# WAMS-Based Online Disturbance Estimation in Interconnected Power Systems Using Disturbance Observer
## 1 Introduction  
With the growth in energy demand and the restructuring of power systems, frequency stability remains an area requiring consistent research. There are many phenomenon that lead to frequency instabilities but all eventually lead to transmission line tripping or generation loss. This upsets the balance between generation and demand and the frequency of a system deviates from the nominal [1; 2; 3; 4]. A scenario where the demand is higher than the generation leads to severe decline in frequency and this is of critical concern for modern power systems. In such cases, advanced frequency restoration techniques with a high speed of response are required. Under frequency load shedding (UFLS) techniques have been used to restore the power system frequency in the event of a large disturbance. Traditional UFLS technique is the most common and in this scheme specific portions of load are shed instantly whenever a frequency threshold is violated [5; 6; 7; 8; 9; 10; 11; 12; 13]. This technique only relies on local frequency measurements and the magnitude of thedisturbance is not estimated. As a result, the major shortfall of this technique is that it fails to achieve optimum load shedding and it results in either over shedding or under shedding, which further puts the system at risk. However, it is important that the disturbance amount be accurately estimated in order to maintain system stability. In this regard, adaptive techniques became more preferred to traditional ones [14; 15; 16; 17; 18; 19; 20; 21; 22]. Adaptive techniques make use of the swing equation to estimate the disturbance. In [15], the magnitude of the disturbance is estimated using the swing equation after which the most optimum amount to be shed is determined. In [16], the aim was to maximize the use of primary frequency support; here UFLS was implemented by taking into account the voltage variations. In [17], based on the estimated power system inertia the disturbance amount is constantly updated after each load shedding stage. In order to achieve the least amount of load shed after obtaining the disturbance amount using the swing equation, the governor ramp shape is then considered in [18]. Nearly similar to the work in [15], in [14] the amount of power deficit is determined using the swing equation and an optimization algorithm is used to determine the optimum amount of load to be shed at each stage. It can be concluded that disturbance estimation forms the backbone of adaptive stability restoration schemes. It is therefore critical that for any power system the disturbance be correctly estimated. Even though the swing equation is commonly used, it may fail to accurately estimate the disturbance amount. In [23], it was found out that the estimated disturbances differ from the actual occurred disturbance. It is noted that the direction of frequency drift determines whether the estimated disturbance will be large or small. The authors conclude that the error is within a range of \(\pm 2.5\%\). However, further investigations show that if the power system modeling is done using highly detailed equations and not just the system frequency response (SFR) model, the estimation error can be way larger. The main reasons for the errors in estimation are the unavailability of accurate power system information and the frequency variations during disturbance. In such cases where accurate information about the power system state is required, Wide Area Monitoring Systems (WAMS) can be an essential tool in power system dynamic studies [24; 25; 26]. Since practical electrical grids cover large geographical areas, without the use of WAMS, it was difficult to quickly pick the root cause of system collapse. The extensive use of wide-area measurement systems (WAMS) has brought flexibility in power networks monitoring [27; 28; 29]. With WAMS event monitoring, control and effects mitigation can now be performed more rapidly [30]. This is because most PMUs are capable of producing 60 samples per second and modern PMUs can produce twice as many samples per second [31].