Solar Energy and Solar Charge Controllers

When solar energy is stored in the battery, it’s necessary to protect the battery from being damaged for the reason of overcharging, over discharging. There are many works on it. More or less maximum designing purposes & processes are same. We have gone through several journal papers. From which a brief review is given here.

A work has been done to develop a charge controller which based on Cuk DC/DC converter in order to transfer solar energy (photovoltaic) to battery.

Here the Cuk convert is configured in parallel power transfer mode (PPT) which can be able to achieve higher efficiency. The paraller power transfer can flow the power into the load by two ways. These are (1) power flowing directly to the load without the converter (Pp) and (2) power flowing to the load through the converter (Pm). Thus this controller get more efficient. This is shown in figure 1.2 [17].

Figure 1.2: Power flow of converter in (a) conventional and (b) PPT Configuration.

This paper deals with a charge controller for thin-film (CdTe) PV modules. The output voltage is boosted that refers output voltage is much higher than the popular values of the order of nominal 12V or 24V. In PV technique notion of the ‘higher voltages’ definition are not exceeding 1KV [19]. A charge controller which is incorporating an SGS- Thompson microcontroller, ST62E20 can be able to prolong the battery life [20].

In recent year research and development of low cost flat-panel solar panels, thin-film devices, concentrator system have increased. There are many MPPT algorithm which can be used for implementation viz.

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Incremental conductance method, constant voltage, Fuzzy logic based method etc[10]. Different MPPT algorithms [8],[11],[12] are given in brief about their feature and limitations as below:

  1. Incremental conductance (INC) method [8], [11] of tracking the MPP does not depend upon PV array, tracking efficiency is good, and implementation is medium. Sensing parameters are voltage and current, convergence speed is medium and of analog type.
  2. Fuzzy logic control based MPPT [8], [11] which is PV array dependent, Tracking efficiency is good, implementation is very complex, convergence speed is fast and of digital type.
  3. Neural network based MPPT [11] is also PV array dependent, tracking efficiency is good, implementation is very complex, convergence speed is fast and of digital type.
  4. Linear current control based MPPT [9], [12] is PV array dependent, tracking efficiency is not so good, implementation complexity is medium, convergence speed is fast, sensing parameter is irradiance and of digital type.
  5. Temperature based MPPT [11] depends upon PV array, tracking efficiency is excellent, implementation is simple and MPP is comparatively accurate and sensing parameters are voltage and temperature.
  6. Array reconfiguration based MPPT [11] is PV array dependent, tracking efficiency is poor, convergence speed is slow, implementation complexity is high, sensing parameters are voltage and current and of digital type.
  7. Perturb and observe based MPPT [8], [9], [11] is not PV array dependent, tracking efficiency is good but with unstable operating points, implementation is simple, sensing parameters are voltage and current.
  8. Advanced Perturb and Observe based MPPT is not PV array dependent, tracking efficiency is very good with stable MPPs, implementation is medium, sensing parameters are voltage and current [10].

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Solar Energy and Solar Charge Controllers. (2019, Nov 19). Retrieved from http://studymoose.com/solar-energy-and-solar-charge-controllers-essay

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