NEURAL NETWORK MATHEMATICAL MODEL OF GAS TURBINE POWER PLANT TAKING INTO ACCOUNT DIFFERENT MODES OF ITS OPERATION
Introduction: the model-oriented approach is well established in the tasks of designing, optimizing, and configuring complex technological systems. Using mathematical models at the early stages of testing can significantly speed up and simplify the testing procedure for such objects. It should be noted that any mathematical model is always developed to solve a specific problem. For example, to synthesize, configure, or optimize a control system for a complex technological object, you need a model whose performance allows you to conduct a significant number of experiments on it. At the same time, the adequacy of such a model should be evaluated through the possibility of solving the task. In most cases, this speed is achieved by simplifying the model by discarding factors that are considered insignificant in solving the problem of testing the control system. In our work, we have chosen an approach using artificial neural networks, on the basis of which, during the training algorithm based on experimental data, we obtain neural network mathematical models. Purpose: obtaining a multi-mode neural network mathematical model of a gas turbine power plant. Methods: using the theory of artificial networks to obtain a simplified mathematical model of a gas turbine power plant. Results: on the basis of representative experimental data of the electric power system power surge and discharge, a model of a gas turbine power plant is obtained, taking into account these two modes, the speed and adequacy of which is sufficient for testing the control system on it. Practical relevance: using a mathematical model of a gas turbine power plant for testing control systems allows you to significantly reduce the test procedure, which leads to significant savings in money, material and human resources.