CTC Adds Facility Failure Sign Detection Feature to E-PLSM Energy IoT Cloud

ITOCHU Techno-Solutions Corporation (Satoshi Kikuchi, President & CEO; headquartered in Chiyoda-ku, Tokyo; hereinafter "CTC") has added a feature of detecting signs of facility failure to the E-PLSM^*1 IoT cloud service of managing the consumption of renewable energy. In addition, the E-PLSM has today begun offering an enhanced feature of forecasting the output of wind and photovoltaic power generation. It will support the advanced maintenance of electric power facilities and the widespread use of renewable energy using IoT technologies.
CTC will approach operators seeking stable operation of electric power facilities and plants, power producers using renewable energy, electricity retailers and other companies engaging in energy-related businesses, aiming to win 100 orders in FY 2019.

The E-PLSM is a cloud-type IoT platform for monitoring facilities by collecting data from sensors installed in power generating facilities and plants and power transmission and distribution facilities, as well as for integrated management of energy consumption through data analysis. It adopts the PI System, IoT software from U.S.-based OSIsoft, LLC as the foundation for data collection and analysis. Incorporating the expertise that CTC has developed in the energy sector, the E-PLSM has been offered since 2011.

The new feature of detecting signs of facility failure monitors the status of the power generation facility on a real-time basis using the data of output, temperature, flow rate, pressure, electric current, vibrations, quality and other data reflecting the state of operation of the facility as parameters. The machine learning software learns about the normal state of the facility to detect signs of failure. This allows users to take preventive action before failure becomes a reality. This helps reduce unscheduled stops and increase the facility operation ratio.
The E-PLSM also employs the Predict-It^*2 software of detecting signs of failure offered by Engineering Consultants Group, Inc. (ECG)^*3. CTC became the first Japan-based agent of this American engineering firm in 2016, and it has since been working on predictive maintenance chiefly in the electric power and energy sectors. It has been offering the service to numerous customers in Japan.

The enhanced feature of forecasting power generation output makes use of data from sensors at power plants in addition to weather conditions data, such as wind direction, wind speed, solar radiation intensity and atmospheric temperature for the purpose of output prediction at wind and photovoltaic power plants in Japan. On the basis of requests from services that have been provided, the E-PLSM after the enhancement provides two forecast options. CTC has revised the prediction models based on knowledge obtained from demonstration trials and business operations in the area of renewable energy, including offering of the E-PLSM, to introduce finer time and space meshes and to achieve improved accuracy, including a reduction in prediction errors.
The two forecast options are, namely, the short-term forecast and the short-time forecast. The short-term forecast provides an output forecast for the coming three days in 30-minute time slots, updated every six hours. In connection with electric power trading in the electricity market and maintenance work that affects output fluctuations, it is helpful, for example, in planning of electric power transactions and in thermal power plant operation planning. The short-time forecast provides an output forecast for the coming six hours in 30 minute-time slots updated every hour. It is useful in streamlining plant monitoring and operational processes based on the forecast and adjusting the power demand-supply balance several hours ahead in the power transmission and distribution business. It is possible to use the two forecast options at the same time.
For the power output forecast, Tohoku Electric Power Co., Inc. and CTC jointly won a New Energy Foundation Chairman prize in the New Energy Award for FY 2012.

Amid actions for addressing global warming and deregulation of the electric utility market, the electric power business is diversifying. Consequently, demand is growing for stable operation of electric power facilities and plants and for greater efficiency in electric power purchases and sales. The widespread use of renewable energy necessitates finer forecasts of power generation outputs for ensuring a power demand-supply balance. The recent addition of features to the E-PLSM is meant to answer these requests.

CTC has been engaging in technical development concerning meteorological prediction and wind and photovoltaic power output forecasts, in assessing the feasibility of renewable energy businesses and in output forecast information and other services for at least 25 years. CTC will continue to upgrade the E-PLSM as well as its analysis and facility management features with a view to helping its corporate customers with productivity improvement through IoT technologies and will develop clean energy technologies to help meet the Sustainable Development Goals (SDGs).

• *1 The letter E in E-PLSM refers to Energy, Economy, Environment and Ecology, while PLSM stands for PLatform for Simulation and Management.
• *2 The Predict-It is software for detecting signs of facility failure developed by ECG in the United States and used mainly in power generation facilities in many countries. It adopts a machine learning algorithm called the Advanced Pattern Recognition (APR). It has three principal features. First, it is based on a theory the effect of which has been verified by a large number of examples. Second, it is easy for plant and factory workers to operate. And third, it runs smoothly and is serviceable with limited IT resources.
• *3Engineering Consultants Group, Inc. was established in 1992 and is headquartered in Ohio in the United States. It provides thermal power generation operators among others with engineering, monitoring and maintenance services, software, and support for effective use of the software. Its website is at http://www.ecg-inc.com/.