Application of IoT technology to collect data for integrated power system

In this paper, the authors present the design of a data collection system of an integrated power system including small-scale wind and solar power on Haiwell's IoT application platform. The proposed research system includes 02 digital power meters connecting with PLC via Modbus RTU standard and PLC communicating with Wifi integrated HMI screen via ZigBee wireless communication technology. This system can be easily installed into electrical cabinets to collect electrical information such as power consumption, voltage, current, power, and frequency of the system to be monitored. The application of Haiwell's free cloud computing platform will be very convenient in designing the interface for the on-site monitoring system via HMI or accessible via computer or smartphone. With the actual results of the project, it has been shown that this is one of the solutions that can be deployed at factories and is very convenient in construction and installation because it uses the high-quality ZigBee wireless communication standard.

ELECTRICAL LOAD FORECASTING ON HIERARCHICAL LEVELS OF IPS OF UKRAINE USING LSTM NEURAL NETWORK

The scientific research presents the results of a study of one-factor forecasting of the total electrical load at three hierarchical levels of the integrated power system (IPS) of Ukraine using artificial neural networks, such as LSTM. Based on research, forecasting errors at each hierarchical level of the power system were analyzed. Methods for improving the quality and stability of forecasts were proposed. The obtained results are the basis for the study of the assessment of the accuracy of forecasting the summary electrical load in the IPS of Ukraine. Ref. 9, fig. 4, table.