Selection of features for analysis of reliability of performance in hydropower plants: a multi-criteria decision making approach

Majumder, Priyanka; Majumder, Mrinmoy; Saha, Apu Kumar; Nath, Soumitra

doi:10.1007/s10668-019-00343-2

Cited by 15 publications

(6 citation statements)

References 69 publications

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“…The proposed method is an evolution of the probabilistic neural network (PNN), originally proposed by [16], is generic and can be applied to several decision problems related to the maintenance of old engineering systems. [17] validated the identification of the most significant indicators to represent the reliability of a hydropower plant and also the method to represent the efficiency of the plant by a physical model. The multi-criteria decision-making ensemble methods made the selection of the most important alternative to be considered.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning and q-Weibull Applied to Reliability Analysis in Hydropower Sector

et al. 2020

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Section: Related Workmentioning

confidence: 99%

Machine Learning and q-Weibull Applied to Reliability Analysis in Hydropower Sector

et al. 2020

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“…In addition, intelligent mitigation measures can be adopted in order to manage the trade-off between economic constraints, climatic variability, and increased demand. These measures can be associated with tailored indicators that explain the production reliability of hydropower plants [83]. Plant modelization can be used also to obtain tailored sustainability KPIs in order to assess the sustainability of hydropower plants [84].…”

Section: Main Topic Scope Main Findingsmentioning

confidence: 99%

Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

Pepe,

Zanoli

2024

Energies

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Intelligent water usage is required in order to target the challenging goals for 2030 and 2050. Hydroelectric power plants represent processes wherein water is exploited as a renewable resource and a source for energy production. Hydroelectric power plants usually include reservoirs, valves, gates, and energy production devices, e.g., turbines. In this context, monitoring and maintenance policies together with control and optimization strategies, at the different levels of the automation hierarchy, may represent strategic tools and drivers for energy efficiency improvement. Nowadays, these strategies rely on different basic concepts and elements, which must be assessed and investigated in order to provide a reliable background. This paper focuses on a review of the state of the art associated with these basic concepts and elements, i.e., digitalization, Industry 4.0, data, KPIs, modelization, and forecast.

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“…Furthermore, applications of MCDM in a fuzzy environment are increasingly used (e.g., Tzimopoulos et al, 2013;Dong et al, 2015;Wibowo et al, 2015;Majumder et al, 2020), since the fuzzy sets theory allows including qualitative criteria, for which measuring the exact value is difficult, or criteria affected by imprecision due to the lack of available information (Khanzadi et al, 2016). Some authors have also presented and tested new MCDM techniques.…”

Section: Short Overview Of the Multi-criteria Decision-making Use In mentioning

confidence: 99%

Comparing Multi-Criteria Decision-Making Methods for the Assessment of Flow Release Scenarios From Small Hydropower Plants in the Alpine Area

Vassoney

Mochet

Desiderio

et al. 2021

Front. Environ. Sci.

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Growing energy demand and targets for reducing greenhouse gas emissions are driving a significant hydropower (HP) generation expansion in the Alpine area. However, despite its advantages, HP implementation causes several impacts on the ecological status of river systems and the preservation of characteristic landscapes. The use of decision-making tools, like multi-criteria decision-making (MCDM) methods, can provide helpful support to achieve more sustainable solutions for complex water management problems. In Aosta Valley (NW Italy), an MCDM experimental approach is being applied to define the optimal environmental flow scenario to be released by HP plants, in an attempt to balance energy production needs, economic profitability and environmental safeguard. Four criteria are considered (energy, environment and fishing, landscape, economy) and key stakeholders are involved in the entire decision-making process. However, there is a need to test also other MCDM methodologies in order to understand if an alternative approach could be more suitable for future, more complex, HP management decisions. Therefore, in this paper, seven different MCDM methods are applied to an existing small run-of-the-river HP plant for the selection of the optimal flow release scenario, i.e., SAW, WPM, AHP, TOPSIS, VIKOR, ELECTRE III, and the initially adopted method, SHARE MCA. The results obtained with the application of the different MCDM methods are investigated and statistically compared. The strengths and weaknesses of the different methodological approaches are also discussed. Based on the comparative results and the consequent evaluations, SHARE MCA, WPM, and VIKOR appear to have the most interesting characteristics in terms of overall feasibility.

show abstract

Selection of features for analysis of reliability of performance in hydropower plants: a multi-criteria decision making approach

Cited by 15 publications

References 69 publications

Machine Learning and q-Weibull Applied to Reliability Analysis in Hydropower Sector

Machine Learning and q-Weibull Applied to Reliability Analysis in Hydropower Sector

Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

Comparing Multi-Criteria Decision-Making Methods for the Assessment of Flow Release Scenarios From Small Hydropower Plants in the Alpine Area

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