30,253 research outputs found
An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning
For short-term solar irradiance forecasting, the traditional point
forecasting methods are rendered less useful due to the non-stationary
characteristic of solar power. The amount of operating reserves required to
maintain reliable operation of the electric grid rises due to the variability
of solar energy. The higher the uncertainty in the generation, the greater the
operating-reserve requirements, which translates to an increased cost of
operation. In this research work, we propose a unified architecture for
multi-time-scale predictions for intra-day solar irradiance forecasting using
recurrent neural networks (RNN) and long-short-term memory networks (LSTMs).
This paper also lays out a framework for extending this modeling approach to
intra-hour forecasting horizons thus, making it a multi-time-horizon
forecasting approach, capable of predicting intra-hour as well as intra-day
solar irradiance. We develop an end-to-end pipeline to effectuate the proposed
architecture. The performance of the prediction model is tested and validated
by the methodical implementation. The robustness of the approach is
demonstrated with case studies conducted for geographically scattered sites
across the United States. The predictions demonstrate that our proposed unified
architecture-based approach is effective for multi-time-scale solar forecasts
and achieves a lower root-mean-square prediction error when benchmarked against
the best-performing methods documented in the literature that use separate
models for each time-scale during the day. Our proposed method results in a
71.5% reduction in the mean RMSE averaged across all the test sites compared to
the ML-based best-performing method reported in the literature. Additionally,
the proposed method enables multi-time-horizon forecasts with real-time inputs,
which have a significant potential for practical industry applications in the
evolving grid.Comment: 19 pages, 12 figures, 3 tables, under review for journal submissio
Intelligent flight control systems
The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms
Multi-time-horizon Solar Forecasting Using Recurrent Neural Network
The non-stationarity characteristic of the solar power renders traditional
point forecasting methods to be less useful due to large prediction errors.
This results in increased uncertainties in the grid operation, thereby
negatively affecting the reliability and increased cost of operation. This
research paper proposes a unified architecture for multi-time-horizon
predictions for short and long-term solar forecasting using Recurrent Neural
Networks (RNN). The paper describes an end-to-end pipeline to implement the
architecture along with the methods to test and validate the performance of the
prediction model. The results demonstrate that the proposed method based on the
unified architecture is effective for multi-horizon solar forecasting and
achieves a lower root-mean-squared prediction error compared to the previous
best-performing methods which use one model for each time-horizon. The proposed
method enables multi-horizon forecasts with real-time inputs, which have a high
potential for practical applications in the evolving smart grid.Comment: Accepted at: IEEE Energy Conversion Congress and Exposition (ECCE
2018), 7 pages, 5 figures, code available: sakshi-mishra.github.i
Output Regulation of Stochastic Sampled-Data Systems with Post-processing Internal Model
This paper deals with the output regulation problem (ORP) of a linear
time-invariant (LTI) system in the presence of sporadically sampled measurement
streams with the inter-sampling intervals following a stochastic process. Under
such sporadically available measurement streams, a regulator consisting of a
hybrid observer, continuous-time post-processing internal model, and stabilizer
are proposed, which resets with the arrival of new measurements. The resulting
system exhibits a deterministic behavior except for the jumps that occur at
random sampling times and therefore the overall closed-loop system can be
categorized as a piecewise deterministic Markov process (PDMP). In existing
works on ORPs with aperiodic sampling, the requirement of boundedness on
inter-sampling intervals precludes extending the solution to the random
sampling intervals with possibly unbounded support. Using the Lyapunov-like
theorem for the stability analysis of stochastic systems, we offer sufficient
conditions to ensure that the overall closed-loop system is mean exponentially
stable (MES) and the objectives of the ORP are achieved under stochastic
sampling of measurement streams. The resulting LMI conditions lead to a
numerically tractable design of the hybrid regulator. Finally, with the help of
an illustrative example, the effectiveness of the theoretical results are
verified
Analysis and control of dual-output LCLC resonant converters with significant leakage inductance
The analysis, design and control of fourth-order LCLC voltage-output series-parallel resonant converters for the
provision of multiple regulated outputs, is described. Specifically, state-variable concepts are developed to establish operating mode boundaries with which to describe the internal behavior and the impact of output leakage inductance. The resulting models are compared with those obtained from SPICE simulations and measurements from a prototype power supply under closed loop control to verify the analysis, modeling, and control predictions
"Class-Type" identification-based internal models in multivariable nonlinear output regulation
The paper deals with the problem of output regulation in a “non-equilibrium” context for a special class of multivariable nonlinear systems stabilizable by high-gain feedback. A post-processing internal model design suitable for the multivariable nature of the system, which might have more inputs than regulation errors, is proposed. Uncertainties in the system and exosystem are dealt with by assuming that the ideal steady state input belongs to a certain “class of signals" by which an appropriate model set for the internal model can be derived. The adaptation mechanism for the internal model is then cast as an identification problem and a least square solution is specifically developed. In line with recent developments in the field, the vision that emerges from the paper is that approximate, possibly asymptotic, regulation is the appropriate way of approaching the problem in a multivariable and uncertain context. New insights about the use of identification tools in the design of adaptive internal models are also presented
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