A control system uses a feedforward neural network model to perform control of a steam turbine power system in sliding pressure mode in a more efficient and accurate manner than a control scheme that uses only a multivariate linear regression model or a manufacturer-supplied correction function. Turbine inlet steam pressure of a steam turbine power generation system in sliding pressure control mode has a direct one-to-one relationship with the electrical energy load (output) of the steam turbine power system. This new control system provides a more accurate representation of the turbine inlet steam pressure, such that the power generated by a power plant is more closely controlled to the target (demand). More particularly, the feedforward neural network model prediction of the turbine inlet steam pressure more closely fits with the actual turbine inlet steam pressure with very little error, and thereby providing better control over the electrical energy load.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A power generation system, comprising: a steam turbine power generation unit having a turbine steam inlet system, a steam turbine coupled to the turbine steam inlet system and powered by steam from the turbine steam inlet system, and a steam outlet; an electrical energy generation unit mechanically coupled to the steam turbine and adapted to produce an electrical energy load based on movement of the steam turbine; a control system adapted to develop a process control signal to control pressure in the turbine steam inlet system to thereby control the electrical energy load produced by the electrical energy generation unit; and a feedforward neural network model of a relationship between turbine steam inlet pressure and the electrical energy load operatively coupled to the control system, wherein an input of the feedforward neural network model includes an electrical energy load set-point to produce a pressure set-point control system output and the pressure set-point control system output is coupled to an input of the control system. 2. The power generation system of claim 1, further comprising: a burner system that burns a fuel to generate steam input to the turbine steam inlet system; wherein the control system includes a controller input generation unit and a controller operatively coupled to the controller input generation unit, wherein the output of the feedforward neural network model is coupled to an input of the controller input signal generation unit, and the controller input signal generation unit is adapted to develop a controller input signal for the controller and the controller is adapted to develop the process control signal to control the burner system to thereby control the pressure in the turbine steam inlet system in response to the controller input signal. 3. The power generation system of claim 2, wherein the controller input signal comprises a controller valve input signal for the controller to control a turbine valve to thereby control an input of steam to the turbine steam inlet system. 4. The power generation system of claim 3, wherein the controller valve input signal comprises a value to maximize the opening of the valve to the turbine steam inlet system such that the power generation system is in a sliding pressure mode. 5. The power generation system of claim 1, further comprising: a reheater operatively coupled to the steam turbine power generation unit to reheat steam exiting the steam turbine power generation unit and provide the reheated steam back to the steam turbine power generation unit; and a condenser operatively coupled to the steam outlet of the steam turbine power generation unit to receive steam exhausted from the steam turbine power generation unit; wherein the feedforward neural network model comprises a multivariable input including the electrical energy load set-point, a reheat temperature deviation, a turbine steam inlet temperature deviation, a condenser back pressure deviation, and an auxiliary steam flow, wherein each of the reheat temperature deviation, the turbine steam inlet temperature deviation, the condenser back pressure deviation, and the auxiliary steam flow have an effect on the electrical energy load. 6. The power generation system of claim 1, wherein the feedforward neural network model comprises a neural network having at least one hidden layer of sigmoid-type neurons. 7. The power generation system of claim 1, further comprising a model adaptation unit that adapts a model to produce the pressure set-point control system output. 8. The power generation system of claim 7, wherein the model adaptation unit is operatively coupled to the electrical energy generation unit, wherein an input of the model adaptation unit includes the electrical energy load set-point and the electrical energy load, and wherein the model adaptation unit adapts the model based on a difference between the electrical energy load set-point and the electrical energy load. 9. The power generation system of claim 8, wherein the model adaptation unit adapts the model if the power generation system is operating in a steady-state and the difference between the electrical energy load set-point and the electrical energy load exceeds a threshold value. 10. The power generation system of claim 7, wherein the model adaptation unit is adapted to train a new feedforward neural network model of the relationship between the turbine steam inlet pressure and the electrical energy load using process data from the power generation system as training data. 11. The power generation system of claim 10, wherein the model adaptation unit is adapted to train a multivariate linear regression model of the relationship between the turbine steam inlet pressure and the electrical energy load using the training data. 12. The power generation system of claim 11, wherein the model adaptation unit is adapted to compute a root-mean-square error for each of the new feedforward neural network model and the multivariate linear regression model using process data from the power generation system as testing data. 13. The power generation system of claim 12, wherein the model adaptation unit is adapted to compute a root-mean-square error for each of the feedforward neural network model operatively coupled to the control system, a previous multivariate linear regression model of the relationship between the turbine steam inlet pressure and the electrical energy load, and a design model of the relationship between the turbine steam inlet pressure and the electrical energy load using the testing data. 14. The power generation system of claim 12, wherein the model adaptation unit is adapted to select one of the new feedforward neural network model and the multivariate linear regression model, wherein the model with the minimum root-mean-square error is selected for the power generation system. 15. The power generation system of claim 13, wherein the model adaptation unit is adapted to select one of the new feedforward neural network model and the multivariate linear regression model, the feedforward neural network model operatively coupled to the control system, the previous multivariate linear regression model and the design model based on the root-mean-square error for each model, wherein the model with the minimum root-mean-square error is selected for the power generation system. 16. The power generation system of claim 15, wherein the model adaptation unit is adapted to replace the feedforward neural network model operatively coupled to the control system if the selected model is the new feedforward neural network model, the new multivariate linear regression model, the old multivariate linear regression model or the design model. 17. A power generation system, comprising: a steam turbine power generation unit having a turbine steam inlet system, a steam turbine coupled to the turbine steam inlet system and powered by steam from the turbine steam inlet system, and a steam outlet; an electrical energy generation unit mechanically coupled to the steam turbine and adapted to produce an electrical energy load based on movement of the steam turbine; a control system adapted to develop a process control signal to control pressure in the turbine steam inlet system to thereby control the electrical energy load produced by the electrical energy generation unit; and a model adaptation unit operatively coupled to the electrical energy generation unit to adapt a feedforward neural network model of a relationship between turbine steam inlet pressure and the electrical energy load using process data from the power generation system as training data, wherein the feedforward neural network model is adapted to produce a pressure set-point control system output from an electrical energy load set-point for the control system. 18. The power generation system of claim 17, wherein the model adaptation unit is operatively coupled to the electrical energy generation unit, wherein an input of the model adaptation unit includes the electrical energy load set-point and the electrical energy load, and wherein the model adaptation unit adapts models based on a difference between the electrical energy load set-point and the electrical energy load. 19. The power generation system of claim 18, wherein the model adaptation unit adapts models if the power generation system is operating in a steady-state and the difference between the electrical energy load set-point and the electrical energy load exceeds a threshold value. 20. The power generation system of claim 17, wherein the model adaptation unit is adapted to train a multivariate linear regression model of the relationship between the turbine steam inlet pressure and the electrical energy load using the training data. 21. The power generation system of claim 20, wherein the model adaptation unit is adapted to compute a root-mean-square error for each of the feedforward neural network model and the multivariate linear regression model using process data from the power generation system as testing data. 22. The power generation system of claim 21, wherein the model adaptation unit is adapted to select one of the feedforward neural network model and the multivariate linear regression model, wherein the model with the minimum root-mean-square error is selected for the power generation system to be operatively coupled to the control system, and wherein an input of the selected model includes an electrical energy load set-point to produce a pressure set-point control system output and the pressure set-point control system output of the selected model is coupled to an input of the control system. 23. The power generation system of claim 21, wherein the model adaptation unit is adapted to compute a root-mean-square error for a previous feedforward neural network model of the relationship between the turbine steam inlet pressure and the electrical energy load, a previous multivariate linear regression model of the relationship between the turbine steam inlet pressure and the electrical energy load, and a design model of the relationship between the turbine steam inlet pressure and the electrical energy load using the testing data. 24. The power generation system of claim 23, wherein the model adaptation unit is adapted to select one of the feedforward neural network model, the multivariate linear regression model, the previous feedforward neural network model, the previous multivariate linear regression model and the design model based on the root-mean-square error for each model, wherein the model with the minimum root-mean-square error is selected for the power generation system to be operatively coupled to the control system, and wherein an input of the selected model includes an electrical energy load set-point to produce a pressure set-point control system output and the pressure set-point control system output of the selected model is coupled to an input of the control system. 25. The power generation system of claim 17, further comprising: a burner system that burns a fuel to generate steam input to the turbine steam inlet system; wherein the control system includes a controller input generation unit and a controller operatively coupled to the controller input generation unit, wherein the output of the feedforward neural network model is coupled to an input of the controller input signal generation unit, and the controller input signal generation unit is adapted to develop a controller input signal for the controller and the controller is adapted to develop a process control signal to control the burner system to thereby control the pressure in the turbine steam inlet system in response to the controller input signal. 26. The power generation system of claim 25, wherein the controller input signal comprises a controller valve input signal for the controller to control a turbine valve to thereby control an input of steam to the turbine steam inlet system. 27. The power generation system of claim 26, wherein the controller valve input signal comprises a value to maximize the valve opening to the turbine steam inlet system such that the power generation system is in a sliding pressure mode. 28. The power generation system of claim 17, further comprising: a reheater operatively coupled to the steam turbine power generation unit to reheat steam exiting the steam turbine power generation unit and provide the reheated steam back to the steam turbine power generation unit; and a condenser operatively coupled to the steam outlet of the steam turbine power generation unit to receive steam exhausted from the steam turbine power generation unit; wherein the feedforward neural network model comprises a multivariable input including the electrical energy load set-point, a reheat temperature deviation, a turbine steam inlet temperature deviation, a condenser back pressure deviation, and an auxiliary steam flow, wherein each of the reheat temperature deviation, the turbine steam inlet temperature deviation, the condenser back pressure deviation, and the auxiliary steam flow have an effect on the electrical energy load. 29. The power generation system of claim 17, wherein the feedforward neural network model comprises a neural network having at least one hidden layer of sigmoid-type neurons. 30. A method of controlling a power generation process in a sliding pressure mode, the power generating process having a steam turbine power generation unit and an electrical energy generation unit, the method comprising: receiving a set-point indicating a desired output of the electrical energy generation unit; modeling, via a neural network model, a relationship between an output of the electrical energy generation unit and throttle pressure to the steam turbine power generation unit in response to the set-point indicating the desired output to develop a predicted pressure set-point control system output; and executing a control routine that determines a control signal for use in controlling the operation of the steam turbine power generation unit based on the predicted pressure set-point control system output. 31. The method of claim 30, wherein the power generation process further has a burner system that burns a fuel to generate steam input to the turbine steam inlet system, and wherein executing a control routine that determines a control signal for use in controlling the operation of the steam turbine power generation unit comprises executing a control routine that determines a control signal for use in controlling the burner system to thereby control the pressure in the turbine steam inlet system. 32. The method of claim 30, wherein executing the control routine further comprises executing a control routine that determines a valve control signal for use in controlling the operation of a turbine valve to thereby control an input of steam to the turbine steam inlet system. 33. The method of claim 32, wherein the valve control signal comprises a value to maximize the valve opening to the turbine steam inlet system such that the power generation process is in the sliding pressure mode. 34. The method of claim 30, wherein modeling, via the neural network model, the relationship between the output of the electrical energy generation unit and the pressure within a turbine steam inlet system to the steam turbine power generation unit in response to the set-point indicating the desired output further comprises modeling, via the neural network model, the relationship between the output of the electrical energy generation unit and the pressure within a turbine steam inlet system to the steam turbine power generation unit in response to a reheat temperature deviation, a turbine steam inlet temperature deviation, a condenser back pressure deviation, and an auxiliary steam flow. 35. The method of claim 30, further comprising: measuring an electrical energy load output of the electrical energy generating unit; and adapting a model of the relationship between the output of the electrical energy generating unit and the pressure within the turbine steam inlet system based on a difference between the set-point indicating the desired output and the measured electrical energy load output. 36. The method of claim 35, wherein adapting the model of the relationship between the output of the electrical energy generating unit and the pressure within the turbine steam inlet system comprises adapting the model of the relationship between the output of the electrical energy generating unit and the pressure within the turbine steam inlet system if the power generation process is operating in a steady-state and the difference between the set-point indicating the desired output and the measured electrical energy load output exceeds a threshold value. 37. The method of claim 35, wherein adapting the model of the relationship between the output of the electrical energy generating unit and the pressure within the turbine steam inlet system comprises training a neural network model of the relationship between the output of the electrical energy generating unit and the pressure within the turbine steam inlet system. 38. The method of claim 37, wherein training a neural network model of the relationship between the output of the electrical energy generating unit and the pressure within the turbine steam inlet system comprises training a neural network model of the relationship between the output of the electrical energy generating unit and the pressure within the turbine steam inlet system using process data from the power generation process as training data. 39. The method of claim 37, wherein adapting the model of the relationship between the output of the electrical energy generating unit and the pressure within the turbine steam inlet system further comprises training a multivariate linear regression model of the relationship between the output of the electrical energy generating unit and the pressure within the turbine steam inlet system. 40. The method of claim 39, wherein training a multivariate linear regression model of the relationship between the output of the electrical energy generating unit and the pressure within the turbine steam inlet system comprises training a multivariate linear regression model of the relationship between the output of the electrical energy generating unit and the pressure within the turbine steam inlet system using process data from the power generation process as training data. 41. The method of claim 39, further comprising determining a root-mean-square error for each of the neural network model and the multivariate linear regression model. 42. The method of claim 41, wherein determining the root-mean-square error for each of the neural network model and the multivariate linear regression model comprises determining the root-mean-square error for each of the neural network model and the multivariate linear regression model using process data from the power generation process as testing data. 43. The method of claim 41, further comprising: determining a root-mean-square error for each of a previous neural network model of the relationship between the output of the electrical energy generating unit and the pressure within the turbine steam inlet system, a previous multivariate linear regression model of the relationship between the output of the electrical energy generating unit and the pressure within the turbine steam inlet system, and a design model of the relationship between the output of the electrical energy generating unit and the pressure within the turbine steam inlet system; and selecting one of the neural network model, the multivariate linear regression model, the previous neural network model, the previous multivariate linear regression model and the design model with the minimum root-mean-square error for the power generation process. 44. The method of claim 43, wherein determining the root-mean-square error for each of the neural network model, the multivariate linear regression model, the previous neural network model, the previous multivariate linear regression model and the design model comprises determining the root-mean-square error for each of the neural network model, the multivariate linear regression model, the previous neural network model, the previous multivariate linear regression model and the design model using process data from the power generation process as testing data. 45. The method of claim 30, wherein modeling, via the neural network model, the relationship between the output of the electrical energy generation unit and pressure within a turbine steam inlet system to the steam turbine power generation unit comprises implementing a feedforward neural network model that models the load output of the electrical energy generation unit in response to the predicted set-point control system output provided to the control routine. 46. A method of adapting a model for a steam turbine power generation process in a sliding pressure mode, the power generating process having a steam turbine power generation unit and an electrical energy generation unit, the method comprising: receiving a set-point indicating a desired output of the electrical energy generation unit; executing a control routine that determines a control signal for use in controlling the operation of the steam turbine power generation unit based on a pressure set-point control system output predicted by a first neural network model of a relationship between an output of the electrical energy generation unit and pressure within a turbine steam inlet system of the steam turbine power generation unit in response to the set-point indicating the desired output to develop the predicted pressure set-point control system output; measuring an actual output of the electrical energy generation unit in response to the set-point indicating a desired output of the electrical energy generation unit during a steady-state operation of the power generation process; and adapting a second neural network model of the relationship between the output of the electrical energy generation unit and pressure within the turbine steam inlet system of the steam turbine power generation unit if a difference between the actual output of the electrical energy generation unit and the set-point indicating a desired output of the electrical energy generation unit is greater than a predetermined threshold. 47. The method of claim 46, wherein adapting the second neural network model comprises training the second neural network model using process data from the power generation process as training data. 48. The method of claim 47, further comprising training a first multivariate linear regression model of the relationship between the output of the electrical energy generation unit and pressure within the turbine steam inlet system of the steam turbine power generation unit using the training data. 49. The method of claim 48, further comprising computing a root-mean-square error for each of the second neural network model and the first multivariate linear regression model using process data from the power generation process as testing data. 50. The method of claim 49, further comprising: selecting one of the second neural network model and the first multivariate linear regression model with the minimum root-mean-square error; and operatively coupling the selected model to a control system of the power generation process to produce a pressure set-point control system output, wherein an input of the selected model includes the set-point indicating the desired output of the electrical energy generation unit and the pressure set-point control system output is coupled to an input of the control system. 51. The method of claim 49, further comprising: computing a root-mean-square error for each of the first neural network model, a second multivariate linear regression model of the relationship between the output of the electrical energy generation unit and pressure within the turbine steam inlet system of the steam turbine power generation unit and a design model of the relationship between the output of the electrical energy generation unit and pressure within the turbine steam inlet system of the steam turbine power generation unit; selecting one of the first neural network model, second neural network model, the first multivariate linear regression model, the second multivariate linear regression model and the design model with the minimum root-mean-square error; and operatively coupling the selected model to a control system of the power generation process to produce a pressure set-point control system output, wherein an input of the selected model includes the set-point indicating the desired output of the electrical energy generation unit and the pressure set-point control system output is coupled to an input of the control system.