Abstract: Generator dynamic model parameter estimation and tuning using online data and subspace state space models are disclosed. According to one embodiment, a system comprises a sensor, a data acquisition network in communication with the sensor; a user console and an identification and tuning engine in communication with the data acquisition network, the user console, and a database. The database comprises one or more generator models, and the identification and tuning engine identifies and tunes parameters associated with a selected generator model.

Abstract: Dynamic parameter tuning using particle swarm optimization is disclosed. According to one embodiment, a system for dynamically tuning parameters comprising a control unit; and a system for receiving parameters tuned by the control unit. The control unit receives as input a model selection and definitions, and dynamically tunes a value for each parameter by using a modified particle swarm optimization method. The modified particle swarm optimization method comprises moving particle locations based on a particle's inertia, experience, global knowledge, and a tuning factor. The control unit outputs the dynamically tuned value for each parameter.

Abstract: Dynamic parameter tuning using particle swarm optimization is disclosed. According to one embodiment, a system for dynamically tuning parameters comprising a control unit; and a system for receiving parameters tuned by the control unit. The control unit receives as input a model selection and definitions, and dynamically tunes a value for each parameter by using a modified particle swarm optimization method. The modified particle swarm optimization method comprises moving particle locations based on a particle's inertia, experience, global knowledge, and a tuning factor. The control unit outputs the dynamically tuned value for each parameter.

Abstract: Generator dynamic model parameter estimation and tuning using online data and subspace state space models are disclosed. According to one embodiment, a system comprises a sensor, a data acquisition network in communication with the sensor; a user console and an identification and tuning engine in communication with the data acquisition network, the user console, and a database. The database comprises one or more generator models, and the identification and tuning engine identifies and tunes parameters associated with a selected generator model.

Abstract: Dynamic parameter tuning using particle swarm optimization is disclosed. According to one embodiment, a system for dynamically tuning parameters comprising a control unit; and a system for receiving parameters tuned by the control unit. The control unit receives as input a model selection and definitions, and dynamically tunes a value for each parameter by using a modified particle swarm optimization method. The modified particle swarm optimization method comprises moving particle locations based on a particle's inertia, experience, global knowledge, and a tuning factor. The control unit outputs the dynamically tuned value for each parameter.

Abstract: Generator dynamic model parameter estimation and tuning using online data and subspace state space models are disclosed. According to one embodiment, a system comprises a sensor, a data acquisition network in communication with the sensor; a user console and an identification and tuning engine in communication with the data acquisition network, the user console, and a database. The database comprises one or more generator models, and the identification and tuning engine identifies and tunes parameters associated with a selected generator model.

Abstract: Dynamic parameter tuning using particle swarm optimization is disclosed. According to one embodiment, a system for dynamically tuning parameters comprising a control unit; and a system for receiving parameters tuned by the control unit. The control unit receives as input a model selection and definitions, and dynamically tunes a value for each parameter by using a modified particle swarm optimization method. The modified particle swarm optimization method comprises moving particle locations based on a particle's inertia, experience, global knowledge, and a tuning factor. The control unit outputs the dynamically tuned value for each parameter.

Abstract: Generator dynamic model parameter estimation and tuning using online data and subspace state space models are disclosed. According to one embodiment, a system comprises a sensor, a data acquisition network in communication with the sensor; a user console and an identification and tuning engine in communication with the data acquisition network, the user console, and a database. The database comprises one or more generator models, and the identification and tuning engine identifies and tunes parameters associated with a selected generator model.

Abstract: Dynamic parameter tuning using particle swarm optimization is disclosed. According to one embodiment, a system for dynamically tuning parameters comprising a control unit; and a system for receiving parameters tuned by the control unit. The control unit receives as input a model selection and definitions, and dynamically tunes a value for each parameter by using a modified particle swarm optimization method. The modified particle swarm optimization method comprises moving particle locations based on a particle's inertia, experience, global knowledge, and a tuning factor. The control unit outputs the dynamically tuned value for each parameter.

Abstract: Generator dynamic model parameter estimation and tuning using online data and subspace state space models are disclosed. According to one embodiment, a system comprises a sensor, a data acquisition network in communication with the sensor; a user console and an identification and tuning engine in communication with the data acquisition network, the user console, and a database. The database comprises one or more generator models, and the identification and tuning engine identifies and tunes parameters associated with a selected generator model.

Abstract: Dynamic parameter tuning using particle swarm optimization is disclosed. According to one embodiment, a system for dynamically tuning parameters comprising a control unit; and a system for receiving parameters tuned by the control unit. The control unit receives as input a model selection and definitions, and dynamically tunes a value for each parameter by using a modified particle swarm optimization method. The modified particle swarm optimization method comprises moving particle locations based on a particle's inertia, experience, global knowledge, and a tuning factor. The control unit outputs the dynamically tuned value for each parameter.