Abstract
Currently, there is a need to improve the systems and control of pumping equipment in the oil and gas production and oil and gas transport industries. Therefore, an adaptive neural network control system for an electric drive of a production well was developed. The task of expanding the functional capabilities of asynchronous electric motors control of the oil and gas production system using the methods of neural networks is solved. We have developed software modules of the well drive control system based on the neural network, an identification system, and a scheme to adapt the control processes to changing load parameters, that is, to dynamic load, to implement the entire system for real-time control of the highspeed process. In this paper, based on a model of an identification block that includes a multilayered neural network of direct propagation, the control of the well system was implemented. The neural network of the proposed system was trained on the basis of the error back-propagation algorithm, and the identification unit works as a forecaster of system operation modes based on the error prediction. In the initial stage of the model adaptation, some fluctuations of the torque are observed at the output of the neural network, which is associated with new operating conditions and underestimated level of learning. However, the identification object and control system is able to maintain an error at minimum values and adapt the control system to a new conditions, which confirms the reliability of the proposed scheme.
Highlights
The need of developing mathematical models of control objects, the application of analytical approaches to the interpretation of the control system, as well as other restrictions do not allow the application of the classical theory of automatic control to multidimensional dynamic objects
The proposed system is able to maintain an error at the minimum values and adapt the control system to the new operating modes, which confirms the reliability of the designed identification and prediction of operating modes of production well’s system
The neural network of the proposed system was trained on the basis of the error back-propagation algorithm, and the identification unit worked in the mode of operation modes prediction and the system error prediction
Summary
The need of developing mathematical models of control objects, the application of analytical approaches to the interpretation of the control system, as well as other restrictions do not allow the application of the classical theory of automatic control to multidimensional dynamic objects To solve such problems in 1943 [1], a mathematical model of a neuron and a software implementation of an artificial neural network (ANN) were proposed. In the framework of production processes of transport and oil preparation, an important condition is the real-time control of a given technological regime To solve this problem, in the works [3,4,5], the authors presented management methods based on neural networks and simultaneous identification of the object. The error in the operation of these methods and the considerable time spent working off the disturbing influences do not allow them to be applied to the process under consideration
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