Design of PD-PID Controller with Double Derivative Filter for Frequency Regulation

Cascade‐I λ D μ N controller design for AGC of thermal and hydro‐thermal power systems integrated with renewable energy sources

“…Error criteria ISE/ITSE/IAE/ITAE are obtained via simulating the models for 15 s and T S are obtained for a tolerance band of ±0.0005. Table 2 also demonstrates the advantage of ICA tuned C-I λ D μ N controller over SCA C-PDN-PIDN [14], ABC PID [16], BSO FOPID [20], SOS PIDN [22], hDE-PSO FPID [33] and JA FPIDN [35] control schemes from least ISE (1.07 × 10 −7 ), ITSE (3.32 × 10 −8 ), IAE (0.0006), ITAE (0.0015), T S (∆F 1 = 0.11 s, ∆F 2 = 0.35 s, ∆Ptie 12 = 0.36 s) and U S (∆F 1 = −0.0010 Hz, ∆F 2 = −0.00013 Hz, ∆Ptie 12 = −0.000130 puMW) point of view. Fascinatingly, this observation is not being raised in the corresponding literature that is, in [3] and hence, it establishes a literature gap.…”

“…A cost function (J) is designed based on the desired provisions and limitations. Some of the industry-linked cost functions are integral time absolute error (ITAE), integral square error (ISE), integral time square error (ITSE) and integral absolute error (IAE) [3,6,14,28,29,36,38,40,41]. However, in this paper, ISE is utilized because of its better performance [ ISE incorporates the square of the errors over the time, therefore, big errors are penalized more compared to smaller ones.…”

“…The details of RES are [14], artificial bee colony (ABC) tuned PID [16], bacterial swarm optimization (BSO) tuned fractional order PID (FOPID) [20], SOS algorithm tuned PIDN [22], hybrid differential evolution-PSO (hDE-PSO) tuned FPID [33] and jaya algorithm (JA) tuned FPIDN [35] controllers. The inspection of Figure 6(a-c [14], ABC PID [16], BSO FOPID [20], SOS PIDN [22], hDE-PSO FPID [33] and JA FPIDN [35] control schemes from least ISE (1.07 × 10 −7 ), ITSE (3.32 × 10 −8 ), IAE (0.0006), ITAE (0.0015), T S (∆F 1 = 0.11 s, ∆F 2 = 0.35 s, ∆Ptie 12 = 0.36 s) and U S (∆F 1 = −0.0010 Hz, ∆F 2 = −0.00013 Hz, ∆Ptie 12 = −0.000130 puMW) point of view. Fascinatingly, this observation is not being raised in the corresponding literature that is, in [3] and hence, it establishes a literature gap.…”

“…The results are exposed inFigure 6(a-c). To reveal the contribution of C-I λ D μ N controller, its outcomes are matched with various existing and the best claimed intelligent controllers like sine-cosine algorithm (SCA) tuned cascade PDN-PIDN (C-PDN-PIDN)[14], artificial bee colony (ABC) tuned PID[16], bacterial swarm optimization (BSO) tuned fractional order PID (FOPID)[20], SOS algorithm tuned PIDN[22], hybrid differential evolution-PSO (hDE-PSO) tuned FPID[33] and jaya algorithm (JA) tuned FPIDN[35] controllers. The inspection ofFigure 6(a-c)divulges that the C-I λ D μ N controller significantly displays superiority over other controllers in concerning swift responses, no oscillations and negligible settling times (T S )/peak undershoots (U S )/overshoots (O S ) in ∆F 1 /∆F 2 /∆Ptie 12 results.To inspect the outcome of the C-I λ D μ N controller further, the numerical values of ISE/ITSE/IAE/ITAE defined via Equations (17)-(20), T S , U S and O S are calculated through simulations and shown in…”

A Power Hardware-in-the-Loop Based Method for FAPR Compliance Testing of the Wind Turbine Converters Control

“…A detailed discussion on the limits and possible trade-off between the consumed energy from the FAPR controller and the recovery time can be found in the work by Dreidy et al [4]. FAPR controllers are mainly grouped into three categories, namely, proportional/droop-based controllers [6][7][8][9], derivative-based controllers [10][11][12][13], and other methods, which are mainly based on the swing equation of synchronous generators, thus trying to emulate inertia through Virtual Synchronous Machine (VSM) [14][15][16][17][18]. Frequency regulation during sudden load variations requires a series of control actions to be performed.…”