Mathematical Problems in Engineering

Volume 2013 (2013), Article ID 517608, 11 pages

http://dx.doi.org/10.1155/2013/517608

## Wide-Area Control for Damping Interarea Oscillations with Event-Triggered Scheme

^{1}Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China^{2}College of Electrical Engineering and New Energy, China Three Gorges University, Yichang, Hubei 443002, China^{3}College of Mathematics and Computer Science, Hubei University of Arts and Science, Xiangyang, Hubei 441053, China

Received 12 January 2013; Accepted 4 March 2013

Academic Editor: Engang Tian

Copyright © 2013 Yunning Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

An event-triggered scheme is adopted and applied to the design of a centralized wide-area damping controller (WAHDC) of interconnected power systems with external disturbance. Firstly, based on the linearized multimachine system model, the centralized WAHDC with event-triggered scheme design problem is described as time-delay feedback control problem. Then by using Lyapunov functional method and Jensen inequality technique, criteria for stability with an norm bound and for design of the feedback controller are derived. The linear matrix inequality (LMI) is employed to solve the feedback gain. Finally, case studies are carried out based on two-area four-machine power system. Simulations indicate that the proposed WAHDC with event-triggered scheme can damp the interarea oscillation effectively when the external disturbance is bounded and significantly reduce the number of measured states released to WAHDC. Relationships between event trigger parameter and dynamic performance, and network utilization, and time delay, and and disturbance rejection level are investigated and results obtained can be used to choose an appropriate event trigger parameter.

#### 1. Introduction

Interarea oscillations are the manifestation of consequences of small disturbances in weakly interconnected power systems [1]. The phenomenon is not new. However, it has evolved from being a local problem to becoming a global one. As more regional power systems are integrated for higher reliability and economy of scale, low frequency interarea oscillations have become one of the major challenges to the power system operators. This type of oscillations limits the amount of power transfer on the tie lines between the regions containing coherent generator groups [2].

The traditional approach to damping out interarea oscillations is to install power system stabilizers (PSSs) that provide supplementary control action through the generator excitation systems [2]. The input of PSS is often local signal. Such PSS is effective in damping local modes. While for wide-area complex power systems, the effectiveness in damping interarea modes is limited. It has been proved that under certain operating conditions an interarea mode may be controllable from one area and be observable from another [3]. In such cases, local PSSs are not effective for the damping of that mode. Proposed solutions to interarea oscillation damping control range from new PSS design [4], including multiband PSS concept [5] -approach-based supervisory-level PSS [6], to control systems that deploy wide-area measurement systems (WAMSs) [7–10]. With the technology of phasor measurement units (PMUs), WAMS allows to synchronized signals measured at remote locations to be available in real time in the control center.

The overall objective of WAMS is to provide dynamic power system measurements. Except for damping control mentioned previously, WAMS can also provide complete monitoring, protection, and control of the power system. Reference [11] presents a sectionalizing method based on WAMS for the build-up strategy in power system restoration. Reference [12] uses globally measured output signals to estimate reduced order state-space models of large power systems with many controllable devices for subspace system identification. Based on the WAMS output of a power system, [13] presents a new methodology to construct power system area load models alongside network reduction. In [14], a fault location method based on principal component analysis is proposed, in which synchronized information data are utilized provided by the WAMS/PMU measurement system. In order to improve the precision, the literature [15] constructs a state estimation objective function combined with the constraints of zero-injection nodes and WAMS.

One can imagine that, with the development of the technology and with a detailed study, WAMS will be used more and more widely in deregulated power systems. It brings a lot of opportunities for improving system stability, but also huge challenges such as the communication pressure to network by remote magnanimity data transmission. However, periodic sample and control strategy will lead to the sending of many redundant signals through network as mentioned in [16–19]. About how to reduce communication requirements on network control systems, other sample and control strategies have appeared in the literature [20–25]. References [26, 27] introduce trigger scheme in excitation control of single machine and state estimation of multiarea power system. As pointed out in [28], although these schemes have different triggering conditions, they are based on a similar concept that the signal will be released to network only “when needed.” Different from those “sampled when needed” strategies, Yue et al. propose a novel event-triggered scheme in [16–19]. It uses a periodic sample scheme, but an evaluation of whether every sample fulfils a certain condition should be taken before the sampled signals are sent to the network. In that case, the number of signals to be transmitted to the controller can be reduced. And in turn, the number of control output calculations can decrease. Therefore the network bandwidth can be used fully and the computation load at the controller can be relieved.

In order to enhance the dynamic performance of power systems and make full use of constrained bandwidth of WAMS at the same time, this paper investigates a wide-area damping controller (WAHDC) design with event-triggered scheme and some related issues about the possible implement method in electrical power engineering. As centralized wide-area damping control provides more efficient solutions due to the availability of large amount of system-wide dynamic data and better observation of interarea modes [2], to improve the damping performance, centralized wide-area full state feedback is introduced based on WAMS. For the feature that machines are distributed widely in multimachine power system, different from [16–19] in which an event generator is located at the sampler side, an event detector sited between PMU and WAHDC at the control center is used to determine whether the newly measured state should be released to WAHDC, but every measured signal by PMU will be sent to the event detector through network. Based on a similar model proposed in [16–19], criteria for the stability with an norm bound and controller design are set and expressed in the form of linear matrix inequality (LMI). For the purpose of dealing with the uncertainty introduced by external disturbance, control method is adopted [29]. Simulation results show the effectiveness of WAHDC with event-triggered scheme and also provide a possible method to choose appropriate event trigger parameter to get trade-off to balance the damping performance, utilization of communication and computation resources, delay margin, and disturbance rejection level.

The remainder of the paper is organized as follows. In Section 2, the problem under consideration is formulated after the modeling of multimachine power system. The centralized excitation control performance analysis and synthesis results are studied in Section 3. Case studies are presented in Section 4 using a benchmark two-area four-machine power system controlled by WAHDC with event-triggered scheme. Simulation example is provided to demonstrate the effectiveness of the proposed control scheme. What is more, the relationships between event trigger parameter and damping performance, and delay margin, and and disturbance rejection level are investigated, respectively, in this section. Finally, the conclusions are presented in Section 5.

#### 2. System Modeling and Problem Formulation

##### 2.1. Small Signal Model of Multimachine Power Systems

Generally speaking, low frequency oscillation is a typical problem of small-signal stability. Small-signal (or small-disturbance) stability is the ability of the power system to maintain synchronism under small disturbances. The disturbances are considered to be small and usually will not lead to structure and operating point change of the power systems. For the purpose of analysis, the equations that describe the resulting response of the system may be linearized at certain operating point [30].

Consider a multimachine power system composed of generators. The generator is represented by the third-order model. Assuming the input mechanical torque is constant, that is, neglecting the influence of the governor system, assuming the excitation system is separative stationary controllable silicone excitation system and ignoring the time constants of power unit and excitation regulator, and taking the excitation voltage deviation as excitation control input, the equation of each generator is expressed as follows [31]. The physical meaning of the corresponding parameters is listed in list of symbols: where denotes relative power angle between the th and the th generators. Other parameters are introduced in list of symbols. Taking as the state vector, applying Taylor series approximation at the given operating point, and then rearranging appropriately, linearized model of -machine power system can be written as [32] where where For convenience of excitation controller design, one can choose the th machine as the reference one, and the state vector is reselected as , where , . . By some mathematical similarity transformation and considering the disturbance entering the excitation winding, one can get the state-space description of -machine system as where denotes the disturbance signal and . , and are constant matrices with appropriate dimensions, and can be obtained from (3) by some simple matrix similarity transformation. Note that the order of (8) is one order less than (3); that is, the order of (8) is .

##### 2.2. System Description of Wide-Area Damping Control

For the problem of wide-area damping control, one can choose a regulated output vector as ; thus the multimachine power system considered here can be described as where and are weighting matrices and is constructed by the linear combination of state signals sampled by remote PMU.

As is known, periodic controlling mechanism may often lead to the sending of many redundant signals through the communication network of WAMS, which will in turn increase the load of network transmission and waste the network bandwidth. Therefore, it is significant to introduce a mechanism to decide which sampled state signals should be taken to calculate the control output. A pretreatment mechanism called event detector (ED) is constructed in the wide-area damping control loop located after the phasor data concentrator (PDC) at the control center. If a signal is sent to the WAHDC, we define that an event happens. The framework of WAMS-based wide-area damping control with event-triggered scheme is shown in Figure 1. Focusing on the closed-loop framework shown in Figure 1, the following functionality is expected.(i)The controlled machines are sampled simultaneously by different PMUs with the sampling period , which can be obtained by global positioning system (GPS) inducing time function. The time of the th sampling is written as , . For simplifying the analysis, assume all of the measured signals are time stamped and arrive at the event detector at the same time. Meanwhile, we also assume that the delays from the controller to the actuators distributed widely are the same.(ii)If the th sampled state signal is taken to calculate the control output, the instant will be marked as . Then the released time at the ED is . The meaning of the notation will be depicted later. The ED uses the previously transmitted sampled data to decide whether the current sampled data needs to be released based on the following quadratic condition, that is, the event-triggered release scheme where is a positive matrix and . The current sampled signal will not be released to calculate the control action if condition (10) is satisfied. In other words, only the sampled signals that violate condition (10) will be released by ED to calculate the control output. Assume the signal sampled at the initial time is released, which means . In particular, when , all the sampled signals are transmitted to the control center and used to calculate control action. Then the event-triggered scheme reduces to the periodic sampling and controlling scheme.(iii)When the signal has been sampled by PMU, it is forwarded to the control center, introducing a sensor-to-controller delay . It is noteworthy that can be obtained with the time stamp and the synchronization measurement feature of PMU. The controller forwards the actuation signals to the actuators, introducing another communication delay, controller-to-actuator delay , which includes the time delay introduced by control output computation. The total transmission time delay can be lumped together as , , where is a positive real number. Finally, the actuators perform the actuation at the time given by .

Based on the previous analysis, considering the effect of the transmission delay, the system model with event-triggered scheme can be described as For the ease of analysis, for , the system (11) can be converted to a time-delay system using similar methods in [17, 19] where is the initial time of . , and the definition of is the same as in [19]. Correspondingly, the event-triggered condition is converted as

##### 2.3. Problem Formulation

The purpose of the paper is to develop techniques to deal with event-triggered control problem for -machine power system (12)–(14) based on WAMS. For a given scalar , the performance of system (12)–(14) is defined to be Then the event-triggered control problem addressed in this paper can be stated so as to design a state feedback controller such that system (12)–(14) under the event detector with event triggering condition (10) satisfies the following two requirements.(1)The closed-loop system (12)–(14) should be asymptotically stable under the condition .(2) for all nonzero under the zero initial condition and a given .

#### 3. Performance Analysis and Controller Design

##### 3.1. Some Lemmas

Before giving the main theorems, we need the following two lemmas.

Lemma 1 (see [33]). *For any constant matrix , , scalars , and vector function such that the following integration is well defined, then it holds that
**
where .*

Lemma 2 (see [34]). * For matrices and , one has
**
where is any chosen constant. *

##### 3.2. Performance Analysis and Controller Design

Based on the Lyapunov functional method, we conclude the following results. Different from [17–19] in which free weighing matrix method is used, we use Jensen inequality approach to performance analysis and controller design. Reference [35] has theoretically proved that both of the methods are equivalent. However, using Jensen inequality can decrease decision variables and is easy for computation of LMI.

Theorem 3. *For given parameters , and matrix , the system (12)–(14) under the event-triggered scheme (15) can keep on the internal stability and also the external disturbance rejection index , if there exist positive definite matrices , , , and , such that the following matrix inequality holds:
**
where
*

*Proof. *Construct a Lyapunov functional as
where , , and . For , taking the time derivation on (21),
Applying Lemma 1, we have
where . Considering (22) and (15), we can obtain that
where
Combining (13) and (24), one can conclude that
where
From (19) and by Schur complement, one can conclude from (26) that, for ,
Since , is continuous in since is continuous in . Therefore, on both sides of (26), the integral from to with respect to yields
Under zero initial conditions, the following can be obtained:
That is . This completes the proof.

In order to ensure the damping control performance under the circumstance of time delay of wide-area state feedback with event-triggered scheme and external disturbance, the main result is summarized in the following theorem, which can be derived based on Theorem 3.

Theorem 4. *For given parameters and , the system (12)–(14) under the event-triggered scheme (15) with can keep on the internal stability and also the external disturbance rejection index , if there exist positive definite matrices , , , , and of appropriate dimensions such that the following matrix inequality is feasible. Moreover, the gain matrix of such damping controller can be obtained as :
**
where
*

*Proof. . *Defining , pre- and postmultiplying (19) with , making the following changes in the variables: , , , , and using Lemma 2 and Schur complement, (31) is derived from (19).

#### 4. Results of Case Studies

This section aims to validate the proposed wide-area damping controller design method with event-triggered scheme. There are different performance indices to evaluate the wide-area damping controller, and there are two parameters, that is, event-triggering parameter and disturbance rejection index that should be chosen appropriately. In this section, we focus on the time response of the power system with small signal, release times of sampled state signals during a certain simulation time, maximum allowable delay time (delay margin), and disturbance rejection level. To find the appropriate and , a series of numeric experiments is carried out through the application of WAHDC to the four-machine two-area test system, which is also the benchmark system for the oscillation damping study [30]. Figure 2 shows the four-machine two-area system. The detailed parameters can be found in [30].

In the following simulations, the state-space description of the power system is based on model (8), that is, the relative power angle synchronized reference frame model. The matrices and can be established by the modeling method mentioned previously at the given operating point. For regulated output, we set the weighting matrices and as And also we choose . For the convenience of analysis, the state variables are reordered asThe duration of all the following simulations is always .

##### 4.1. Case Study about the Influence of on System Time Response and Network Utilization

A time domain analysis method of the closed-loop system is carried out to reveal the performance of the proposed controller in terms of improving the damping of interarea oscillation. The exogenous disturbance is assumed to be The damping action of the controller is examined under different event-triggering parameter . According to the time-delay analysis of WAMS in [36, 37], we set , , and then the transmission delay is correspondingly. For , the dynamic response with different chosen from the feasible range, which is shown in Figure 7 and will be described in detail later, is displayed in Figures 3 and 4, and the corresponding network utilization is reported in Table 1. The data transmission rate is also listed in Table 1, where denotes the number of total measured data release times with , and indicates the number of release times with .

To illustrate the quality of the systems response, we introduce two indices, that is, the integrated absolute error (IAE) between the system response of periodic release (or time-based) strategy and event-triggered scheme [38] and the time response of the error (TRE) between the time-based strategy and the event-triggered scheme, where The s and s for different are illustrated in Figures 5 and 6 and Table 2, respectively. In Figure 5, denotes the relative angle between and with time-based strategy, and represents the relative angle between and with different , , where , , and . In Figure 6, , has similar meaning.

The curves depicted in Figures 3 and 4 show that the oscillation between Area and Area is damped quickly for different event-triggering parameter . Even when , the oscillation is damped well. It shows that the WAHDC with event-triggered scheme can provide effective damping of interarea oscillations when the power system is subjected to external interference and communication time delay exists in the damping control loop. Moreover, from Table 1, it is found that the times when the sampled signals are released to be used to calculate the control output at the control center decrease with the increase of . It can be seen from the last row of Table 1 that, for , with event-triggered scheme, only 21.45%, 16.19%, and 12.59% of measured states need to be released to the WAHDC, respectively. In other words, the network utilization by the event-triggered scheme can be obtained by 78.55%, 83.81%, and 87.41% improvement, respectively. Thus the burden of the network communication between control center and the actuators widely distributed is reduced and the communication bandwidth is saved. That validates the effectiveness of the design approach.

However, according to the definition of IAE we can see a lower value of IAE meaning that the control strategy provides a better damping performance. The data in Table 2 show that the dynamic response is degraded to a certain degree with increase of ; that is, it is expected that larger will yield larger IAE with less network utilization. From the point of view of WAHDC design, the relationship between event trigger parameter and network utilization can provide an effective guide for the design of WAHDC, and the event-triggering parameter can be chosen to achieve trade-off between the network utilization and damping performance for certain delay margin and perturbation attenuation index.

As stated previously, for certain delay margin and perturbation attenuation index, event trigger parameter can be chosen in a feasible range. The following studies are aimed at finding the feasible range.

##### 4.2. Case Study about the Relationship between and Time Delay

In this section, simulation studies are carried out to find a guiding method to choose an appropriate for certain . For given time delay and , a feasible range of can be found by manually increasing the value of and checking the feasibility of (31). The upper bound of is calculated by selecting different sets of and . In other words, the delay margin that resulted from different and can also be obtained in similar way. Results of the upper bound of with respect to different and the delay margin for different are shown in Figure 7 and Table 3 separately.

Results in Figure 7 show that, for a certain , the curve declines slowly at first and after a turning point it declines sharply with the increase of . The trend of the relationship between and the upper bound of for different is similar except that with the increase of , the value of the upper bound of decreases for the same . Namely, with the increase of , one can choose a in an increasing feasible range. From another perspective, from Table 3 we can also conclude that for given , increasing the value of , delay margin of WAHDC decreases. Thus for the full use of network bandwidth one can choose a larger . For example, for , one can choose from to about when the maximum delay in damping control loop is . However, as addressed previously, considering the damping performance and delay margin of the controller, the value of cannot be too large.

##### 4.3. Case Study about the Relationship between and the Disturbance Rejection Level

Different selection of disturbance rejection level also influences the feasible range of . The relationship between and the disturbance rejection level is investigated in this section. By applying Theorem 4, we can get the upper bound of for different set of and time delays and also obtain the smallest for certain combination of and time delay. Results of upper bound of with respect to different and the smallest for different and time delay are shown in Figure 8 and Table 4 separately.

As is well known, for control problem, smaller means better disturbance rejection level. However, from Figure 8 and Table 4 we can know that, when is set to less than , the feasible range of decreases rapidly for both and time delay. Since larger means less network overheads generally, for reduction of network overheads, the value of cannot be set too small. Moreover, when we can know the smallest value of for given time delay. Therefore, the findings can provide an effective guide for the WAHDC design for certain disturbance rejection performance and delay margin to achieve full use of network resource.

#### 5. Conclusion and Future Work

A centralized wide-area damping controller for multi-machine power system is proposed in this paper. Besides using the wide-area signals from remote generators to improve the damping of the interarea oscillation in the power system, to reduce the amount of data transfer, an event-triggered scheme is introduced. Moreover, an control criterion is obtained for the controller design based on the Jensen inequality and LMI approach. By solving a set of LMIs, Theorem 4 provides a useful way of design for feedback gain with given trigger parameter . Then to validate the presented WAHDC with event-triggered scheme and to find the method to choose an appropriate event trigger parameter , case studies are carried out based on the two-area four-machine power system. From the simulation results, the following conclusion can be drawn.(i)The presented WAHDC with event-triggered scheme can damp the interarea oscillation effectively when the disturbance is energy bounded. Increasing the value of in the feasible range can deduce the traffic volume between WAHDC and machines. But the dynamic response of power system under WAHDC is degenerated to some extent.(ii)The introduction of event-triggered scheme has some influence on system dynamic performance, network utilization, delay margin, and disturbance rejection level. In view of WAHDC design, increasing the value of can decrease the delay margin and disturbance rejection performance. Findings from previous simulations give a guiding method to select appropriate to get compromise between damping performance, network utilization, delay margin, and disturbance rejection level.

The previous researches are promising, but also have some limits. The real power system is nonlinear and of high dimensionality, and it is neither practical nor necessary to design the full state feedback controller based on the linearized model. Further research is still needed to combine the model reduction theory and the event-triggered scheme presented in this paper. Furthermore, the proposed WAHDC with event-triggered scheme only reduces the data transfer from control center to distributed machines. Another direction of our future research is to construct distributed event detectors at the PMUs side to reduce the whole network overheads. In addition, the algorithm and method proposed in [39, 40] may be used to find the optimal value of .

#### Symbols

: | Power angle of the ith generator, in deg |

: | The angular velocity of the ith generator, in p.u. |

: | Synchronous machine speed, in p.u. |

: | q-axis transient potential, in p.u. |

: | Control input, excitation voltage, in p.u. |

: | d-axis synchronous reactance, in p.u. |

: | d-axis transient reactance, in p.u. |

: | Modulus of the transfer admittance between the ith and jth generator, in p.u. |

: | Mechanical input power, in p.u., which is a constant |

: | Electrical power, in p.u. |

: | Damping coefficient of ith generator |

: | Stored energy at rated speed, inertia constant, in seconds |

: | d-axis open circuit transient timeconstant, in seconds |

: | Direct axis current, in p.u. |

: | Quadrature axis current, in p.u. |

#### Acknowledgments

The authors would like to thank the anonymous reviewers for providing their valuable comments and constructive suggestions that have improved the presentation of this paper. This work was supported by the National Natural Science Foundation of China (nos. 60834002 and 61074025) and the Ph. D. Programs Foundation of Ministry of Education of China (no. 20110142110036).

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