Hasanov A.I.
TECHNICAL DIAGNOSTICS OF ELECTRICAL EQUIPMENT BASED ON FUZZY LOGIC *
Аннотация:
voltage stability is one of the important factors in power system. The generation of harmonics, which can lead to voltage surges, voltage surges, and system instability. To compensate for all these problems of the transmission system, svc is used. A Static VAR Compensator (SVC) was used as an additional control to improve the transient stability and power droop of the system. The problem of svc with fuzzy logic controller is more adapted to the prevailing load requirement to maintain the stability of heavy load or lightening system. Static VAR compensator is chosen as a low cost solution to replace conventional capacitor bank, thus durable and flexible allowing the modal voltage to be adjusted
Ключевые слова:
fuzzy logic, reactive power, SVC, voltage stability
УДК 621.3:681.2
Hasanov A.I.
Laboratory assistant of the Department Power Industry
Azerbaijan State Oil and Industry University
(Baku, Azerbaijan)
TECHNICAL DIAGNOSTICS OF ELECTRICAL
EQUIPMENT BASED ON FUZZY LOGIC
Abstract: voltage stability is one of the important factors in power system. The generation of harmonics, which can lead to voltage surges, voltage surges, and system instability. To compensate for all these problems of the transmission system, svc is used. A Static VAR Compensator (SVC) was used as an additional control to improve the transient stability and power droop of the system. The problem of svc with fuzzy logic controller is more adapted to the prevailing load requirement to maintain the stability of heavy load or lightening system. Static VAR compensator is chosen as a low cost solution to replace conventional capacitor bank, thus durable and flexible allowing the modal voltage to be adjusted.
Keywords: fuzzy logic, reactive power, SVC, voltage stability.
Introduction. The development of electric power systems leads to a complication of their structure and an increase in the requirements for the quality of functioning. Along with this, the requirements for assessing the current technical condition of the electrical equipment of the electric grid complex of electric power systems are increasing. At the same time, higher requirements for operational reliability are imposed on modern electrical equipment of power plants, substations and electrical networks, which is one of the main directions for improving the technical and economic indicators of the functioning of electric power systems.
Repair according to the actual condition gives a significant economic effect. Its successful use allows:
reduce the time and volume of repairs by at least a third;
reduce the number of sudden failures by dozens of times;
reduce lost profits due to downtime by several times.
To carry out repairs according to the actual state, a detailed diagnostics of the object is necessary, and it is desirable to detect all defects long before failure in order to prepare for repairs.
In favor of the decision to switch to the repair and operation of equipment according to the actual state, facts that are known to many repairmen speak:
it turns out that at least 50% of all technical services under the scheduled preventive maintenance are performed without their actual need;
very often maintenance and repair according to the regulations does not reduce the frequency of equipment failure. There is evidence that simple disassembly – assemblyequipment can reduce the resource by 30%, there is also no guarantee that you will install better quality components during repairs;
This technology radically changes the system of equipment maintenance at the enterprise and allows:
control the real current technical condition of the mechanisms;
technically justified to determine the timing and content of repair and adjustment work, control the quality of their implementation;
reduce financial and labor costs for equipment operation;
extend the overhaul period and the service life of your mechanisms;
reduce the need for spare parts, materials and equipment;
get rid of "sudden" breakdowns of mechanisms and production stops;
improve the overall production culture and staff qualifications.
Modern diagnostic systems necessary for the analysis of signs of malfunctions of power grid equipment should be based on current or expert information about the location and causes of malfunctions when monitoring the current technical condition and controlling the deviation limits of certain technical characteristics.Recently, new various effective approaches based on artificial intelligence methods have been developed, namely: expert systems, fuzzy logic, pattern recognition using artificial neural networks and fuzzy relationships. The scope of diagnostic systems is monitoring the operation of electrical installations of the power grid complex, monitoring trends, monitoring tools in complex automated production and monitoring the quality of electricity.
The nature of the fuzzy relationships between the elements of a certain set of alternatives or any other objects, which in the general case can also be considered alternatives, in technical problems is usually carried out through consultations with the decision maker. More common is the solution of such problems with the participation of experts, who usually do not have clear judgments about certain relationships between alternatives.In such situations, the fuzzy relation itself turns out to be a more convenient and more adequate form of presenting the necessary information to reality than the usual clear relations. Moreover, in fact, in the representation of people, the relations between many objects most often really have a fuzzy character.
It is very convenient to use fuzzy relations as an integral part of fuzzy models for diagnosing a technical conditionelectrical equipment or other technical objects. In this case, communication is carried out on a set of possible alternatives for the signs of malfunctions and causes of failures. Such a connection is implemented using the production rule "If, ..., then ..." or "if ..., then ...".The first part of the “If” production rule represents the cause and is called the antecedent, the second part of the “then” production rule characterizes the effect, which is called the consequent. Then, if some hypotheses of possible failures of electrical equipment are known (antecedent), then it is necessary to determine the true cause of the damage (consequent). In other words, the consequent gives a solution to the problem in the form of some fuzzy values for one reason or another, which previously acted as hypotheses.
A fuzzy relation is defined as any fuzzy subset of ordered tuples built from elements of certain basic sets, which in this case are universes. In this case, a tuple, as well as in the case of ordinary sets, is understood as an arbitrary set or list of ordered elements.
Fuzzy relations are not only an important mathematical concept, but also a logical concept that allows you to form and analyze mathematical models of real problems of various nature and find their solutions. Usually, fuzzy relations are built on a set of some alternatives, from which a possible fuzzy logical conclusion follows.
Fuzzy relations are some matrix of fuzzy coefficients between some vectors of possible alternatives, for which they serve as a transformation operator.
where - fuzzy logical conclusion.
It should be noted that there are other ways to define fuzzy relationships. So, for example, you can pre-specify fuzzybasis setsA1, A2,¼, Akand define fuzzy cartesianthe product of these fuzzy sets in the form
then define the fuzzy relation Q as some fuzzy subset of this fuzzy Cartesian product.
RESULTS. It is shown that fuzzy set theory and fuzzy logic are a promising scientific direction that allows formalizing expert knowledge in the form of verbal assessments and a linguistic variable to create a formalized fuzzy mathematical model of cause-and-effect relationships. A mathematical model of current diagnostics based on a system of fuzzy linear equations has been proposed and developed, the solution of which makes it possible to determine the vector - a column of priority of the possible causes of the identified defects in the equipment of the power grid complex.
REFERENCES:
Номер журнала Вестник науки №5 (62) том 2
Ссылка для цитирования:
Hasanov A.I. TECHNICAL DIAGNOSTICS OF ELECTRICAL EQUIPMENT BASED ON FUZZY LOGIC // Вестник науки №5 (62) том 2. С. 444 - 448. 2023 г. ISSN 2712-8849 // Электронный ресурс: https://www.вестник-науки.рф/article/8151 (дата обращения: 19.04.2024 г.)
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