Pricing

Choose your service requirements from our offers below and know more before you pay!

Single request, a simple and easy HTTP form prediction

Batch prediction, multiple assets prediction via CSV  uploads

Necessity is the mother of all choices

The BASIC plan is especially designed for inexperienced users/engineers who may be operating less than 10 transformers within a premise.
On the other hand, the PRO and ENTERPRISE plans are especially designed for grid owners who are looking for real-time analysis of transformer fleets to make short/long-term maintenance plans.

If you need more assistance, then drop an email to info@seetalabs.com or chat with us!

Frequently Asked Questions

What kind of transformers can I check using RONIN?

RONIN can be used for health index prediction of fluid filled power transformers that are rated more than 1MVA using key gas concentration, oil and paper quality parameters only. 

What are the other domain of actions for SeetaLabs?

Due to the cross-functionality, SeetaLabs is expandable in utility management of other sectors such as oil & gas, waste management, energy and life-sciences. 

What is enterprise asset management ?

Enterprise asset management (EAM) is a systematically-designed set of activities  for maximum utilization of physical assets in any organization during its service life. These activities are necessary for the techno-economic justification of maintenance strategies intended to optimize the asset performance and life-cycle. Some of the key activities associated with EAM are diagnostic testing, data collection and preservation of knowledge.

What is condition monitoring of assets?

The performance of industrial assets reduces gradually  due to mechanical wear, ageing of key components and fluctuating operational conditions. Condition monitoring refers to the early identification of incipient faults within these assets using various diagnostic tools. This can be crucial for failure avoidance and strategizing asset maintenance activities. Besides, condition monitoring  is the key to improve asset reliability and operational safety.

Why maintain power transformers?

Power transformers are key components of energy supply and distribution network. Due to the severe operational stresses, the expected efficiency and life-time of these transformers can decrease gradually. Furthermore, service disruption due to abrupt failure of transformers can also lead to socio-economic backlash for the asset and grid owners. Appropriate maintenance of transformers can reduce repair/reinstallation necessities and costs, lower the operational risks, ensure safety and improve reliability of these assets.

What is predictive maintenance of assets?

Predictive maintenance refers to monitoring of asset condition using various on-line and off-line methods. The maintenance activities are scheduled only when certain conditional expectations are met and there is a definitive indication of risk. This reduces the downtime and cost of asset maintenance. However, with assets such as transformer, there are various sub-components that require meticulous condition monitoring for fault detection that may lead to an overwhelming amount of data accumulation.  

What is artificial intelligence (AI) ?

Artificial Intelligence (AI) is a branch of computer science dealing with the creation of intelligent machines that behave like humans, where “intelligence” is the ability to acquire and apply knowledge. It can change the quality of life by improving healthcare (e.g. making diagnosis more precise, enabling better prevention of diseases), increasing the efficiency of farming, combating the effects of climate change, improving the efficiency of production systems through predictive maintenance, increasing the security of citizens and the protection of workers, and so much more!

Why use AI for condition monitoring?

Unlike the traditional methods, artificial intelligence (AI) depends on data mining, pattern recognition, and computation intelligence to produce hyper-precision results for quick and fast decision making. Furthermore, it is relatively free from human intervention to preserve and learn from the condition monitoring data instead of human opinion. This makes planning of maintenance activities easier, faster, cheaper and unbaised. Furthermore, industrial application of AI-based solutions can be accessible 24/7 thus making it useful for real-time prediction, maintenance and management of assets such as transformers. 

What are “Duval Triangle” and “Duval Pentagon” in transformer maintenance?

Dr. Michel Duval introduced the famous Duval Triangle during the 60s to identify various transformer faults using ratio of key hydrocarbon gases. The gases are generated as a result of degradation of mineral oil/ester and paper insulation under operational stresses. Duval pentagons are used for interpretation of DGA results in mineral oil-filled transformers and similar equipments using five hydrocarbon gases only i.e. hydrogen, methane, ethane, ethylene and acetylene. Duval pentagons are complimentary to the Duval triangles to say, study a mixture of various faults in transformers. Application of such methods for non-mineral oil filled equipments is currently under progress. 

Are there any international bodies publishing guidelines for condition monitoring of transformers?

 

The guidelines and recommendations provided by these bodies are necessary for regulating the frequency of diagnostic testing and its interpretation.

ANSI – American National Standards Institute

ASTM -American Society for Testing and Materials 

IEC –  International Electrical Committee 

IEEE – Institute of Electrical and Electronics Engineers

CIGRE- Conférence de Grandes Reseaux Electriques

What is health index of transformers?

Health index (HI) refers to the prediction of overall health, immediate and long-term risks and remaining life of transformers using condition monitoring data.  In case of transformer fleets, it is used either in identifying the assets that are at a higher risk of failure, or closer to the end-of-service-life. Existing methods of transformer HI determination are based either on decision theory  or computational intelligence.

How does RONIN calculates transformer HI?

Transformer HI depends on condition monitoring data from various diagnostic tests. This can lead to an overwhelming amount of data accumulation, especially in case of transformer fleets. RONIN uses a hybrid AI-algorithm for HI prediction that meets the experience and knowledge of an expert by rigorous training of its core algorithm.

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