This paper deals with the estimation of the energy production by means of pumps used as turbines to exploit residual hydraulic energy, as in the case of available head and flow rate in water distribution networks. To this aim, four pumps with different characteristics are investigated to estimate the producible yearly electric energy. The performance curves of Pumps As Turbines (PATs), which relate head, power, and efficiency to the volume flow rate over the entire PAT operation range, were derived by using published experimental data. The four considered water distribution networks, for which experimental data taken during one year were available, are characterized by significantly different hydraulic features (average flow rate in the range 10-116 L/s; average pressure reduction in the range 12-53 m). Therefore, energy production accounts for actual flow rate and head variability over the year. The conversion efficiency is also estimated, for both the whole water distribution network and the PAT alone.

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Nowadays, the importance of gas turbine monitoring and diagnostics pushes OEM operators to exploit historical data to search for early indicators of incipient failures. One of the most disrupting events that affect GT operation is gas turbine trip, since its occurrence causes a reduction of equipment remaining useful life as well as revenue loss because of business interruption. Thus, early detection of incipient symptoms of gas turbine trip is crucial to ensure efficient operation and lower operation and maintenance costs. This paper presents a data-driven methodology aimed at investigating and disclosing the onset of trip symptoms. The goal of the methodology is the identification of the time point at which trip symptoms are triggered, by exploring multiple scenarios characterized by different trigger positions. For each scenario, a time window of the same length is considered before and after the trigger time point. For classification purposes, the former is supposed to be representative of normal operation and thus is labeled as “No trip”, whereas the latter is labeled as “Trip”. A Long Short-Term Memory (LSTM) neural network is employed as the classification model. A predictive model is first trained for each scenario and subsequently tested on new observations (i.e., trip events) by considering the whole available timeframe. Finally, trips are clustered into homogeneous groups according to their most likely trigger position, which identifies the time point of onset of trip symptoms. The methodology is applied to two real-world case studies composed of a collection of trips, of which the causes are different, taken from various fleets of Siemens gas turbines. Data collected from multiple sensors during three days of operation before trip occurrence are employed and analyzed. The methodology provides the most likely trigger position for four clusters of trips within the two days before trip occurrence with a confidence in the range 66%–97%.

This paper presents the application of a physics-based simulation model, aimed at predicting the performance curves of pumps as turbines (PATs) based on the performance curves of the respective pump. The simulation model implements the equations for estimating head, power and efficiency for both direct and reverse operation. Model tuning on a given machine is performed by using loss coefficients and specific parameters identified by means of an optimization procedure, which simultaneously optimizes both the pump and PAT operation. The simulation model is calibrated in this paper on data taken from the literature, reporting both pump and PAT performance curves for head and efficiency over the entire range of operation. The performance data refer to twelve different centrifugal pumps, running in both pump and PAT mode. The accuracy of the predictions of the physics-based simulation model is quantitatively assessed against both pump and PAT performance curves and best efficiency point. Prediction consistency from a physical point of view is also evaluated.

This paper deals with the comparison of different methods which can be used for the prediction of the performance curves of pumps as turbines (PATs). The considered approaches are four, i.e., one physics-based simulation model (“white box” model), two “gray box” models, which integrate theory on turbomachines with specific data correlations, and one “black box” model. More in detail, the modeling approaches are: (1) a physics-based simulation model developed by the same authors, which includes the equations for estimating head, power, and efficiency and uses loss coefficients and specific parameters; (2) a model developed by Derakhshan and Nourbakhsh, which first predicts the best efficiency point of a PAT and then reconstructs their complete characteristic curves by means of two ad hoc equations; (3) the prediction model developed by Singh and Nestmann, which predicts the complete turbine characteristics based on pump shape and size; (4) an Evolutionary Polynomial Regression model, which represents a data-driven hybrid scheme which can be used for identifying the explicit mathematical relationship between PAT and pump curves. All approaches are applied to literature data, relying on both pump and PAT performance curves of head, power, and efficiency over the entire range of operation. The experimental data were provided by Derakhshan and Nourbakhsh for four different turbomachines, working in both pump and PAT mode with specific speed values in the range 1.53–5.82. This paper provides a quantitative assessment of the predictions made by means of the considered approaches and also analyzes consistency from a physical point of view. Advantages and drawbacks of each method are also analyzed and discussed.