5 Yadda na Gano Abin da ke Masu Karamin Inganci a Turbin Mai Tsawo

Haddamar Dijagnoistikin Afini na Transformer

1. Haddamar Ratio don Tacciyar Gas Mai Yawa

Don karamin transformer da mai yawa da kashi, wasu gaso da suka yawa ana samu a cikin tankin transformer daga faduwar hawa da kashi. Gaso da suka yawa a cikin kashi zai iya amfani a kan nuna kayayyakin tacciyar jiki-kwakwa na transformer daga binciken gaso da muhimmancinta da ratios. Wani teknologi ya fara amfani a kan dijagnoistikin transformer da mai yawa. Bada Barraclough da sauran suka bayar wani haddamar dijagnoistikin da ke amfani da gaso na ratio: CH4/H2, C2H6/CH4, C2H4/C2H6, da C2H2/C2H4. A nan IEC standards masu ba, an yi nasara C2H6/CH4 ratio, kuma haddamar ratio uku ta shafi da ya zama mafi yawan amfani. Rogers ya bayar tushen bayanin da ke amfani da gaso na ratio a IEEE da IEC standards. Amfani na IEC 599 tsakanin lokacin da suka fi sani cewa bana daidai da yanayin da suka faruwa a wasu abubuwa, kuma bana daidai da tacciyar wasu abubuwan fault. Saboda haka, China da Japan Electrical Association sun yi nasara a IEC coding, kuma wasu haddamar tacciyar gaso da suka yawa suka samu yawan amfani.

2. Haddamar Dijagnoistikin Fuzzy Logic

L.A. Zadeh, wanda ya fara amfani da haddamar fuzzy diagnosis, ya fara amfani da haddamar wanda ya zama mafi yawan amfani. Fuzzy logic ya shafi da kyau a nuna ilimi da taurari da kuma ma'ana mai yawa. Ta amfani da concept of membership functions, ita shafi fuzzy sets, ita shafi fuzzy relationships, ita shafi human rule-based reasoning, kuma ita shafi various uncertainty problems a practical applications. A cikin practice, transformers suna da faults da bane da kyau sababon da mechanisms da suka shiga manya uncertain and fuzzy relationships da haddamar traditional bana daidai da tacciyar. Fuzzy logic methods zai iya amfani da kyau a nuna uncertain relationships a cikin transformer faults, tare da wani hanyar da za a iya amfani a kan dijagnoistikin transformer da kyau.

Don neman koyar lack of critical ratio criteria a haddamar Rogers ratio method da ake amfani a dijagnoistikin transformer, an yi haddamar da ke amfani da fuzzy set theory. Haddamar ya shafi da fuzzy logic technology a cikin haddamar ratio traditional da ke amfani da fuzzy boundaries. Haddamar ya samu yawan amfani a cikin dijagnoistikin multiple transformer faults, kuma ya zama series of fault diagnosis methods, including coding combination methods, fuzzy clustering techniques, Petri networks, da grey systems. Waɗannan models sun shafi da kyau data na inherent fuzziness, sun shafi da performance da complex datasets, kuma sun shafi da accuracy of transformer fault diagnosis.

3. Haddamar Dijagnoistikin Expert System

Expert systems suna da muhimmanci a artificial intelligence. Suna da computer program systems da suke shafi da experience da reasoning processes na human expert to a certain extent. Based on data provided by users, they apply stored expert knowledge or experience to make inferences and judgments, ultimately providing conclusions with confidence levels to assist user decision-making. Power transformer fault diagnosis is an extremely complex problem involving multiple factors.

Making accurate judgments based on various parameters requires solid theoretical foundations and rich operational maintenance experience. Additionally, due to variations in transformer capacity, voltage levels, and operating environments, the same fault may manifest differently across various transformers. Expert systems possess strong fault tolerance and adaptability, allowing them to modify their knowledge base based on acquired diagnostic knowledge to ensure completeness. Therefore, they can effectively diagnose different types of power transformers. Power transformer fault diagnosis expert systems can determine fault characteristics by synthesizing knowledge of fault causes and types, incorporating fault detection knowledge including dissolved gas analysis in oil. They can effectively handle fuzzy problems in fault diagnosis using fuzzy logic, address the bottleneck of difficulty in obtaining complete knowledge through rough set methods, and establish structures suitable for multi-expert collaborative diagnosis using blackboard model architecture.

4. Haddamar Dijagnoistikin Artificial Neural Network

Artificial neural networks mathematically model neuron activity and represent an information processing system based on mimicking the structure and function of brain neural networks. ANNs possess self-organizing, adaptive, self-learning, fault-tolerant capabilities, and strong nonlinear approximation abilities. They can implement prediction, simulation, and fuzzy control functions, making them powerful tools for processing nonlinear systems. Using artificial neural networks for transformer fault diagnosis based on dissolved gas components and concentrations in oil has been a research focus in recent years. This has led to the development of various fault diagnosis methods based on ANNs, such as the two-step ANN method, backpropagation artificial neural networks, decision tree neural network models, combined neural network hierarchical structure models, and radial basis function neural networks. These methods continuously improve the convergence speed, classification performance, and accuracy of neural network algorithms.

5. Wasu Haddamar Dijagnoistikin Sauran

Beyond the four methods mentioned above, several other approaches are also used for transformer fault diagnosis. By organically combining neural networks and evidence theory to leverage their complementary advantages, a comprehensive transformer fault diagnosis method integrating multiple neural networks with evidence theory can be developed. Drawing inspiration from the efficient recognition and memory mechanisms of antibodies against antigens in biological immune systems, self-organizing antibody networks and antibody generation algorithms can be applied to solve power transformer fault diagnosis problems. Additionally, other transformer fault diagnosis methods include those based on information fusion, rough set theory, combined decision trees, Bayesian networks, artificial immune systems, novel radial basis function networks, and support vector machines.