Abstract
The subsystem for optimal control of voltage and reactive power of EPS is developed. The proposed solution uses state of art methods for state estimation, forecasting and dynamic optimization. A new architecture of an artificial neural network is proposed – a neuro-analytical network. Algorithms are proposed that allow reliable combination of classical automatic control methods and methods using machine learning. The proposed methodology is designed for use in a real power system for automatic voltage control.
Highlights
The voltage and reactive power control system is designed to maintain the optimal regime of the Magadan power system
The voltage is maintained in the optimal range by coordinated control of compensation devices, in combination with the coordinated control of the voltage on the buses of the power stations
For reliability the system is designed in two levels of control: the lower level, which provides basic control functions, and the upper one, represented by the optimal control subsystem, which performs intelligent voltage control in the power system
Summary
The voltage and reactive power control system is designed to maintain the optimal regime of the Magadan power system. In the Magadan power system, the main generation is concentrated hydro generation, which, due to its concentration, creates difficulties with optimal voltage control in the power system. In our proposed solution, compensating devices are installed at important substations. The voltage is maintained in the optimal range by coordinated control of compensation devices, in combination with the coordinated control of the voltage on the buses of the power stations
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