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.

中文翻译：

---

模块化水电站发电量实时监控：最重要的参数方法

水基可再生能源系统的不确定性降低了工厂的产能。然而，水力发电厂的实时监控可确保发电厂的最佳性能和持续无故障性能。但是，实时系统的实施总是会增加发电厂的整体运营成本，因为它需要持续监测、分析和决策 (MAD)，以确保对不确定性进行长时间的原位检测和解决。要求观察代表电厂性能的多个指标，提高了管理成本并影响电厂的经济回报。此外，启用实时监控发电厂所需的基础设施调整也将导致支出增加。本研究旨在降低智能系统的成本和基础设施要求，以通过单加权函数监测代替多指标跟踪的要求来表示工厂性能，以即时缓解系统故障。这种监控升级将降低系统的过程成本，从而提高发电厂的盈利能力。功能跟踪还将提高 MAD 的效率，并最大限度地减少实时监控的内存需求，因为在做出决定之前需要对单个指针进行分析和评估。在这方面，使用客观的多标准决策技术来发现每个指标在水电生产中的重要性，以便可以根据它们破坏系统稳定的潜力进行跟踪。结果表明，与多项式神经网络相结合的新型多准则决策方法可以通过便携式独立平台根据参数的重要性识别不确定性，该平台可与基于监控的系统集成以监测水电中的不确定性系统。根据结果​​，在研究中考虑的指标中，运行和维护成本其次是排放指标被认为具有最高的显着性。结果表明，采用多项式神经网络的新多准则决策方法可以借助可集成到监控系统中以监控水电系统中的不确定性的便携式独立平台，基于参数的重要性来识别不确定性实时。