# Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis
## 4 Simulation Results, Experimental Validation, Comparisons and Discussion
### HIL Validation  
In order to validate the developed FDI solutions in more realistic working situations, the WT process and the designed algorithms have been implemented and executed by means of a HIL tool. This test-bed allows to reproduce experimental tests that are oriented to the verification of the results achieved in simulations. This test-bed is sketched in Figure 7, which highlights 3 main modules. The WT simulator that was used to describe the system dynamics, its actuator, measurement sensors, and the WT controllers were implemented in the LabVIEW environment. Realistic effects such as uncertainty, measurement errors, disturbance and the model-reality mismatch were also included, as recalled Section 2. The overall system is converted into the C++ code running on a standard PC, which allows also to test and monitor the signals generated by the proposed fault diagnosis strategies. The FDI schemes summarised in Section 3.1 were also compiled as executable code and implemented in an AWC 500 industrial system that features typical WT requirements. This industrial module receives the signals acquired from the PC simulating the realistic WT plant that represent the monitored signals reported in Table 3. Therefore, the on-board electronics elaborate these signals according to the FDI algorithms and produce the monitoring signals transmitted back to the WT simulator running on the PC. Figure 6: Estimated and actual faults for cases 6, 7, 8, and 9. An intermediate module represents the interface circuits providing the communications between the PC with the WT simulator and the on-board electronics running the fault diagnosis algorithms. In this way, it manages the signals and exchanges the data between the WT simulator and the AWC 500 system. In the following, the data sequences generated by the WT simulator are briefly described. With reference to the WT benchmark model of Section 2 and its HIL implementation, all simulations are driven by realistic wind sequences, as depicted in Figure 8.  
Figure 8 shows an example of the realistic acquisition of wind speed data from a WT, as described in [20,26]. It should represent a good coverage of typical operating conditions, as it ranges from 5 to 20 m/s, with a few spikes at 25 m/s. As further example of WT sequences used for the validation of the FDI schemes, Figure 9 shows the measured outputs \(P_{g,m}\) and \(\omega_{g,m}\) with respect to the reference signals, in partial and full load working conditions [20]. Figure 8: Measured wind speed signal example driving the WT benchmark. Figure 7: HIL tool for real-time validation. These working conditions represent the operating regions of a WT that motivate the switching blocks used for the torque and the pitch controls sketched in Figure 1. Therefore, the WT data sequences are generated according to the nominal operating trajectory to satisfy different demands below and above a certain wind speed. A condition monitoring system determines the actual working point of the WT and drives the switches accordingly, as shown in Figure 1.  
When the WT operates in partial load condition, it is driven by the generator torque controller, which should maximise the produced energy, as shown in Figure 9. On the other hand, when the WT operates in full load condition, both the speed and the power controller are active. These controllers should track the rated generator speed and limit the output power, as depicted in Figure 9. This behaviour is obtained by properly setting the two switches in Figure 1. In this way, the data sequences exploited for the validation of the designed FDI schemes are generated. As final example of data sequences used for the identification of the FS and NN prototypes, the measured control inputs of the WT blades \(\beta_{i,mj}\) and the generated power \(P_{g,m}\) are depicted in Figure 10 in full load conditions. Figure 6:0 Pitch sensor measurements \(\beta_{i,mj}\) and generated power \(P_{g,m}\) in full load conditions. Figure 6:1 Reference and measured \(P_{g,m}\) and \(\omega_{g,m}\) outputs.