Multidimensional cavitation monitoring update

In two earlier papers published in IWP&DC, an innovative approach to diagnostics and monitoring of turbine cavitation was introduced, first for a high [1] and then for a low [2] number of runner blades. This multidimensional approach is based on a detailed study of sensors and signal and data processing problems [3] and the specific theory [4]. The application examples presented in references 1-6 show that the approach is efficient both as a diagnostic tool and as a means for highly sensitive monitoring. The advantages of multidimensional cavitation monitoring over simpler systems is that it delivers almost as detailed a description of cavitation as imaginable and thus enables:

• recognition of different cavitation mechanisms (different types of cavitation or the same type appearing in different locations);

• assessment of the role played by turbine parts in cavitation;

• estimation of the spatial distribution of cavitation in the turbine; and

• achievement of extremely high sensitivity in detecting changes in turbine cavitation performance and the onset of detrimental effects.

In this paper, typical results of multidimensional cavitation monitoring are reviewed, a simple implementation of such a monitor is illustrated (related to the one for the multidimensional hydro-unit general monitor, presented in reference 7), and the results of the multidimensional cavitation monitoring are contrasted with those of a simple cavitation monitor and with model test results.

Turbine cavitation characteristics

The multidimensional cavitation characteristics of a turbine [4], which are included in the monitoring algorithm of the multidimensional cavitation monitor and used in the diagnostic tests, are defined in Table 1. With respect to cavitation mechanisms, each of these characteristics can be defined for:

• the total cavitation in the turbine,

• each cavitation mechanism separately, or

• the erosive mechanism or the group of erosive mechanisms.

The calibration of the characteristics can be:

• relative, with an arbitrary reference, or

• absolute, in kg of the metal mass lost in a specified time interval (e.g. 10,000hr) while the turbine is being operated in the operation point considered.

An example

The characteristics defined above, as obtained by detailed cavitation monitoring, are illustrated by the example of two 48MW vertical Francis turbines in a plant operated at an almost constant head and rather constant tail water level. The turbines differ slightly in the runners but all other parts are the same. They have 20 guide vanes (v in Table 1) and 17 runner blades (b in Table 1). Figure 1 presents four types of the characteristics; turbine A is considered in all of them, and in the global characteristic (figure 1d), turbine B is also shown. The set of the operation parameters P is reduced to the power.

Four cavitation mechanisms are found by monitoring the two turbines considered: areal cavitation on the suction side of the runner blades; leading edge cavitation; areal cavitation which disappears in free water downstream from the blade; and cavitation in the free vortices in the draft tube. The first three, which are related to the runner, are shown in figure 2. Among them, only the first mechanism proved to be erosive. The global characteristics (figure 1d) are given for the erosive mechanisms and for the sum of all the mechanisms.

In figures 1 and 2, relative calibration of the cavitation intensity is applied. All the values are expressed as percentages of the intensity of the erosive mechanism in Unit A at 48MW. Such a way of presenting the intensities is useful for comparing turbines (as in figure 1d) or for assessing the share of turbine parts in the total cavitation intensity. In figure 3, the absolute calibration yielding metal loss in unit time is used. In the case shown, the weight in kg of electrodes needed for repair is used as a measure of this loss, instead of the kg of the runner body itself. By combining the absolutely calibrated global characteristic (which thus yields the erosion rate for each operation point of interest) and the turbine operation statistics (presented in figure 3 by the probability density of various power settings), the erosion accumulated in a chosen time interval can be derived. This can be done on historical data while analysing the role of various operation regimes or optimising the maintenance schedule, or on assumed operation statistics, while planning the optimal operation schedule.

Implementation

Use of a rather large number of sensors is characteristic for the multidimensional monitor (see figure 4); four, six or eight per turbine are typical. The sensors are mounted on various places on the dry side of a turbine, in a way to sense structure-born sound generated in turbine metal parts by cavitation. For an unbiased assessment of cavitation intensity, the entire energy of cavitation noise must be taken into account. To pick it up, and also to enable sufficiently high resolution with respect to the instantaneous angular position of the runner, fast-reacting, broad-band sensors (up to 1MHz) must be used. Their response is made free of side effects and independent of the influence of mounting details by means of a spectrum normalisation technique [8] and a specially devised in-situ calibration procedure.

A computer used as a processor in the cavitation monitor reduces a large quantity of high-frequency data into a small set of scalar and vector data which represents the intensities according to the four types of cavitation characteristics. While this output data is similar to data used to monitor other dynamic processes in hydro units, the large quantity and high rate of input cavitation signals and the processing speed needed to process it in quasi real time, make cavitation processor different from the processors used in the general monitors.

The multidimensional cavitation monitoring system can be designed:

• as a stand-alone unit incorporating the operator interface,

• as a passive, separate system that reduces cavitation signals to data on cavitation and delivers this to the general plant monitoring system; or

• as an integrated monitoring system that monitors all the quantities needed, including cavitation. Here the processing power needed for cavitation usually suffices for all the functions of the general monitor.

The operator interface does not depend on the implementation variant. On users' screens (on the monitor itself or on the users' PC) the cavitation data can be presented either:

• independently, in a manner illustrated in figure 5; or

• incorporated into the set of all monitored data, in the way shown in reference 7.

Simple vs. Multidimensional cavitation monitor

The characteristics of simple and multidimensional cavitation monitoring are contrasted in table 2.

Monitoring vs. model tests

• There are strong scale effects in incipient cavitation modelling [9]. Thus, it is advisable to control cavitation on the prototype, e.g. in the acceptance test. The monitor or a diagnostic test is needed for this.

• Turbine cavitation performance varies in time, making continuous control necessary.

• In a typical model cavitation test, much less useful data for practical operation of the prototype is obtained than by means of an in-plant multidimensional vibro-acoustic monitor (see figure 6).

• In some cases, not all types of cavitation can be seen in a model test. All can be heard and assessed in a good, multi-dimensional in-plant vibro-acoustic test.

Conclusion

The multidimensional cavitation monitor delivers detailed diagnostic data and reliable estimates of cavitation intensity. It detects detrimental effects with a very high sensitivity. An introductory diagnostic test is needed in order to design the multidimensional cavitation monitor for a given turbine. The cavitation monitor can be implemented as a stand-alone unit, a passive system connected to the general plant monitor, or integrated into the general monitor.

Dr Brank Bajic is managing director of Korto Cavitation Services