CC BY
11L
I h.
n
= ILA . o.
(4)
i=1
where - ht.o. is the sum of the maximum possible measured current values passing along the operating OL, A. which is calculated by the formula:
L
I
t .o.
n
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Induced voltage measurement system (IVMS) and induced voltage meter (IVM-15) made in Russia may be used as a measuring device (fig.6 ) It is designed to measure root-mean-square values of alternating voltage with industrial frequency of 50 Hz generated on the de-energized towards the earth sections of electricity generating plants to guarantee personnel safety involved in overhead line operation.
This device represents a two-terminal network one of which is a probe with induced voltage meter (IVM-15) and the other is a contacting wire with a clamp fixed to the grounding electrode.
To measure the induced voltage the second method will be used as it is more informative and meets safety requirements during the measurements.
Figure 6 shows induced voltage measurement system with IVM-15.
The research given will be helpful when making a procedural instructions for the RCPDN personnel involved in the field of electrical engineering.
References
1. Bessonov L.A. Theoretical basis of electrical engineering: Electrical circuits: Textbook for students of electrical, power-engineering, instrumentation-technology specialties. -7th edition, revised and supplemented - M.; Higher School, 2008 - 258p.
2. Organization standard "FGC UES": "Procedural instructions on induced voltage definition on disconnected overhead lines located near active overhead lines." M: OL - Spetsenergo, 2009, - 27p.
3. Inter-branch labor protection (safety rules) in electrical installation maintenance « 153-34.0-03.15000», - Moscow: STOCK COMPANY "NC ENAS PUBLISHERS", 2003. - 180p.
4. Tselebrovsky U. "Safety of works on overheard lines under induced voltage" I&R edition "Electrical Engineering News" NSTU
http://www.news.elteh.ru/arh/2008/51/07.php -
2018
DEVELOPMENT MICROPROCESSOR DEVICE MEASURING POWER FOR COMPUTER SYSTEMS MEASURE THE DISTANCE
Dudnik A.S.
Candidate of technical sciences, associate professor Associate Professor of the Department of Network and Internet Technologies
Kyiv National Taras Shevchenko University
ABSTRACT
At the moment, wireless sensor networks are an important tool for researching the physical world. Their importance is connected with new possibilities of use, due to such characteristics of the BSM, as the lack of the need for cable infrastructure, miniature nodes, low power consumption, built-in radio interface, high enough computing power, relatively low cost. All this made possible their widespread use in many spheres of human activity in order to automate the processes of information gathering, monitoring, control over the characteristics of various technical and natural objects. At the same time due to the limited resources of individual nodes for solving many problems, the cooperative work of all nodes of the network is necessary for the achievement of the goal. One of these tasks is the task of locating nodes in a wireless sensor network with self-organization. It consists in determining the coordinates of individual sensors without the use of external infrastructure. The problem of localization has been researched in the past, as in many applications, information about the location of objects or people is important, and a large number of systems have been developed to address them. The most famous of them is the Global Positioning System (GPS). However, the GPS approach can not be applied to the BSM in connection with its requirements for the availability of a large number of additional infrastructure (for example, satellites). In this
study, it is proposed to apply a method of measuring the distance between objects, based on the measurement of signal strength. The structural scheme of microprocessor frequency meter, which allows to implement the given algorithm, is proposed. Future studies will explore some of the basic methods used by localization systems to estimate distances / angles. These methods include RSSI, ToA / TDoA, AoA, and communication ranges. The distance / angle estimation, identifies the distance or angle between the two nodes. Such assessments form an important component of localization systems, because they are used by position calculations, as well as by the localization algorithm.
Keywords: Wireless sensor network, localization, distance, microprocessor power meter, error.
The issue of this article is the study of a wireless sensor network in which it is proposed to use a microprocessor power meter that measures the power of signal reception as a magnitude that is the proximal distance between the objects of the sensor network.
Analysis of recent research and publications.
Issues of the study of information-measuring systems, including the study of technologies for modeling, control and interaction of computerized systems for measuring mechanical quantities (in particular, the distance between objects), are devoted to the work of modern scientists, among them:
- works [1-3], which are devoted to the measurement of the distance to the means of measuring equipment;
- works [4-8], devoted to the measurement of distance by means of wireless sensory networks;
- works [9-13], which, in addition to measuring the distance, are also devoted to the analysis of the characteristics of the sensory networks themselves.
In the paper [1], it is proposed to use the Internet to control the measuring head, but in the analysis and correction of the measurement results, the Internet does not take part. The content of work [2] is devoted to the development of analog interfaces of information measuring systems, but it does not consider the means of increasing their productivity. In [3], this refers to the correction of measurement errors through the information-measuring system, but it is proposed to use cable communication. In work [4] a general overview of existing technologies of sensor networks is conducted and only their weaknesses are analyzed. In papers [5, 6] localization algorithms are considered which can improve the process of measuring the distance between objects. The works [7, 8] consider the existing problems of the integration of sensor networks and the ways of their solution. Works [9-13] describe the localization methods used by satellite navigation systems, in particular, [13] also refers to energy-saving technologies for sensor networks.
Allocation of unresolved parts of a common problem.
In this paper, it is proposed to consider recommendations for the improvement of the technical characteristics of wireless sensor networks through the additional use of microprocessor power meters in the devices of the network. In order to improve the accuracy of the measurement.
The research objective is:
• Analysis of the distance measurement method based on the signal strength;
• Development of the principle of measuring power microprocessor and its block diagram.
Statement of the main material
Localization systems can be divided into three distinct components:
1. Distance or angle estimation: This component is responsible for assessing distance information and / or angles between two nodes. This information will be used by other components of the localization system.
2. Positioning calculation: This component is responsible for calculating the position of the node, based on available information of distances / angles and positions of nodes of the reference information.
3. Algorithm of localization: this is the main component of the localization system. It determines how the information available will be processed so that all or almost all of the BCM nodes evaluate their positions.
In addition, each component has its own purpose and methods of decision. They can be considered as under the localization area of the problem, which should be separately analyzed and studied.
Methods of determination of coordinates. The distance / angle estimation, identifies the distance or angle between the two nodes. Such assessments form an important component of localization systems, because they are used by position calculations, as well as by the localization algorithm.
Different methods can be used to evaluate such information. Some of them are very precise, but with an additional cost of equipment, while others are less accurate, but are already available on most nodes.
The following sections will examine some of the main methods used by localization systems to estimate distances / angles. These methods include RSSI, ToA / TDoA, AoA, and communication ranges.
Method of determination of coordinates on the basis of measuring signal strength. The most simple method of determining the range to the node is the indication of the received signal strength (Received Strength Signal Indication, RSSI). RSSI - in telecommunication, a device for measuring the signal strength level. The simplest circuits are designed to accept the input signal and form the analog output voltage (or the corresponding digital code obtained after applying this voltage to the ADC), proportional to the power of the received signal. You can use this metric to estimate the distance to the transmitter (from cell phones to the base station).
As a rule, the signal is measured at intermediate frequencies before the amplifier (for example, in cellular phones and other GSM devices). In devices that operate without intermediate frequencies, measurements are made at the main frequency [2].
Any wireless channel according to IEEE 802.15.4 (ZigBee) has a link quality indicator (Link Quality Indicator) function, which reduces to determining the
power of the received signal. The result of this measurement can be deduced, calibrated at a known distance and estimate the range to the source.
As shown in Figure 1, the node sends a signal with a certain force, which is reduced, as this signal is multiplied. The greater the distance to the receiver node, the lower the signal strength, at the receiver node.
Communication Rang*
Figure 1. Reducing signal strength. The signal is sent with a certain power that decreases, theoretically, in
proportion to the length of the distance
Traffic loss in distribution is best described by the model of losses on the free-propagation trail. The lossfree model on the free path assumes that the transmitting antenna is isotropic, that is, the transmitter emits energy of equal intensity in all directions, and there are no objects on the propagation pathway between the transmitter and receiver that could block the signal or create conditions for it. reflection It is also assumed that the transmission medium does not absorb energy.
The power received by the receiving antenna in the free space model is determined by the Friss formula for free space [3]:
(1)
where Pt is the power transferred, Pr (d) is the received power, which is the function of the distance between the transmitter and the receiver, Gt is the gain of the transmission antenna, Gr is the gain of the receiving antenna, X is the signal wavelength, d is the distance between the transmitter and the receiver and L is the non-proliferation system loss factor.
One can see that the power of the received signal is back proportional to the square of the distance between the transmitting and receiving antennas. Consequently, if the distance between the transmitter and the receiver increases, the power of the received signal decreases. Equation 1 can be written as follows:
(2)
where d0 - the marginal distance for the distant antenna area. Loss on the track, PL (d) is the signal loss between the transmitting and receiving antennas and is defined as:
(4)
This method has a number of significant constraints, since the signal level is a very variable parameter due to the influence of the following factors:
• Fast and slow damping of signals on the route due to changing conditions of radio wave propagation;
• Multi-beam propagation, due to reflections from various metal objects;
• Distribution of output power of transmitters and sensors of receivers;
• The influence of the antenna orientation on the irregularity of the directional diagram.
Also, this method, like others, has certain advantages and disadvantages. The main advantage is the low price, because most receivers are able to estimate the received signal strength.
The disadvantage of this method is that it is very subject to distortion and interference, resulting in higher errors in distance estimates. Some experiments, display errors from 2 to 3 m in scripts, where all nodes are placed in the field of the plane, 1.5 m from the base, and with a range of communications of 10 m [4].
Although RSSI shows some results in simulations and experiments, its use in real applications is not always justified. But, considering its low price, the use of this method has certain advantages. When using sophisticated and precise devices (for example, with better transmitters), RSSI is the most affordable distance / cost estimation technology.
To measure the power of the received signal of sensor network it is necessary to conduct current and voltage measurements. Most simply, schematically, this is realized by connecting current and voltage sensors through an ADC to a microcontroller, which monitors the instantaneous values of current and voltage at discrete moments of time. The accuracy of the measurements increases with the increase in the sampling
rate, which in turn leads to software complications, An example of a power converter is the AD7750,
since signal processing (filtration, averaging) is carried a product-to-frequency converter , with an error of less
out in real time. In addition, the lack of such systems is than 0.3%, developed by Analog Devices. Figure 2
their relatively high cost. shows the block diagram of the AD7750 chip.
REFOUT REFIN F3 51 32 DGND
Figure 2 - The structural diagram of the transmitted and received power measurement device
The inputs of the current and voltage channels are differential. Each input is designed for a voltage of not more than 1V. The current channel to which the received power is given - Pr (d), has an amplifier with a variable gain of Gr (1 or 16) simulating the receiving antenna, the voltage channel to which the transmitted power is fed - Pt, contains an amplifier with an amplification factor Gt = 2 simulating the transmitter antenna. After amplification, both signals transform ADC into numeric code and multiply. The high-frequency components are filtered by a digital low-pass filter LPF, then the power code is fed into the frequency converter (Digital to Frequency Converter, DTF).
To reduce the power measurement error due to the presence of a constant current component in the neutral wire, you can enable the current filtration mode with the high-pass filter HPF.
Conclusions and suggestions
The application of the method of measuring the distance between objects, based on the comparison of the power of the sent and received signal is substantiated.
The structural scheme of the device for measuring the power of the sent and received signal is proposed.
In further research, the distance measurement method based on the phase measurement will be projected, and the time of sending and receiving the signal is compared with the use of an ultrasonic pulse.
References
1. Kvasnikov V. P. Kontseptsiya povirky koordynatno-vymiryuval'nykh mashyn cherez Internet
/ V. P. Kvasnikov, T. M. Khaeyn // Metrolohiya ta prylady. - 2013 . - # 6. - S. 48-53.
2. Kvasnikov V.P. Sposoby pobudovy analoho-vykh interfeysiv informatsiyno-vymiryuval'nykh system mekhanichnykh velychyn / V. P. Kvasnikov, D. P. Ornats'kyy, T. P. Nichikova, I. V. Havrylov // Mizhnarodnyy naukovo-tekhnichnyy zhurnal «Vymiryuval'na ta obchyslyuval'na tekhnika v tekhnolohichnykh protsesakh». - 2013. - # 1. - S. 164169.
3. Ornat s'kyy D. P. Analohovyy interfeys dlya dystantsiynykh vymiryuvan' peremishchen' dyferentsi-al'no-transformatornymy induktyvnymy datchykamy / D. P. Ornat s'kyy, M. V. Mykhalko, O. I. Os-molovs'kyy // Skhidno-Yevropeys'kyy zhurnal peredovykh tekhnolohiy. - 2014. - # 1/2 (67). - S. 5257.
4. Akyildiz I. F. Wireless sensor networks: A survey. Computer Networks // IEEE Communications Magazine. - 2008. - P. 250.
5. Boukerche A., Oliveira H. Towards an integrated solution for node localization and data routing in sensor networks // In ISCC ' 17: 22th IEEE Symposium on Computers and Communications, Aveiro, Portugal, July 2017. - P. 449-454.
6. Boukerche A., Oliveira H., Nakamura E., A novel location-free greedy forward algorithm for wireless sensor networks // In Proceedings of the 2008 IEEE International Conference on Communications (ICC 2008), Beijing, China, May 2008.
7. Brooks R. R., Iyengar. S. S. Multi-Sensor Fusion: Fundamentals and Applications / R. R. Brooks, S.
S. Iyengar // Prentice Hall, Englewood Cliffs. - NJ. -2009. - P. 120.\
8. Hofmann-Wellenho B., Lichtenegger H., Collins J. Global Positioning System: Theory and Practice, 14th edition // Springer-Verlag, Berlin. - 2013.
9. Intanagonwiwat C., Govindan R., Estrin D. Directed diffusion: A scalable and robust communication paradigm for sensor networks // In Proceedings of the 6th ACM International Conference on Mobile Computing and Networking (MobiCom '00), Boston, MA, August 2008, ACM Press, New York, P. 56-67.
10. Niculescu D., Nath B. Ad hoc positioning system (aps) using aoa // I Proceedings of INFOCOM 2009, San Francisco, CA. - 2009. - P. 238.\
11. Priyantha N., Balakrishnan H., Teller S. The cricket compass for context aware mobile applications // In 17th ACM International Conference on Mobile Computing and Networking. Rome, Italy, July 2016. -P. 325.
12. Savvides A., Han C. Strivastava M. Dynamic fine-grained localization in ad-hoc networks of sensors
// In 7th ACM/IEEE International Conference on Mobile Computing and Networking, Rome, Italy, 2010. -P. 166-179.
13. Yu Y., Govindan R., Estrin D. Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks. Technical Report CSD-TR-01-0023, UCLA Computer Science Department, 2011.
14. He T., Huang C., Blum B. Range-free localization schemes for large scale sensor networks // In MobiCom '03: Proceedings of the 9th Annual International Conference on Mobile Computing and Networking, New York, 2011, ACM Press, New York. - P. 8195. 83
15. Derivation of Friis Transmission Formula [Електронний ресур]. - Режим доступу: http://www.antenna-theory.com/basics/friis.php. The Friis Equation - Назва з титул. екрану.
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ПОЛ1ПШЕННЯ ЯКОСТ1 ВУГЛЕВОДНЕВИХ ПАЛИВ З ВИКОРИСТАННЯМ МУЛЬТИФУНКЦЮНАЛЬНИХ ПРИСАДОК
Рок 1.В.
Нацюнальний технгчний утверситет Украти «Кшвський полтехтчний iнститут 1мет 1горя Сжорського», асистент
Кшв
QUALITY IMPROVEMENT OF HYDROCARBON FUELS WITH USING MULTYFUNCTIONAL ADDITIVES
Roik I.V.
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Teaching Assistant
Kyiv
АНОТАЦ1Я
Проаналiзовано сучасний ринок присадок до палива та розроблено компонентний склад паливних добавок на основi поверхнево-активних речовин та антиоксиданпв. Було розроблено ряд добавок, яш скла-даються з антиоксиданпв рiзноi хiмiчноi природи та поверхнево-активних речовин. Паливна добавка за сво!м компонентним складом е рослинного походження i бюлопчно розкладаеться. Вперше було отримано композицшну багатофункцюнальну присадку до бензину, яка складаеться з антиоксиданпв з рiзною тер-мiчною стаб№нютю, i пiдтверджено синергiзм !х ди. Виявлено, що використання добавок у визначеному дiапазонi значень покращуе експлуатацшш та екологiчнi властивостi палива. Концентрация оксиду Карбону (II) СО знижуеться на 4-10 %, вмют вуглеводнiв CxHy - на 3-6 %. Отримано зменшення споживання палива на 3-5 %.
ABSTRACT
It was developed some series of additives, which consist of antioxidants of different chemical nature and surfactants. This technology is a fuel additive of vegetal origin and biodegradable, thus resulting in a renewable and inexhaustible source of energy due to its vegetal origin. For the first time, it was obtained the multifunctional fuel additive that consists of antioxidants with different thermal stability and it was determine the synergetic effect of their action. It was found that using of the additives in determined volume diapason significantly improves operational and environmental properties of fuels. The concentration of carbon monoxide (II) CO reduced by 410%, total hydrocarbons CxHy- by 3-6%. There was also a reduction of fuel consumption by 3-5%.
Ключовi слова: автомобшьний бензин, присадки, антиоксиданти, поверхнево-активш речовини, ви-трата палива.
Keywords: automobile gasoline, additives, antioxidants, surfactants, fuel consumption.
