Особливості перетворення гільберта та їх використання в енергетичній інформатиці

• Дата: 2022
• Автори: Babak , Vitalii, Zaporozhets , Artur, Kuts , Yurii, Shcherbak , Leonid
• Формат: Стаття
• Мова: Ukrainian
• Опубліковано: General Energy Institute of the National Academy of Sciences of Ukraine 2022
• Теми: енергетична інформатика; інформаційні сигнали; обробка сигналів; дискретне перетворення Гільберта; амплітудна характеристика сигналів; фазова характеристики сигналів
• Онлайн доступ: https://systemre.org/index.php/journal/article/view/558
• Назва журналу: System Research in Energy

Information-measuring technologies (IMT) are an important instrument for solving problems of energy informatics. They allow to form primary information based on the interaction of energy facilities with IMT sensors that form information signals. In many practical applications, the constructive model of information signals is the model of narrowband signals. The article summarizes the features of the discrete Hilbert transform and its application to obtain the primary characteristics of information signals – bypass and phase as functions of time. The main advantages of using the discrete Hilbert transform in signal processing for energy informatics are considered, including the consistency of obtaining frequency and time characteristics, high information content, the ability to analyze the dynamics of changes in signal characteristics, the possibility of obtaining samples of characteristics of information signals of significant volumes, etc. It is proposed to use a phase characteristic to select the time interval that limits the signal sample and sets it to a multiple of the signal period, and the sampling rate of information signals to reduce the errors in estimating their spectrum. The possibility of obtaining on their basis secondary deterministic (voltage level, voltage deviations from the nominal level, attenuation coefficient, signal period, signal phase shift, oscillation frequency, etc.) and statistical (sample characteristic, sample variance, sample median, sample circular variance, sample circular median, sample circular kurtosis, etc.) of signal information characteristics, which allows more complete to use their information resource. These characteristics can be used both for assessing power quality characteristics and for monitoring and diagnosing of energy facilities.