Intelligent Energy Management Technologies: ICAEM 2019 (Algorithms for Intelligent Systems)

19 Detection of GSM Signal Using Energy Detection … 197 that energy detection starts working at –10 dB of SNR in our simulated result while matched filter detection performance is better under lower SNR values. 4 Conclusion The performance of energy detection and matched filter increases with increasing SNR as the probability of detection of signal increases and correspondingly the probability of false detection decreases. At lower SNR, matched filter performance is better than energy detection (Below –5 dB). It is evidenced from Fig. 6 that with increase in SNR value the performance of energy detection improves over matched filter and for lower value of SNR matched filter method performance is improved compared to energy detection method. References 1. Ali et al (2017) Advances on spectrum sensing for cognitive radio networks: theory and applications. IEEE Commun Surv Tutor 19(2):1277–1304 2. Dobariaa A, Sodhatarb S (2015) A literature survey on efficient spectrum utilization: cognitive radio technology. Int J Innov Emerg Res Eng 2(1) 3. Shahzad A et al (2010) Comparative analysis of primary transmitter detection based spectrum sensing techniques in cognitive radio systems”, Aust J Basic Appl Sci 4522–4531. INS Inet Publication 4. Haykin S, Thomson DJ, Reed JH (2009) Spectrum sensing for cognitive radio. IEEE Proc 97(5):849–877 5. Arjoune Y, Mrabet ZE, Ghazi HE, Tamtaoui A (2018) Spectrum sensing: enhanced energy detection technique based on noise measurement. In: IEEE 8th annu comput commun workshop conf (CCWC), Las Vegas. 6. Abbas N, Nasser Y, Ahmad KE (2015) Recent advances on artificial intelligence and learning techniques in cognitive radio networks. EURASIP J Wirel Commun Network. 7. Masonta MT, Mzyece M, Ntlatlapa N (2013) Spectrum decision in cognitive radio networks: a survey. IEEE Commun Surv Tutor 15(3):1088–1107, 3rd Quart. 8. Salama U, Sarker PL, Chakrabarty A (2018) Enhanced energy detection using matched filter for spectrum sensing in cognitive radio networks. In: Joint 7th international conference on informatics, electronics & vision (ICIEV) and 2018 2nd international conference on imaging, vision & pattern recognition (icIVPR), Kitakyushu, Japan, pp 185–190 9. Singh BK, Bhadada R (2020) Enhancing spectral efficiency of mobile communication by store forward base transceiver system, 2020 7th Int Conf Signal Process Integrated Networks (SPIN), Noida, India, 2020, pp. 781–784, http://doi.org/10.1109/SPIN48934.2020.9071058 10. Talukdar J, Mehta B, Aggrawal K, Kamani M (2017) Implementation of SNR estimation based energy detection on USRP and GNU radio for cognitive radio networks. In: IEEE WiSPNET 11. Eduardo AF, Caballero RGG (2015) Experimental evaluation of performance for spectrum sensing: matched filter vs. energy detector. In: IEEE Colombian conference on communication and computing, Popayan, Colombia

Chapter 20 Simulation of Performance Characteristics of Different PV Materials Harish Kumar Khyani and Jayashri Vajpai 1 Introduction The research in the area of generation of solar electricity is motivated by both improvement of the performance for different Photovoltaic (PV) materials and by a decrease in the technological realization cost. Study and development of new photovoltaic materials is an emerging area of research. The variety of photovoltaic semiconductor materials, developed over the past two decades, possess different optical, electrical, and mechanical properties. However, the deployment of these new materials and their associated design technologies for manufacturing solar cell depend essentially on the availability of material and often impose an elevated price. These different materials are characterized by their typical properties, which can considerably influence the price and the performance of the variety of solar cells manufactured by employing them. The present stake in the growth of photovoltaic systems is to find a compromise between the cost and performance to guide the selection of photovoltaic material for designing modules for bulk energy genera- tion. MATLAB/Simulink-based modeling of photovoltaic module has already been accomplished by the authors [1] in a previous work. This model can be adapted to represent different types of photovoltaic materials by changing certain parameters. The simulation of performance features of six commonly used different types of photovoltaic materials, and their comparative study is offered in this paper. This paper is divided into five sections. The next section discusses the PV module tech- nologies being used presently. The third section presents MATLAB/Simulink-based model of PV system followed by the simulation of different PV materials. Finally, H. K. Khyani (B) · J. Vajpai 199 M.B.M Engineering College, JNVU, Jodhpur, India e-mail: [email protected] J. Vajpai e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Shorif Uddin et al. (eds.), Intelligent Energy Management Technologies, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-15-8820-4_20

200 H. K. Khyani and J. Vajpai the comparison of the performance characteristics of these models has been carried out before concluding comments. The research for mathematical modeling of PV cells was initiated by physicists in the year 1883 but engineering-oriented modeling began quite recently around the year 2000. With the emergence of computational modeling techniques along with increased interest of researchers toward simulation-based studies, the use of MATLAB software has become the ruling trend in this research area. Tsai et al. [2] pioneered the progress of generalized MATLAB/Simulink® model for solar PV cell to examine the consequence of solar irradiance and cell temperature. This was followed by a circuit-based simulation model designed by González-Longatt [3], in order to evaluate the electrical behavior of the cell with respect to changes in tempera- ture and irradiance. Further, Bhatt and Thakker demonstrated MATLAB-based simu- lation of photovoltaic array at different temperatures to obtain their electrical charac- teristics as a function of temperature [4]. Yousef Mahmoud, Xiao, and Zeineldin have simplified the model and extended it for the PV modules while employing precise estimates of the model parameters directly from manufacturer datasheets [5]. Panwar and Saini have studied the problem of model parameter determination based on the four-parameter model using MATLAB/Simulink [6]. These researchers simulated MATLAB-based models of photovoltaic modules of crystalline silicon to examine the effect of temperature and irradiance on the performance characteristics. The authors of this paper have attempted to develop a simplified MATLAB/Simulink model that has been validated for performance characteristics by comparison with commercially available polycrystalline module from Easy Photovoltaic Private Limited, Ghaziabad, Uttar Pradesh. Then the devel- oped model has been extended to simulate presently used commercial materials like Monocrystalline Silicon (Si-mono), Polycrystalline Silicon (Si-poly), Amorphous Silicon (a-Si) and the emerging like Cadmium Telluride (CdTe), Copper Indium Selenide (CIS), and Gallium Arsenide (GaAs). 2 PV Module Technologies Several crystalline silicon based and thin-film PV technologies have been demon- strated commercially on a large scale in the past few years. In addition, several emerging PV technologies may be technically and economically competitive in the future. This subsection briefly describes some of these PV module technologies and compares their performance, particularly conversion efficiency. Efficiency is a signif- icant characteristic for comparison of performance of different materials for design of modules. The efficiency of a PV cell or module is defined as the percentage of the solar energy striking the cell or module that is converted into electricity [7]. The PV materials considered here can be broadly classified as follows:

20 Simulation of Performance Characteristics of Different … 201 2.1 Crystalline Silicon PV Technologies Crystalline Silicon (c-Si) technologies that constitute about 80% of the current PV market have reliable performance with working lifetime of more than 25 years. There are two types of crystalline silicon PV technologies: monocrystalline (Si-mono or Sc-Si) and multi-crystalline (Si-poly or mc-Si). The rated DC efficiencies of standard c-Si PV modules are about 14–16% [7]. 2.2 Thin-Film PV Technologies Thin-film PV cells have a few microns (µm) thick semiconductor layer which is about 100 times thinner than commercial c-Si cells. The most common thin-film semi- conductor materials include Amorphous Silicon (a-Si), Cadmium Telluride (CdTe), Gallium Arsenide (GaAs), and alloys of Copper Indium Selenide (CIS). Thin-film modules have lower DC efficiencies than c-Si modules: about 9–12% for CdTe and 6–9% for a-Si. CdTe-based PV has proficient expressively higher market growth during the last decade than the other thin-film PV technologies [7]. 3 Design of MATLAB-Simulink Model of PV System The equivalent circuit and mathematical equations used to design MATLAB- Simulink model of the generalized PV cell have been described in this section. The model of [1] has been further extended for modeling a module and an array as described in Sect. 3.2 and simulated as described in Sect. 3.3. It is modified to suit the modeling of cells of different important materials in Sect. 3.4 to build a generalized model that is suitable for scaling at all levels of model, i.e., the PV cell, module, and an array. The generalized PV model has been developed by using the following nonlinear voltage-current (V-I) characteristic equation of a solar cell [8]: I = IPH − IS q(V +I RS ) −1 − V + I RS (1) RSH exp AkTC where IPH Photocurrent, IS Saturation current, q Charge on an electron, 1.6 × 10−19C,

202 H. K. Khyani and J. Vajpai k Boltzmann constant = 1.38 × 10−23 J/K, A Ideality factor, T C Working temperature, RS Series resistance, RSH shunt resistance. The series loss and the leakage to ground is neglected in this model, i.e., RS = 0 and RSH = ∞. The photocurrent IPH is obtained by IP H = ISC + K I TC − TRef G (2) where ISC Short-circuit current. KI Short-circuit current temperature coefficient. T Ref Reference temperature. G Solar insolation. The saturation current IS is obtained by ⎡⎤ q EG −1 1 TC 3 TRe f TC ⎦ IS = IRS TRe f (3) exp⎣ kA where IRS Reverse saturation current. EG Energy bandgap of the semiconductor material used. The reverse saturation current IRS is represented by IRS = ISC −1 (4) exp (5) q VOC kTC A Finally, the output current of the PV cell is given as follows: I = IPH − IS exp q VOC −1 kTC A These equations form the base for the development of model.