Neuron-Adaptive PID Based Speed Control of SCSG Wind Turbine System

and Applied Analysis 3 Cooling system Wind turbine Generator AC-DC-AC-convertor To power grid Rotor dewar Superconducting coils Excitation power supply 13.8KV, 10MW, 2Hz 13.8KV, 10MW, 2Hz 3 phase,


Introduction
With the fast development of wind power generation systems, the generating capacity of wind turbines is expected to reach up to 10 MW [1].Consequently, the wind turbine weight and size have to be increased simultaneously, with the bald diameter reaching up to 10 meters [2], as shown in Figure 1, which imposes technical difficulty in designing, transporting, and installing such large turbine blades.To address this challenge, novel concept of wind turbine generators with high energy density is urgently needed.High-temperature superconducting (HTS) technology is an expected solution.The research of wind turbines with SCSG has gained worldwide attention during the past decade [3][4][5][6][7][8][9][10][11].
Among various issues related to SCSG wind power generation systems, speed control represents one of the most crucial ones.Because of the inherent nonlinear and uncertain characteristics of the system, traditional PID control, although simple in structure and used widely in industry, is difficult to achieve reliable variable speed control performance in the blow-rated speed region.
To address this issue, several advanced control approaches have been studied, such as single neuron-adaptive PID control approach, BP neural network PID control approach, fuzzy RBF neural network PID control approach, genetic algorithm PID control approach, and adaptive fuzzy PID control approach.However, previous studies show that the response time of single neuron-adaptive PID is comparatively long, and most of the existing algorithms are computationally expensive, and some of them even lead to larger overshoot than traditional PID.The neural network control approach with self-learning and strong self-adaptive characteristics can effectively reduce the negative impact arising from the system parametric uncertainties and stochastic disturbances.Motivated by this fact, in this paper, a single neuronadaptive PID controller based on Delta learning regulation is introduced, in which the RBF neural network is employed to estimate the uncertain but continuous function.Analysis and  simulation results show that the proposed control approach has better performance in terms of robustness, stability, and computational cost as compared with other modified PID methods, thus being mode suitable for the speed control of SCSG wind turbine systems.

Dynamic System Modeling
2.1.Configuration of the Superconducting Generator.The SCSG for wind turbine system has a multiple synchronous high-temperature superconducting (HTS) field winding for direct drive train and has been widely studied worldwide.
Figure 2 shows the configuration of the 10 MW SCSG wind power generation system, including the wind turbine, the generator, and the convertor [12].Physical properties and electrical properties of the designed SCSG are given in Tables 1 and 2, respectively [1].

Modeling of the Superconducting Synchronous Generation
System.It is well known that the expression for power produced by a wind turbine is simply given by where  is air density,  is the radius of rotor, and V is wind speed passing the rotor.  denotes power coefficient of wind turbine, which is a function of the tip-speed ratio  and the pitch angle  [13].Note that the tip-speed ratio is defined by where V Tip is the tip-speed and  is the rotor speed.In the lower-rated wind speed region, the maximum power point tracking (MPPT) control approach is adopted.The maximum power of the wind turbine is expressed as [14]  max = 1 2 Table 3 shows the specifications of the modeled 10 MW SCSG wind turbine [2].
For the purpose of the studies presented in this paper, a standard (-frame) synchronous machine simulation model has been used with parameters resembling the main characteristics of a notional 10 MW HTS generator along with the associated controls.This notional simulation model of the HTS generator is loosely based on the parameters of an actual prototype in order to safeguard the proprietary information of the manufacturer.The mechanical input for the notional HTS generator originates from the wind turbine model.
In the rotor -rotating coordinate system, the generator flux model is shown in (4) Ψ  , Ψ  are the orthogonal axes flux of the stator; Ψ  is rotor excitation flux; Ψ  , Ψ  are orthogonal axes flux of the damping winding;   ,   are the orthogonal axes currents of the stator;   is rotor excitation current;   ,   are orthogonal where   ,   are the orthogonal axis inductances of the motor;   is the self-inductance of the rotor field winding;   ,   are the self-inductances of orthogonal axis damper winding.
According to formula (5), the flux mathematical model in formula (4) could be further simplified.
x kq r kq i fd The voltage balance equations of stator and rotor windings are shown in ( 6) where   ,   ,   , and   are the resistances of the stator winding, rotor field winding, and orthogonal axis damping winding, respectively.The electromagnetic torque expression is shown in formula where  is the number of pole pairs of the generator.Figure 3 shows the d-q equivalent circuit model of the designed SCSG based on Park's transformations.
In order to allow operation at different speeds, the designed SCSG model is linked to the power grid via a fullscale frequency inverter.The frequency converter consists of a generator side converter, a grid side rectifier, and a DC-link.The generator side converter executes the MPPT control through the control of the q-axis current.The grid side rectifier performs reactive power control and constant DC voltage control through control of the d-axis and qaxis currents.Figure 4 depicts the structure of the full-scale frequency inverter.Figure 5 shows controller implementation of the generator side converter and the grid side rectifier.
The simulation model of the generator drive used in this paper utilizes ideal voltage sources representing the fundamental frequency component of a PWM-type variable speed drive.It includes a current control and a speed control.The control target of the speed control is to keep the expected steady state and dynamic characteristic of the rotor speed .The paper utilizes a single neuron-adaptive PID control approach based on RBF to achieve this control goal [15].

Design of Controller
Equation (8) shows that the three control parameters (  ,   , and   ) cannot be real-time adjusted, which result in a decrease in the response speed of system.

Single Neuron-Adaptive PID Controller Algorithm.
Combining single neuron to the incremental PID can address the challenge of real-time adjustment of control parameters.The single neuron-adaptive PID control structure is illustrated in Figure 7.
Improved single neuron-adaptive PID control algorithm based on Delta learning rule implements the adjustment of the three weights in incremental PID controller, therefore, achieving adaptive control.
Delta learning rule based on the steepest descent method completes the aim to adjust the three weights by minimizing the introduced performance indicator.
Assume the weight value at time  in the incremental PID controller is to be expressed as   (),  = , , , and the weight value adjusting formula of next moment is expressed as where Δ  () is the weight increment.Introduce the output error square function as the performance indicator, namely, where () and () are input and output of the reference.Each time the amendments are to meet First-order Taylor expansion of [  ()] is expressed as where () = ∇[  ()].
In the above formula, () is the gradient vector of [] when  =   ().Assume Δ  () = −  (), where   is the learning rate, which is a small positive value.Formula ( 11) is bound to meet.
The value of Δ  () is expressed as where (())/(Δ()) is unknown and in the case when calculation accuracy is not strictly required, replace it with the sign function sgn((())/(Δ()) where Introducing a gain factor K, we can get

Improved Single Neuron PID Using RBF Neural Network.
Neural network (NN) is a powerful approach to linearize and approximate any continuous nonlinear control system, in which radial basis function (RBF) network is a three-layer forward network.RBF network is composed of an input layer, a single hidden layer with nonlinear nodes, and an output layer with a linear node.The topological structure of a typical RBF network is depicted in Figure 8.
The control block diagram of a typical RBF neural network is illustrated in Figure 9.

Simulations and Analysis
The proposed neuron-adaptive PID speed control system of SCSG based on RBF neural network identification is depicted in Figure 6.The entire control module of the SCSG wind generation system is presented in Figure 10 and the simulation module of SCSG is depicted in Figure 11.Parameters of SCSG used in the simulation are listed in Tables 1, 2, and 3.The reference model used in the simulation is taken as a constant speed.The simulation is tested with the MATLAB/Simulink and FAST (FAST is a fully coupled aero-hydro-servo-elastic code, freely available and developed at the National Renewable Energy Laboratory and used to simulate the loads and performance of modern wind turbines).
To verify the dynamic and static performances of the neuron-adaptive PID controller based on RBF NN proposed in this paper, simulation tests are designed in two cases: (1) no-load operation with a given speed of 10 r/min; (2) system starts with no-load, after being in a steady state, given a sudden load   = 10 MN ⋅ m at  = 2 s.Simultaneously compared with the conventional PID controller, available system speed control response curves are demonstrated 8 Abstract and Applied Analysis from Figure 12 to Figure 16.Furthermore, after verifying the validity of the proposed control approach, the paper applies the proposed controller in the speed control of SCSG wind generation system in the below-rated wind speed region to better track wind speed and maintain a stable tip-speed ratio, therefore, achieving the maximum utilization of wind energy.Figures 12 and 13 show that compared with the conventional PID controller, the speed response with the proposed controller is more rapid and smooth, with a smaller overshoot and no static error, solving the contradiction between response time and overshoot of the conventional PID control.
Figures 14 and 15 depict that compared with the conventional PID controller, system with the proposed controller is more robust.When the parameter perturbation occurs, the entire system is stable, with better transient characteristics and nonsteady state static error, and has a strong ability to inhibit the impact of load disturbance to generator speed.
Figure 16 shows the adaptive tuning curve of PID parameters (  ,   ,   ).
Figures 17 and 18 show the compared simulating results of two algorithms in speed control of SCSG tracking wind speed in wind power generation system.Obviously, the proposed control approach makes the SCSG speed better track the wind speed and achieve a more stable tip-speed ratio when the wind speed is below the rated wind speed 12 m/s, such that the SCSG wind power generation system makes better use of wind energy.

Conclusion
In this paper, the 10 MW class superconducting wind turbine generator has been studied, and a single neuron-adaptive PID controller based on Delta learning regulation is proposed, using the RBF neural network to estimate the uncertain continuous function.Analysis and simulation results show that the proposed control approach in SCSG system has a strong robustness and good dynamic performance by keeping a stable output in the presence of disturbances.Furthermore,    comparative study between the proposed controller and the conventional PID controller in the speed control of SCSG wind turbine system has also been conducted.Overall, the proposed control approach is able to achieve smooth and satisfactory rotor speed tracking, achieving the maximum wind energy utilization, in the below-rated wind speed region, and outperforms the traditional PID.

Figure 1 :
Figure 1: Comparison of nacelle sizes for technology options for large systems (image from American Superconductor).

Figure 3 :
Figure 3: Equivalent circuits of the modeled SCSG for dq-axes.

Figure 4 :
Figure 4: Structure of the full-scale frequency inverter.

Figure 6
Figure 6 represents the overall structure diagram of the SCSG wind turbine generation system mounted with associated controllers.

Figure 10 :Figure 11 :
Figure 10: Simulation module of the SCSG wind generation system.

Figure 12 :
Figure 12: SCSG speed response curve to no-load operation with traditional PID controller.

Figure 13 :
Figure 13: SCSG speed response curve to no-load operation with neuron-adaptive PID controller with RBF NN.

Figure 14 :
Figure 14: SCSG speed response curve to a sudden load with traditional PID controller.

Figure 15 :
Figure 15: SCSG speed response curve to a sudden load with neuron-adaptive PID controller with RBF NN.

Figure 18 :
Figure 18: Simulation results of the speed control for SCSG wind turbine with neuron-adaptive PID controller with RBF NN.

Table 1 :
Physical properties of the designed SCSG.

Table 2 :
Electrical properties of the designed SCSG.

Table 3 :
Model parameters of the designed SCSG.
axes currents of damper winding.  ,   ,   , and   are leakage inductances, respectively, for stator winding, field winding, and orthogonal axis damper winding.  ,   are armature reaction inductances for orthogonal axis.  ,   , and   are mutual inductances between the stator winding, rotor field winding, and the orthogonal axis damper winding.  ,   , and   are inductances corresponding to the main magnetic circuit in the self-inductance of the field winding and the orthogonal axis damper winding.Assume 2 , . ..,   , . ..,   ]  is the central vector of th hidden neuron.= [ 1 ,  2 , . ..,   , . ..,   ]  is the basis-width vector,   > 0 is the base width constant of th mode, and the weight vector of the network is  = [ 1 ,  2 , . ..,   , . ..,   ]  .The output of the th neuron in the output layer at time  is defined as