US-12618375-B2 - Systems and methods for estimating integrity and efficiency of an inlet filtration system for turbine systems and for recommending mitigation actions

US12618375B2US 12618375 B2US12618375 B2US 12618375B2US-12618375-B2

Abstract

A control system for turbine systems configured to provide accurate interpretations of detected particle accumulation, improve performance of turbine systems, and/or minimize costs due to downtime and maintenance are disclosed. The control system may build an intelligent model of fluid flow based on measured data provided by a sensor in a fluid flow path of the turbine system. The intelligent model consults a filter efficiency framework and determines an impact value that quantifies an operational efficiency of the turbine system and may identify a location of possible leakage, estimate a total amount of ingress of particles, identify components of the turbine system that may be operating in a diminished capacity, estimate a risk of damage to components of the turbine system, and/or recommend mitigation actions.

Inventors

Maruthi Manohar Jupudi
KAMEL ABDELKADER TAYEBI
Abdurrahman Abdallah Khalidi
JOSE ANTONIO CUEVAS ALVAREZ
Alston Ilford Scipio
Bradly Aaron Kippel

Assignees

GENERAL ELECTRIC COMPANY

Dates

Publication Date: 20260505
Application Date: 20210728

Claims (20)

1 . A system for assessing the efficiency and integrity of an inlet filtration system of a turbine, the system comprising: one or more sensors operative to generate at least one measured data value of air intake particles within a fluid flow path of the turbine; a controller in electronic communication with one or more sensors and operative to: utilize an intelligent model to process the at least one measured data value, the intelligent model consulting a filter efficiency framework, wherein the system for assessing the efficiency and integrity of an inlet filtration system of a turbine is configured to use the filter efficiency framework to relate the at least one data value of the air intake particles to a filter efficiency of the air filtration system of the turbine, wherein the system for assessing the efficiency and integrity of an inlet filtration system of a turbine is configured to use the filter efficiency framework to identify a location of possible leakage in the system for assessing the efficiency and integrity of an inlet filtration system of a turbine, wherein the controller is configured to compare the location of possible leakage in the system for assessing the efficiency and integrity of an inlet filtration system of a turbine with an offline computational fluid dynamic evaluation to estimate a risk of further damage to at least one component of the system, wherein the controller is configured to determine a dirt load estimate based on a particulate concentration multiplied by a rate of air flow multiplied by a number of hours of operation, wherein the intelligent model is configured to utilize the filter efficiency framework to determine one or more of a forecast degradation, a fouling rate, and/or an erosion rate.
2 . The system of claim 1 , wherein the system uses the filter efficiency framework to identify a duration of possible leakage in the turbine system.
3 . The system of claim 1 , wherein the system uses the filter efficiency framework to derive an expected data value of an air intake particle using computational fluid dynamics and to compare the at least one data value of the air intake particles to the expected data value of the air intake particle to identify the location of possible leakage in the air filtration assembly.
4 . The system of claim 1 , wherein the system uses the filter efficiency framework to estimate an amount of total ingress of particles over a set time period.
5 . The system of claim 1 , wherein the system uses the filter efficiency framework to identify a component of the turbine system that is operating in a diminished capacity.
6 . The system of claim 1 , wherein the system uses the filter efficiency framework to estimate the risk of damage to the at least one component of the turbine system if the turbine system continues to operate for a set period of time.
7 . The system of claim 1 , wherein the intelligent model determines an impact value based on the at least one measured data value, the impact value including an efficiency of flow of intake air and an effect of particle accumulation, and wherein the impact value quantifies an operational efficiency of the turbine system.
8 . The system of claim 7 , wherein the impact value includes a key performance indicator that provides quantifiable advisory analytics to an operator of the turbine system, and wherein the advisory analytics include at least one of minimizing the revenue loss, minimizing the cost of replacing a component of the turbine system, and minimizing the cost of shutdown of the turbine system.
9 . The system of claim 8 , wherein the key performance indicator recommends a mitigation action including at least one of recommending inspection of a component of the turbine system, recommending replacement of a component of the turbine system, and recommending replacement of a component of the turbine system with an alternate component.
10 . The system of claim 9 , wherein the mitigation action further comprises at least one of recommending a cleaning of a component of the turbine system, forcing a cleaning of a component of the turbine system, and establishing or updating a frequency of a cleaning of a component of the turbine system.
11 . The system of claim 1 , wherein at least one sensor is placed within an exhaust fluid flow path of the turbine system to generate at least one measured data value of air exhaust particles within the exhaust fluid flow path.
12 . The system of claim 11 , wherein the controller utilizes the intelligent model to verify at least one measured data value of air intake particles with the at least one measured data of air exhaust particles.
13 . The system of claim 1 , wherein the one or more sensors include at least one electrostatic sensor that detects at least one measured data value of air intake particles.
14 . The system of claim 1 , wherein the one or more sensors include at least one of an infrared sensor, an acoustic wave sensor, an optical sensor, and a laser sensor.
15 . The system of claim 1 , wherein the system modifies the intelligent model over a set period of time.
16 . A method of quantifying an effect of continued fluid flow within an air filtration assembly of a turbine system, the method comprising: consulting an intelligent model of fluid flow tailored to the turbine system; determining an impact value using the intelligent model of fluid flow based on at least one detected data value of an air intake particle traveling through a fluid flow path of the turbine system; determining a location of a possible leakage in the system using the intelligent model of fluid flow; calculating a key performance indicator from the impact value; comparing the location of the possible leakage in the system with an offline computational fluid dynamic evaluation to estimate a risk of further damage to at least one component of the system; and notifying an operator of the turbine system of the key performance indicator, wherein the key performance indicator provides quantifiable advisory analytics of at least one of a revenue loss due to inefficient operation of the turbine system, a cost of replacing an element of the turbine system, and a cost of a shutdown of the turbine system for a set time period.
17 . The method of claim 16 , wherein the intelligent model notifies the operator of an optimal key performance indicator that minimizes at least one of the revenue loss, the cost of replacing a component of the turbine system, and the cost of shutdown of the turbine system.
18 . The method of claim 16 , further comprising: recommending a mitigation action to the operator after the step of notifying the operator of the turbine system of the key performance indicator, wherein the mitigation action includes at least one of recommending inspection of a component of the turbine system, recommending replacement of a component of the turbine system, and recommending replacement of a component of the turbine system with an alternate component.
19 . The method of claim 18 , wherein the mitigation action further comprises at least one of recommending a cleaning of a component of the turbine system, forcing a cleaning of a component of the turbine system, and establishing or updating a frequency of a cleaning of a component of the turbine system.
20 . A controller for a turbine system, the system having at least one sensor, the controller operative to: receive at least one measured data value of air intake particles; and process the at least one data value via an intelligent model to generate an impact value including an efficiency of flow of intake air and an effect of particle accumulation on the turbine system, process a filter efficiency framework via the intelligent model to identify a location of possible leakage in the turbine system, compare the location of the possible leakage in the turbine system with an offline computational fluid dynamic evaluation to estimate a risk of further damage to at least one component of the turbine system, determine a dirt load estimate based on a particulate concentration multiplied by a rate of air flow multiplied by a number of hours of operation, and wherein the impact value relates the at least one measured data value to an operational efficiency of the turbine system.

Description

BACKGROUND The disclosure relates generally to systems and methods for turbine systems, and more particularly, to systems and methods configured to build and consult an intelligent model of particulate presence and accumulation within gas turbine systems to quantify an operational efficiency of the gas turbine system and may identify a location of possible leakage, estimate a total amount of ingress of particles, identify components of the gas turbine system that may be operating in a diminished capacity, estimate a risk of damage to components of the gas turbine system, and/or recommend mitigation actions. Gas turbines are used throughout the world in many diverse applications and environments. This diversity creates a number of challenges to air filtration systems, necessitating different particle accumulation estimates and/or estimates of effects on components of the gas turbine system for each type of environmental contaminant(s), gas turbine platform technology, and/or fuel quality. For example, gas turbines which operate in hot and harsh climates, in environments in which the turbine is exposed to severe air quality contaminations, and/or high efficiency gas turbines operating at high operational temperatures, face significant challenges with respect to engine performance, reliability, and/or maintainability, particularly where there is a compromise or breach in the inlet system of the gas turbine system. Such challenges may include the erosion, corrosion and/or failure of various turbine components. Different operating environments for gas turbines having substantially different structures cannot be adequately monitored by an operator of the turbine system with a single, common monitoring system. When conventional filtration systems fail, and sand and other undesirable particles can enter the gas turbine and the components of the turbine may become damaged and/or inoperable. Additionally, undesirable particles flowing through components of the gas turbine may reduce the operational efficiency of the turbine itself. To prevent debris and/or particles from entering the turbine, the filtration systems typically include multiple stages of filtration components that filter various sizes of debris and/or particles prior to the working fluid (e.g., filtered air) entering the compressor of the gas turbine. However, these components included in conventional filtration systems can become damaged by the same debris and may no longer filter out the debris and particles as desired. Additionally, or alternatively, the components included in conventional filtration systems may not operate as desired (e.g., filter out debris) due to improper installation, extended operational life or use, and/or other degradation factors. In conventional turbines, there is no warning or indication system that relates such filtration components being damaged and/or inoperable to an overall cost to replace, perform maintenance, and/or to recommend mitigation actions. Likewise, conventional turbines typically do not provide for predictive filter maintenance. As such, it is only when components of the gas turbine become damaged/altered, and/or when operational efficiency of the turbine degrades to a significant degree that an operator of the turbine may determine that components of the filtration system need repair and/or replacement. Additionally, in order to repair the damaged filtration components of the filtration system, the filtration system, or even the entire turbine, must be shut down for maintenance on the damaged filtration components and/or the damaged components of the turbine, resulting in a significant loss of power and/or revenue generation. BRIEF DESCRIPTION A first aspect of the disclosure provides a system for assessing the efficiency and integrity of an inlet filtration system of a turbine. The system includes one or more sensors that generate at least one measured data value of air intake particles within a fluid flow path of the turbine and a controller electrically communicating with the sensor(s). The controller utilizes an intelligent model to process the at least one measured data value. The intelligent model consults a filter efficiency framework that relates the at least one data value of the air intake particles to a filter efficiency of the air filtration system of the turbine. A second aspect of the disclosure provides a method of quantifying an effect of continued fluid flow within an air filtration assembly of a turbine system. The method includes: consulting an intelligent model of fluid flow tailored to the turbine system; determining an impact value using the intelligent model of fluid flow based on at least one detected data value of an air intake particle traveling through a fluid flow path of the turbine system; calculating a key performance indicator from the impact value; and notifying an operator of the turbine system of the key performance indicator. The key performance indicator provides quant