Article citation info: (*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl IntroductionIn extensive fittings used for transporting large quantities of gas under high pressure for long distances, monitoring of the fittings condition becomes a significant problem, in respect of correct functioning of the measuring devices, as well as the occurrence of possible leakage. Exploitation of gas network requires periodic tightness controls and elimination of faults and leaks. When a leak is discovered in the gas pipeline, it undergoes repair work, which is conducted after shutting down a certain section of the network by shut-off valves or temporary closure. Works on active gas pipelines are considered hazardous and must be performed by qualified teams.Difficult conditions of exploitation are placing increasingly high demands on long duration and high degree of reliability of control systems. Due to flammability, any breakdowns causing unsealing of the fittings and gas effusion pose a risk of explosion and environmental contamination. These risks may be eliminated by current detection which enables prediction of the possible necessity of switching off pumping or excluding the leaky section of the pipeline.In the current exploitation of gas networks a number of solutions can be used allowing for monitoring and diagnostics, with particular consideration of leakage detection. The methods of detection of transmission networks can be divided into two general categories [2,11,21]: direct (external), when the detection is conducted from the outside of the pipe by applying specialized devices and visual observation, and indirect (internal), when the detection is based on the measurements and analysis of parameters of flow process, such as pressure, flow, temperature. Among the direct methods we can differentiate acoustic methods [12], which are based on the detection of noise generated by a leak and require installing acoustic sensors along the pipeline. Indirect methods are divided into methods based on detecting sound waves caused by effusion, methods based on balancing the medium inflowing to-and outflowing from the pipeline and analytical methods based on mathematical model and measuring data of the object, obtained from telemetric system [7,8,17,22].Natural gas is a viscous and compressible gas, the physicochemical parameters of which are strongly dependent on pressure and temperature conditions. For description of such a medium, application of complicated equations of state is necessary, such as virial or cubic equations of state of the gas [5,23]. The dynamics of elementary section of the gas pipeline can also be described by partial differential equations system [7,17], which can be derived from mass and momentum conservation principles and solved by explicit or implicit methods.Optimization algorithms based on neural networks or swarm intelligence [1,9,16] can also be applied for the analysis of work of certain sections of transmission network. It is a t...
The article presents an identification method of the model of the ball-and-race coal mill motor power signal with the use of machine learning techniques. The stages of preparing training data for model parameters identification purposes are described, as well as these aimed at verifying the quality of the evaluated model. In order to meet the tasks of machine learning, additive regression model was applied. Identification of the additive model parameters was performed on the basis of iterative backfitting algorithm combined with nonparametric estimation techniques. The proposed models have predictive nature and are aimed at simulation of the motor power signal of a coal mill during its regular operation, startup and shutdown. A comparative analysis has been performed of the models structured differently in terms of identification quality and sensitivity to the existence of an exemplary disturbance in the form of overhangs in the coal bunker. Tests carried out on the basis of real measuring data registered in the Polish power unit with a capacity of 200 MW confirm the effectiveness of the method.
The reliability of a coal mill's operation is strongly connected with optimizing the combustion process. Monitoring the temperature of a dust–air mixture significantly increases the coal mill's operational efficiency and safety. Reliable and accurate information about disturbances can help with optimization actions. The article describes the application of an additive regression model and data mining techniques for the identification of the temperature model of a dust–air mixture at the outlet of a coal mill. This is a new approach to the problem of power unit modeling, which extends the possibilities of multivariate and nonlinear estimation by using the backfitting algorithm with flexible nonparametric smoothing techniques. The designed model was used to construct a disturbance detection system in the position of hot and cold air dampers. In order to achieve the robust properties of the detection systems, statistical measures of the differences between the real and modeled temperature signal of dust–air mixtures were used. The research has been conducted on the basis of the real measuring data registered in the Polish power unit with a capacity of 200 MW. The obtained high-quality model identification confirms the correctness of the presented method. The model is characterized by high sensitivity to any disturbances in the cold and hot air damper position. The results show that the suggested method improves the usability of the statistical modeling, which creates good prospects for future applications of additive models in the issues of diagnosing faults and cyber-attacks in power systems.
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