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FDIA theory based on nonlinear model is much more complicated because the state variable and observation value has nonlinear relationship.
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In a memristor model, the state variable is an internal unknown because its value gets changed with different states.
However, because suitable initial guesses can be found for both the state variable and parameter variables (see Figure 1), relatively efficient convergence can be achieved.
This modification was necessary because the FBSL-SPKS requires the state variables to have zero mean.
There is no single universally accepted methodology forrepresenting damping because of the nature of the state variables whichcontrol damping.
One advantage is that it is real-time, and it is almost plausible for the control system to take proper action for the instantaneous situation of the process, because it is based on observing the state variables of the process and diagnosing the faults or malfunctions of the control system.
Taking into account of all possible input and output of relevant cells, we have the following stochastic equations for the state variables: (2) Because the transition variables are random variables, the above equations are stochastic equations.
However, the state variables are sensitive to perturbations, for example, because of multi-stability.
This is because there are many applications that demand the knowledge of many of the state variables, if not all of them.
Because agents can be developed independently and only come together at runtime to create the agent-based model, keeping track of the state variables can be challenging.
FDIA is a harmful attack to the secure and economic operations of the whole power system because the estimated state variable used in all the higher layer applications in the control center is different from the actual one.
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