DATA MINING TECHNIQUES APPLIED TO POWER PLANT PERFORMANCE MONITORING

2005 ◽  
Vol 38 (1) ◽  
pp. 369-374 ◽  
Author(s):  
D. Flynn ◽  
J. Ritchie ◽  
M. Cregan
Keyword(s):  
Data Mining ◽  
Power Plant ◽  
Performance Monitoring ◽  
Plant Performance ◽  
Data Mining Techniques

scholarly journals Application of Data Mining Techniques for Sensor Drift Analysis to Optimize Nuclear Power Plant Performance

2019 ◽  
Vol 9 (1) ◽  
pp. 3087-3095
Keyword(s):  
Data Mining ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
Response Times ◽  
Protective Action ◽  
Plant Performance ◽  
Data Mining Techniques ◽  
Prime Concern

The Power Plants are engineered and instrumented to ensure safety in all modes of operation. Hence they should be continuously monitored and maintained with necessary Instrumentation to identify performance degradation and the root causes to avoid calling for frequent maintenance. The degraded performance of Instrumentation & Control systems may also lead to plant outages. Different studies have suggested that a well maintained instrumentation with errors and response times within the permissible limits may increase the availability minimizing outages. The I&C systems are designed for monitoring, control and safety actions in case of an event in a power plant. The sensors used are single, redundant, triplicated or diverse based on the type of application. Where safety is of prime concern, triplicated and 2/3 voting logic is employed for initiating safety actions. Diverse instruments are provided for protecting the plant from any single abnormal event. Redundant sensors are used to improve plant availability. Wherever 2/3 logics are used, the sensors shall uniformly behave and the drifts across the sensor may lead to crossing the threshold, initiating a protective action. Instead of waiting for the regular preventive maintenance schedule for recalibrating the sensors, the drift in the sensors are analyzed by developing a combined overall online monitoring parameter which will give an early warning to the operator the need for recalibration of the redundant sensors. This paper deals with development of one such parameter through data mining techniques for a representative process in a nuclear power plant.

The Performance Mining Method

Big Data ◽  
2016 ◽  
pp. 181-199
Author(s):  
Stella Pachidi ◽  
Marco Spruit
Keyword(s):  
Data Mining ◽  
Financial Management ◽  
Performance Monitoring ◽  
Software As A Service ◽  
Software Performance ◽  
Mining Method ◽  
Data Mining Techniques ◽  
Software Products ◽  
Software Product ◽  
Operation Knowledge

Software Performance is a critical aspect for all software products. In terms of Software Operation Knowledge, it concerns knowledge about the software product's performance when it is used by the end-users. In this paper the authors suggest data mining techniques that can be used to analyze software operation data in order to extract knowledge about the performance of a software product when it operates in the field. Focusing on Software-as-a-Service applications, the authors present the Performance Mining Method to guide the process of performance monitoring (in terms of device demands and responsiveness) and analysis (finding the causes of the identified performance anomalies). The method has been evaluated through a prototype which was implemented for an online financial management application in the Netherlands.

Diagnostic Techniques for Improving Power Plant Performance, Reliability and Availability: A Case Study

2006 ◽  
Author(s):  
Komandur S. Sunder Raj
Keyword(s):  
Power Plant ◽  
Power Plants ◽  
Performance Monitoring ◽  
Monitoring Program ◽  
Heat Rate ◽  
Diagnostic Techniques ◽  
Lessons Learned ◽  
Plant Performance ◽  
Reliability And Availability

The objectives of an effective power plant performance monitoring program are several-fold. They include: (a) assessing the overall condition of the plant through use of parameters such as output and heat rate (b) monitoring the health of individual components such as the steam generator, turbine-generator, feedwater heaters, moisture separators/reheaters (nuclear), condenser, cooling towers, pumps, etc. (c) using the results of the program to diagnose the causes for deviations in performance (d) quantifying the performance losses (e) taking timely and cost-effective corrective actions (f) using feedback techniques and incorporating lessons learned to institute preventive actions and, (g) optimizing performance. For the plant owner, the ultimate goals are improved plant availability and reliability and reduced cost of generation. The ability to succeed depends upon a number of factors such as cost, commitment, resources, performance monitoring tools, instrumentation, training, etc. Using a case study, this paper discusses diagnostic techniques that might aid power plants in improving their performance, reliability and availability. These techniques include performance parameters, supporting/refuting matrices, logic trees and decision trees for the overall plant as well as for individual components.

scholarly journals Designing Single Slop Single Basin Solar Water Distillation Plant Performance Monitoring and Prediction System

2019 ◽  
Vol 9 (1) ◽  
pp. 2445-2451
Keyword(s):  
Data Mining ◽  
Performance Monitoring ◽  
Plant Performance ◽  
Prediction System ◽  
Sample Collection ◽  
Water Distillation ◽  
Solar Water ◽  
Solar Distillation ◽  
Mining Model ◽  
Distillation Plant

the issue of the water crisis is rising day by day, due to global warming and other environmental effects. That is not only the issue for India, but it is also for the entire world. However, the solar-based water distillation plants are not much efficient but we can use this method for producing pure and drinkable water. In this paper, we proposed to design a solar water distillation plant using the single slop method. In addition to that for monitoring and measuring the performance of the distillation plant a data mining based prediction system is implemented. The experiments are performed on the real-world implemented single slop solar water distillation plant-based observations. The observations are collected using the IoT (Internet of things) device for each five-minute time difference for each sample collection. The data samples are collected between 10:00 AM to 4:00 PM for 7 days. Additionally by using the collected samples the data mining model is trained and tested on the prepared syntactic dataset. The experimental results demonstrate accurate predictions for the solar distillation water plant. After this implementation and system model, the future directions of the research are also provided.

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