Performance Assessment of Solar-Transformer-Consumption System Using Neural Network Approach

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Ritu Gupta
C.M. Batra


Solar energy is one of the immeasurable renewable energy in power generation for a green, clean and healthier environment. The silicon-layer solar panels absorb sun energy and converts it into electricity by off-grid inverter. Electricity is transferred either from this inverter or from transformer, consumed by consumption unit(s) available for residential or economic purposes. The artificial neural network is the foundation of artificial intelligence and solves many complex problems which are difficult by statistical methods or by humans. In view of this, the purpose of this work is to assess the performance of the Solar - Transformer - Consumption (STC) system. The system may be in complete breakdown situation due to failure of both solar power automation subsystem and transformer simultaneously or consumption unit; otherwise it works with fully or lesser efficiency. Statistically independent failures and repairs are considered. Using the elementary probabilities phenomenon incorporated with differential equations is employed to examine the system reliability, for repairable and non-repairable system, and to analyze its cost function. The accuracy and consistency of the system can be improved by feed forward- back propagation neural network (FFBPNN) approach. Its gradient descent learning mechanism can update the neural weights and hence the results up to the desired accuracy in each iteration, and aside the problem of vanishing gradient in other neural networks, that increasing the efficiency of the system in real time. MATLAB code for FFBP algorithm is built to improve the values of reliability and cost function by minimizing the error up to 0.0001 precision. Numerical illustrations are considered with their data tables and graphs, to demonstrate and analyze the results in the form of reliability and cost function, which may be helpful for system analyzers.


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Gupta R, Ekata, Batra C. Performance Assessment of Solar-Transformer-Consumption System Using Neural Network Approach. Baghdad Sci.J [Internet]. 2022 Aug. 1 [cited 2024 Feb. 29];19(4):0865. Available from:


Kaurav S, Yadav P. Hybrid Power System Using Wind and Solar Energy. Int J Inno Res Sci Engg Tech. 2016 Jan; 5(1): 54-58. Available from: DOI: 10.15680/IJIRSET.2015.0501007 .

Report of the Expert Group on 175 GW RE by 2022. Nat Ins Trans India Aayog. 2015. Available from:

Mudgal SM, Yadav AK, Mahajan V. Reliability Evaluation of Power System Network With Solar Energy. IEEE. 2020 Jul. Available from: DOI: 10.1109/ICPS48983.2019.9067364.

Muthusamy S, Meenakumari R. Optimal Planning of Solar PV/WTG/DG/Battery Connected Integrated Renewable Energy Systems for Residential Applications using Hybrid Optimization. Int J Indust Engg. 2018; 2(1):15-20.

Rausand M, Hoyland A. System Reliability theory. #2. New Jersey: John Wiley & Sons , Inc, Publication; 2004.

Balagurusamy E. Reliability engineering. India: Tata McGraw Hill Publishing Co. Ltd.; 1984.

Bazovsky Igor. Reliability Theory and Practice. NJ: PHI Englewood cliff; 1961.

Chauhan SK, Malik SC. Reliability Evaluation of Series-Parallel and Parallel-Series Systems for Arbitrary Values of the Parameters. Int J Stat Reliab Engg. 2016; 3(1): 10-19.

Paula CPD, Visnadi LBH, Castro FD. Multi-objective optimization in redundant system considering load sharing. Reliab Engg Sys Saf. 2019; 181: 17–27.

Gertsbakh IB, Shpungin Y. Models of Network Reliability: Analysis. Combinatorics, and Monte Carlo. London New York: CRC press Taylor & Francis Group; 2016.

Sanghavi M, Tadepalli S, Boyle T, Downey M, Nakayama M. Efficient Algorithms for Analyzing Cascading Failures in a Markovian Dependability Model. IEEE. Trans Reliab. 2017; 66(2): 258 – 280

Li XY, Huang HZ, Li YF. Reliability analysis of phased mission system with non-exponential and partially repairable components. Reliab Engg Sys Saf. 2018 Jul; 175: 119-127.

Nandal J, Chauhan SK, Malik SC. Reliability and MTSF of series and parallel systems. Int J Stat Reliab Engg. 2015 May; 2(1): 74-80.

Levitin G, Xing L, Dai Y. Reliability versus expected mission cost and uncompleted work in heterogeneous warm standby multiphase systems. IEEE. Trans Sys Man Cybe Sys. 2017; 47(3): 462 – 473.

Gurney K. An introduction to neural networks. London: UCL Press; 1997.

Karunanithi N, Whitley D, Malaiya YK. Using neural networks in reliability. IEEE. 1992; 9(4): 53-59.

Lakshman I, Ramaswamy S. An Artificial Neural-Network Approach to Software Reliability Growth Modeling. Pro Comput Sci. 2015; 57: 695-702. Available from: .

Bisht S, Singh SB, Ekata. Reliability and profit function improvement of acyclic transmission network using artificial neural network. Mathe Engg Sci Aeros. 2020; 11(1): 127-141.

Quiles E, Roldán-Blay C, Escrivá-Escrivá G, Roldán-Porta C. Accurate Sizing of Residential Stand-Alone Photovoltaic Systems Considering System Reliability. MDPI. Sustaina. 2020 Feb 10; 12(3):1274- 1292. Available from: DOI:10.3390/su12031274.

Arévalo JC, Rivera S, Santos F. Uncertainty cost functions for solar photovoltaic generation, wind energy generation and plug-in electric vehicles: Mathematical expected value and verification by Monte Carlo simulation. Int J Pow Ener Conv. 2019 March; 10(2):171-207.

Bhagat BA, Bhondve RR, Maske AV, Ravate SV, Karpe G. Hybrid Power Generation System using Wind Energy and Solar Energy. Int J Res Engg Sci Mngmt. 2019 February; 2(2): 805-808.

Yousif JH, Al-Balushi HA, Kazem HA, Chaichan MT. Analysis and forecasting of weather conditions in Oman for Renewable Energy Applications. Elsevier. Cas Stud Therm Engg. 2018 Nov. 15; 13: 1-12. Available from:

Hussien ZK, Dhannoon BN. Anomaly Detection Approach Based on Deep Neural Network and Dropout. Bagh Sci J. 2020 Jun 23; 17(2): 701-709.

Shaikh MRS, Waghmare SB, Santosh B, Labade S, Tekale A. A Review Paper on Electricity Generation from Solar Energy. Int J Res App Sci Engg Tech. 2017; 5(IX): 1884-1889. Available from: URL: .