Eye in the Sky
Eye in the Sky

CLP has created an intelligent, cloud-based digital platform to keep close watch over the performance of its renewable energy assets spanning a vast geographical area in Mainland China and India.

CLP’s energy network in Mainland China is widely distributed – drawing from a diversity of sources and spanning thousands of miles across 15 provinces, autonomous regions and municipalities of the world’s most populous country.

 

The task of managing this vast portfolio of energy assets is equally immense. To do it efficiently requires enhanced resources and capabilities, and the application of common standards and processes across the portfolio.

 

To achieve this, CLP China has embarked on a project to digitalise its operations and adopt data analytics to provide a holistic and comprehensive oversight of its renewable energy portfolio.

 

The initiative came after CLP teamed up with Envision Digital, a provider of artificial intelligence and Internet of Things technology solutions for the energy industry, to conduct a trial of applying big data analytics on wind and solar assets in Yunnan province in China and Chandgarh in the state of Madhya Pradesh in India in 2016. 

 

The results were encouraging and CLP has partnered with the company to develop a Centralised Analytics Platform (CAP) across its wind and solar fleets in 2019, with the objective of optimising asset utilisation and reducing operational and maintenance costs.

20/20 Vision

In Mainland China, the platform has been adopted in four wind farms and two solar power stations to date, and by mid-2021 its use will be extended to two other wind farms and four additional solar farms.

 

The CAP system helps the daily operations of CLP power stations in many ways. It is a cloud-based centralised system, consolidating the operational data of all energy assets on a single platform, providing the management team with visibility of the condition of remote renewable assets, and offering the convenience of monitoring all assets easily through a computer or mobile app.

 

For the frontline teams, the system can easily identify areas with the most energy loss, and specific problems in power plant components such as turbines or inverters. The system can also provide instructions to the site teams to increase operational efficiency.

Map showing the adoption of CAP system in Mainland China
Frontline team using the CAP system
The CAP’s real-time monitoring function helps the frontline team identify any abnormal alteration of operational data in a timely and efficient manner.

Based on the recommendations provided by the platform, site managers can prevent energy loss in advance by correcting configurations, repairing equipment, recalibrating sensors, or changing their device control strategies.

 

“Thanks to the installation of the CAP system in September 2020, we can now monitor the operation of our solar panels more effectively. This helps optimise the performance of the plant throughout its lifetime,” says Zhao Yuquan, Station Manager of Lingyuan Solar Farm in Liaoning province.

The CAP system has already detected power reductions and abnormal changes of direction of turbines at a wind farm in China, as well as a premature defect at a solar power station. The data collected at the solar station enabled managers to calculate the conversion losses of the inverter, the loss of solar radiance, and the loss of power resulting from snow, dirt, or dust. 

 

Detecting and resolving these issues early is essential for the optimal power generation performance of wind and solar farms, and can help them generate greater revenue as more energy is produced. 

 

Solar farm in Mainland China
The CAP system helps improve operation efficiency at wind and solar farms by identifying defects at an early stage.

Cloud cover

The data analytics behind the CAP system provides cloud-based solutions, including real-time remote monitoring, operational performance analysis, and natural resources and energy forecasting.

The CAP system
Advanced data mining and machine learning techniques are used by the CAP system to detect and diagnose underperformance of the renewable energy assets.

The platform’s real-time monitoring function offers data visualisation tools showing operational data. Built on big data architecture, the system provides an efficient way to process large volume of data. It helps CLP monitor its assets and gain valuable insights into operational trends from anytime, anywhere.

 

The CAP system also offers big data analysis. It provides CLP with statistical underperformance detection and diagnosis using advanced data mining, filtration, and machine learning techniques. The prognostic health monitoring of key components of the power stations has helped lower the possibility of operational maintenance, minimising power loss.

For instance, to address the problem of power loss due to snow, dirt, and dust, the platform automatically generates a performance alarm and provides users with the best cleaning recommendation based on solar panel cleaning costs and weather forecasts.

 

The platform further incorporates renewable resource forecasting capability. It integrates site level meteorological data to provide accurate site-specific local weather forecasts. It can provide accurate regional wind and solar power forecasts according to the meteorological features and power output characteristics of individual turbines and inverters.

 

CLP is providing training and workshops on the CAP system for both frontline engineers and managerial staff, and is continuing to evaluate the benefits and impact of the platform.

 

The early indications demonstrate that the platform has the potential to be a transformative factor in more efficiently and effectively monitoring and managing a broad spectrum of assets spread over a vast geographical area.