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Figure 11. Impact of inductances variation L;, L,and Lyn: (a) active power and (b) reactive power

Figure 11 Impact of inductances variation L;, L,and Lyn: (a) active power and (b) reactive power

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Based on (7) and (8), the stator power can be given as (9): Starting from (4), the strong linkage between the stator and rotor components is appearing, which makes DFIG difficult to control, in order to simplify DFIG controlling. The field orientation technique was applied in literature [12], [30]. By orienting the stator flux ®, with the direct d axis and when taken into account the stator resistance, R, is ignored, we have (7) and (8) [32]: In (9) prove that the DFIG stator power can be regulated via the control of the currents rotor. The relationship between the rotor voltages and its current is given as (10):
Based on (7) and (8), the stator power can be given as (9): Starting from (4), the strong linkage between the stator and rotor components is appearing, which makes DFIG difficult to control, in order to simplify DFIG controlling. The field orientation technique was applied in literature [12], [30]. By orienting the stator flux ®, with the direct d axis and when taken into account the stator resistance, R, is ignored, we have (7) and (8) [32]: In (9) prove that the DFIG stator power can be regulated via the control of the currents rotor. The relationship between the rotor voltages and its current is given as (10):
Figure 1. Hybrid SM-ANN controller
Figure 1. Hybrid SM-ANN controller
Figure 2. ANN structure of stator power controller and its progression (a) active power and (b) reactive power a a a, a In our case, the training process used is that of the Levenberge-Marquardt algorithm, in order to determine the optimal synaptic weights. It’s an excellent optimization method due to its quick convergence properties and robustness. the structure of the ANN controller designed to replace the switching section of the SM regulator, for controlling the stator active power, consists of one linear input, 17 nodes in the hidden layer and, one neuron in the output layer as depicted in Figure 2(a), besides that, the stator reactive power loop controlled by an ANN controller with one linear input, 8 nodes in the hidden layer, and one neuron in the output layer showing in Figure 2(b), the curve of training, test and validation of the controller ANN of stator active power and stator reactive power are depicted in Figure 3(a) and Figure 3(b) respectively.
Figure 2. ANN structure of stator power controller and its progression (a) active power and (b) reactive power a a a, a In our case, the training process used is that of the Levenberge-Marquardt algorithm, in order to determine the optimal synaptic weights. It’s an excellent optimization method due to its quick convergence properties and robustness. the structure of the ANN controller designed to replace the switching section of the SM regulator, for controlling the stator active power, consists of one linear input, 17 nodes in the hidden layer and, one neuron in the output layer as depicted in Figure 2(a), besides that, the stator reactive power loop controlled by an ANN controller with one linear input, 8 nodes in the hidden layer, and one neuron in the output layer showing in Figure 2(b), the curve of training, test and validation of the controller ANN of stator active power and stator reactive power are depicted in Figure 3(a) and Figure 3(b) respectively.
Figure 3. Performance curve of training (a) stator active power and (b) stator reactive power
Figure 3. Performance curve of training (a) stator active power and (b) stator reactive power
Figure 5. Average daily wind speed values (April 2020) Figure 4. NEAL station in URER/MS Adrar The data used in this work are data from April 2020 taken from measurements of the weather stations called new energy Algeria (NEAL) as shown in Figure 4, placed in research unit in renewable energies in saharan medium (URER/MS), in Adrar region, which is a section of renewable energy development center (CDER), in Algeria capital. Knowing that the Adrar area in south Algeria has even greater potential for wind power. The yearly average wind speed is over 6 m/s [36]. The curve of average daily wind speed for April 2020 at Adrar zone. are displayed in Figure 5.
Figure 5. Average daily wind speed values (April 2020) Figure 4. NEAL station in URER/MS Adrar The data used in this work are data from April 2020 taken from measurements of the weather stations called new energy Algeria (NEAL) as shown in Figure 4, placed in research unit in renewable energies in saharan medium (URER/MS), in Adrar region, which is a section of renewable energy development center (CDER), in Algeria capital. Knowing that the Adrar area in south Algeria has even greater potential for wind power. The yearly average wind speed is over 6 m/s [36]. The curve of average daily wind speed for April 2020 at Adrar zone. are displayed in Figure 5.
Table 1. Characteristics of the wind turbine system Figure 6. Schematic diagram of direct-drive vertical axis wind system based SM-ANN
Table 1. Characteristics of the wind turbine system Figure 6. Schematic diagram of direct-drive vertical axis wind system based SM-ANN
Figure 7. DFIG stator power with PI, SMC and SM-ANN controllers (a) active power and (b) reactive power
Figure 7. DFIG stator power with PI, SMC and SM-ANN controllers (a) active power and (b) reactive power
Figure 8. DFIG one-phase stator current (a) I,a of SMC and (b) THD of SMC Furthermore, using the fast fourier transform (FFT) approach, the THD of stator currents are presented in Figure 8 and 9, as shown, the SM-ANN controller reduces THD down to 2.05% (Figure 9(b)), compared to SMC, where THD is 4.40% (Figure 8(b)), which helps to reduce the chattering phenomenon effect. The stator current signal quality of the SM-ANN controller has a good sinusoidal shape and, is less corrugated (Figure 9(a)) compared to SM controllers (Figure 8(a)).
Figure 8. DFIG one-phase stator current (a) I,a of SMC and (b) THD of SMC Furthermore, using the fast fourier transform (FFT) approach, the THD of stator currents are presented in Figure 8 and 9, as shown, the SM-ANN controller reduces THD down to 2.05% (Figure 9(b)), compared to SMC, where THD is 4.40% (Figure 8(b)), which helps to reduce the chattering phenomenon effect. The stator current signal quality of the SM-ANN controller has a good sinusoidal shape and, is less corrugated (Figure 9(a)) compared to SM controllers (Figure 8(a)).
Figure 9. DFIG one-phase stator current (a) Isa of SM-ANN and (b) THD of SM-ANN
Figure 9. DFIG one-phase stator current (a) Isa of SM-ANN and (b) THD of SM-ANN
Figure 10. Impact of resistances variation R;, R,: (a) active stator power and (b) reactive stator power
Figure 10. Impact of resistances variation R;, R,: (a) active stator power and (b) reactive stator power
Related topics:
Sliding mode controlDoubly fed induction generatorArtificial Neural Network Controller
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