Improved power output forecastingtechnique for effective battery management in photovoltaic system / Utpal Kumar Das

Utpal, Kumar Das (2019) Improved power output forecastingtechnique for effective battery management in photovoltaic system / Utpal Kumar Das. PhD thesis, University of Malaya.

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      Photovoltaic (PV) is widely used to mitigate the impact of global warming and climate change, and meet the growing electricity demand. However, inaccurate forecasting of PV power generation is a great concern in the planning and operation of stable and reliable electric grid system, and large-scale PV deployment. In order to forecast the PV power generation accurately, this study proposes a particle swarm optimization (PSO) optimized support vector regression (SVR) based forecasting model. In this process, an SVR-based model is developed based on the historical PV output power and most influential meteorological data of real PV station. A PSO-based algorithm is adopted for the appropriate selection of dominated parameters of SVR-based model to achieve better performance. In addition, a novel data preparation algorithm is also developed to prepare the solar irradiance (SI) pattern of the forecasted day for forecasting PV output power based on the online-weather-report. The proposed model is applied and experimentally validated by deploying it to three different PV stations. One of the most important uses of PV power is to charge the electric batteries for night time electricity supply in rural and isolated areas, and electric vehicles (EVs). However, a significant amount of PV power is wasted due to the inability to exploit the maximum power to maintain the appropriate charging rate of the batteries. Therefore, the maximum utilization of PV generated power is improbable especially in stand-alone PV based battery charging system. In order to ensure the maximum utilization of PV generated power, a novel battery charging management (BCM) algorithm based on the forecasted PV output power has been proposed in the present study. To extract the maximum power from the PV system, the proposed BCM algorithm selects a suitable set of battery cells to charge for a particular time by referring to the forecasted PV output power. In most of the applications of electric battery, especially Li-ion battery, the battery pack consists of a number of series connected cells to meet the required voltage demand. However, the voltage variation among the series connected cells is a great obstacle for safe and effective battery operation. In order to equalize the cell voltages completely within a very short time, this study proposes a new structure of resonant switched capacitor (SC) equalizer. In this case, the resonant tank is designed besides considering the zero-current-switching (ZCS) and zero-voltage-gap (ZVG) for minimizing the switching losses and equalizing the cell voltages completely. Additional capacitor tier is included to improve the balancing speed. The nRMSE of the proposed forecasting model is found as 2.841% and 9.422% in testing and online dataset, respectively. The proposed algorithm ensures 87.47% utilization of PV generated power in battery charging. The proposed cell equalizer confirms 99.95% equalization of the cell voltages in a very short time at an energy transferring efficiency of 97.34%. It reduces the 61.25% and 46.55% balancing time from conventional SC and conventional resonant SC equalizers, respectively. The results show that the proposed model, algorithm, and circuit perform better compare to the existing system.

      Item Type: Thesis (PhD)
      Additional Information: Thesis (PhD) – Faculty of Computer Science & Information Technology, University of Malaya, 2019.
      Uncontrolled Keywords: photovoltaic (PV) power forecasting; Maximum utilization of PV power; Cell voltage equalization; Renewable energy; Battery management system (BMS)
      Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
      Divisions: Faculty of Computer Science & Information Technology
      Depositing User: Mr Mohd Safri Tahir
      Date Deposited: 06 Jan 2020 02:06
      Last Modified: 18 Jan 2020 10:51

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