervals of the two adjacent measuring points can be either constant or variant. To address the deficiency that the existing grey model is unable to describe the fluctuation of the predicted object in time domain, the Markov chain is introduced to improve the accuracy of the grey forecasting model. An adaptive method to automatically divide the state space based on the number of states and the relative error of the grey model is presented by using Fibonacci sequences. Practical measurements are used to verify the accuracy of the proposed forecasting model. The numerical results show that there is high probability (86%) that the proposed grey-Markov model acquires a smaller prediction residual as compared to the original GM(1,1) grey model.

Dynamic Fault Prediction of Power Transformers Based on Hidden Markov Model of Dissolved Gases Analysis

2019 ◽  
Vol 34 (4) ◽  
pp. 1393-1400 ◽  
Author(s):  
Jun Jiang ◽  
Ruyi Chen ◽  
Min Chen ◽  
Wenhao Wang ◽  
Chaohai Zhang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Fault Prediction ◽  
Power Transformers ◽  
Dissolved Gases

scholarly journals A New Adaptive Method for the Extraction of Steel Design Structures from an Integrated Point Cloud

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3416
Author(s):  
Pawel Burdziakowski ◽  
Angelika Zakrzewska
Keyword(s):  
Data Integration ◽  
Point Cloud ◽  
Laser Scanning ◽  
Detection Threshold ◽  
Measurement Data ◽  
Steel Structures ◽  
Adaptive Method ◽  
Edge Extraction ◽  
Geometric Structures ◽  
Redundant Data

The continuous and intensive development of measurement technologies for reality modelling with appropriate data processing algorithms is currently being observed. The most popular methods include remote sensing techniques based on reflected-light digital cameras, and on active methods in which the device emits a beam. This research paper presents the process of data integration from terrestrial laser scanning (TLS) and image data from an unmanned aerial vehicle (UAV) that was aimed at the spatial mapping of a complicated steel structure, and a new automatic structure extraction method. We proposed an innovative method to minimize the data size and automatically extract a set of points (in the form of structural elements) that is vital from the perspective of engineering and comparative analyses. The outcome of the research was a complete technology for the acquisition of precise information with regard to complex and high steel structures. The developed technology includes such elements as a data integration method, a redundant data elimination method, integrated photogrammetric data filtration and a new adaptive method of structure edge extraction. In order to extract significant geometric structures, a new automatic and adaptive algorithm for edge extraction from a random point cloud was developed and presented herein. The proposed algorithm was tested using real measurement data. The developed algorithm is able to realistically reduce the amount of redundant data and correctly extract stable edges representing the geometric structures of a studied object without losing important data and information. The new algorithm automatically self-adapts to the received data. It does not require any pre-setting or initial parameters. The detection threshold is also adaptively selected based on the acquired data.

scholarly journals Time interval measurement module implemented in SoC FPGA device

2016 ◽  
Vol 62 (3) ◽  
pp. 237-246 ◽  
Author(s):  
Grzegorz Grzęda ◽  
Ryszard Szplet
Keyword(s):  
Mass Measurement ◽  
Measurement Data ◽  
Measurement Instrument ◽  
General Concept ◽  
Time Interval ◽  
Processing Unit ◽  
Test Results ◽  
Interval Measurement ◽  
Time Interval Measurement ◽  
On Chip

Abstract We presents the design and test results of a picosecond-precision time interval measurement module, integrated as a System-on-Chip in an FPGA device. Implementing a complete measurement instrument of a high precision in one chip with the processing unit gives an opportunity to cut down the size of the final product and to lower its cost. Such approach challenges the constructor with several design issues, like reduction of voltage noise, propagating through power lines common for the instrument and processing unit, or establishing buses efficient enough to transport mass measurement data. The general concept of the system, design hierarchy, detailed hardware and software solutions are presented in this article. Also, system test results are depicted with comparison to traditional ways of building a measurement instrument.

Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting

2016 ◽  
pp. 1161-1183 ◽  
Author(s):  
Tuncay Ozcan ◽  
Tarik Küçükdeniz ◽  
Funda Hatice Sezgin
Keyword(s):  
Support Vector Regression ◽  
Electricity Market ◽  
Load Forecasting ◽  
Forecast Accuracy ◽  
Grey Model ◽  
Support Vector ◽  
Forecasting Model ◽  
Data Sets ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is crucial for electricity generation companies, distributors and other electricity market participants. In this study, several forecasting techniques are applied to time series modeling and forecasting of the hourly loads. Seasonal grey model, support vector regression, random forests, seasonal ARIMA and linear regression are benchmarked on seven data sets. A rolling forecasting model is developed and 24 hours of the next day is predicted for the last 14 days of each data set. This day-ahead forecasting model is especially important in day-ahead market activities and plant scheduling operations. Experimental results indicate that support vector regression and seasonal grey model outperforms other approaches in terms of forecast accuracy for day-ahead load forecasting.

Parallelization Analysis of Dissolved Gases in Transformer Oil Based on Random Forest Algorithm

2014 ◽  
Vol 519-520 ◽  
pp. 98-101
Author(s):  
De Wen Wang ◽  
Zhi Wei Sun
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gases ◽  
Random Forest Algorithm ◽  
Dissolved Gas ◽  
Important Method ◽  
Transformer Fault

Dissolved gas analysis (DGA) in oil is an important method for transformer fault diagnosis. This paper use random forest parallelization algorithm to analysis the dissolved gases in transformer oil. This method can achieve a fast parallel fault diagnosis for power equipment. Experimental results of the diagnosis of parallelization of random forest algorithm with DGA samples show that this algorithm not only can improve the accuracy of fault diagnosis, and more appropriate for dealing with huge amounts of data, but also can meet the smart grid requirements for fast fault diagnosis for power transformer. And this result also verifies the feasibility and effectiveness of the algorithm.

scholarly journals Predication of life cycle cost of equipment base on unbiased grey Markov models

2020 ◽  
Vol 309 ◽  
pp. 05005
Author(s):  
Yonghong Chen ◽  
Ping Hu ◽  
Dong Zhang
Keyword(s):  
Life Cycle ◽  
Markov Model ◽  
Life Cycle Cost ◽  
Prediction Accuracy ◽  
Markov Models ◽  
Grey Model ◽  
Model Accuracy ◽  
Integrated Logistics ◽  
And Control ◽  
Whole Life Cycle

Life cycle cost(LCC) is an important content of equipment integrated logistics support. While the LCC includes the whole life cycle of equipment from development, production, service and maintenance to retirement, in order to effectively manage and control the LCC and better develop integrated logistics support, it is necessary to analyze and predict it. The unbiased grey markov model(UGMM) was introduced into the LCC prediction in the paper, in order to check model accuracy, the posterior difference method(PDM) was used, also the influence by the number of state intervals in UGMM on the prediction accuracy is analyzed and studied. The result indicate that UGMM can be used to predict the LCC, also have the highest prediction accuracy comparing with unbiased grey model and grey separating model, and in order to ensure the prediction accuracy, the state interval should be divided according to the number of sequence.

scholarly journals Pasang Surut, Arus dan Gelombang Berdasarkan Data Pengukuran Acoustic Doppler Current Profiler di Perairan Pulau Cilik, Karimunjawa

2020 ◽  
Vol 9 (1) ◽  
pp. 37-44
Author(s):  
Elis Indrayanti ◽  
Diah Permata Wijayanti ◽  
Hendry Syahputra Ropinus Siagian
Keyword(s):  
Coral Cover ◽  
Measurement Data ◽  
Acoustic Doppler Current Profiler ◽  
Ocean Currents ◽  
Time Interval ◽  
Live Coral ◽  
Actual Measurement ◽  
M 10 ◽  
Current Profiler ◽  
Sample Rate

Pulau Cilik merupakan gugusan pulau di Karimunjawa yang menjadi salah satu destinasi wisata bahari di Indonesia. Perairan ini kaya akan terumbu karang dan ikan berwarna-warni dengan  tutupan karang hidup yang masih tinggi. Proses hidrodinamika seperti pasang surut, arus laut dan gelombang laut secara langsung maupun tidak langsung berpengaruh terhadap kondisi terumbu karang, oleh karena itu penelitian ini perlu dilakukan. Tujuan penelitian adalah untuk mengetahui karakteristik pasang surut, arus laut dan gelombang berdasarkan data pengukuran Acoustic Doppler Current Profiler (ADCP) di Perairan Pulau Cilik, Karimunjawa. Pengukuran dilaksanakan selama 7x24 jam dengan interval waktu 600 dt dan sample rate 300 dt. Posisi ADCP pada -5.8177°S  dan 110.5096°E. Kedalaman total pengukuran 14 m dengan kedalaman aktif pengukuran 12 m, yang terbagi menjadi 6 lapisan kedalaman yaitu 2 m, 4 m, 6 m, 8 m, 10 m, dan 12 m.  Hasil pengukuran menunjukkan bahwa Perairan Pulau Cilik, Karimunjawa memiliki tipe pasang surut campuran condong harian tunggal dengan nilai formzahl sebesar 2.55. Kecepatan arus bervariasi dengan rata-rata pada seluruh lapisan antara 5.57–6.35 cm/dt, sedangkan arahnya bi-directional yaitu memiliki dua muka arah (timur dan barat-barat daya). Tinggi dan periode gelombang yang didapatkan pada saat pengamatan relatif kecil. Cilik Island Waters in Karimunjawa is one of the marine tourism destinations in Indonesia. These waters are rich in coral reefs and colorful fish with high live coral cover. Hydrodynamic processes as tides, ocean currents, and waves influence the performance of coral reef through direct or indirect effects. Therefore this research needs to be done. The purpose of this study was to determine the characteristics of tides, ocean currents, and waves based on Acoustic Doppler Current Profiler (ADCP) measurement data in Cilik Island Waters, Karimunjawa. Measurements were carried out for 7 x 24 hours (2 April 2017 - 9 April 2017) with a time interval of 600 s and a sample rate of 300 s. The ADCP position is -5.8177 ° S and 110.5096 ° E. The total depth is 14 m with an actual measurement depth of 12 m, which is divided into 6 layers of depth namely 2 m, 4 m, 6 m, 8 m, 10 m, and 12 m. Result shown that  Cilik Island, Karimunjawa, have a single mixed daily tidal type with a formzahl value of 2.55. Current velocity varies with the average in all layers between 5.57-6.35 cm/s, while the direction is bi-directional, which has two faces (east and west-southwest). The height and wave period obtained at the time of observation are relatively small. 

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