Збірники наукових праць ЦНТУ
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Item Синтез електромагнітної системи діагностування дефектів опор повітряних ЛЕП із використанням нейро-нечіткого моделювання(ЦНТУ, 2019) Бондаренко, В. Б.; Петрова, К. Г.; Серебренніков, С. В.; Петрова, Е. Г.; Серебренников, С. В.; Bondarenko, V.; Petrova, K.; Serebrennikov, S.Показано, що за умов невизначеності під час діагностування дефектів опор повітряних ЛЕП в режимі реального часу, раціональним є побудова електромагнітної системи із використанням нейро-нечіткого моделювання. Розглянуто найінформативніші детерміновані та ймовірнісні ознаки образу дефекту. Доведено, що застосування гібридних нейронних мереж призводить до істотного збільшення швидкодії вихрострумового контролю та надійності розпізнавання дефектів. Показано, что в условиях неопределенности при диагностировании дефектов опор воздушных ЛЭП в режиме реального времени, рациональным является построение электромагнитной системы с использованием нейро-нечеткого моделирования. Рассмотрены наиболее информативные детерминированные и вероятностные признаки образа дефекта. Доказано, что применение гибридных нейронных сетей приводит к существенному увеличению быстродействия вихретокового контроля и надежности распознавания дефектов. The purpose of the work is to develop a system for diagnosing defects (damages of the armature) of air transmission line supports based on the combination of electromagnetic control principles with the interpretation of the results of diagnosis using neuro-fuzzy modeling. It is shown that in the case of uncertainty in the diagnosis of defects of air transmission line supports in real time, it is rational to build an electromagnetic system using neuro-fuzzy modeling. Practice shows that the bars of the reinforcement bars of the transmission lines support fractures oriented perpendicularly forming, and stress-corrosion cracks - parallel to it. Optimal results of the electromagnetic control are achieved in the scan direction, which intersects the defects perpendicular to their long side. In the case of electromagnetic diagnosis, the conclusion about the defect and its size is usually based on the values of the amplitude, phase and frequency of the current signal. However, the large amount of information that comes from controlling the tens and hundreds of power lines in the field under live conditions exceeds the human ability to adequately perceive it in real time and requires automation of data analysis; this will significantly increase the speed of control, reduce errors and errors associated with the actions of the staff, will allow to coordinate the results to the object of control. Under such conditions, it is rational to synthesize an electromagnetic system for diagnosing defects of air transmission lines using neuro-fuzzy modeling. That is, create a neural network and teach it to look for defects based on real data. The most informative deterministic and probable signs of defect image are considered. It is proved that the use of hybrid neural networks leads to a significant increase in the speed of eddy current control and the reliability of recognition of defects. An analysis of the causes of emergency situations of the transmission line elements showed that 30-35% are damage to reinforced concrete supports, and the existing methods of non-destructive diagnosis of the condition of reinforcement and bearing capacity of the supports are not effective enough. This requires improvement of the non-contact electromagnetic control method. The study of the complex of characteristic parameters allowed us to select the 9 most informative deterministic and probabilistic features of the defect image. The training of the neural network was carried out using samples of artificial defects such as through-through fracture of the reinforcement according to the hybrid method of training with the error level 0 and the number of cycles 400, which allowed to improve the quality of recognition of the defect image. The correctness of network training is ensured by dividing the experimental data into test and training ones. For data modeling, 2 data blocks were used: the training sample volume was 180 datasets and 60 test sets. The information blocks were filled taking into account the features of neural network synthesis - the data should change alternately (in the direction of growth) and as much as possible fill the entire area of their values. Diagnostic inputs include transmission line voltage, resistance type, lifetime, failure statistics, climatic and geological conditions, VCT signal. It is also possible to enter additional user data and more. Combining the benefits of an electromagnetic defect diagnosis system with the use of neuro-fuzzy modeling will significantly increase the speed of eddy current control and the reliability of recognition of defects of supports.Item Проблеми визначення ефективності та ранжування енергоощадних заходів на об'єктах бюджетної сфери(ЦНТУ, 2019) Плєшков, П. Г.; Серебренніков, С. В.; Петрова, К. Г.; Савеленко, І. В.; Сіріков, О. І.; Плешков, П. Г.; Серебренников, С. В.; Петрова, Е. Г.; Савеленко, И. В.; Сириков, А. И.; Pleshkov, P.; Serebrennikov, S.; Petrova, K.; Savelenko, I.; Sirikov, O.Проаналізовано енергетичні особливості об’єктів бюджетної сфери за результатами енергоаудитів, розглянуто критерії оцінювання ефективності енергоощадних заходів та їх ранжування за пріоритетністю. Проанализированы энергетические особенности объектов бюджетной сферы по результатам энергоаудитов, рассмотрены критерии оценки эффективности энергосберегающих мероприятий, их ранжирование по приоритетности. The purpose of the work is to formulate and solve the problem of determining the energy efficiency of energy-saving measures when performing energy audits and ranking them by priority. We have developed investigation of energy flows of budgetary objects by conducting energy audits and finding ways to improve their energy efficiency by implementing energy-efficient measures. Energy peculiarities of the objects of the budget sphere according to the results of energy audits are analyzed, criteria of estimation of efficiency of energy saving measures and their ranking by priority are considered. Permanent tariff increases for all types of energy stimulate the search for energy savings, which will allow consumers to reduce their energy bills. In addition, to obtain financing from the state and / or local budgets, investments from international funds for the energy modernization of budgetary objects, it is necessary to justify a complex of energy-saving measures based on the results of the energy audit and the availability of energy certificates of buildings. Energy audits shows that the customer does not always clearly understand the priority of energy efficient measures and the real level of losses due to elements of fencing structures, in engineering systems of power supply, heating, ventilation etc. This leads to irrational allocation of funds for the implementation of energy efficient measures. Only a comprehensive energy audit of the building and its engineering systems will help the customer understand the tasks. The investment budget for energy efficiency measures is always limited and, as a rule, less than the amount of investment proposals, measures should be ranked in order to choose the most effective solutions. Choosing the criteria for ranking energy-saving measures is a difficult task that cannot always be based on technical and economic calculations, so it is important to search for natural and economic criteria by which energy-saving measures should be ranked in packages. The division of energy-saving measures into small and high-cost ones depends on the financial capacity of the customer and is therefore conditional. To obtain the correct results of calculating the savings from the implementation of energy-saving measures, it is necessary to focus on the baseline of energy consumption, which it is based on the design mode of operation and the regulatory microclimate in the premises. The problem of determining the baseline level of energy consumption is an urgent one and it needs further discussion and research.