Main Article Content

Article Sidebar

Published Sep 8, 2021
Nageswara Rao Gudipudi


Among several renewable energy resources, Solar has great potential to solve the problem of shortage of nonrenewable energy resources. Now-a-days a greater number of houses are installed with PV system. As installation increases monitoring the system is a need to protect the system from weather and atmospheric conditions. Environmentally friendly power is free, spotless and interminable, for example, sunlight based photovoltaic (PV), wind energy, flowing energy, and so forth… and the utilizations of sustainable assets have been respected by all clients [1, 2]. In electrical business sectors, PV is becoming quickly because of their benefits like expense decrease of sun powered board, life time of the board, simple establishment and support [3]. In that support has become the more significant part for extricating the most extreme energy from the sunlight based board for the duration of the existence season of the module. By appropriate upkeep, Maximum Power (Pm) extraction is guaranteed by Maximum Power Point Tracker (MPPT). In writing, bunches of papers are introduced in the space of most extreme force point following [4-6].Throughout the most recent couple of many years, the utilization of PV frameworks has spread quickly everywhere on the world in various applications, from space to the private, business, and modern applications [7]. High utilization of PV boards has brought high PV segment disappointment rate too. This high rate is a direct result of numerous reasons, for example, cut off modules; an open circuit in various strings, and so forth These disappointments extraordinarily impact on the working proficiency of the PV age frameworks and the presentation of the PV framework work. In this way, numerous kinds of examination and improvements in ref. [8] gained great headway utilizing on-line deficiency determination to build framework unwavering quality and execution by early issue discovery.Various shortcomings can be classified into impermanent and perpetual blames in PV clusters, and both are liable for lessening the yield power and sun based energy created contrasted and solid working conditions [9]. Albeit the brief flaws happened for a brief period truly, the security gadgets need to segregate this condition from perpetual shortcoming conditions for forestalling incorrectly closures [10]. In this way, to decrease the inaccessibility time, increment the steadiness and effectiveness of PV frameworks, distinctive issue determination and location strategies are expected to guarantee the progression of sun based age [11]. There are many shortcoming location techniques utilized in the GCPV frameworks. In [12], programmed oversight by OPC innovation based observing by figuring Voltage and Current Ratios VR, and IR is utilized to distinguish the various flaws. Some different strategies, for example, in ref. [13,14] are executed dependent on the constant natural conditions and some PV boundaries, notwithstanding, the disadvantage of this strategies is the significant expense of hardware. Also, Partial Shading PS in the GCPV plant utilizing measurable techniques is talked about and approved utilizing Ratios VR& PR [15,16].As of late, various flaw determination strategies dependent on man-made reasoning methods are embraced to build the identification rate and right grouping, for example, Fuzzy Logic Control FLC [13, 16], Neural Network [8,17], both Neural and FLC [16,18], Genetic Algorithm [19], or MATLAB [10, 12]. As of now, the FLC is broadly utilized with GCPV frameworks, Such as [16, 18] which introduced FLC to distinguish the flawed modules, yet couldn't separate between open circuit and short out deficiencies.The work introduced in this paper is to acquaint another procedure with recognize and analyse the various kinds of flaws happening in the PV power plants utilizing sugeno FLC strategy. This strategy is utilized as a counterfeit technique to expand the exactness of flaw recognizable proof and quick conclusion. The proposed discovery strategy relies upon the examination set of two information proportions which are Current Ratio IR, and Voltage Ratio VR. Computing these proportions is performed utilizing the deliberate and mimicked under ordinary and defective conditions. The flaw discovery technique is executed utilizing FLC-based strategy and MATLAB Simulink device to distinguish the sort of the shortcoming. The chose blames in this work are PS, PS with bypass diode failure, open circuit, short circuit, snow falling, and bird or tree leaves dropping on PV plant.

This paper proposes a novel technique for fault detection in the photovoltaic array by using fuzzy logic controller. By using a simulation model, the voltage and current variation under eight different faults. They are open circuit, short circuit, partial shading, bypass diode failure, snow falling, bird or tree leaves dropping faults. The simulated attributes are given to the fuzzy logic controller to predict the type of fault occur in or between photovoltaic modules.

How to Cite

Abstract 8 |

Article Details


Maximum Power Point Tracker, photovoltaic, Maximum Power, Fuzzy Logic Control

[1] Liqun Liu, Xiaoli Meng andChunxia Liu, “ A review of maximum power point tracking methods of PV power system at a uniform and partial shading”, International Journal on Renewable and Sustainable Energy Reviews, Vol.53, pp.1500-1507,2016.
[2] Kashif Ishaque and Zainal Salam, “ A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition”, International Journal on Renewable and Sustainable Energy Reviews, Vol.19, pp.475-488,2013.
[3] M. SabbaghpurArani and M.A Hejazi, “ The comprehensive study of electrical faults in PV array”, Journal of Electrical and Computer Engineering, Volume 2016.
[4] S.Saravan and N.Ramesh Babu, “Maximum power point tracking algorithms for the photovoltaic system- A review”, International Journal on Renewable and Sustainable Energy Reviews, Vol.57, pp.192-204, 2016.
[5] K.L.Lian, J.H.Jhang, and I.S.Tian, “ A maximum power point tracking method based on perturb-and-observe combined with particle swarm optimization”, IEEE Journal of Photovoltaic, Vol.4, No.2,pp.626- 633,2014.
[6] Santi Agatino Rizzo and Giacomo Scelba, “ANN based MPPT method for rapidly variable shading conditions”, International Journal on Applied Energy, Vol.145, pp.124-132, 2015.
[7] E. Kaplani, Detection of degradation effects in field- aged c-Si solar cells through IR thermography and digital image processing, Int. J. of Photovoltaic Energy, 11, (2012).
[8] A.T. Lahiania, A. B. Abdelghani, I. S. Belkhodja, Fault detection and monitoring systems for photovoltaic installations: A review, Renew. and Sustain. Energy Review, 82, 12 (2018)
[9] A. Chokor, M. El Asmar, S. V. Lokanath, A Review of PV DC Systems Prognostics and Health Manag.: Challenges and Opportunities, PHM Conf., 6 oct., Denver, Colorado, 12(2016).
[10] R. Hariharan, M. Chakkarapani, G. S. Ilango, A Method to Detect Photovoltaic Array Faults and PS in PV Systems, IEEE J. of Photovoltaic, 6(5), 7(2016).
[11] R. Khenfer, et. al, Faults Detection in a PV Generator by Using Matlab Simulink and the chipKIT Max32 Board, Int. J. of Photovoltaic Energy, 9(2014).
[12] S. Silvestre, et. al., Remote supervision and fault detection on OPC monitored PV systems, Sol. Energy, 137, 9(2016).
[13] W. Chine, A. Mellit, A. M. Pavan, S. A. Kalogirou., Fault detection method for grid-connected photovoltaic plants". Ren. Energy, 66, 11(2014).
[14] R. Platon, J. Martel, N. Woodruff, T. Y. Chau, Online fault detection in PV systems". IEEE Trans. Sustain. Energy, 6(4), 7(2015).
[15] M. Dhimish, V. Holmes, B. Mehrdadi, M. Dales, Fault detection algorithm for GCPV plants, Sol. Energy., 137, 9(2016).
[16] M. Dhimish, V. Holmes, B. Mehrdadi, M. Dales, Diagnostic method for photovoltaic systems based on six layer detection algorithm, Elec. Power Sys. Res. 151, 13(2017).
[17] W. Chine, A. Mellit, V. Lughi , A. Malek, G. Sulligoi, A. M. Pavan., A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks, Renew. Energy, 90, 11(2016).
[18] M. Dhimish, V. Holmes, B. Mehrdadi, M. Dales, Comparing Mamdani Sugeno fuzzy logic and RBF ANN network for PV fault detection, Renew. Energy, 10 (066), 2017.
GE2- Electrical