Power Plant Advisory System for Startup Optimization
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Solution Overview
Problem
Power plant operators face challenges in efficiently and reliably starting generators due to various factors, requiring years of experience to optimize startup processes such as time, fuel consumption, and equipment stress, which can be lost when experienced operators retire.
Innovation Solution
A power plant advisory system that provides custom advisory information based on the initial state of the plant and past startups, using data compilation, clustering analysis, and optimization methods to guide operators in achieving efficient startup conditions, including real-time data processing and visualization tools for optimal decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If operators rely on years of experience to determine efficient startup methods, then startup efficiency is improved, but operator availability deteriorates when experienced operators retire
Solution Approach 1:
The system captures and stores startup procedures and decision-making logic from experienced operators into a digital knowledge base. This copying of expertise allows the system to provide guidance based on accumulated experience without requiring the actual presence of experienced operators, thus maintaining startup efficiency while overcoming operator availability issues.
Solution Approach 2:
The system enables operators to access startup guidance and decision support independently through the knowledge base and recommendation engine. Operators can query the system for specific startup scenarios and receive tailored advice, making the expertise self-available without requiring senior operator intervention.
2Productivity
If multiple factors are considered for efficient startup (time, fuel consumption, equipment stress), then startup optimization is improved, but system complexity increases
Solution Approach 1:
The system segments the complex startup optimization problem into distinct factors (time, fuel consumption, equipment stress) and evaluates them separately through dedicated modules. Each factor is analyzed independently and then integrated through the recommendation engine, making the overall complex system manageable through modular segmentation.
Solution Approach 2:
The recommendation engine acts as an intermediary that processes multiple startup factors and translates them into coherent startup guidance. This intermediary layer simplifies the complexity by automatically integrating multiple factors and presenting unified recommendations, shielding operators from the underlying system complexity.
3Reliability
If custom advisory information is provided based on initial state and past startups, then startup reliability is improved, but data processing requirements increase
Solution Approach 1:
The system performs preliminary processing and organization of historical startup data during system setup and maintenance phases. By pre-processing and structuring the data in advance, the system reduces the computational burden during actual startup scenarios, enabling reliable custom advisory information without excessive real-time data processing requirements.
Solution Approach 2:
The system replaces manual data analysis and pattern recognition with automated computational algorithms. The recommendation engine uses computer-based processing to analyze initial plant state and historical data, substituting human cognitive processing with automated systems that can handle large data volumes efficiently and reliably.
Data Source
AI summary
Disclosed herein are methods and systems for advising and operating a power plant and related devices. In an embodiment, a power plant operator via a client 135 requests from a server 115 advisory information regarding a current power plant startup. The client 135 may receive custom advisory information based on data of an initial state of the power plant and data from past power plant startups.