Research Article | Open Access

Zhen Zhang, Yanhong Liu, "Stochastic Small Signal Interval Stability of Power Systems with Asynchronous Wind Turbine Generators", *Mathematical Problems in Engineering*, vol. 2018, Article ID 7617394, 13 pages, 2018. https://doi.org/10.1155/2018/7617394

# Stochastic Small Signal Interval Stability of Power Systems with Asynchronous Wind Turbine Generators

**Academic Editor:**Quanxin Zhu

#### Abstract

The stochastic dynamic interval model of power systems with asynchronous wind turbine generators is established with consideration of the interval uncertain parameters and random small excitation disturbances. The conditions of interval mean stability and interval mean square stability of the power systems are proposed. The relationship between the bounds of the mean (mean square) error and the parameter interval range of the systems is discussed. Finally, we simulate the power systems to demonstrate the effectiveness of the proposed results.

#### 1. Introduction

With the rapid development of wind power and other emerging new energy and grid, the random excitation has a significant impact on the power systems stability and power quality [1–3]. In addition, the system parameters are generally uncertain because of the effects of the temperature, humidity, and other environmental conditions and the restriction of the information collection technology. It is of great importance to consider the influence of the stochastic excitation and uncertain parameters in the stability analysis of power systems [4–6].

In recent years, a lot of researches have been done on the stability analysis and feedback control of stochastic systems; see [7–10] and the references therein. For power systems under stochastic excitations, Humberto et al. [11] discussed the almost sure asymptotic stability of linear stochastic systems with bounded Markov diffusion process perturbation by the means of Lyapunov exponents and applied the theoretical results to one machine infinite bus electric power systems. Ma et al. established a continuous Markov power system model with multiple operating conditions considering the stochastic characteristic of wind speed and proposed a Lyapunov function based method for the stability analysis of the system [4]. Robust stochastic stability of power systems under stochastic excitation and random perturbations was discussed in [12, 13]. Noticing that the mean and mean square stability [14, 15] are of great importance for power systems, the small signal mean stability and mean square stability of power systems under random excitation were investigated by some researchers. In [2], Zhang et al. put forward a stochastic differential equation model for power system under Gauss type random excitation and proposed some conditions for the mean stability and mean square stability of the system. Yuan et al. further investigated the the steady-state expectation and covariance of the system state variables of the power systems with asynchronous wind turbines [16]. For a general case of mean stability and mean square stability, Lu et al. discussed the th stochastic stability and dynamic characteristics of power systems under small Gauss type random excitations [17]. For power systems whose network parameters are being known to be within certain bounds, under the assumption of with mixed phasor and conventional power measurements, some state estimation methods were put forward based on the interval linear system model [18, 19]. So far, there have been no researches carried out for the stochastic mean stability and mean square stability analysis of power systems with both consideration of random excitation disturbances and interval parameter uncertainties.

In this paper, we discuss the stochastic small signal stability of power systems with wind turbines and parameter uncertainties. First, the stochastic interval dynamic model of the power systems is established with consideration of the interval parameter uncertainties and small random excitations. Then, we propose some results for the mean stability and mean square stability by analyzing the eigenvalues of the system matrix. The relationship between the bounds of the mean and the mean square error and the small random excitation and interval uncertain parameters is also discussed. Finally, we simulate a single machine infinite bus power system with asynchronous wind turbine Generator to verify the effectiveness of the proposed method.

The paper is organized as follows. First, the stochastic dynamic interval model of power system is established considering interval uncertain parameters under small random excitation in Section 2. The interval mean stability and interval mean square stability of the system are given in Section 3. Section 4 gives the simulation of power systems. Finally, conclusions are drawn in the last section.

#### 2. Stochastic Dynamic Model of Power Systems with Asynchronous Wind Turbine Generators

##### 2.1. Dynamic Model of Asynchronous Generators

Ignoring the electromagnetic transient process of asynchronous generator, the simplified equivalent circuit of the asynchronous generator can be shown in Figure 1 [20], where , , , , , , and are the stator resistance, the stator reactance, the rotor resistance, the rotor reactance, the excitation resistance, the excitation reactance, and the voltage of the asynchronous generator, respectively, is the slip, and is the stator current.

From Figure 1, we have

The active power consumption in the rotor windings can be formulated as

The electromagnetic power and the mechanical power of the system are respectively. Writing the parameters ware as per unit values, we have and

Ignoring the high-end items, the Taylor series expansion of at is

Assuming , we have

##### 2.2. Stochastic Model of Asynchronous Wind Turbine Generators

The asynchronous wind power generation systems is mainly composed of the wind turbine, gear box, and asynchronous generator, as shown in Figure 2.

Without consideration of the random excitation, the determinate shaft model of the wind turbines can be written aswithwhere is the angle speed of the wind turbine, is the synchronous angle speed, is the angle speed of the asynchronous generator, is the rotary inertia of the wind turbine, is the rotary inertia of the asynchronous generator, , , are output mechanical torque, shaft torque, and generator electromagnetic torque of wind turbine, respectively, is the shaft twist angle, is the shaft stiffness coefficient, and is the damping coefficient.

Considering the power fluctuations of the wind turbine and the asynchronous generator, which can be regarded as Gaussian random small excitation in a short time [9, 10], the stochastic dynamic model of the asynchronous wind turbine generators can be formulated as where and are the random excitation intensities and is the standard Wiener process.

Substituting (9) into (10), we have the stochastic dynamic model of the asynchronous wind turbine generators as follows:

##### 2.3. Stochastic Interval Model of Power Systems with Wind Turbines

In this section, the stochastic dynamic interval model of power systems with asynchronous wind turbine generators is established with consideration of the parameter uncertainty of .

Considering the random small excitation and denoting , we have

Noting that and , we can obtain

When the asynchronous wind turbine generators are working at a stable operating point, we have

Let ; we can obtain

So the stochastic state equation of power systems with asynchronous wind turbine generators can be written as where , ,

For power systems, the damping coefficient is always uncertain with some upper and lower bound. Denoting , where is the lower bound and is the upper bound of , respectively, we have , where Let so we have and . The stochastic dynamic interval model of the power systems with asynchronous wind turbine generators can be formulated as

#### 3. Stochastic Small Signal Interval Stability Analysis of the Power Systems

In this section, we analyze the interval mean stability and interval mean square stability of the power systems and establish the relationship between the bounds of the mean and the mean square error and the system uncertain parameter and random excitation.

*Definition 1 (see [8]). *System (20) is interval -moment stable, if where is a constant. Especially, when and , the system is interval mean stable and interval mean square stable, respectively.

Lemma 2 (see [21]). *Let be real symmetric matrix and be -dimensional column vector; we havewhere and are the maximum and minimum eigenvalue of , respectively.*

Lemma 3 (see [22]). *Consider the system If there exists a continuous positive definite function with infinitesimal upper bound and infinity lower bound such that system (23) is asymptotically stable.*

Theorem 4. *Suppose there exists a reversible matrix such that ; we have where is the order of the same roots, and is an even number.*

*Proof. *The Taylor expansion of at can be written as Assume matrix is similar to a Jordan matrix ; that is, where , with , Substituting into (27) gives Let and we have whereIf there exists a matrix such that , we haveAssume are real roots, are plurals, and is a pair of conjugate complex roots of . It is obvious that is a pair of conjugate complex roots of and is an even number. Let then we have According to the structure of , we have and So can be written as thus the proof is complete.

*Deduction 1. *For the two order power systems with , we have if has two same real roots, if has two different real roots, and if has a pair of conjugate complex roots, where .

*Proof. *Suppose has two same real roots; then and where According to Theorem 4, we have If has two real roots with , choosing , then we have There exists a matrix such that . So we have If has a pair of conjugate complex roots, denote and we have thus the proof is complete.

Theorem 5. *System (20) is interval mean stable if where is the maximum eigenvalue of , and*(1) *if has two same real roots, we have*(2) *if has two different real roots, we have*(3) *if has a pair of conjugate complex roots, we have*

*Proof. *Consider the following system: where is the same as the system matrix in (20).

Choosing a Lyapunov function , there exist positive constants and such that and has the infinitesimal upper bound and the infinite upper bound. Moreover, we have where is a constant. Let and ; we have Thus and system (52) is stable under condition (48). Furthermore, we have .

Now consider the stochastic power system (20). The solution process of (20) can be expressed as [8] Taking expectation of the both sides, we haveNoticing that the expectation of the nonrandom variable is equal to itself and the expectation of the random variable is equal to 0, we have Substituting (58) into (57), we can getAccording to Deduction 1, if has two same real roots, choose and we have Direct calculation shows thatSubstituting (60)-(61) into (59) givesAccording to Cauchy-Schwarz inequality and noticing that we haveSimilarly, if has two different real roots, we have and if has a pair of conjugate complex roots, we get So there exists such that and system (20) is interval mean stable. Thus the proof is complete.

Theorem 6. *System (20) is interval mean square stable, if Moreover,*(1) *if has two the same real roots, we have*(2) *if has two different real roots, we have*(3) *if has a pair of conjugate complex roots, we get*

*Proof. *The proof is similar to Theorem 5 and is omitted here.

*Remark 7. *Consider -machine stochastic power systems described byThe system can be reformulated as where is state matrix, is coefficient matrix, is gain matrix, and is random excitation matrix. From the proof procedure of Theorems 5 and 6, we can see that similar results can be obtained for the interval mean and mean square stability of multimachine stochastic power systems.

#### 4. Simulation

In this section, we simulate the power system to verify the effectiveness of the proposed results. The parameters of the system are chosen as , = 10 p.u., = 2.5 p.u., = 1 p.u., = 0.08 p.u., = 0.003 p.u., = 0.125 p.u., = 0.004 p.u., = 0.05 p.u., = 2.5 p.u., and = 10.52 p.u. and we can get ,

According to Theorems 5 and 6, we know that the system is interval mean stability and interval mean square stability.

Choosing , we can get