**NNPID-based Stator Voltage Oriented Vector Control**

for DFIG based Wind Turbine Systems

for DFIG based Wind Turbine Systems

**Shanzhi LI ^{1}, Haoping WANG^{1}, Yang TIAN^{1},**

Nicolai CHRISTOV^{2}, Abdel AITOUCHE^{2}

^{1 }*S*ino-French International Joint Laboratory of Automatic Control and Signal Processing (LaFCAS), Nanjing University of Science & Technology (NUST),

Nanjing 210094, China

**LAGIS- CNRS UMR 8219, LaFCAS, University Lille Nord de France,**

^{2 }Lille France, 59600

**Abstract**: Doubly fed induction generators are widely adopted for the wind turbine systems since it is cheap and reliable. Based on the traditional stator voltage oriented vector control method, the performance of the proposed vector control is largely influenced by the variations of the DFIG parameters. And the classical PID algorithm cannot achieve the maximum power point tracking (MPPT) in time (owing to the transient wind). Hence, in this paper, to eliminate parameters variations on the power output and capture the MPPT rapidly, we propose a stator voltage oriented vector control which is based on a Neural Network PID (NNPID) technology. The weights which are being similar to the PID coefficients are adapted by Hebb rule to decrease the power error online according to the error gradient descent method, while the classical PID coefficients will be a constant. The effectiveness of the proposed method is demonstrated by corresponding simulation results: even in the case of wind mutation change, the proposed NNPID can track the variation of the wind energy, and robust to the DFIG parameters variations.

**Keywords**: wind turbine system; maximum power point tracking; DFIG; NNPID.

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**CITE THIS PAPER AS**:

Shanzhi LI, Haoping WANG, Yang TIAN, Nicolai CHRISTOV, Abdel AITOUCHE, **NNPID-based Stator Voltage Oriented Vector Control for DFIG based Wind Turbine Systems**, *Studies in Informatics and Control*, ISSN 1220-1766, vol. 23 (1), pp. 5-12, 2014.

** 1. Introduction**

As a renewable and clean energy, wind energy which growths rapidly worldwide and attracts widespread attention by governments [1]. Recently, high efficiency and low cost power production control methods and technologies are always an eternal working goal for researchers and engineers.

Doubly fed induction generators (DFIG) which are widely adopted for the wind turbine systems occupy most parts of the global market because of the lower-cost and smaller inverter capacity. However, the DFIG’s characteristics of non-linearties, strong coupling, and varying parameters as well as the wind randomness are intractable problems. It also reduces the efficiency of the wind energy. Moreover, the model of wind is complicated, and affected by many factors. Besides, dynamic and static characteristics of nature wind need to be reflected by the wind model. And wind turbine system also requires an appropriate wind model for different performances. The emitted power usually cannot catch up with capture wind energy because of both the transient wind and the lag inertia of the blades. Considering wind uncertainties and wind turbine systems nonlinearities, it is difficult to selecting appropriate classical PID coefficients to obtain good performances.

In addition, these coefficients usually maintain constant once the PID controller is installed. Recently, artificial intelligent controls have been developed rapidly and applied gradually to wind turbine systems, such as neural network control and fuzzy control are discussed in [2-4]. However, to achieve good control performance needs complex computations in the neural network based control algorithms. A rule table for fuzzy control is also difficult to design [6]. For fuzzy controllers which select this rule table by self-adjusting needs to spend a longer time in calculations

In this paper, to improve the efficiency of capturing wind energy and simplify the referred control methods computations, a particular PID controller which is based on a single-neuron network is developed and called Neural Network PID (NNPID). Known that three layers of neural networks (NNs) can be used to approximate an arbitrary input-output mapping [7]. The weights of the proposed NNPID can be adjusted adaptively online with tracking errors. This used adaptive mechanism is useful to overcome both the wind turbine systems external disturbances and parameters uncertainties.

The rest paper is organized as follows: a wind model is presented in 2^{nd} section. And in 3^{rd} section, a wind turbine model which includes an aerodynamic model of the wind turbine rotor, DFIG model and drive trains model is described.

After that, for the considered system, a fundamental stator voltage oriented vector control method and a NNPID controller is developed in 4^{th} section. Then to validate the proposed method**,** some numerical simulation results are illustrated in 5^{th} section. Finally, some conclusion remarks are presented in 6th section.

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