Abstract

This work analyzes the practical results from implementation and operation of a modern predictive monitoring system in high voltage transformers of the Braskem unit substations in São Paulo (PE8 Cubatão and PP4 ABC). This project has achieved the purpose of guaranteeing better company reliability and profitability by applying the right actions at the right time at an adequate cost, since these are the input transformers of the 88kV substations.

The efficient management of substation electric assets demands tools able to show the current equipment status and the future equipment scenarios; this allows the best technical and financial decisions to be made, so the park is kept intact and available. Inside industries, electric power is an irreplaceable input, and its outage can cause great losses to the organization. In seeking maintenance excellence and the continuity of its electric system, Braskem has implemented online monitoring in 2011 as a way to prevent catastrophic failures in power transformers in 88kV substations.

This article discusses Braskem’s most successful experience in applying this technology and in practicing predictive maintenance to increase reliability. The system, — made of a set of intelligent sensors (IEDs), and specialist software to generate diagnoses and prognoses — has been operating at full capacity for over three years. The software counts on mathematical engineering models, based on artificial intelligence, to help in the decision-making process.

Every three years Braskem’s industrial plants, in Mauá and Cubatão, interrupt their activities for inspection and general maintenance of their facilities. Equipment and structures are inspected, in addition to the electric power conditions, which receive special attention with specific activities, mainly at the power substation.

Since electric power is a crucial input for factory production, it is crucial that all assets in the substation are working perfectly. Therefore, each detail of the system is analyzed, such as maneuver equipment (circuit breakers and disconnect switches), insulators (CTs and PTs) and especially the power transformer.

Both substations have only one transformer which cannot have any inconsistency because this may bring great loss to the company.

The actions of Braskem technical and maintenance teams aim at keeping employees safe, reaching the highest possible asset performance level and also keeping power supply uninterrupted to meet the demands of a constant growth in production.

Therefore, from 2011 on, both units started to operate under online transformer monitoring, so predictive analyses could be performed and power reliability could be increased.

An international research of operating power transformers performance [1], carried out by Cigré with data from failures occurred between 1968 and 1978 involving over 1,000 failures, revealed the main causes of downtime, both forced and planned, for several transformer kinds and applications.

Figure 3 shows those data for OLTC transformers in power plants. According to those statistic data, the OLTC is the main cause of transformer failure, followed by the active part and the bushings.

Based on this and on the need to increase operational reliability of the assets the variables to be monitored were selected, in addition to the data processing functions needed so the measured variables were transformed into useful information for transformer diagnosis and prognosis.

That aimed at covering the main transformer subsystems and therefore reducing failure and unavailability risks.

Fig. 3  Statistics of the causes of OLTC transformer downtime [1].

The variables measured in step-up transformers in Mauá and Cubatão plants are shown in table 1, grouped by subsystem.

All data obtained by the sensors installed directly on the transformer are sent to the server located in Braskem’s own plant and then, after they are processed, are made available online on the computer display. Through the intranet, any authorized terminal can access the system.

To handle the data obtained from the sensors mentioned above, the installed monitoring system has in its software an Engineering Module, which includes the engineering models shown in table 2.

Therefore, the raw data from sensors allow the obtention of useful information for diagnoses and prognoses of the transformer status [2], [3].

Engineering Models for transformer condition diagnosis and prognosis

1. ELECTRA, “An International Survey on Failures in Large Power Transformers in Service”, Paris, CIGRE, Ref. no. 88, 1983.
2. Amom, Jorge, Alves, Marcos, Vita, André, Kastrup Filho, Oscar, Ribeiro, Adolfo, et. al., “Sistema de Diagnósticos para o Monitoramento de Subestações de Alta Tensão e o Gerenciamento das Atividades de Manutenção: Integração e Aplicações”, X ERLAC – Encontro Regional Latinoamericano do CIGRÉ, Puerto Iguazu, Argentina, 2003.
3. Alves, Marcos, “Sistema de Monitoração On-Line de Transformadores de Potência”, Revista Eletricidade Moderna, Maio/2004.
4. Melo, Marcos A. C., Alves, Marcos, “Experiência com Monitoração On-Line de Capacitância e Tangente Delta de Buchas Condensivas”, XIX SNPTEE – Seminário Nacional de Produção e Transmissão de Energia Elétrica. Rio de Janeiro, Brasil, 2007.
5. Alves, Marcos, Silva, Gilson, “Experiência de Campo com Monitoração On-Line de um Transformador 343MVA 230kV com 2 Comutadores sob Carga”, IV Workspot – Workshop on Power Transformers, Recife, Brasil, 2005.
6. Alves, Marcos, Vasconcellos, Vagner, “Monitoramento da Umidade no Óleo Isolante de Transformadores de Potência Visando o Aumento da Confiabilidade Operativa”, V Workspot – Workshop on Power Transformers, Belém, Brasil, 2008.