Hitachi Case Studies Wind turbines using digital technology

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	Wind turbines using digital technology Hitachi

Wind turbines using digital technology

Hitachi

Technology Category	Analytics & Modeling - Data Mining Analytics & Modeling - Predictive Analytics
Applicable Industries	Renewable Energy
Applicable Functions	Maintenance
Use Cases	Predictive Maintenance
Challenge	Issues involved in expanding commercialization of wind power In 2015, the worldwide capacity of renewable energy facilities exceeded that of coal-fired power.*1 With the aim of creating a low-carbon society, in July 2012, Japan put into effect the feed-in tariff scheme for renewable energy, stimulating the construction of solar and wind farms. In 2016, the full liberalization of the electrical retail business resulted in an increasing number of companies planning either to enter the power generation field or to expand their business. These market conditions engendered a need for the development, design, manufacturing, and sales of wind turbines optimized for Japan's environmental conditions. Utilities considering entering the field of wind power also sought assistance in the streamlining of maintenance and other such business operations. Read More
About Customer	Saibu Gas, a Japanese gas company based in Fukuoka, Japan. It supplies gas to the Northern Kyushu region, including in the area of Fukuoka, Saga, Nagasaki, and Kumamoto. Read More
Customer Name	Saibu Gas Read More
Solution	Lumada IoT platform: Using proprietary data mining technology, this predictive diagnostics solution can infer causes of failures by collecting large amounts of operational data from sensors incorporated into the equipment of wind turbines and other power generation facilities, analyzing such with automated diagnostics technology, and detecting signs of failure. It also makes use of accumulated data on historical events. Another of its features is a user-friendly interface that contributes to the standardization of facility maintenance by removing the need to rely on the personal experience and instincts of expert engineers to determine faults. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Data Mining

Analytics & Modeling - Predictive Analytics

Applicable Industries

Renewable Energy

Applicable Functions

Maintenance

Use Cases

Predictive Maintenance

Challenge

Issues involved in expanding commercialization of wind power

In 2015, the worldwide capacity of renewable energy facilities exceeded that of coal-fired power.*1 With the aim of creating a low-carbon society, in July 2012, Japan put into effect the feed-in tariff scheme for renewable energy, stimulating the construction of solar and wind farms. In 2016, the full liberalization of the electrical retail business resulted in an increasing number of companies planning either to enter the power generation field or to expand their business. These market conditions engendered a need for the development, design, manufacturing, and sales of wind turbines optimized for Japan's environmental conditions. Utilities considering entering the field of wind power also sought assistance in the streamlining of maintenance and other such business operations.

About Customer

Saibu Gas, a Japanese gas company based in Fukuoka, Japan. It supplies gas to the Northern Kyushu region, including in the area of Fukuoka, Saga, Nagasaki, and Kumamoto.

Customer Name

Saibu Gas

Solution

Lumada IoT platform:

Using proprietary data mining technology, this predictive diagnostics solution can infer causes of failures by collecting large amounts of operational data from sensors incorporated into the equipment of wind turbines and other power generation facilities, analyzing such with automated diagnostics technology, and detecting signs of failure. It also makes use of accumulated data on historical events. Another of its features is a user-friendly interface that contributes to the standardization of facility maintenance by removing the need to rely on the personal experience and instincts of expert engineers to determine faults.

Impact #1	[Efficiency Improvement - OEE] Causes of failures can be investigated before faults occur.
Impact #2	[Cost Reduction - Maintenance] Inspections can be carried out at suitable times to reduce maintenance costs and prevent major accidents.

Impact #1

[Efficiency Improvement - OEE]

Causes of failures can be investigated before faults occur.

Impact #2

[Cost Reduction - Maintenance]

Inspections can be carried out at suitable times to reduce maintenance costs and prevent major accidents.

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Overview

Wind turbines using digital technology

Operational Impact