**5. WAMS applications**

In general, the information about any system can be extracted from its raw data, which is measured by its data resources. In power systems, this can be achieved by a kind of computer aided tools known as "WAMS Applications" (Shahraeini et al., 2010). Typically, WAMS applications process the raw data measured by data resources and extract usable information for system operator, consumers and customers. Shahraeini et al. (2010) have classified WAMS applications into the three main groups: generation, transmission and distribution applications. Three next sub-sections are going to describe these three groups of applications.

#### **5.1 Generation aplications**

Generation applications (GEN): These applications are run in generation level in the way that they acquire and process data of generators in the control center(s). As its consequence, generator information can be obtained in the control center(s) all at once. Generator operation status monitoring and transient angle stability are some examples of such applications (Xiaorong et al., 2006).

In the above mentioned applications, generator status monitoring (GSM) is the most important GEN application since it provides all or part of real time information of generators in the control center. The first kind of GSM was implemented by using DFR as a data recorder (Lee et al., 2000). As DFRs can record the operational and non-operational data with very high sampling rates, they can be used as online recorders in generation sides. If the recorded data is transmitted to the control center in real time, the generator status can be monitored in the control center. After introducing PMUs to the power systems, the information provided by these units can also used for GSM application (Xiaorong et al., 2006). This has been resulted from the fact that PMUs provide phasor data in real time with very high sampling rate (up to 60 samples per second).

#### **5.2 Transmission and Sub-Transmission applications**

Transmission and sub-transmission applications (TRAN): In deregulated power industries, some applications are performed at transmission (or sometimes sub-transmission) level by independent system operator (ISO). Historically, these functions are performed by group of computer aided tools called energy management systems (EMS). State estimation (SE), load flow (LF), optimal power flow (OPF), load forecast (LF) and economical dispatch (ED) are

Wide Area Measurement Systems 313

Data resources of SA are located in distribution substations including bus phase voltages, transformer and feeder active and reactive power, feeder currents, statuses of circuit breakers, capacitors and reclosers cut-off switches, load tap changer and voltage regulator

Typically, measurement data and status data are measured by pre-mentioned data resources. Then, Remote Terminal Units (RTUs) collect data and send it to SCADA systems. Finally, communication infrastructure has the responsibility of data transmitting from

Nowadays, due to rapid growth in metros, distribution networks have been one of the most extensive infrastructures in metros. In such networks, Feeder Automation (FA) is one of the key elements for efficient management of the power distribution networks. The main purposes of FA are twofold. Firstly, FA aims to automate feeder switching. Secondly, FA

The main data resources and controllable devices of this function are line reclosers, load break switches, sectionalizers, capacitor banks and line regulators. Typically, these data resources are much more than resources of SA and are located at distribution poles

Consumer side automation tries to automate the final points of electricity delivery i.e. metering devices of customers. Beyond this, customer equipments may be automated by this application and may be controlled through the control center. Advanced Metering Infrastructure (AMI) and Automatic Meter Reading (AMR) are two main systems utilized to

The AMI may be assumed as the central nervous system of the smartgrid architecture in distribution systems. AMI collects consumption data from smart meters and transmits it to the control center. This function of AMI is similar to the purposes of AMR systems. In addition to reading functions, AMI also relays demand signals and pricing information to smart meters in near real-time; and thereby, feedback loop is closed by AMI system

Data resources of AMI /AMR systems are metering devices. But the difference between AMI and AMR is that metering devices of AMI can also be remotely controlled by system operator. Since huge numbers of metering devices are distributed in entire system, the cost of communication system, which is utilized by an AMI/AMR system, is vital. In the view of

The communication system of WAMS is responsible for data delivery from data resources to the control center(s) and from control center(s) to the system actuators. Indeed, the communication network of WAMS is similar to the neural network of humans. As in case of failure or mal-functioning of neural network paralyzed may happen, failure of

this fact, low cost communication infrastructure means more automated customers.

positions and statuses, transformer temperatures and relay settings (Cassel, 1993).

SCADA to the control center(s) (Shahraeini & Alishahi, 2011).

controls voltages and active/reactive powers of feeders (Cassel, 1993).

**5.3.2 Feeder automation** 

(Shahraeini & Alishahi, 2011).

automate consumer sides.

(Shahraeini & Alishahi, 2011).

**6. Communication infrastructure of WAMS** 

**5.3.3 Consumer side automation** 

some examples of conventional EMS applications (Shahidehpour & Wang, 2003). The aforementioned applications may be considered as conventional WAMS applications since data is fundamental part of all of them.

After introducing phasor measurement units to the power systems, phasor data may contribute conventional WAMS applications or may introduce some new modern WAMS applications. For instance, if a state estimation uses only phasor data as its input, the state equations of the system will be linear. While conventional SEs (that use conventional data) are non-linear and they must use numerical method to solve their equations, iteratively.

Xiaorong et al. (2006) have summarized modern WAMS applications in the power systems. Some of these applications use only phasor data (e.g. Integrated Phasor Data Platform) and some ones may use both phasor and conventional data e.g. hybrid SEs.

Some modern WAMS applications are as follows (Xiaorong et al., 2006): Integrated Phasor Data Platform, Wide-Area Dynamic Monitoring and Analysis, Synchronized Disturbance Record and Replay, Online Low-Frequency Oscillation Analysis, Power Angle Stability Prediction and Alarming, and PMU based State Estimation. Combination of these modern applications with the conventional ones forms a modern EMS in the control center.

In the above mentioned applications, the state estimation is the most important WAMS application and is considered as kernel of EMS (Shahraeini et al., 2011). This has been resulted from the fact that this application extracts creditable data from raw data provided by data resources. Other WAMS applications use obtained creditable data as their input (Shahidehpour & Wang, 2003).

State estimations, based on their utilized data, are classified into the three different types: conventional, PMU based and hybrid state estimations (Shahraeini et al., 2011).

Conventional state estimations use conventional operational data i.e. voltage and current magnitude, active and reactive power flow, and active and reactive power injection.
