Gas pipeline leakage detection in the presence of parameter uncertainty using robust extended Kalman filter

2021 ◽  
pp. 014233122198911
Author(s):  
Mohadese Jahanian ◽  
Amin Ramezani ◽  
Ali Moarefianpour ◽  
Mahdi Aliari Shouredeli
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Parameter Uncertainty ◽  
Oil And Gas ◽  
Estimation Error ◽  
Real Data ◽  
Gas Pipeline ◽  
Pipeline System ◽  
Parameter Uncertainties ◽  
Simulation Results

One of the most significant systems that can be expressed by partial differential equations (PDEs) is the transmission pipeline system. To avoid the accidents that originated from oil and gas pipeline leakage, the exact location and quantity of leakage are required to be recognized. The designed goal is a leakage diagnosis based on the system model and the use of real data provided by transmission line systems. Nonlinear equations of the system have been extracted employing continuity and momentum equations. In this paper, the extended Kalman filter (EKF) is used to detect and locate the leakage and to attenuate the negative effects of measurement and process noises. Besides, a robust extended Kalman filter (REKF) is applied to compensate for the effect of parameter uncertainty. The quantity and the location of the occurred leakage are estimated along the pipeline. Simulation results show that REKF has better estimations of the leak and its location as compared with that of EKF. This filter is robust against process noise, measurement noise, parameter uncertainties, and guarantees a higher limit for the covariance of state estimation error as well. It is remarkable that simulation results are evaluated by OLGA software.

Real-Time Signal Denoising Algorithm in Wheel Force Transducer

2014 ◽  
Vol 615 ◽  
pp. 244-247
Author(s):  
Dong Wang ◽  
Guo Yu Lin ◽  
Wei Gong Zhang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Force Transducer ◽  
Time Signal ◽  
Signal Denoising ◽  
Observation Function ◽  
Simulation Results ◽  
Wheel Force Transducer

The wheel force transducer (WFT) is used to measure dynamic wheel loads. Unlike other force sensors, WFT is rotating with the wheel. For this reason, the outputs and the inputs of the transducer are nonlinearly related, and traditional Kalman Filter is not suitable. In this paper, a new real-time filter algorithm utilizing Quadrature Kalman Filter (QKF) is proposed to solve this problem. In Quadrature Kalman Filter, Singer model is introduced to track the wheel force, and the observation function is established for WFT. The simulation results illustrate that the new filter outperforms the traditional Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF).

Planning for Purging and Loading of a Newly Constructed Gas Pipeline System Using a Pipeline Simulator

2000 ◽  
Author(s):  
Mo Mohitpour ◽  
J. Kazakoff ◽  
Andrew Jenkins ◽  
David Montemurro
Keyword(s):  
Natural Gas ◽  
Planning Process ◽  
Gas Pipeline ◽  
Simulation Technique ◽  
Pipeline System ◽  
Methane Gas ◽  
Natural Gas Pipeline ◽  
Air Interface ◽  
Simulation Results ◽  
Constant Practice

Purging of a gas pipeline is the process of displacing the air/nitrogen by natural gas in an accepted constant practice in the natural gas pipeline industry. It is done when pipelines are put into service. Gas Pipelines are also purged out of service. In this case they are filled with air or other neutral gases. Traditionally, “purging” a newly constructed pipeline system is carried out by introducing high pressure gas into one end of the pipeline section to force air out of the pipeline through the outlet until 100% gas is detected at the outlet end. While this technique will achieve the purpose of purging air out of the pipeline, it gives little or no consideration to minimizing the emission of methane gas into the atmosphere. With the advances of the pipeline simulation technology, it is possible through simulation to develop a process to minimize the gas to air interface and thereby minimize the emission of methane gas. In addition, simulation can also be used to predict the timing of purging and loading of the pipeline. Therefore, scheduling of manpower and other activities can be more accurately interfaced. In this paper a brief background to purging together with a summary of current industry practices are provided. A simplified purging calculation method is described and a simulation technique using commercially available software is provided for planning purging and loading operations of gas pipeline systems. An Example is provided of a recently constructed pipeline (Mayakan Gas Pipeline System) in Mexico to demonstrate how the planning process was developed and carried out through the use of this simulation technique. Simulation results are compared with field data collected during the actual purging and loading of the Mayakan Pipeline.

A New Friction Estimation Approach for Precision Positioner

2007 ◽  
Author(s):  
Wen Wang ◽  
Xinxin Li ◽  
Zichen Chen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Taylor Series ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Low Velocity ◽  
Friction Estimation ◽  
Simulation Results ◽  
Long Stroke

Precision positioner has been significantly developed as the rapid growth of MEMS and IC industries. As for short-stroke position, the loss of friction can be avoided by using flexible hinges. Long-stroke postioner, however, in which moved-to-be mass always stands on the guide-way part, a main source of friction, makes friction unavoidable. Friction estimation is based on certain filters, such as Extended Kalman filter (EKF). However, estimation accuracy of Kalman filter, especially at low-velocity movement, is not very well. To solve this problem, the paper proposes an estimation method based on DD2 to make an accurate estimation. And the result shows this method is promising in real-time friction estimation. After background introduction, in section 2, the relation of EKF and Taylor series and EKF implementation are reviewed and its limitations are noted as well. A briefly introduction to DD2 is given in Section 3 and friction estimation case comparing the simulation results of DD2 estimation with that of EKF described in Section 4, respectively. At last, conclusions are summarized.

Reputation Damage Minimization at Accident on Gas Pipeline System with Use of Smart Contracts and SAP Integration Technologies (on the Example of Gazprom PJSC)

2018 ◽  
Vol 6 (5) ◽  
pp. 48-55
Author(s):  
Кирилл Мельников ◽  
Kirill Melnikov
Keyword(s):  
Business Processes ◽  
Oil And Gas ◽  
Linear Part ◽  
Theoretical Models ◽  
Integrated Approach ◽  
Gas Pipeline ◽  
Pipeline System ◽  
Clear Understanding ◽  
Problem Solution ◽  
Smart Contracts

In this paper an integrated approach to a problem related to minimization of gas supplier’s reputation losses connected with accidents on a gas pipeline and disruption of gas delivery to a consumer with the help of smart contracts is stated. Nowadays analysts and methodologists of pipeline companies perform assessment of expected and actual material expenses at an accident on GTS objects. Account and analytics of reputation losses either isn’t conducted in all, or is implemented in the frame of separate business processes which aren’t integrated into processes of enterprise budget’s management and planning. Such situation is disconcertingly. In the era of globalization the cost of a company and its business reputation depends significantly on the attitude towards it in society. In Europe and USA the major oil and gas companies invest huge money in increasing of their positive image in the eyes of population. Any significant accident which has happened in unforeseen time and unsuccessful place can completely destroy reputation of any company. Modern corporate IT-decisions allow unite a number of data flows in the uniform analytical module now. Information collected in such modules in Gazprom PJSC helps to solve optimizing problems both within planning of material inputs, and within accounting of reputation expenses. In this work a theoretical model for assessment of reputation losses at an accident on a linear part of a gas transmission system (GTS) of Gazprom PJSC is given. Modern theoretical models for definition of function of supplier’s reputation losses have been used. The review of possible application for SAP decisions and Blockchain technology in current business processes of Gazprom JSC in the frame of the problem solution is given. For a clear understanding how to integrate this decision into existing business processes the graphic description of the decision work scheme is given. The principles of this approach can be applied not only in working processes of Gazprom PJSC, but also adapted according to other pipeline companies’ needs. Possible fast introduction of cryptoruble in Russia can offer new prospects for mass introduction of this tool in the context of smart contracts.

scholarly journals Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor

2016 ◽  
Vol 14 (1) ◽  
pp. 934-945
Author(s):  
Cenker Biçer ◽  
Levent Özbek ◽  
Hasan Erbay
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Stochastic Systems ◽  
Estimation Error ◽  
The State ◽  
Forgetting Factor ◽  
The Matrix ◽  
Stochastic Framework ◽  
Theoretical Results

AbstractIn this paper, the stability of the adaptive fading extended Kalman filter with the matrix forgetting factor when applied to the state estimation problem with noise terms in the non–linear discrete–time stochastic systems has been analysed. The analysis is conducted in a similar manner to the standard extended Kalman filter’s stability analysis based on stochastic framework. The theoretical results show that under certain conditions on the initial estimation error and the noise terms, the estimation error remains bounded and the state estimation is stable.The importance of the theoretical results and the contribution to estimation performance of the adaptation method are demonstrated interactively with the standard extended Kalman filter in the simulation part.

Automotive Battery Prognostics Using Dual Extended Kalman Filter

2009 ◽  
Author(s):  
Matteo Rubagotti ◽  
Simona Onori ◽  
Giorgio Rizzoni
Keyword(s):  
Experimental Data ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Remaining Useful Life ◽  
State Of Charge ◽  
Future Evolution ◽  
On Line ◽  
Automotive Batteries ◽  
Useful Life ◽  
Simulation Results

This paper proposes a strategy for estimating the remaining useful life of automotive batteries based on dual Extended Kalman Filter. A nonlinear model of the battery is exploited for the on-line estimation of the State of Charge, and this information is used to evaluate the actual capacity and predict its future evolution, from which an estimate of the remaining useful life is obtained with suitable margins of uncertainty. Simulation results using experimental data from lead-acid batteries show the effectiveness of the approach.

scholarly journals Moving Horizon Estimation for Cooperative Localisation with Communication Delay

2014 ◽  
Vol 68 (3) ◽  
pp. 493-510 ◽  
Author(s):  
Wei Gao ◽  
Jian Yang ◽  
Ju Liu ◽  
Hongyang Shi ◽  
Bo Xu
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Extended Kalman Filter ◽  
Acoustic Communication ◽  
Underwater Vehicle ◽  
Random Time ◽  
Computation Method ◽  
Moving Horizon Estimation ◽  
Underwater Environment ◽  
Simulation Results

Cooperative Localisation (CL) technology is required in some situations for Multiple Unmanned Underwater Vehicle (MUUVs) missions. During the CL process, the Relative Localisation Information (RLI) of the master UUV is transmitted to slave UUVs via acoustic communication. In the underwater environment, the RLI is subject to a random time delay. Considering the time delay characteristic of the RLI during the acoustic communication, a Moving Horizon Estimation (MHE) method with a Delayed Extended Kalman Filter (DEKF)-based arrival cost update law is presented in this paper to obtain an accurate and reliable estimation of present location. Additionally, an effective computation method for the MHE method is employed, in which the “Lower Upper” (LU) factorization is used to compute the solution of the Karush-Kuhn-Tucker (KKT) system. At the end of this paper, simulation results are presented to prove the superiority and practicality of the proposed MHE algorithm.

Autonomous Aerial Refueling for UAVs Based on GPS/MV

2012 ◽  
Vol 433-440 ◽  
pp. 4087-4094 ◽  
Author(s):  
Long Wang ◽  
Xin Min Dong ◽  
Jun Guo ◽  
Hai Yan Jia
Keyword(s):  
Kalman Filter ◽  
Machine Vision ◽  
Extended Kalman Filter ◽  
Reference Trajectory ◽  
Sensor Failure ◽  
Aerial Refueling ◽  
Control Laws ◽  
Measurement Models ◽  
Simulation Results ◽  
Docking Control

According to the UAV autonomous aerial refueling based on GPS/Machine Vision integration, the restrictions on the sensors during docking are analyzed. An adaptive Federal Kalman Filter (AFKF) is proposed, which is based on extended Kalman filter arithmetic, after modeling the sensors measurement models. Reference trajectory of docking is planed using cubic interpolators and docking control laws are designed with LQR. Simulation results show that the controller ensure the stabilized tracking and docking, and the AFKF outputs is continuous and stabilized during sensor failure comparing to centralize Kalman filter.

scholarly journals A hypothesis of state covariance decorrelation effects to partial observability SLAM

2019 ◽  
Vol 14 (2) ◽  
pp. 588 ◽  
Author(s):  
H Ahmad ◽  
N.A Othman ◽  
M M Saari ◽  
M S Ramli ◽  
M M Mazlan ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Matrix Inversion ◽  
Matrix Norm ◽  
Main Element ◽  
Partial Observability ◽  
Localization And Mapping ◽  
The Matrix ◽  
Simulation Results ◽  
Partially Observable

<span>This paper analyze the performance of partial observability in simultaneous localization and mapping(SLAM) problem. The study focuses mainly on the effect of having a decorrelation technique known as Covariance Inflation to the estimation. The matrix inversion will be the main element to be investigated through two conditions with respect to some defined environment namely as unstable partially observable SLAM and partially observable SLAM via matrix norm analysis. For assessment purposes, the Extended Kalman Filter estimation is referred as the estimator to understand how the conditions can influence the results. The simulation results depicted that, the matrix norm is able to determine the efficiency of estimation and is proportional to the uncertainties of the system.</span>

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