New approach for optimizing energy by adjusting the trade-off coefficient in wind turbines
© Fakharzadeh J et al.; licensee Springer. 2013
Received: 16 August 2012
Accepted: 8 September 2013
Published: 18 September 2013
As fossil fuels run out, more attention should be paid to renewable energies, among which wind energy is one of the best. Therefore, the optimization of its energetic efficiency in variable speed wind turbines is an important focus of this recent study.
Based upon linearization, a trade-off between energy conversion maximization and minimization of damage caused by mechanical fatigue, the resulting energy produced by a wind turbine, is optimized. Mathematically, the objective is defined as a stochastic criterion, belonging to the class of linear quadratic regulator (LQR) optimal control problems.
A linear control law has been obtained using an LQR stochastic approach, and the optimal value for α has been calculated using a real-value genetic algorithm. The numerical simulations show a better efficiency for this method compared to other methods used thus far. They also present a better stability when the optimal trade-off coefficient is applied.
The results demonstrate that the curves of the state variables and output variables for the different valves of α converge to zero, which shows that the design controller was fully able to reduce the effectiveness of the white noise.
KeywordsLinearization Linear quadratic regulator Genetic algorithm Optimal control Wind energy
Renewable energies are obtained from natural resources such as sunlight, wind, rain, tides, and geothermal heat. As fossil fuels become scarce, more attention should be paid to new energy sources or technically new energies. Among the renewable energies, wind energy is known to provide one of the most economical ways to produce electricity because it is inexhaustible and causes no environmental pollution. Moreover, wind turbines normally do not need any extra fuel, water, and other intermediary. Therefore, the exploitation of wind energy using wind turbines for producing electricity has been taken into consideration.
On the other hand, variable-speed fixed-pitch wind turbines are well suited for small- to medium-scale wind power markets due to their simple structure, low cost, and high reliability . Therefore, the optimization of energy efficiency in variable speed for wind turbines is the focus of studies about the designing and exploitation of wind turbines. Indeed, the exclusive goal of wind energy conversion systems is the optimization of the energy conversion with the aim of maximizing the energy captured from wind.
One of the previous studies used variable speed of electrical generators in conjunction with a nonlinear control algorithm (see ). Also, Wood (in 2004) employed differential evolution to optimize wind turbine blades (see ). Liu et al. (in 2007) presented an optimization model for rotor blades of horizontal axis wind turbines. Their model refers to the wind speed distribution function (see ). In another study, a power optimization objective is gained by computing the optimal control settings of wind turbines using data mining and an evolutionary strategy algorithm (see ). In this study , an approach to perform another linearization for determining an optimal control design was applied. A stochastic model of wind turbines which convert wind speed signals into power output signals with appropriate multifractal statistics was suggested in . Munteanu et al.  have presented a review of both the operational methods for the analysis of the stochastic data and the reconstruction of the detailed stochastic evolution equations from the available data.
Among the recent new works in this area, it is preferred to emphasize not only on an optimization-based approach to reduce extreme structural loads during rapid and emergency shutdown , but also on predicting the maximum generation capacity to obtain power control  and extreme seeking to perform maximum point tracking  as well dynamic responses of land-based and floating wind turbines under pitch system faults .
Regarding the mentioned studies, in this current research, we have attempted to optimize the energy in wind turbines by means of simplification using linearization in order to achieve the best adjusting coefficient for the trade-off action via the application of the stochastic LQR method.
Where Ω is the rotational speed of the blades and R is the blade length; and in fact, λ is the ratio of the linear speed of blades to wind speed.
In order to reduce mechanical fatigue on the wind turbine system, the torque variations should be minimized by controlling the generator torque variation, ; hence, due to the stochastic behavior of wind, a stochastic control system is provided.
The most common methods that have been applied for solving this optimal problem can be classified as maximum power point tracking (MPPT) approach, based on an on-off controller , fuzzy control techniques , linear quadratic Gaussian (LQG) approach , and sliding mode techniques . In all of the mentioned methods, the main goal is the maximization of energy efficiency.
In this paper, based on the LQR approach, a new optimal control structure is proposed, which optimizes the combined stochastic criterion that includes the identification of the optimal coefficient for adjusting the given trade-off. Thus, the aim of this paper is to optimize the energy produced by the turbine. To achieve this goal, a real-valued genetic algorithm (GA) and Matlab software tools were applied to obtain the best trade-off coefficient for the variable speed, fixed pitch turbine. Moreover, the LQR stochastic approach is used to design an optimal strategy, which could lead to the optimal solution of the dynamical system.
This paper is organized as follows: in the ‘Methods’ section, the modeling of the control system is explained, the linearization of the wind energy conversion system is presented, the optimization problem formulation is the focus, and the performance index is introduced. Furthermore, the stochastic LQR controller and the optimal control law are presented. In ‘Results and discussion’ section, some simulation results are summarized. This paper finishes with some closing remarks in the ‘Conclusions’ section.
Modeling of the wind power system
Where Γ is denoted as the torque (Γwt is the electrical and Γ G is the mechanical torque) and .
Where J t expresses the total inertia of the turbine.
Here, the positive coefficient α is introduced in the model to adjust the trade-off between the two above-mentioned contrary requirements. We also would like to mention that the scalar λopt will be introduced in the next section.
Linearization of the system
where and is called the torque parameter.
Where and . Substituting into Equation 12 and using gives
where is the mechanical time constant.
Optimal control structure
The linearized relations (11) and (13) are used to represent the state space matrix equations.
where the disturbance input e ( t ) is a white noise random signal with spectral density.
As mentioned before, up to now, problem (19) has been solved by different methods such as LQG, MPPT, sliding mode, and fuzzy techniques. But as a different view, Equation 19 is an LQR stochastic problem. So, we present a new and simple solution method in the next subsection.
Optimal LQR stochastic controller design
Results and discussion
This provides us with the following values for the linearized system's parameters around the operating point corresponding to and γ = −1. The standard deviation of e(t) is σ e = 0.0475. The results have shown that the performance index values are sensitive to the α values. So, the real-value genetic algorithm from  is used to find the optimal value of α.
where the parameters ‘gensize’ denotes the population size and ‘itergen’ denotes the number of iteration of the population (see ).
Obviously, these results show that the best obtained numerical results belong to the optimal value α = 0.0099, given by applying GA.
Comparing these simulation results with those taken from  intuitively shows that the corresponding results to α = 0.0099 do not only have better performance index and stability, but also quite reduced torque variations; this is a good reason for the suitability of α = 0.0099 when we know that its related eigenvalues of the A − BK matrix are 0.0467 and −1.7843. It may be necessary to bear in mind that in , for a certain case of the objective function, the optimal value of α was obtained as 0.0011 with I = 3 × 10−5, but there, the optimal control resulted in an approximated piecewise constant function.
For further research, it is a very interesting and useful idea to measure the noise and find out how much it is reduced. As mentioned in  and , ‘it is a big challenge’ and needs some deep experience on concepts like the Langevin and Ornstein-Uhlenbeck process.
This paper proposes an optimal control strategy for variable-speed fixed-pitch wind turbines. The optimality of the whole system is defined in relation to the trade-off between wind energy conversion maximization and the minimization of the generator torque variation. This optimal problem is treated using an LQR stochastic approach, whose effectiveness was proven by a numerical solution. Since this combination is dependent on the definition parameter for the required trade-off, this method is able to define the parameter in an optimal way by genetic algorithms. Applying the best of the obtained trade-off coefficients in an LQR stochastic approach allows us not only to produce a larger amount of energy, but also to obtain a better stability; moreover, the torque variations were extremely reduced and the numerical conclusion showed the desired ability and application of this method. These results demonstrated that the curves of the state variables and output variables for the different values of α converge to zero, which shows that the design controller was fully able to reduce the effectiveness of the white noise.
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