Artificial Neural Network Controller in Two-Area and Five-Area System with Security Attack and Game-Theory Based Defender Action
Abstract
:1. Introduction
2. Materials and Methods
Algorithm 1. ALO algorithm | |
1: | Random initialization of ant(X(t)) is performed ( and values are the vectors of X(t)). |
2: | Calculate the fitness (after running the LFC the setting time is taken) of ants and antlions. |
3: | The elite identification is performed from the best antlion (determined optimum Kp and Ki). |
4: | Check for the satisfaction of termination criteria for ants of each type. Choice of an antlion using a Roulette wheel. c and d are updated using The randomized walk and normalization are performed using the equation below. walk of the i-th variable, is the minimum of the i-th variable at the t-th iteration, and indicates the maximum of the i-th variable at t-th iteration. Additionally, is the random walk around the antlion selected by the roulette wheel at t-th iteration, is the random walk around the elite at t-th iteration, and indicates the position of i-th ant at t-th iteration End for loop. For all the ants, calculate the fitness. If the ant is fitter than other ants, stack that ant as the elite one. End while. |
5: | Return elite |
2.1. Applying an Artificial Neural Network (ANN) Controller in the Place of an ALO-PI Controller
2.2. Attacks Applied in LFC
2.2.1. Constant Injection
2.2.2. Injecting the Bias
2.2.3. Overcompensation
2.2.4. Negative Compensation
2.3. Preliminary Defenses
2.3.1. Saturation Filter
2.3.2. Redundancy
2.3.3. Detection
3. Results
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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ANN Unit | Availability Status of Training Data |
---|---|
Controller | Available to train the controller |
Plant | LFC model |
Integral Controller [25] | NN Controller | |||||
---|---|---|---|---|---|---|
Δf1 (Hz) | Δf2 (Hz) | ΔPtie (p.u) | Δf2 (Hz) | Δf2 (Hz) | ΔPtie (p.u) | |
Rise Time(s) | 0.000816 | 0.000149 | 0.02057 | 6.6132 | 0.00714 | 0.01136 |
Settling Time (s) | 27 | 27.851 | 38.771 | 17.376 | 18.285 | 25.846 |
Settling Min(s) | −1.0612 | 4.8252 | −0.89933 | −0.2872 | −0.2957 | −0.7447 |
Settling Max(s) | 0.53052 | 0.55137 | 1.4133 | 0.10266 | 0.10431 | 1.297 |
Overshoot (ratio) | 16,209 | 2.70 × 105 | 19,560 | 394 | 1323.1 | 66,981 |
Undershoot (ratio) | 1.70 × 105 | 30,907 | 12,510 | 24,241 | 54,439 | 38,515 |
Peak Value | 5.5215 | 4.8252 | 1.4133 | 5.0377 | 3.9904 | 1.297 |
Peak Time (s) | 1.25 | 1.95 | 0.75 | 1.23 | 1.893 | 0.741 |
PI Controller | NN Controller | |
---|---|---|
Rise Time (s) | 2.0968 | 0.30207 |
Settling Time (s) | 34.451 | 24.842 |
Settling Min (s) | 0.063062 | 0.78426 |
Settling Max (s) | 25.779 | 27.734 |
Overshoot (ratio) | 40,315 | 3435 |
Undershoot (ratio) | 0 | 0 |
Peak value | 25.779 | 27.734 |
Peak Time (s) | 9.5024 | 2.301 |
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Khadarvali, S.; Madhusudhan, V.; Kiranmayi, R. Artificial Neural Network Controller in Two-Area and Five-Area System with Security Attack and Game-Theory Based Defender Action. Energies 2022, 15, 5715. https://doi.org/10.3390/en15155715
Khadarvali S, Madhusudhan V, Kiranmayi R. Artificial Neural Network Controller in Two-Area and Five-Area System with Security Attack and Game-Theory Based Defender Action. Energies. 2022; 15(15):5715. https://doi.org/10.3390/en15155715
Chicago/Turabian StyleKhadarvali, S., V. Madhusudhan, and R. Kiranmayi. 2022. "Artificial Neural Network Controller in Two-Area and Five-Area System with Security Attack and Game-Theory Based Defender Action" Energies 15, no. 15: 5715. https://doi.org/10.3390/en15155715