**8. References**


entire system. The WAMS process can be divided into the three interconnected subprocesses; data acquisition, data delivery and data processing. These sub-processes are respectively performed by measurement, communication and energy management subsystems. Each sub-system has different tasks to perform on system data. As a result, it is definitely important that the functions and the equipments of these sub-systems are deeply

This chapter has extensively reviewed the equipments and the functions of each subprocess, separately. It has been shown that WAMS contributes monitoring systems to shift from the "data acquisition" systems to the "dynamic information" systems. Dynamic information of power systems helps power system operators to overcome generation, operation and planning challenges that may be resulted from system restructuring. Furthermore, it has also been shown that from the big generators to the small home equipments, WAMS systems are capable of monitoring and controlling various functions in real time. It can be concluded that in modern power systems, WAMS is an essential part of

In particular, this chapter shows that dynamic information of power systems, as a result of WAMS implementation; contributes system operators to make better decisions for system operation and planning. However, in addition to the power systems, dynamic information of any interconnected system (e.g. natural gas pipelines) helps system operators/administrators to reduce operational cost and increase efficiency of such interconnected systems. Consequently, WAMS concepts may be also generalized to other interconnected systems in order to form a dynamic information system and to deliver

As a conclusion, it can be stated that although the WAMS was firstly introduced to the power systems in order to obtain dynamic information of such systems, it can also be well established in other critical infrastructures (e.g. natural gas, petroleum, water supply, emergency services, telecommunication and etc.) to operate, monitor and control such

Cai, J. Y.; Huang, Z.; Hauer, J. & Martin, K. (2005). Current status and experience of WAMS

Cassel, W. R. (1993). DISTRIBUTION MANAGEMENT SYSTEMS: FUNCTIONS AND PAYBACK, *IEEE Transactions on Power Systems*, Vol. 8, NO. 3, pp. 796-801 Clarke G.; & Reynders D. (2004). Practical Modern SCADA Protocols: DNP3, 60870.5 and

EPG & CERTS, (2006). Phasor Technology and Real Time Dynamics Monitoring System (RTDMS) frequently asked question (FAQs), Electric Power Group and CERTS Eshraghnia, R.; Modir Shanehchi, M. H.; & Rajabi Mashhadi, H. (2006). Generation

*2006 IEEE Power Systems Conference and Exposition*, 2006, pp. 1814 – 1819 Fourty, N. Val, T. Fraisse, P. & Mercier, J. J. (2005). Comparative analysis of new high data

*Distribution Conference Exhibition*, Aug. 2005, pp. 1–7

implementation in North America, *Proceeding IEEE/Power Eng. Soc. Transmission and* 

maintenance scheduling in power market based on genetic algorithm, *Proceeding* 

rate wireless communication technologies "From Wi-Fi to WiMAX"," *Joint* 

investigated from data point of view.

power system operation and control.

infrastructures.

**8. References** 

system data to the related applications in real time.

Related Systems. Oxford: Elsevier

*International Conference on Autonomic and Autonomous Systems and International Conference on Networking and Services - (ICAS/ICNS 2005)*


**16** 

**Dynamic State Estimator Based on Wide Area** 

The wide area measurement system (WAMS) developed rapidly in recent years [1-3]. It has been applied to the studies on many topics in monitoring and control of power systems. But as a kind of measurement system, WAMS has the measurement error and bad data unavoidably. The steady measurement errors of WAMS have been prescribed in corresponding IEEE standard [4], but the dynamic measurement errors now become the focus of discussion [5-6] and attract the attention of PSRC workgroup H11. If the dynamic raw data is applied directly, the unpredictable consequence will be resulted in, which will do a lot of damage to power systems. Therefore, the dynamic estimation for the state variables during electromechanical transient process is the backbone for WAMS based

There was no effective means to measure the power system dynamic process before WAMS come forth; therefore, the dynamic estimation for power system state variables during electromechanical transient process was not feasible. In reference [7], a dynamic estimator for generator flux state variables during transient process is proposed, but the dimension of the flux state variables is relative high, and the accurate values of parameters can not be achieved easily. In reference [8], a non-linear dynamic state observer for generator rotor angle during electromechanical transient process is proposed, but the method is only applicable to one machine infinite bus system (OMIB), and the fault scenarios is required to

After WAMS come forth, many references focused on the steady state estimation with PMU measurements and had many achievements [9-11]. Comparing with the steady state estimation, the traditional dynamic state estimation [12-15] aims at the relative slow load fluctuation, which is different with the proposed dynamic state estimation during electromechanical transient process. The traditional dynamic state estimation employs the measurement equations based on the network constraints, and predicts the state variables using exponential smoothing techniques. But during the power system fault stage and consequent dynamic process, the network topology is changed and can not be acquired in time; the bus voltage phase angles has jump discontinuities and are not easy to be predicted. Thereby, the centralized dynamic estimation which adopts the measurement equations

**1. Introduction** 

satisfy the preset mode.

dynamic applications and real-time control.

**Measurement System During Power System** 

**Electromechanical Transient Process** 

*Power System Department, China Electric Power Research Institute,* 

Xiaohui Qin, Baiqing Li and Nan Liu

*The People's Republic of China* 

Shahraeini, M. Javidi, M. H. & Ghazizadeh, M. S. (2011). Comparison between Communication Infrastructures of Centralized and Decentralized Wide Area Measurement Systems, IEEE Transaction on Smart Grid, Vol. 2, No. 1, pp. 206-211

Stallings, W. (1997). Data and Computer Communications, fifth edition, Prentice-Hall Inc

