Category Control of Solar Energy Systems

S/T Mixed Sensitivity Problem-Based Controller Design

The Hx control theory has received a lot of attention in the last decade within the research community due to the robustness characteristics supplied by its controllers. These features make it of a priori interest to be used in controlling such solar refrig­eration systems. The basic idea is to minimize the ratio between the energy of the error vector and the energy of the exogenous signals [353]. The sub-optimum so­lution of the problem based on the S/T or S/KS/T mixed sensitivity problem for building up the generalized plant, allows the controller to be obtained by just de­signing a nominal model and some suitable weighting matrices.

In this application [119], the Hx control is used to regulate the absorption ma­chine inlet temperature which establishes the evaporator and absorber pre...

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Single Operating Mode

Many controllers have been tested at the actual plant. In [119] a robust controller based on the Иж mixed sensitivity problem was applied to regulate the genera­tor inlet temperature. The controller includes a feedforward action to deal with the measurable disturbances. In [275] the Smith predictor generalized predictive control (SP-GPC) is used to control the generator inlet temperature. The controller includes the robustness filters and a feedforward action and also considers system constraints. A robust sliding mode predictive control is developed in [148]. It is based on the idea of a combination of model predictive control and sliding mode control. The main idea is to introduce the prediction of the sliding surface into the control objective function of a model predictive control...

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Controllers for the Solar Air Conditioning Plant

The main problem encountered when controlling a solar energy process is that the primary source of energy cannot be manipulated and, from a control point of view, the solar irradiance acts as a perturbation. The control research community has contributed considerably in this field by designing advanced controllers for so­lar processes [85]. Some controllers applied to the solar air conditioning plant are described in the following. They are classified according to the operating modes considered by the controller, i. e. single and multiple operating modes.

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Solar Refrigeration

3.3.1 Introduction

Air conditioning consumes a lot of electric energy. An important factor of condition­ing systems is the relationship between solar irradiance and the ambient tempera­ture and the refrigeration demand (related to temperature). Most people use their air

Подпись: Fig. 7.20 Solar plant
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conditioning units when it is hot and this high ambient temperature usually occurs together with high solar radiation during daytime. In a passive solar energy system such as solar collectors, solar radiation is the main energy source and it is therefore, appropriate as the energy source for a cooling system [284].

There are many advantages coming from the use of solar energy for air condition­ing systems...

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Summary

An application of automatic control strategies to a solar plant for material treatment has been shown. This kind of plant is manually operated by skilled operators and so the development of an automatic control strategy aimed at achieving adequate results throughout the wide range of operating conditions under which these plants operate, supposes an important improvement toward facilitating operation and ob­taining the desired performance. The first control scheme presented is based on a

Table 7.3 Fuzzy vs. human statistics (courtesy of D. Lacasa et al., [221])

Step

Rise time [s] Rise rate [°C/s]

Overshoot [%]

Fuzzy control

650-700

42

1.1906

5.99

700-800

47

2.1276

4.59

800-1000

136

1 . 4706

2.45

1000-800

149

-1.3423

-1.92

800-...

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Plant Results

The fuzzy controller was tested at the plant in the aforementioned versions, i. e. soft, intermediate and hard approaches, each of them varying most of the parameters of the fuzzy paradigm, such as partition of the input universes and knowledge rule base. Quality, not quantity, should be stressed, thus two experiments were devoted to tun­ing the initial simulation-based candidate controllers which yielded the final generic controller. Another fact to be noted is the ease of implementation and deployment of these kinds of system. Additionally, more than decent results were obtained from the very early tests performed at the plant. Figure 7.18 corresponds to a step-response test where a wide range of sample temperature is covered (from ambient to 600°C)...

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Rule Base and Method of Inference

Two features left to give life to this FLC are knowledge and reasoning capabilities. The former will come in the form of a rule base while the latter will fire rules and work with them to return, in the end, an incremental crisp value to add to the current state of the output variable. In this case (two input-single output system), the rules take the form: IF error is positive very large (+VLarge) AND change in error is negative medium (-Medium) THEN control signal increment must be medium (+M).

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Because of the partial matching attribute of fuzzy control rules and the fact that the antecedents of rules overlap, two in each input dimension, up to four rules can fire at the same time...

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Universe of Discourse of Output Variable

The output variable has been selected as an increment of the shutter aperture. The discretization of the output universe into fuzzy sets plays a vital role in the final

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Fig. 7.16 Universe of discourse of the error derivative variable (courtesy of D. Lacasa et al., [221])

behavior of the controlled system. Moreover, the plant presents a non-linearity of slew-rate type in the actuator, which basically means that its speed is constrained and varies with direction (opening or closing) and beginning and end positions. Therefore, much attention and time was devoted to its study, i. e. the actuator un­derwent rigorous tests of opening/closing rates at a series of positions, throughout the whole range available...

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