Real-time monitoring of power production in modular hydropower plant: most significant parameter approach

  • Priyanka MajumderEmail author
  • Mrinmoy Majumder
  • Apu Kumar Saha


The uncertainty in the water-based renewable energy systems reduces the plant capacity. However, real-time monitoring of hydropower plants ensures optimality and continuous faultless performance from the plant. But the implementation of real-time systems has always increased the overall operation cost of the power plant due to the continuous monitoring, analysis and decision-making (MAD) to assure prolonged and in situ detection and solution of uncertainties. The requirement to observe multiple indicators which represent the plant performance, elevate the cost of managing and impact the economical returns from the power plant. Also the infrastructural adjustments required to enable real-time monitoring of a power plant will also induce increased expenditure. The present study aimed to reduce the cost and infrastructural requirements of a smart system to represent the plant performance for instant mitigation of system failures by replacing the requirement of multi-indicator tracking by single weighted function monitoring. This monitoring upgradation will reduce the process cost of the system, thereby elevating the profitability of the power plant. The functional tracking will also increase the efficiency of the MAD and minimize the memory requirement of the real-time monitoring as single pointer will be required to be analysed and evaluated before taking a decision. In this aspect, an objective multi-criteria decision-making technique was used to find the significance of each indicator in hydropower production such that they can be tracked as per their potential for destabilizing the system. The results show that the new multi-criteria decision-making method which hybridizes with polynomial neural networks can identify uncertainty based on the significance of parameters by a portable and independent platform that can be integrated with supervisory control-based systems to monitor uncertainty in a hydropower system. According to the results, operation and maintenance cost followed by the discharge indicator was found to have the highest significance among the indicators considered in the study. The results depict that the new multi-criteria decision-making method with polynomial neural networks can identify uncertainty based on the significance of parameters with the help of a portable and independent platform that can be integrated in supervisory control systems to monitor uncertainty in a hydropower system at real time.


Real-time monitoring Power production Hydropower plant MCDM 



Analysis and decision-making


Real-time monitoring


Multi-criteria decision-making


Analytical hierarchy process


Analytical network process


Measuring attractiveness by a categorical-based evaluation technique


Polynomial neural network


Statistical control chart


Relative significance


Geometric mean


Supervisory control and data analysis


Plant efficiency function


Return on investment


Utilization factor


New multi-criteria decision-making methods


Pairwise comparison matrix


Evaluation of mixed data


Group method of data handling


Root mean square error


Correlation coefficient


Nash–Sutcliffe efficiency


Performance and profitability






Arc tangent training


Arc tangent testing




Model performance efficiency


Central control system


Ness Sutcliffe Efficiency



  1. Bana E Costa, C. A., & Vansnick, J. C. (1997). Applications of the MACBETH approach in the framework of an additive aggregation model. Journal of Multi-Criteria Decision Analysis, 6(2), 107–114.CrossRefGoogle Scholar
  2. Bilgili, M., Bilirgen, H., Ozbek, A., Ekinci, F., & Demirdelen, T. (2018). The role of hydropower installations for sustainable energy development in Turkey and the world. Renewable Energy, 126, 755–764.CrossRefGoogle Scholar
  3. Chakraborty, T., & Majumder, M. (2017). Application of statistical charts, multi-criteria decision making and polynomial neural networks in monitoring energy utilization of wave energy converters. Environment, Development and Sustainability, 21, 1–21.Google Scholar
  4. Chao, P. Y., Ferreira, P. M., & Liu, C. R. (1988). Applications of GMDH-type modeling in manufacturing. Journal of Manufacturing Systems, 7(3), 241–253.CrossRefGoogle Scholar
  5. Christodoulakis, G. A., & Satchell, S. (Eds.). (2007). The analytics of risk model validation. Amsterdam: Elsevier.Google Scholar
  6. Cristian, B., Stelian, S., Maria, C. A., & Adina, C. (2014). Modeling the causal relationships and measuring the degree of risk and uncertainty on the romanian financial market. Procedia-Social and Behavioral Sciences, 143, 509–513.CrossRefGoogle Scholar
  7. Delgado-Galván, X., Pérez-García, R., Izquierdo, J., & Mora-Rodríguez, J. (2010). An analytic hierarchy process for assessing externalities in water leakage management. Mathematical and Computer Modelling, 52(7–8), 1194–1202.CrossRefGoogle Scholar
  8. Ebtehaj, I., Bonakdari, H., Zaji, A. H., Azimi, H., & Khoshbin, F. (2015). GMDH-type neural network approach for modeling the discharge coefficient of rectangular sharp-crested side weirs. Engineering Science and Technology, an International Journal, 18(4), 746–757.CrossRefGoogle Scholar
  9. Fan, J. L., Hu, J. W., Zhang, X., Kong, L. S., Li, F., & Mi, Z. (2018). Impacts of climate change on hydropower generation in China. Mathematics and Computers in Simulation. Scholar
  10. Fan, G., Zhong, D., Ren, B., Cui, B., Li, X., & Yue, P. (2016). Real-time grouting monitoring and visualization analysis system for dam foundation curtain grouting. Transactions of Tianjin University, 22(6), 493–501.CrossRefGoogle Scholar
  11. Galton, F. (1886). Regression towards mediocrity in hereditary stature. The Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.CrossRefGoogle Scholar
  12. Ghosh, S., Chakraborty, T., Saha, S., Majumder, M., & Pal, M. (2016). Development of the location suitability index for wave energy production by ANN and MCDM techniques. Renewable and Sustainable Energy Reviews, 59, 1017–1028.CrossRefGoogle Scholar
  13. Goyal, M. K., & Goswami, U. P. (2018). Teesta river and its ecosystem. In: The Indian Rivers (pp. 537–551). Singapore: Springer.
  14. Gu, H., & Xu, J. (2011). Grey relational model based on AHP weight for evaluating groundwater resources carrying capacity of irrigation district. In Water resource and environmental protection (ISWREP), 2011 international symposium on (Vol. 1, pp. 308–310). IEEE.Google Scholar
  15. Iqbal, Z., Javaid, N., Iqbal, S., Aslam, S., Khan, Z. A., Abdul, W., et al. (2018). A domestic microgrid with optimized home energy management system. Energies, 11(4), 1002.CrossRefGoogle Scholar
  16. Jahan, A., Edwards, K. L., & Bahraminasab, M. (2016). Multi-criteria decision analysis for supporting the selection of engineering materials in product design. Oxford: Butterworth-Heinemann.Google Scholar
  17. Landry, M., Malouin, J. L., & Oral, M. (1983). Model validation in operations research. European Journal of Operational Research, 14(3), 207–220.CrossRefGoogle Scholar
  18. MacKenzie, J. J. (1998). Oil as a finite resource. Nonrenewable Resources, 7(2), 97–100.CrossRefGoogle Scholar
  19. Majanne, Y., Korpela, T., Judl, J., Koskela, S., Laukkanen, V., & Häyrinen, A. (2015). Real time monitoring of environmental efficiency of power plants. IFAC-PapersOnLine, 48(30), 495–500.CrossRefGoogle Scholar
  20. Majumder, P., Majumder, M., & Saha, A. K. (2016). Application of decision making for optimal condition method to analyze operational efficiency of hydropower plants. International Journal of Control Theory Applications, 9(42), 79–94.Google Scholar
  21. Majumder, P., Majumder, M., & Saha, A. K. (2018). Climate change and urbanization impact on hydropower plant by neural network-based decision-making methods: Identification of the most significant parameter. Water Conservation Science and Engineering, 3(3), 169–179.CrossRefGoogle Scholar
  22. Majumder, P., & Saha, A. K. (2018). Efficiency assignment of hydropower plants by DEMATEL-MAPPAC approach. Water Conservation Science and Engineering, 3(2), 91–97.CrossRefGoogle Scholar
  23. Majumder, P., Saha, A. K., & Majumder, M. (2017). Identification of most important parameter for efficiency performance of hydro power plant by harmonic mean hierarchy process (HMHP). Skit Research Journal, 7, 60–66.Google Scholar
  24. Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10(3), 282–290.CrossRefGoogle Scholar
  25. Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455.CrossRefGoogle Scholar
  26. Pal, S. (2016). Impact of Massanjore Dam on hydro-geomorphological modification of Mayurakshi River, Eastern India. Environment, Development and Sustainability, 18(3), 921–944.CrossRefGoogle Scholar
  27. Roos, A., & Bolkesjø, T. F. (2018). Value of demand flexibility on spot and reserve electricity markets in future power system with increased shares of variable renewable energy. Energy, 144, 207–217.CrossRefGoogle Scholar
  28. Rykiel, E. J., Jr. (1996). Testing ecological models: the meaning of validation. Ecological Modelling, 90(3), 229–244.CrossRefGoogle Scholar
  29. Saaty, T. L. (1980). The analytic hierarchy process. New York: McGrawHill.Google Scholar
  30. Saaty, T. L. (2004). Fundamentals of the analytic network process—Dependence and feedback in decision-making with a single network. Journal of Systems Science and Systems Engineering, 13(2), 129–157.CrossRefGoogle Scholar
  31. Sarkar, A., & Majumder, M. (2018). Real-time monitoring of water requirement in protected farms by using polynomial neural networks and image processing. Environment, Development and Sustainability. Scholar
  32. Turcksin, L., Bernardini, A., & Macharis, C. (2011). A combined AHP-PROMETHEE approach for selecting the most appropriate policy scenario to stimulate a clean vehicle fleet. Procedia-Social and Behavioral Sciences, 20, 954–965.CrossRefGoogle Scholar
  33. Voogd, H. (1983). Multicriteria evaluation for urban and regional planning (Vol. 207). London: Pion.Google Scholar
  34. Whaiduzzaman, M., Gani, A., Anuar, N. B., Shiraz, M., Haque, M. N., & Haque, I. T. (2014). Cloud service selection using multicriteria decision analysis. The Scientific World Journal,. Scholar
  35. Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82.CrossRefGoogle Scholar
  36. Zavadskas, E. K., & Turskis, Z. (2011). Multiple criteria decision making (MCDM) methods in economics: An overview. Technological and Economic Development of Economy, 17(2), 397–427.CrossRefGoogle Scholar
  37. Zhang, M., He, C., & Liatsis, P. (2012). A D-GMDH model for time series forecasting. Expert Systems with Applications, 39(5), 5711–5716.CrossRefGoogle Scholar
  38. Zhong, D. H., Liu, D. H., & Cui, B. (2011). Real-time compaction quality monitoring of high core rockfill dam. Science China Technological Sciences, 54(7), 1906–1913.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Priyanka Majumder
    • 1
    Email author
  • Mrinmoy Majumder
    • 2
  • Apu Kumar Saha
    • 1
  1. 1.Department of MathematicsNational Institute of Technology, AgartalaBarjala, JiraniaIndia
  2. 2.Department of Civil EngineeringNational Institute of Technology, AgartalaBarjala, JiraniaIndia

Personalised recommendations