Digital transformation and upgrading of equipment, intelligent operation and maintenance to ensure the safe operation of hydropower stations

Power plants include thermal power plants, hydropower plants, wind power plants, nuclear power plants, oil-fired power plants, gas-fired power plants, etc. Take hydropower plants as an example. They not only generate electricity but also play a role in flood control, drought resistance, irrigation, ecological protection, environmental pollution reduction, and saving non-renewable resources. Therefore, the operation and management of hydropower plants are particularly important, as it is related to the stable operation of hydropower plants and people's livelihood.

• For a long time, the operation and maintenance inspection of hydropower plants has mainly relied on manual periodic inspections and on-site monitoring, which is inefficient. Daily inspection and maintenance requires a lot of manpower and material resources, resulting in high maintenance costs.
• Due to factors such as the wide distribution of substations and difficulties in wiring and communication, data cannot be fully uploaded, making real-time monitoring difficult. This means that maintenance personnel cannot understand the actual operating status of the equipment in time, increasing the possibility of failures.
• Monitoring subsystems from different manufacturers are difficult to interconnect, forming data islands. In addition, online monitoring data is relatively simple and not comprehensive enough to meet the data requirements for intelligent analysis, early warning, and diagnosis of the status of important oil, gas, and water equipment.

Therefore, Zhongyun Technology has tailored an intelligent operation and maintenance solution for hydropower plants, using advanced technologies such as big data, cloud computing, and artificial intelligence to perform real-time monitoring and intelligent analysis of the operating status of hydropower equipment.

Equipment management, intelligent construction

01 Data acquisition, intelligent diagnosis

By collecting equipment operation data in real time, the system can automatically identify equipment failures and issue early warnings, providing maintenance personnel with timely and accurate fault information.

Combining pump and motor equipment design data, historical monitoring data, and real-time monitoring data, based on artificial intelligence data analysis and mining and production equipment fault diagnosis mechanisms, to achieve equipment feature extraction and establish a diagnostic model, thereby building a mechanical and electrical equipment anomaly warning and health management system. Based on real-time operation monitoring, abnormal status judgment and warning are performed, and key equipment life prediction is realized.

02 Data-driven, algorithm support

Using big data analysis and machine learning technologies to process and analyze the collected data, through the equipment asset digital management model, build an equipment database, manage the data of important auxiliary equipment parts, measuring points, and operating status of oil, gas, and water in hydropower plants as resources, and establish a digital twin model of the hydropower plant.

Zhongyun Technology has nearly 100 diagnostic algorithm models for component-level, covering more than 90% of industrial equipment failure models, including failure frequency, shaft trajectory, waveform characteristic values, pulse speed, power spectrum, etc. At the same time, data is organized and cleaned to ensure data quality. Statistical analysis, machine learning, and data mining technologies are used to analyze equipment data to provide strong support for equipment management decision-making.

Prior knowledge + small sample algorithm achieves 95% accuracy in intelligent diagnosis of equipment failures, with zero missed reports.

03 Optimized management, safety assurance

Provides maintenance personnel with a unified operation and maintenance management platform for convenient comprehensive monitoring and management of hydropower plants. Through data visualization technology, complex operation data is presented to maintenance personnel in an intuitive way, making it easy to quickly understand the operation status of the power plant.

Supports remote control and collaboration functions to improve maintenance response speed and processing capabilities.

Provides professional training and technical support to help maintenance personnel improve their skills and work efficiency.

Application cases, digital advancement

A hydropower plant's online monitoring data for important auxiliary oil, gas, and water equipment is relatively simple and not comprehensive enough to meet the data requirements for intelligent analysis, early warning, and diagnosis of the status of important auxiliary oil, gas, and water equipment.

After on-site investigation, vibration, temperature, and sound sensors, and current harmonic sensors were installed on the oil pumps, water pumps, and gas turbines of the hydropower plant to collect online monitoring data such as vibration, temperature, sound, and current. Real-time online monitoring data such as vibration, temperature, sound, and current of oil pumps, water pumps, and gas turbines are obtained, and the data is transmitted to the power plant's industrial big data platform.

Based on online monitoring data of temperature, vibration, sound, and current, typical fault diagnosis models are established to realize intelligent early warning, intelligent diagnosis, and health prediction of oil pumps, water pumps, and gas turbines, realizing real-time monitoring and predictive maintenance of the status of oil pumps, water pumps, and gas turbines, reducing the possibility of equipment failures, improving equipment stability and reliability, and ensuring the continuous and stable operation of the hydropower plant.

Today, the intelligent operation and maintenance system has been successfully applied to multiple hydropower projects, not only significantly improving the efficiency and accuracy of equipment operation and maintenance, but also ensuring safe production and achieving cost reduction and efficiency increase.