Proceedings of 6th International Conference of Young Scientisis on Solutions of Applied Problems in Control and Communications

Conclusion

In this paper, the main functions performed by EMS have been discussed, along with EMS’s main components and the purpose of the proposed system’s creation. Was considered structure, composition and prospects of the EMS developed for implementation in the laboratory of power drive of PNRPU. In addition, the results of creation of a simplified EMS have been presented. The main feature of the system is utilization of the information system OpenJEVis, which allows to access data and to operate that data remotely from any operating system.

It is possible to detect a variety of profiles and develop mathematical models for identification of consumer’s state, equipment troubleshooting and detection of irrational use of laboratory equipment and lighting systems.

The system can be recommended for use in local electric networks: at the factories, equipped with power drive and electric machines; in housing sector and for analysis of energy consumption of lighting systems.

References

1. Kychkin A.V., Khoroshev N.I., Eltyshev D.K. The concept of an automated information system to support energy management // Energy security and energy saving. – 2013. – № 5. – P. 12–17.

2.Envidatec GmbH. – URL: www.envidatec-ost.ru (acc essed: 03.03.2015).

3.Khoroshev N.I., Eltyshev D.K., Kychkin A.V. Comprehensive assessment of the effectiveness of technical support retrofits // Fundamental

Research. – 2014. – № 5–4. – P. 716–720.

4. Kychkin A.V. Long-term energy monitoring based on software platform OpenJEVis // Bulletin of PNRPU. Electrotechnics, information technologies, control systems. – 2014. – № 9. – P. 5–15.

5.Denisenko V., Kilmentov P., Metelkin E., Trubachev O., Khalyavko A. Distributed data collection systems RealLab! // Electronic components. – 2007. – № 4. – P. 1–6.

6.Janitza GmbH. – URL: www.janitza.com (accessed: 03.03.2015).

7.OpenJEVis – The Open Data Monitoring and Storage Solution, available at: www.OpenJEVis.org (accessed: 03.03.2015).

8.Kuznetcova I.Y. Mathematical model of prediction of energy consumption // Bulletin of YFU. Technical Sciences. – 2013 . – № 4. – P. 121–125.

21

АВТОМАТИЗИРОВАННАЯ СИСТЕМА ЭНЕРГОМОНИТОРИНГА ЛАБОРАТОРИИ ЭЛЕКТРОПРИВОДА ПНИПУ

Георгий МИКРЮКОВ1, Алексей КЫЧКИН2

Пермский национальный исследовательский политехнический университет, Пермь, Россия

(1е-mail: mikriukov.georgii@yandex.ru, 2e-mail: aleksey.kychkin@gmail.com)

Аннотация. Рассматриваются основные принципы организации автоматизированной системы мониторинга энергетических показателей на примере лаборатории электропривода ПНИПУ. В работе описываются основные требования к составу системы мониторинга. Выполнен краткий обзор используемых технических и программных средств автоматизации, на основании результатов которого построена архитектура системы. В качестве аппаратного обеспечения системы энергомониторинга выбраны анализа-

торы мощности фирмы Janitza electronics UMG 104 и UMG 604, в качестве программного обеспечения – GridVis и OpenJEVis. Система обладает широкими возможностями по сбору, отображению и анализу полученных энергетических параметров. В практической части работы рассматриваются технические решения по реализации системы. Производится обзор перспектив использования системы, выделяются два основных направления: прогнозирование потребления электроэнергии и расчет корреляционных функций объектов мониторинга.

Ключевые слова: энергомониторинг, система сбора данных, Janitza, OpenJEVis.

22

APPROACHES TO TASK SOLUTION OF COMPLEX OBJECTS IDENTIFICATION AS EXAMPLE OF AN INDUCTION MOTOR

Natalya ANDRIEVSKAYA1, Nail MUBARAKZYANOV2

Perm National Research Polytechnic University, Perm, Russia

(1e-mail: zav@msa.pstu.ac.ru, 2е-mail: nail-93@mail.ru)

Abstract. In the identification task statement features article of modern objects and control system are considered. Application limited of classical identification methods for object this class is proved. Review of modern identification methods is adduce apparatus of fuzzy logic and artificial neural networks are using. Parameters identification task of induction motor is stated. Modeling and identification software is offered.

Keywords: Identification, artificial neural network, fuzzy logic, induction motor.

Introduction

Object identification task and control system is reverse tasks of automatic control theory initially, in modern development conditions of science and technology it is become very important due to extending field of application:

− control system designing of complex technological processes and plants;

−diagnostics of operability control system, technological complexes, automated and automatic manufactures;

−adaptive control system development of manufacturing and technological processes;

−automated technological complexes designing;

−efficient control production;

−expert statistic system of decision-making support and marketing control of commercial company based on adaptive and robust algorithms;

−analysis and optimization of economical routes efficacy and timetable and transport motion system;

Therefore development of efficacy identification algorithms of complex objects is topicality.

1. Review of identification methods

Identification of nonlinear control objects is one of the most important tasks in this area.

23

Majority of technological objects have nonlinear properties and influenced by external and internal perturbation often having stochastic nature that results in nonstationary character of changing their parameters.

Because of this at designing of automatic control system as one basic task is identification of such object. However use of classic identification methods for such objects is difficult as they have some disadvantages:

−impossibility of use under uncertainty conditions. Under uncertainty in this case is understood the uncertainty due to the lack of information as necessary to obtain a quantitative description of the processes occurring in the system, and the complexity of the control object [1, 2];

−complexity of high-order systems identification , multivariable systems, nonlinear systems, etc. That is bad formalized objects;

−limited operability under conditions of limited measurability [3];

−low models building accuracy.

2. Fuzzy algorithms

It are based on fuzzy ranges and fuzzy logic. There are two types of fuzzy models is Mamdani and Sugen. This models are distinguished format of knowledge base and defuzzification procedure. Fuzzy inference procedure in a type model of Mamdani are understood fuzzy models costumers: technologists, economists, doctors, biologists. Therefore, for tasks where accuracy is more important identification, it is necessary to use fuzzy models of Sugen type, and for tasks where is explanation of adopted solution more important fuzzy models of Mamdani type will have advantage [4].

3. Artificial neural networks

Main feature of neural network is ability it to training. It is realized by means specially developed algorithms, most popularly rule “backward propagation of errors” among it. For training neura l network don’t need none a priori information about sought functional dependence structure. Need only learning sample as experimental pairs <input-output>. Cost of it is trained neural network - arc-weighted graph – in formal interpretation is beggared [5].

4. Fuzzy neural network

Obviously knowledge representation in neural network as weight matrix not allow to build explanation performed recognition or prediction while deduction system on fuzzy rules base allow to build explanation as

24

reverse deduction protocols. Neural network is trained by means universal algorithm i.e. laborious knowledge is extracted replace collection of enough size learning sample [6].

5. Parameters identification task statement of induction motor

One of relevant designing and researching tasks of induction motor is parameters estimation. For reliable and continuous operation of the engine must clearly know a large number of its technical parameters that make up the technical certificate of product, and characterize the different modes of operation. These parameters include the energy data of the motor – the loss in its separate parts, efficiency, rated torque on the shaft, and the constructional parameters; resistances and reactances.

Consider, identification of induction motor model with a squirrel cage. Induction motor is nonlinear and nonstationary object since processes of iron circuit saturation, motor windings resistance changing at temperature changing occur in it (Figure).

Fig. Generalized model of the induction motor

The work of the induction motor is described by differential equations:

25

instantaneous values of the rotor voltage; ψ A , ψB , ψC – interlinkage of stator phases; ψa , ψb , ψc – interlinkage of rotor phases;

LAA , LBB , LCC , Laa , Lbb , Lcc – windings leakage inductance, all the rest in-

ductance are magnetizing inductance between windings; M – electromag-

netic torque; M С – static torque of load; J – moment of inertia of the drive, the motor shaft; ω – angular frequency of rotor.

For efficient operation of the induction motor is necessary to know the resistance. Since the induction motor is a complex, essentially nonlinear object with partial observability, then solution to the task of estimation the parameters, in particular resistances is impossible to classical methods. Therefore, for this task are invited to consider identification algorithms using fuzzy logic, artificial neural networks and fuzzy neural networks.

26

Such software is recommended to use tools for identification:

∙Neural Network Toolbox – bump pack of MatLab contai ning means for designing, simulation, development and visualization of neural networks.

∙Fuzzy Logic Toolbox – bump pack of MatLab containin g tools for fuzzy logic system designing.

Conclusions

Research of the induction motor model with a squirrel cage has showed that the induction motor is a nonlinear and non-stationary object and the estimation of its parameters by means of classical methods is rather difficult. As synthesis procedure algorithms we offer to use identification methods based on fuzzy logic and artificial neural networks allowing to create versatile and work capable methods of general electrical system parameters estimation.

References

1. Mikhailov A.S., Staroverov B.A. Problems and prospects of artificial neural networks applying for identification and diagnostics of technical objects // Vestnik IGEU. – 2013. – Vol. 3. – № 3. – P. 64–68.

2. Andrievskaya N.V., Reznikov A.S., Cheranev A.A. Features of ap-

plication of Neuro Fuzzy Systems in systems of automatic control // Fundamental research. – 2014. – Vol. 11. – P. 1445–144 9.

3.Andrievskaia N.V. The use of neural network approach for solving of tasks of estimation of parameters and state variables // Neurocomputers.

–2014. – Vol. 12. – P. 3–9.

4.El’-Aidubi S.D., Dorofeev U.I. Identification of nonlinear dependence with the help fuzzy logical inference. – URL: www.k pi.kharkov.ua/archive/ Conferences/VIII%20Університетська%20науково-практична%20 студентська%20конференція%20магістрантів/2014/ S2/Идентификация%20

нелинейных%20зависимостей.pdf (accessed: 25.01.2015).

5.Hybrid systems based on soft computing. – URL: h ttp://www.swsys.ru/ index.php?id=687&lang =en&page =article (accessed: 25.01.2015).

6.Rotshteyn A.P. Intelligence technology of identification. – URL: http://matlab.exponenta.ru/ fuzzylogic/book5/6_1.php (accessed: 25.01.2015).

27

7.Ignat'ev I.V., Prihod'ko M.A., Bulatov Yu.N. Development and program implementation of algorithm fuzzy neural network identifications of parameters of the synchronous generator // Systems. Methods of Technology. – 2012. – Vol. 4. – P. 52–56.

8.Dyageterev A.V., Zaporozhets O.V., Ovcharova T.A. Nonlinear transfer functions identification by using artificial neural network //

Ukrainian Journal of Metrology. – Kharkiv, 2013. – Vol. 2. – P. 4–8.

9.Kondratenko Yu.P., Gordienko E.V. Neuronets going near decision of task of authentication of non-stationary parameters of technological objects // Bulletin of the National Technical University «Kharkiv Polytechnic Institute». Series: Computer science and modeling. – Kharkiv, 2010. – Vol. 21. – P. 102–109.

10.Zvyagintseva E.A., Dudnik A.V. Application of recurrence neural network for DC-motor parameters identification // Bulletin of the National Technical University «Kharkiv Polytechnic Institute». Automation and Instrumentation. – Kharkiv, 2011. – Vol. 57.

28

ПОДХОДЫ К РЕШЕНИЮ ЗАДАЧИ ИДЕНТИФИКАЦИИ СЛОЖНЫХ ОБЪЕКТОВ НА ПРИМЕРЕ АСИНХРОННОГО ДВИГАТЕЛЯ

Наталья АНДРИЕВСКАЯ1, Наиль МУБАРАКЗЯНОВ2

Пермский национальный исследовательский политехнический университет, Пермь, Россия

(1e-mail: zav@msa.pstu.ac.ru, 2е-mail: nail-93@mail.ru)

Аннотация. Рассмотрены особенности постановки задачи идентификации современных технических объектов и систем управления. Доказана ограниченность применения классических методов идентификации для объектов данного класса. Приведен обзор современных методов идентификации, использующих аппарат нечеткой логики и искусственных нейронных сетей. Поставлена задача идентификации параметров асинхронного двигателя. Предложены программные средства моделирования и идентификации.

Ключевые слова: идентификация, искусственные нейронные сети, нечеткая логика, асинхронный двигатель.

29

AUTOMATION OF NC-CODE GENERATION FOR NC MACHINES

IN THE ENGINEERING INDUSTRY

Egor OBUKHOV1, Sergey BOCHKAREV2

Perm National Research Polytechnic University, Perm, Russia

(1e-mail:Obuhov2014@bk.ru, 2e-mail: Bochkarev@msa.pstu.ru)

Abstract: The research is important because the process of preparing NC-codes for CNC machines is key stage of technological preparation of production. The main issue which I want to highlight is how to make more effective process of NC-code generation, by means CAD/CAM decision. The T-FLEX decision is really effective; it allows to perform designing and technological tasks in one module.

Keywords: CNC-code generation, T-FLEX, CAD/CAM.

Introduction

For now the process of preparing NC-codes for CNC machines is key stage of technological preparation of production. From one hand, quality of NC-code depends on quality of NC-code generation process. From the other hand, there is dependence among quality of NC-code, quality of manufactured product and production process efficiency in general [1].

In order to improve quality of manufactured products and to save time on manufacture the products, necessary to improve process efficiency, one of them is process of preparing NC-codes for CNC machines [2].

Thus, actual task is increase in efficiency of preparing NC-codes process, by means of automation tools.

1. Objectives

At the Proton-PM there is a process of NC-code generation, which based on CAM-CNC system. The system has just CAM functions, so there is no connection with a CAD system and that is a problem, because the transferring model can distort the model and one needs additional time for this process too (Fig. 1).

By analyzing the existing problems, we have set following purposes:

∙To reduce time on the NC-codes preparing.

∙To improve the processes of design, development and manufacturing of products in general.

30