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26Современные инновации, системы и технологии // Modern Innovations, Systems and Technologies
год; 2022 2(2) eISSN: 2782-2818 https://www.oajmist.com
УДК: 658.012.011.56 EDN: NDPJWC
DOI: https://doi.org/10.47813/2782-2818-2022-2-2-0251-0263
Вопросы разработки интеллектуальных информационных электроэнергетических систем
Г.К. Ишанходжаев, М.Б. Султанов, Б.Б. Нурмамедов
Институт энергетических проблем Академии наук Республики Узбекистан, Ташкент,
Узбекистан
Аннотация. В статье осуществлен системный анализ функционирования существующих электроэнергетических систем. На основание результатов системного анализа выявлены проблемы и недостатки процессов функционирования электроэнергетических систем, для устранения которых предложено применение интеллектуальной информационной электроэнергетической системы. В данной работе сформированы принципы и требования к созданию и применению, а также разработана структура интеллектуальной информационной электроэнергетической системы, которая включает в свой состав подсистемы обеспечения, баз данных, знаний и функциональные блоки, и задачи. С использованием методов системного анализа и обработки информации разработаны структурная схема информационного обеспечения интеллектуальных информационных электроэнергетических систем.
Ключевые слова: системный анализ, модель, задача, структура, информационная модель, функциональные блоки.
Для цитирования: Ишанходжаев, Г.К., Султанов, М.Б. & Нурмамедов, Б.Б. (2022). Вопросы разработки интеллектуальных информационных электроэнергетических систем. Современные инновации, системы и технологии - Modern Innovations, Systems and Technologies, 2(2), 02510263. https://doi.org/10.47813/2782-2818-2022-2-2-0251-0263
Issues of development of intelligent information electric power
systems
G.Q. Ishankhodjayev, M.B. Sultanov, B.B. Nurmamedov
Institute of energy problems of the Academy of sciences of the Republic of Uzbekistan,
Tashkent, Uzbekistan
Abstract. The article carried out a systematic analysis of the functioning of existing electric power systems. Based on the results of the system analysis, problems and shortcomings of the processes of functioning of electric power systems were identified, for the elimination of which the use of an intelligent information electric power system was proposed. In this work, the principles and requirements for the creation and application are formed, and the structure of the intelligent information
© G.Q. Ishankhodjayev, M B. Sultanov, B.B. Nurmamedov, 2022
0251
electric power system is developed, which includes the subsystem of support, databases, knowledge and functional blocks, and tasks. Using the methods of system analysis and information processing, a block diagram of the information support of intelligent information electric power systems has been developed. This article also provides generalized goals for improving the efficiency of using the potential capabilities of the control object, the main functions in the formation and adoption of control decisions in intelligent information electric power systems.
Keywords: system analysis, model, task, structure, information model, functional blocks, data representation structures.
For citation: Ishankhodjayev, G., Sultanov, M., & Nurmamedov, B. (2022). Issues of development of intelligent information electric power systems. Modern Innovations, Systems and Technologies, 2(2), 0251-0263. https://doi.org/10.47813/2782-2818-2022-2-2-0251-0263
INTRODUCTION
At present, the existing unified electric power system (UEPS) of the republic, created more than sixty years ago, is a unique organizational and technical facility, the structure and management of which is built according to a hierarchical principle, which ensured a balanced unity of generation, distribution and consumption of electricity in the territorial context to ensure energy security of the regions and the possibility of intersystem exchange of power and energy flows in normal and emergency modes to improve the efficiency of the energy association. At the same time, it should be noted that the UEPS, which was created a long time ago, needs a serious modernization of fixed assets and renewal, both in terms of replacing physically and morally obsolete equipment, and in the application of new technologies and equipment, information and diagnostic systems. The restructuring of the electric power industry, the market conditions for the functioning of the electric power industry bring their own characteristics and problems. Solving these problems requires the use of an intelligent information electric power system (IIEPS), which ensures cost reduction in the production and transmission of electricity, a decrease in the level of losses in the transport of heat and electricity, and optimization of the size and placement of reserve capacities. The use of the IIEPS in the energy sector and the reform of the national energy sector pose new important tasks for the development of the UEPS of the republic. Modernization of energy management will lead to the financial independence of the fuel and energy complexes, which is provided by funds received for the production and transportation of energy. With an increase in the number of enterprises of fuel and energy complexes and a decrease in the size of each separately, in comparison with pre-reform vertically integrated enterprises of fuel and energy complexes, the
risks and significance of management decisions increase significantly. The responsibility of enterprises of the fuel and energy complexes for their own energy consumption increases the importance of energy saving issues, reducing excess energy losses and improving the quality of energy metering systems. Modernization of equipment and improvement of information technologies require more focused attention to the formation of the scientific and technical policy of the enterprise of fuel and energy complexes. The construction of the IIEPS structure is primarily associated with the construction of a system model, in which both traditional elements of the control system and the knowledge processing models implemented by the intelligent system should be defined. In an intelligent control system, new elements compared to a traditional control system are all intelligent transformations or knowledge management elements that are associated with the implementation of artificial intelligence, i.e. using technologies of expert systems, database, goals and knowledge, decision making, associative memory, fuzzy logic, semiotic networks, structural dynamics control, etc. [1].
FORMULATION OF THE PROBLEM
For the efficient and reliable operation of electric power facilities (energy associations, power systems, grid and generating enterprises), it is necessary to create and implement modern information management systems. During the last quarter of a century, domestic information systems have undergone a progressive evolution, both in terms of the development of theoretical principles for their construction, and in the field of implementation of these systems. A significant contribution to this difficult work was made by V.A. Barinov, A.I. Bartolomey, F.D. Goldenberg, A.F. Bondarenko, V.V. Bushuev, V.P. Vasin, V.A. Venikov, N.I. Voropay, V.E. Vorotnitsky, A.Z. Gamm, A.F. Dyakov, Yu.S. Zhelezko, A.G. Zhuravlev, N.I. Zelenokhat, Gustav Olsson, Gianguido Piani, V.I. Idelchik, G.L. Kemelmacher, I.N. Kolosok, V.G. Kitushin, JI.A. Koshcheev, L.A. Krumm, Yu.N. Kucherov, Yu.Ya. Lyubarsky, M.I. Londer, KG. Mityushkin, V.L. Nesterenko, V.G. Ornov, Yu.I. Morzhin, M.A. Rabinovich, S.I. Palamarchuk, V.I. Rozanov, Yu.N. Rudenko, V.A. Semenov, S.S. Smirnov, Yu.A. Tikhonov, Yu.A. Fokin, E.V. Tsvetkov, M.I. Londer, A.P. Chepkasov and others.
At present, with the development of high-performance computer technology, information systems (IS) are an effective means of solving systemic problems. The works of M.K. Chirkova, S.P. Maslova, V.N. Petrov, D. Mark, K. McGowan. The issues of developing information systems for various purposes by methods of system analysis using modern object-oriented programming languages and database technologies are widely covered in the works of
V.P. Agaltsova, K.Yu. Bogacheva, V.I. Vasilyeva, B.G. Ilyasov, E. Jordan, D.M. Mutushev and others. To a lesser extent, this affected the problems of creating adapted methods for developing special information systems for electric power complexes. Separate aspects devoted to the methods of system analysis and decision-making on the creation and deployment of information systems for monitoring the parameters of electric power complexes for smart grid technologies are considered in the works of S.V. Rodygina, V.A. Kamaeva, A.I. Zaitseva, I.V. Blinova, B.B. Kobets, I.O. Volkova and others. In this area, there is also a certain number of legal documents, both international and domestic, partially describing the direction of development of standards in the field of intelligent grid [2].
DESCRIPTION OF EXISTING METHODS
The construction of the IIEPS structure is primarily associated with the construction of a system model, in which both traditional elements of the control system and the knowledge processing models implemented by the intelligent system should be defined. In an intelligent control system, new elements compared to a traditional control system are all intelligent transformations or knowledge management elements that are associated with the implementation of artificial intelligence, i.e. using technologies of expert systems, database, goals and knowledge, decision making, associative memory, fuzzy logic, semiotic networks, structural dynamics control, etc.
Let us consider in more detail the block diagram of the IIEPS, which is presented in Fig.1. In this figure, the input of the system is the information input block (IIB), designed to enter numerical data, text, and speech. Information at the input of the system can come (depending on the problem being solved) from the user, the external environment, the control object. Further, the input information goes directly to the database (DB) - a set of tables that store, as a rule, symbolic and numerical information about the objects of the subject area or a control information generation block (CIGB) [3].
CIGB using database information, goal base (GB is a set of local goals of the system, which is a set of knowledge activated at a particular moment and in a particular situation to achieve a global goal) and a knowledge base (KB is a totality of knowledge, for example, a system of production rules, about the regularities of the subject area) provides solutions for the fuzzy formalized tasks of the IIEPS, and also carries out action planning and the formation of control information for the user or control object based on the database, knowledge base, business center and using a block of algorithmic decision methods (ADMB) contains
algorithms, models and software modules for solving problems in the subject area. The knowledge assimilation block (KAB) analyzes dynamic knowledge in order to assimilate it and save it in the knowledge base. The Decision Explanation Block (DEB) interprets to the user the inference sequence applied to achieve the current result.
Figure 1. Structural diagram of IIEPS (MS - mathematical software; PS - program software;
IS - information support; TS - technical support; DB-database; GB - bases of goals; KB-knowledge base; IIB - information input block; CIGB - control information generation block;
ADMB - block of algorithmic decision methods; KAB - block of knowledge acquisition; DEB - decision explanation block; IOB - block of logical output of information; CO-object of
control; U-user).
At the output of the system, the information inference block (IOB) provides the output of data, text, speech, images and other results of inference to the user (U) and / or the Control Object (CO).
The feedback loop makes it possible to realize the adaptability and learning properties of the IIIEPS. At the design stage, knowledge specialists fill the knowledge base and the goals base, and programmers develop programs modules based on algorithmic methods for solving problems. The database is created and updated, as a rule, during the operation of the IIEPS. The dynamics of the IIIEPS operation can be described as follows. When information in the external language of the system is received at the input of the IIB, it is interpreted into an internal representation to work with the symbolic model of the system. The CIGB selects from the KB a set of rules activated by the received input information, and places these rules in the GB as the current goals of the system. Further, the CIGB, according to a given strategy, for example, the strategy of maximum reliability, selects a rule from the GB and tries to complete the definition of the variables of the model of the external world and the executive system with the control object. Based on this, new KB rules are activated and logical inference begins in the system of productions (rules). This procedure ends as soon as a solution is found, or when the GB is exhausted. The solution found from the internal representation is interpreted by the IOB into the external language of the lower-level control subsystem and the CO [4, 5].
In modern conditions, when making managerial decisions, the role of predictive information is increasing. The multi-criteria nature of decision-making tasks, the lack of a rigorous mathematical model describing the behavior of the UES in the time context, the lack of a complete amount of information and its possible unreliability lead to the fact that management decisions are often based on the experience and intuition of the manager. A tool is needed to improve the objectivity and quality of decisions made, using both the technical and economic indicators of the UEPS, and the experience of qualified specialists. In terms of information, the UEPS can be represented as a multi-level, multi-layer structure of a sufficiently large dimension with a complex multi-connected system of relations. To solve the problems of functioning and development of the UEPS, it is necessary to develop and implement an adequate information model. Such a model should be built on the basis of a multidimensional, hierarchical information system consisting of subsystems united by a set of functional links. It is these connections that make it possible to assess the functional state of subsystems and the system as a whole. In turn, the functional state of the UEPS is characterized by the following indicators: technical condition of power equipment, reliability of power supply, energy
efficiency, environmental friendliness, financial stability, etc.
In domestic and foreign practice, attempts were made to resolve only certain aspects of the issues of creating a comprehensive information model for the functioning and development of the UEPS. In the current situation, in the absence of a single toolkit, it is impossible to solve the existing problems of managing the UEPS. That is why it is fundamentally necessary to create tools for monitoring power equipment and assessing the functional state of power equipment [6].
An information system that provides support for decision-making on the development and functioning of the UEPS must meet such an important requirement as the availability and reliability of the information used. This means that the models and decision-making methods used must be provided with information. The requirement of information security significantly affects the formation of mathematical models and methods used to solve energy problems. Part of the necessary information may be missing for objective reasons related to the impossibility of obtaining it (lack of measuring systems, lack of communication channels, etc.). In addition, the lack of information is associated with the shortcomings of the information system of the enterprise of fuel and energy complexes, the fragmentation of its information subsystems, the lack of exchange between databases and software systems. (Fig.2).
Improving the quality of the decision-making system is associated both with the improvement of its information security, and with the development of mathematical methods of decision-making [7-8].
Information support of the tasks of synthesis and operation of the UEPS is proposed to be considered based on the information model. Modern requirements for the representation and use of information in the IIEPS make it expedient to use a new information technology - the so-called Common Information Model (CIM) - systems. The generalized information model CIM - hereinafter CIM - is a certain conceptual model for describing various objects (subjects) of the surrounding world, using object-oriented terminology. If until recent years the concepts of object-oriented technology were related to programming languages (C ++, Java, etc.), then CIM expands these concepts to describe data, deliberately using such terminology of object-oriented programming as classes, properties, methods, etc. associations. In essence, CIM is an information model, the task of which is a single unified representation of data structures, regardless of the source of data origin and the purposes of their use (Fig. 3).
Figure 2. Exchange of information in IIEPS (DCTD-data collection and transmission device;
LDPC-local data processing center; DR-data recorder; RPC-request processing center; DB-
database).
It was already noted that CIM - the model uses a standard object-oriented visual representation. The main elements of the CIM model are classes, associations, and packages. The class is the main element of the CIM model. A class is an abstract description of some objectively existing entity of an electric power system. Examples of classes are «transformer», «load», «ac line», «dc line», «measurement», etc. The fundamental difference between the concept of a class in CIM and object-oriented programming languages is that in CIM a class describes only an interface and is completely independent of both the computer technology platform and the implementation. The main properties of a class are encapsulation, polymorphism, and associations. Encapsulation means concentrating all the properties of a class as its attributes within the class declaration. Polymorphism means that the same symbolic attribute name can be used in different classes, but the class name must be unique.
Figure 3. Relationship diagram when applying the CIM model in IIEPS (DCTD-data collection and transmission device; LDPC-local data processing center; DR-data recorder; RPC-request processing center; DB-database; CIM- common information model).
Association means the possibility of connecting classes to each other, that is, any pair of classes can be connected by an association, which in turn is also a class. Associations represent a semantic relationship between two classes, with the help of which one class can obtain information about the attributes and associations of another class. An association has two association ends, each attached to one of the association classes. The end of an association may be marked with a label called «role name» or «role». In the CIM model, the role name almost always contains the class name, and in some cases simply repeats it. The end of the association (role) also has a multiplicity, which indicates how many objects of the class can participate in this association [9].
EXPERIMENTAL RESULTS
In this work: - a new integration mechanism is considered for organizing information interaction of heterogeneous information systems of the UEPS - the so-called CIM-systems; -
investigated the benefits of using such systems; - analyzed the methodology for building CIM models in relation to energy applications, as well as data access interfaces in CIM systems.
In the future, work will be carried out to adapt international standards for CIM systems to the real UEPS, a number of methodological documents will be developed that determine the possibility, rules and techniques for building information models, for the first time CIM for interactive creation of information models.
In general, IIEPS can be viewed as a set of interrelated management processes and objects. The general purpose of the IIEPS is to increase the efficiency of using the potential capabilities of the control object. Thus, a number of goals can be distinguished: - providing the decision maker (DM) with relevant data for decision-making; - acceleration of data collection and processing operations; - reducing the number of decisions that the DM must make; -increasing the efficiency of management, the level of control and performance discipline; -reducing the costs of the DM for the implementation of auxiliary processes; - increasing the degree of validity of decisions made.
The main functions of the IIEPS in the formation and adoption of control decisions: -information processing functions (computing functions) - carry out accounting, control, storage, search, display, replication, transformation of the information form; - functions of exchange (transfer) of information - are associated with bringing the developed control actions to the CO and exchanging information with the DM; - decision-making functions (transformation of information content) - creation of new information in the course of analysis, forecasting or operational management of an object.
The main stages of the decision-making process according to the theory of decision-making are decomposed into the following stages:
• determination of the purpose of solving the problem;
• choice of the most preferred course of action leading to the achievement of the goal;
• implementation of the solution (chosen variant of action).
Determining the goal of solving the problem that has arisen is implemented in the intelligent converter unit, which receives and processes information about the external environment from the sensor system. In conflict situations, the goal may depend on the available resources and factors that form a problematic situation, i.e. decision-making in conflict situations. The method of action for controlling an object in the decision-making process is called strategies, and the result that the chosen strategy can lead to is called the outcome.
Conflict conditions give rise to factors that affect the strategy and, accordingly, the control implemented by the intelligent system. Depending on the origin, uncertain factors are divided into random and uncertain non-stochastic nature, consisting of natural and strategic ones.
The mathematical model of decision-making is formed taking into account all the factors and the information available about them. A simplified decision-making model in this case can be described by the following system
Pr = < I, M, U, L, J, O> (1)
where I is the set of outcomes (results); M - model of preferences for outcomes (decisions made); U - many decision strategies; L - many possible values of uncertain factors; J - a function that determines the relationship of an uncertain factor and the outcome resulting from the decision; O - all other information about the decision being made in a formalized form (information about the conflict, preferences of other persons participating in the conflict, etc.) [9, 10].
The convenience of using model (1) in a conflict is determined by the fact that it allows you to simply and clearly connect the values of uncertain factors and strategies with the control implemented by an intelligent system. The sets I, M, U, L and the function J formally define the components of the decision being made and determine the connection with the control system through the concepts of the criterion and system performance indicators. In management theory, preference relations are most often described using special functions of quality indicators and criteria. An indicator of the quality or effectiveness of a management system is a measure of the degree to which the real result of management corresponds to the one required to achieve the goal and obtain estimates or measurements of the intensity of outcomes. A criterion is a rule that makes it possible to compare decisions and strategies made in terms of selected indicators of outcome assessments. Criteria are introduced on the basis of a certain concept of rational behavior of an intelligent system: suitability, optimization and adaptability [9, 10].
CONCLUSIONS
To solve the above tasks, one must first perform the following work:
1. Create a system of industry standards for building unified information models for both the UEPS as a whole and its individual elements.
2. Create a system of industry standards that describe a unified system of application-level interfaces and provide application integration.
3. To create in the IIEPS a network environment of a common information bus that supports a single information model focused on WEB-services technology and allows creating applied heterogeneous systems based on the same platform-independent network technologies.
4. To create the necessary organizational «vertical» structure of the IIEPS, supporting the unity of information models and interface models for all market participants.
REFERENCES
[1] Ishankhodjaev G.K., Sultanov M.B, Mirzaakhmedov D.M. Azimov D.T. Optimization of information processes of multilevel intelligent systems, ICFNDS 2021: The 5th International Conference on Future Networks & Distributed Systems, December 15-16, 2021. Pages 713717. DOI:10.1145/3508072.3508212
[2] Morzhin Yu.I. Development of information technologies for automation of operational dispatch and technological management to improve the efficiency of the functioning of the UES of Russia, Doctor of Technical Sciences thesis, Moscow - 2006
[3] Ishankhodzhaev G.K., Sultanov M.B. The concept of creating intelligent energy systems, Energy and resource saving: new research, technologies and innovative approaches. Collection of materials of the proceedings of the international conference. September 24-25, 2021 - T.: 155-160
[4] Ishankhodzhaev G.K., Sultanov M.B., Mirzaakhmedov D.M., Nurmamedov B.B. Models and algorithms of the energy management information system. Tashkent -2021.
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[9] Sultanov M.B. Models and algorithms of the optimum solution of the problems of the energy management system, European science review. Volume 5-6. - Austria, June 2018. -P.
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ИНФОРМАЦИЯ ОБ АВТОРАХ / INFORMATION ABOUT THE AUTHORS
Ишанходжаев Гайрат Кудратович, доктор технических наук, заведующий лабораторией, Институт энергетических проблем Академии наук Республики Узбекистан, Ташкент, Узбекистан e-mail: igayratk@gmail.com
Ishankhodjaev Gayrat Kudratovich, Dr. of
Engineering, professor, head of laboratory, Institute of Energy Problems Academy of Sciences of the Republic of Uzbekistan e-mail: igayratk@gmail.com
Султанов Муроджон Бахтиярович, PhD, старший научный сотрудник, Институт энергетических проблем Академии наук Республики Узбекистан, Ташкент, Узбекистан
e-mail: wise_man@list.ru ORCID: 0000-0001-5107-2424
Sultanov Murodjon Baxtiyarovich, PhD, senior researcher, Institute of Energy Problems Academy of Sciences of the Republic of Uzbekistan
e-mail: wise_man@list.ru ORCID: 0000-0001-5107-2424
Нурмамедов Бахром Бахтиерович,
Институт энергетических проблем Академии наук Республики Узбекистан, Ташкент, Узбекистан
e-mail: nurmamedov@ungd.uz
Nurmamedov Baxrom Baxtiyorovich,
independent applicant, Institute of Energy Problems Academy of Sciences of the Republic of Uzbekistan
e-mail: nurmamedov@ungd.uz
Статья поступила в редакцию 25.05.2022; одобрена после рецензирования 21.06.2022; принята
к публикации 28.06.2022. The article was submitted 25.05.2022; approved after reviewing 21.06.2022; accepted for publication
28.06.2022.
