Method and system for optimization of combination cycle gas turbine operation

A system for optimizing an operation of a combined cycle gas turbine (CCGT) plant, the system comprises:

• an input/output interface;
• one or more hardware processors;
• a memory in communication with the one or more hardware processors, wherein the one or more first hardware processors are configured to execute programmed instructions stored in the memory, to:
• receive a plurality of data from a one or more databases of the CCGT plant at a predetermined frequency, wherein the plurality of data comprises of a real time and a non-real time data, and wherein the real-time data is obtained from plant automation systems via a communication server;
• preprocess the plurality of data;
• estimate a set of soft sensor parameters using a plurality of soft sensors;
• integrate the set of soft sensor parameters with the pre-processed plurality of data, wherein the integrated data comprises a first set of manipulated variables;
• train a plurality of anomaly detection models and a plurality of anomaly diagnosis models using a historical data of the CCGT plant and built using statistical, machine learning and deep learning techniques including principal component analysis, Mahalanobis distance, isolation forest, random forest classifiers, one-class support vector machine, artificial neural networks, elliptic envelope and auto-encoders and Bayesian networks;
• detect process and equipment anomalies related to the CCGT plant and individual units of the CCGT plant, using plurality of anomaly detection models, wherein the plurality of anomaly detection models are retrieved from the database;
• identify at least one cause of the detected anomalies using the plurality of anomaly diagnosis models, wherein the plurality of anomaly diagnosis models are retrieved from the database, and wherein a real-time process optimization is triggered only in an absence of the anomaly;
• determine a state of the operation of the CCGT plant using a plurality of state determination models wherein the state can be steady or unsteady state;
• classify the state of the operation of the CCGT plant into the steady state and the unsteady state in real-time using a subset of the CCGT plant variables comprising a total generated power, a frequency of power generated or a rotational speed of shaft, a fuel flow rate and an inlet air flow rate using a plurality of state determination which are data-driven classifiers, wherein the unsteady state is further classified into one of a steady, load-up, load-down, start-up and shut-down state;
• predict a plurality of key performance parameters of CCGT plant using a plurality of predictive models and the integrated data, wherein the plurality of predictive models are retrieved from the database;
• configure an optimizer using the plurality of predictive models to optimize the plurality of key performance parameters of the CCGT plant;
• generate a second set of manipulated variables using the configured optimizer;
• determine an optimal set of manipulated variables using the first set of manipulated variables and the second set of manipulated variables based on
• the cause of the detected anomalies,
• the determined state of the CCGT plant, and
• an importance of the plurality of key performance parameters of the CCGT plant, herein the importance is either defined by a user or obtained from the database, wherein the importance of the plurality of key performance parameters is defined based on instantaneous condition of the CCGT plant;
• calculate rating points for each of the plurality of key performance parameters using the determined importance for each of the key performance parameters, for both the first set and the second set of manipulated variables;
• compute a reward value utilizing the rating points calculated for first set and second set of manipulated variables;
• recommend the optimal set of manipulated variables using a predefined set of conditions involving the comparison of the reward value with an upper threshold value and a lower threshold value;
• and
• provide the optimal set of manipulated variables to optimize the operation of the CCGT plant for generation of electric power.

One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving a plurality of data from a one or more databases of the combined cycle gas turbine (CCGT) plant at a predetermined frequency, wherein the plurality of data comprises of a real time and a non-real time data, and wherein the real-time data is obtained from plant automation systems via a communication server;
• preprocessing the plurality of data;
• estimating, a set of soft sensor parameters using a plurality of soft sensors;
• integrating the set of soft sensor parameters with the pre-processed plurality of data, wherein the integrated data comprises a first set of manipulated variables;
• training a plurality of anomaly detection models and a plurality of anomaly diagnosis models using a historical data of the CCGT plant and built using statistical, machine learning and deep learning techniques including principal component analysis, Mahalanobis distance, isolation forest, random forest classifiers, one-class support vector machine, artificial neural networks, elliptic envelope and auto-encoders and Bayesian networks;
• detecting process and equipment anomalies related to the CCGT plant and individual units of the CCGT plant, using the plurality of anomaly detection models, wherein the plurality of anomaly detection models are retrieved from the database;
• identifying at least one cause of the detected anomalies using the plurality of anomaly diagnosis models, wherein the plurality of anomaly diagnosis models are retrieved from the database, and wherein a real-time process optimization is triggered only in an absence of the anomaly;
• determining, via the one or more hardware processors, a state of the operation of the CCGT plant using a plurality of state determination models wherein the state can be steady or unsteady state;
• classifying the state of the operation of the CCGT plant into the steady state and the unsteady state in real-time using a subset of the CCGT plant variables comprising a total generated power, a frequency of power generated or a rotational speed of shaft, a fuel flow rate and an inlet air flow rate using a plurality of state determination which are data-driven classifiers, wherein the unsteady state is further classified into one of a steady, load-up, load-down, start-up and shut-down state;
• predicting a plurality of key performance parameters of CCGT plant using a plurality of predictive models and the integrated data, wherein the plurality of predictive models are retrieved from the database;
• configuring an optimizer using the plurality of predictive models to optimize the plurality of key performance parameters of the CCGT plant;
• generating a second set of manipulated variables using the configured optimizer;
• determining an optimal set of manipulated variables using the first set of manipulated variables and the second set of manipulated variables based on
• the cause of the detected anomalies,
• the determined state of the CCGT plant, and
• an importance of the plurality of key performance parameters of the CCGT plant, wherein the importance is either defined by a user or obtained from the database, wherein the importance of the plurality of key performance parameters is defined based on instantaneous condition of the CCGT plant;
• calculating rating points for each of the plurality of key performance parameters using the determined importance for each of the key performance parameters, for both the first set and the second set of manipulated variables;
• computing a reward value utilizing the rating points calculated for first set and second set of manipulated variables;
• recommending the optimal set of manipulated variables using a predefined set of conditions involving the comparison of the reward value with an upper threshold value and a lower threshold value;
• and
• providing the optimal set of manipulated variables to optimize the operation of the CCGT plant for generation of electric power.