Identification of Frequency Ranges for Subharmonic Oscillations in a Relay Feedback System

This paper examines the behaviour of a single loop relay feedback system (RFS) which is driven by an external signal. It is well known that such a RFS exhibits a variety of oscillation patterns including forced and subharmonic oscillations (SO). This paper focuses on the conditions for SO. It will be shown that for an external signal with a fixed amplitude, it is possible for SO with different orders to occur simply by changing the frequency of the external signal. Similarly, for an external signal with a fixed frequency, it is possible for SO with different orders to occur when the amplitude of the external signal is varied. The conditions under which these different scenarios will occur are explored. An analysis of these conditions identifies the frequency ranges where certain orders of SO are possible for a given amplitude of the external signal. The effects of the initial conditions on the SO are illustrated and discussed. Simulation studies are presented to illustrate the result

Modelling and Parameter Identification Using Reduced I-V Data

No abstract available

Evaluation of the Students' Learning Experience and Competency Gain with YACRS

No abstract available

An Improved Oblique Asymptote Method for Parameter Identification of PV Panels

A single-diode model is the most important and broadly used tool for PV module design and analysis. The model has 5 parameters to be identified from the I-V characteristics curves. However, due to the lack of explicit form of I or V with the unknown 5 parameters, parameter identification is very difficult. Recent progress in PV model identification are discussed in this paper with the simulation of MATLAB against the measured data from a real PV module. An improved Oblique Asymptote Method is then proposed and compared with existing identification methods. Test results show that the proposed method achieves lower RMSE with less knowledge of I - V data points

Low-Cost Precision Motion Control for Industrial Digital Microscopy

This paper presents a reliable but low-cost way for industrial digital microscopy to implement μm-level precision of X/Y stage motion control. Other than the prevailing designs using stepper motors with open-loop control algorithms, the proposed method uses DC motors with closed-loop sliding mode control (SMC) to save the cost and allow a smooth switching between manual and motorized mode for stage movement. Boundary layer (saturator) method is then applied to alleviate the chattering cause by SMC, and its accuracy loss is completely eliminated by a simple position fine-tune trick to limit the error within ±2 μm. Comparing with the main stream μm-level industrial microscopies with stepper motors, the proposed solution achieves similar performance with almost half costs

High-precision XY stage motion control of industrial microscope

This paper presents an economic way to implement a high precision (um level) XY stage motion control for the industrial microscope using DC motors. Other than the prevailing design of using stepper motors where the stage is always locked under the motorized mode, the proposed design allows users to manually move the stage by introducing the friction engagement in between. The nonlinearity from the friction is then fully compensated by the sliding mode control (SMC) so that the stage can strictly follow the predefined motion profile. Possible chattering suppression methods are discussed and the accuracy loss is analyzed using LuGre friction model. Finetuning algorithm is then proposed to limit the position error within u2 um. Comparing to the other um-level industrial microscopes using stepper motors, the proposed solution achieves comparable performance with much lower costs

Very short term irradiance forecasting using the lasso

We find an application of the lasso (least absolute shrinkage and selection operator) in sub-5-min solar irradiance forecasting using a monitoring network. Lasso is a variable shrinkage and selection method for linear regression. In addition to the sum of squares error minimization, it considers the sum of ℓ1-norms of the regression coefficients as penalty. This bias–variance trade-off very often leads to better predictions.<p></p> One second irradiance time series data are collected using a dense monitoring network in Oahu, Hawaii. As clouds propagate over the network, highly correlated lagged time series can be observed among station pairs. Lasso is used to automatically shrink and select the most appropriate lagged time series for regression. Since only lagged time series are used as predictors, the regression provides true out-of-sample forecasts. It is found that the proposed model outperforms univariate time series models and ordinary least squares regression significantly, especially when training data are few and predictors are many. Very short-term irradiance forecasting is useful in managing the variability within a central PV power plant.<p></p&gt

A linear method to extract diode model parameters of solar panels from a single I–V curve

The I-V characteristic curve is very important for solar cells/modules being a direct indicator of performance. But the reverse derivation of the diode model parameters from the I-V curve is a big challenge due to the strong nonlinear relationship between the model parameters. It seems impossible to solve such a nonlinear problem accurately using linear identification methods, which is proved wrong in this paper. By changing the viewpoint from conventional static curve fitting to dynamic system identification, the integral-based linear least square identification method is proposed to extract all diode model parameters simultaneously from a single I-V curve. No iterative searching or approximation is required in the proposed method. Examples illustrating the accuracy and effectiveness of the proposed method, as compared to the existing approaches, are presented in this paper. The possibility of real-time monitoring of model parameters versus environmental factors (irradiance and/or temperatures) is also discussed

Conditions for forced and subharmonic oscillations in relay and quantized feedback systems

Ph.DDOCTOR OF PHILOSOPH

PV panel modeling and identification

In this chapter, the modelling techniques of PV panels from I-V characteristics are discussed. At the beginning, a necessary review on the various methods are presented, where difficulties in mathematics, drawbacks in accuracy, and challenges in implementation are highlighted. Next, a novel approach based on linear system identification is demonstrated in detail. Other than the prevailing methods of using approximation (analytical methods), iterative searching (classical optimization), or soft computing (artificial intelligence), the proposed method regards the PV diode model as the equivalent output of a dynamic system, so the diode model parameters can be linked to the transfer function coefficients of the same dynamic system. In this way, the problem of solving PV model parameters is equivalently converted to system identification in control theory, which can be perfectly solved by a simple integral-based linear least square method. Graphical meanings of the proposed method are illustrated to help readers understand the underlying principles. As compared to other methods, the proposed one has the following benefits: 1) unique solution; 2) no iterative or global searching; 3) easy to implement (linear least square); 4) accuracy; 5) extendable to multi-diode models. The effectiveness of the proposed method has been verified by indoor and outdoor PV module testing results. In addition, possible applications of the proposed method are discussed like online PV monitoring and diagnostics, noncontact measurement of POA irradiance and cell temperature, fast model identification for satellite PV panels, and etc