Multi-model Predictive Control of Ultra-supercritical Coal-fired Power Unit

The control of ultra-supercritical(USC) power unit is a difficult issue for its characteristic of the nonlinearity, large dead time and coupling of the unit. In this paper, model predictive control(MPC) based on multi-model and double layered optimization is introduced for coordinated control of USC unit. The linear programming(LP) combined with quadratic programming(QP) is used in steady optimization for computation of the ideal value of dynamic optimization. Three inputs(i.e. valve opening, coal flow and feedwater flow) are employed to control three outputs(i.e. load, main steam temperature and main steam pressure). The step response models for the dynamic matrix control(DMC) are constructed using the three inputs and the three outputs. Piecewise models are built at selected operation points. Double-layered multi-model predictive controller is implemented in simulation with satisfactory performance.

Model Identification Based on Subspace Model Identification of Main Steam Temperature in Ultra-Supercritical Coal-Fired Power Unit

Ultra-supercritical(USC) unit is more and more popular in coal-fired power industry.In this paper,closed-loop identification based on subspace model identification(SMI) is introduced for superheated steam temperature system of USC unit.Closed-loop SMI is applied to building step response model of the unit directly.The parameters selection method is proposed to deal with the parameter sensitivity and improve the reliability of the model.Finally,the model is used in model identification of real USC unit.

A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression

A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is un-der normal condition, then kernel regression is further used for quality prediction and estimation. If faults have oc-curred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can ef-fectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.

Design and Performance of a Novel Pancake Rogowski Coil for Measuring Pulse Currents

A novel pancake Rogowski coil without magnetic core is introduced in this paper. Owing to its special pancake winding structure, the coil is of low self-resistance and high self-inductance, and thus has excellent low frequency characteristic in the self-integral mode. Moreover, because of its unique installation method, the coil has a flexible sensitivity and can be applied under situations where toroidal air-core Rogowski coils or printed aircuit board （PCB） coils are not available. The parameters and performance of the pancake Rogowski coil are presented, and the principle of shielding is given. Measurements of step pulse current and oscillating pulse current by the coil are studied experimentally to illustrate its good linearity, reliable and flexible sensitivity and excellent frequency characteristic, especially its advantage in low frequency characteristic. The pancake Rogowski coil can be designed to assume round, square or rectangle shape, and has thus broad prospects in its application to the current measurement in such areas as plasma physics and pulsed power technology.

Application of Kernel GDA to Performance Monitoring and Fault Diagnosis for Rotating Machinery

Faults in rotating machine are difficult to detect and identify,especially when the system is complex and nonlinear.In order to solve this problem,a novel performance monitoring and fault diagnosis method based on kernel generalized discriminant analysis(kernel GDA,KGDA)was proposed.Through KGDA,the data were mapped from the original space to the high-dimensional feature space.Then the statistic distance between normal data and test data was constructed to detect whether a fault was occurring.If a fault had occurred,similar analysis was used to identify the type of faults.The effectiveness of the proposed method was evaluated by simulation results of vibration signal fault dataset in the rotating machinery,which was scalable to different rotating machinery.

Optimal operation strategy for distributed battery aggregator providing energy and ancillary services

The high penetration of distributed renewable energy raises a higher concern for the safe and economic operation of the smart grid. Distributed batteries equipped in demand-side can not only contribute to the reliability and security of the grid, but also make profits by participating in the electricity market, especially when distributed batteries are combined and operated by an aggregator.Considering the well-operated mechanism of performance based regulation(PBR) in the U.S. electricity market, it becomes increasingly lucrative for batteries to participate not only in energy markets for energy arbitrage, but also in ancillary service markets to provide regulation and peakload shaving services. In this study, distributed batteries are operated and coordinated by the aggregator, which simultaneously submits offers to the energy and the ancillary service markets as an individual entity. An optimaldecision model is formulated for the aggregator to determine the operation and bidding strategy for the distributed batteries by considering the characteristics of batteries,including the terms of capacity, efficiency and degradation cost. Finally, a numerical case is conducted to evaluate the benefits of the decision model.