Abstract: According to some embodiments, system and methods are provided comprising: receiving data; providing a simulation model for the data; generating one or more simulations via a Bayesian module based on the data, wherein the simulation includes one or more nodes in a chain; executing the Bayesian module to determine the acceptability of the nodes in the simulation based on a Bayesian rule, wherein execution of the Bayesian module further comprises: generating a binary decision tree representing the chain in the simulation, wherein the chain includes one or more nodes; prioritizing which nodes in the tree to simulate; generating one or more simulations; executing the simulation model with data associated with the prioritized nodes in the tree in parallel to determine a posterior probability for each prioritized node; and determining whether each prioritized node is accepted or rejected based on the posterior probabilities. Numerous other aspects are provided.

Abstract: A system for estimating data in large datasets for an equipment system is provided. The system includes a data estimation and forecasting (DEF) computing device. The DEF computing device arranges a dataset in a primary matrix and parses rows of the primary matrix and generates a sample matrix by selecting primary matrix rows having non-null values for each variable. The DEF computing device adds to the sample matrix rows that include non-null values for each variable except one. The DEF computing device generates normalized values for this augmented matrix, applies several techniques including probabilistic principal component analysis (PPCA) and Markov processes, and scales the augmented matrix to normalized values. The DEF computing device generates non-null values for the variable, scales the augmented matrix back to the sample matrix, and generates a forecast for the equipment system, directing a user to update logistics processes for the equipment system.

Abstract: A method and system for use in controlling an electric network are provided. The system includes an Integrated Volt-VAr Control (IVVC) component configured to determine optimization parameters for slow dynamics electromechanical devices and fast dynamics DER devices coupled to the network. The slow dynamics devices are controlled by a present state of the electric network and a voltage rise table that is adaptively updated in real-time using a command output, or a power flow-based complete optimization routine that generates optimal setpoints for the traditional controllable assets and for at least some of the fast dynamics DER devices. The fast dynamics devices are controlled locally using a control algorithm that uses a reactive power contribution based on IVVC settings, based on photo-voltaic (PV) plant active power variations, based on power factor, or based on a voltage of the local electric network.

Abstract: According to some embodiments, system and methods are provided comprising: receiving data; providing a simulation model for the data; generating one or more simulations via a Bayesian module based on the data, wherein the simulation includes one or more nodes in a chain; executing the Bayesian module to determine the acceptability of the nodes in the simulation based on a Bayesian rule, wherein execution of the Bayesian module further comprises: generating a binary decision tree representing the chain in the simulation, wherein the chain includes one or more nodes; prioritizing which nodes in the tree to simulate; generating one or more simulations; executing the simulation model with data associated with the prioritized nodes in the tree in parallel to determine a posterior probability for each prioritized node; and determining whether each prioritized node is accepted or rejected based on the posterior probabilities. Numerous other aspects are provided.

Abstract: A system for similarity analysis-based information augmentation for a target component includes an information augmentation (IA) computer device. The IA computer device identifies a target component input variable with unavailable data. The IA computer device executes a similarity analysis function, identifying at least two test components with data for the input variable exceeding a threshold. The IA computer device generates parameter distributions for test data for each test component. The IA computer device generates model coefficients using the parameter distributions, determining a proportional mix of the parameter distributions. The IA computer device authors a predictive model configured to generate at least one predicted value for the target data for the at least one input variable for the target component by including the at least one model coefficient in the predictive model. The IA computer device generates, using the predictive model, the at least one predicted value.

Abstract: A system for estimating data in large datasets for an equipment system is provided. The system includes a data estimation and forecasting (DEF) computing device. The DEF computing device arranges a dataset in a primary matrix and parses rows of the primary matrix and generates a sample matrix by selecting primary matrix rows having non-null values for each variable. The DEF computing device adds to the sample matrix rows that include non-null values for each variable except one. The DEF computing device generates normalized values for this augmented matrix, applies several techniques including probabilistic principal component analysis (PPCA) and Markov processes, and scales the augmented matrix to normalized values. The DEF computing device generates non-null values for the variable, scales the augmented matrix back to the sample matrix, and generates a forecast for the equipment system, directing a user to update logistics processes for the equipment system.

Abstract: A system for determining a decrease in service life to a target component is provided. The system includes a service life modeling (SLM) computing device, which identifies a physics variable for a test component. The SLM computing device generates a likelihood function for the physics variable. The SLM computing device applies probabilistic techniques to the physical measurements together with a set of coefficients. The SLM computing device generates a hybrid service life model for the test component. The SLM computing device calibrates the hybrid service life model. The SLM computing device applies the hybrid service life model to a target component that shares characteristics with the test component. The SLM computing device identifies a predictive metric for the target component. The SLM computing device outputs the metric. The SLM computing device directs an operator to modify a maintenance plan for the target component based on the metric.

Abstract: A system, method and computer-readable medium for monitoring a life of a gas turbine component is disclosed. A numerical model of the gas turbine component is created and parameter measurements are obtained in real-time for at least a portion of the gas turbine component. The parameter measurements are fused with a subset of the numerical model corresponding to the portion of the gas turbine component to obtain a subset of a fused parameter model corresponding to the portion of the gas turbine component. The subset of the fused parameter model is expanded to obtain the fused parameter model that corresponds to at least a location outside of the portion of the gas turbine component. The life of the gas turbine component is monitored using the fused temperature model.

Abstract: A method and system for use in controlling an electric network are provided. The system includes an Integrated Volt-VAr Control (IVVC) component configured to determine optimization parameters for slow dynamics electromechanical devices and fast dynamics DER devices coupled to the network. The slow dynamics devices are controlled by a present state of the electric network and a voltage rise table that is adaptively updated in real-time using a command output, or a power flow-based complete optimization routine that generates optimal setpoints for the traditional controllable assets and for at least some of the fast dynamics DER devices. The fast dynamics devices are controlled locally using a control algorithm that uses a reactive power contribution based on IVVC settings, based on photo-voltaic (PV) plant active power variations, based on power factor, or based on a voltage of the local electric network.