Application of Standard ZET-algorithm for Gaps Filling in Wide Area Measurement System Data Assets

This article explores the application of Standard ZET-algorithm for gaps filling in Synchronized Phasor Measurement data assets received from Wide Area Measurement Systems. In spite of Synchronized Phasor Measurement data assets apparent advantages, they have gaps affecting the accuracy of solving power engineering tasks. Authors analyzed the reasons of gap occurrence in data assets, provided experiments with voltage and current data assets and collected the key points about application of Standard ZETalgorithm

Data Science With Excel

The stages in data science consist of several stages, one of which is data preparation. At this stage, many things are done so that the dirty data becomes clean data that is ready for modeling. Many applications offer data science convenience in terms of processing data. One of them is excel, this application from Microsoft can perform data processing so that the data is ready for modeling. However, there are limitations in using excel. The maximum number of rows that excel has is only 1,048,576 and the number of columns is 16,384. However, if you process data of no more than 1 million rows, excel can still handle it by using features such as error detection, removing duplicate data, correcting error values, detecting outlier values, handling missing data and validating data. This study shows some of these features along with examples of their use

An improved pre-forecasting analysis of electrical loads of pumping station

Relevance of research. In order to reduce energy losses, an accurate and timely forecast of the amount of consumed electricity is necessary. Accurate forecasting of electrical loads of industrial enterprises and their divisions (productions, workshops, departments etc.) allows planning of normal operating conditions, concluding contracts for the electricity supply with the electricity supply company under more favorable conditions, and improving the electricity quality, which ultimately affects the final cost of the products produced by an enterprise. So far, more than 150 forecasting methods of electrical loads have been developed. Usually, the most convenient one is selected based on the forecaster experience by creating and analyzing several forecasting models in order to identify the best. Therefore, in order to simplify the forecasting procedure, it is necessary to develop the methodology for forecasting analysis. This methodology should enable canceling forecasting algorithms that will create lower quality forecasts. The main objective is to develop the methodology for making a forecasting analysis of power consumption on the example of a pumping station of an enterprise with a continuous cycle of work to increase the efficiency of energy consumption and implementation of energy-saving measures. Objects of research: the process of forecasting electrical loads of a pumping station of the enterprise with a continuous cycle of work. Methods of research: fundamental principles of the theory of electrical engineering, statistical methods for power consumption forecasting, the method for detecting the trend of radio signals, and fractal analysis of time series. Research results. The methodology for forecasting analysis of power consumption, which makes it possible to apply the most appropriate methods to forecast the operational power consumption, is developed. For the first time, the radio signal trend detection method is applied to identify the trend of electrical loads. The variation ranges of the fractal parameters of time series of electrical loads are established depending on the variation coefficient of the time series for different periods of time. The Brown method of exponential smoothing that is used to forecast the electrical loads, in the case of identifying the smoothing constant α is in the beyond set ( 1 2 < a < ), is further improved. The regularity of changes in the fractal parameters of time series of power consumption of a pumping station with an increase in the time series duration and their field of application are explained