Requirement Analysis
In order to improve the power generation efficiency of photovoltaic power station , reduce the cost of manual maintenance , to realize the goal of unattended or less manned photovoltaic power station , the system design based on intelligent monitoring of the equipment , the application of algorithmic models for intelligent diagnosis of the equipment is proposed . Firstly, analyze the failure characteristics of PV system, build a platform based on big data analysis and nonlinear diagnostic model, and establish a new fault investigation mechanism; secondly, study the fault location method of array branch strings, and categorize the fault characteristics of PV strings such as real-time zero current, PV strings with continuous zero current, and PV strings with low power generation, etc.; combine with the power limit status of the inverter, the power-limited range of strings will be removed, and get the final and accurate equipment fault diagnosis results.
Working Principle
Photovoltaic power plant fault online monitoring intelligent diagnosis system based on discrete rate, deviation rate and neural network algorithms, the application of " Internet + " related Internet of Things, big data technology, comprehensive coverage, online intelligent monitoring of photovoltaic power plant equipment, real-time localization and accurate identification of faults, to avoid the "purposeless routine inspection", targeted and accurate fault diagnosis results. To avoid "purposeless routine inspection", targeted, planned guidance to carry out manual on-site testing and verification of defect elimination, significantly reducing the difficulty and labor intensity of elimination of operation and maintenance work, and improve the efficiency of operation and inspection; can be deployed at multiple levels, hierarchical control, to achieve the quantitative assessment of power plant operation and maintenance; can be accumulated to form a knowledge base of faults to guide the field personnel operation and inspection, reducing personnel training costs. The deployment of the system, if combined with rigorous operation and inspection procedures, can greatly reduce the hidden loss of power generation caused by equipment failure, to maintain the power station equipment in the 25-year life cycle is always in a very good state of power generation, thus providing a strong support for the efficient and intelligent operation and maintenance of photovoltaic power plants, and to improve the efficiency of the power station to provide a new means, a new method.
Product Characteristics
(1) One-key operation and inspection
Friendly interface, simple and easy to use, convenient, query power station failure rate, through the health degree indicator macro grasp of the whole power plant equipment operation. Automatically generate work orders for faulty equipment, and users can arrange daily maintenance tasks according to work order management.
(2) Real-time fault diagnosis and monitoring
Fault diagnosis type: For power station power generation equipment faults, it provides 13 types of diagnostic content for four major items, such as line communication faults, power loss faults, equipment loss faults, and power limitation faults. Diagnostic equipment includes box transformers, inverters, convergence boxes, DC distribution cabinets, PV strings and so on.
Fault diagnosis level: PV string level.
Fault diagnosis algorithm: Combined with the meteorological data information, the clustering algorithm divides the small and very good data, comparing the data units, along the deviation rate, discrete rate, neural network algorithms, to accurately locate the faulty equipment, avoiding the inspectors' purposeless inspection work in a large area.
Fault Knowledge Base: The rich fault knowledge base enables the power station operation and maintenance personnel to quickly troubleshoot equipment faults, and can share the fault knowledge base with other power stations to share problem-solving programs.
(3) Equipment Fault Management
Establish a closed-loop process for O&M personnel to eliminate faults. From fault execution to the end of the fault, the system will automatically diagnose its status and record it, marking the duration of fault elimination. The operation and maintenance personnel can enter the fault elimination experience into the system for other operation and maintenance personnel to share, saving the same type of faulty equipment overhaul time.
The system provides digital view analysis of different fault types, clearly displaying the data characteristics of equipment failure.
(4) Component Dust Warning
Based on big data processing technology, distributed retrieval analyzes string power generation efficiency, provides component cleaning early warning function, locates component cleaning scope and cleaning cycle. Gradient color image, intuitively show the degree of accumulation of dust in each string. Continuously and effectively track the power station string power generation efficiency, and provide decision support for the power station to formulate an effective cleaning program.
(5) Multi-dimensional fault comparison
Based on big data, multi-dimensional comparison and analysis tool can assess the power generation performance of various manufacturers, form a post-assessment of the equipment, and provide equipment selection program for future station construction, and the user can customize the multi-dimensional comparison of the parameters of the power generation equipment of the PV power station.
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