The extensive practice of open communication links in the power system network introduces unavoidable communication time delays (CTDs). These CTDs demean the performance of the load frequency control (LFC) system, and in the worst condition, the LFC system becomes unstable. Thus, this brief proposes a robust proportional integral derivative double derivative (PIDD2) controller design for the perturbed LFC of the interconnected time-delayed power system. In this brief, the worst case plant selection approach is applied to find the worst case plant model using Kharitonov’s stability theorem of the interval system. The proposed PIDD2 controller is designed for the selected worst case plant model of the original interval plant. Furthermore, the tuning of PIDD2 controller is carried out using an internal model control (IMC) approach. The notable feature of the presented IMC scheme is that the IMC filter coefficient ( $\lambda $ ) is determined in terms of maximum sensitivity ( $M_{s}$ ) and CTD, which shows the tradeoff between robustness and performance. The proposed control scheme is validated on a two-area time-delayed power system model. The effectiveness and robustness of the proposed IMC-PIDD2 control approach are assessed under parametric uncertainties in system parameters as well as CTD, nonlinearities, and step load demand disturbances. Besides, delay margin (DM) is also computed in the sense of Walton and Marshall stability theorems for the proposed control system. The efficacy of the proposed IMC-PIDD2 control scheme is verified by comparing it with the recently reported control schemes.

This paper addresses the robust and resilient PIDA control design for frequency regulation of the cyber-physical power system. PIDA control design is carried-out using a recently develop supply demand optimization algorithm (SDOA). This SDOA inspires by the supply-demand mechanism in economics theory. Besides, SDOA is a global optimization algorithm and simple in design. Further, this paper is introduced a new fitness function to obtain the unknown parameters of PIDA controller. From application point of view, the proposed control design is validated on the interconnected time-delayed microgrid system configuration with cyber-attacks. The effectiveness and efficacy of the proposed control design are analyzed under various practical conditions such as cyber-attacks, communication time-delay, parametric uncertainty in system parameters, stochastic load deviation, stochastic wind power deviation, and stochastic solar power deviation. Further, the superiority of SDOA shows in comparison to the grey wolf optimizer (GWO), and whale optimization algorithm (WOA). Finally, it is concluded that the proposed PIDA control design performs better, robust and resilient in comparison to the existing PID control design.

This paper presents the design and implementation of three-phase four-wire shunt active power filter (SAPF). It consists of insulated gate bipolar transistors, IGBT based current-controlled voltage source inverter (CC-VSI), series coupling inductor and self-supported DC bus. Power electronics based converters and non-linear loads generate waveform-driven power quality issue as harmonics. Three-phase four-wire SAPF mitigates harmonics, compensates for reactive power, neutral current and power factor correction. Conventionally, the positive sequence detection control strategy using phase-locked loop (PLL) is applied as the synchronizing unit vector element to generate reference source currents. Conventional controller tuning process is difficult and fails to perform satisfactorily under supply voltage variation conditions. In this paper, Levenberg-Marquardt back propagation training algorithm based artificial neural network (ANN) controller is proposed to regulate DC link voltage due to its self-adapting and rapid calculation characteristics that allow the controller to handle high nonlinearity and uncertainty in a non-linear system. Weights of a neuron are adapted to minimize total harmonic distortion (THD) of source current under the step, ramp, time series amplitude variation and frequency and amplitude of modulation conditions. The proposed system is modelled in MATLAB/SIMULINK environment and laboratory prototype with dSpace1104 control card is developed. Experimentation results validate the simulated results of the proposed scheme under supply voltage variations for three-phase four-wire distribution system.

The present era of advances in desalination plants revolves around the involvement of artificial intelligence techniques in ameliorating their modeling and operational performance. Among the two objectives, an accurate modeling of the plant’s behavior may certainly help the design engineers to operate the plant in more stable and controlled operating conditions so as to achieve higher plant efficiency. Furthermore, this helps eliminate the risk to the operator’s life and reduces production time, energy, and money. From the literature, it is observed that Artificial Neural Network (ANN) has been the most extensively used approach for modeling and simulation of the desalination plant. However, ANN has the concept of biases and weights updation for better prediction and accuracy of the predicted model, but the conventional methods do not yield desirable results. So, the updation of biases and weights using optimization algorithms is preferred in the literature. Therefore, this paper presents the Grey Wolf Optimizer based ANN (GWO-ANN) approach for desirable prediction and accuracy of models. Further, six models (GWO-ANN Model-1 to Model-6) are proposed to more accurately predict the Reverse Osmosis (RO) desalination plant’s performance. For this investigation, we have considered four experimental inputs (feed water salt concentration, condenser inlet temperatures, evaporator inlet temperatures, and feed flow rate) and one output (permeate flux). The simulation results predict output performance in quite proximity to the experimental datasets. The simulated hybrid GWO-ANN models (best of best results of GWO-ANN Model-2: $\text{R}^{2} \,\,=98.9$ %, Error = 0.007) show superior results than the reported results from the existing Response Surface Methodology (RSM) ( $\text{R}^{2} \,\,=98.5$ %, Error = 0.100) and ANN models ( $\text{R}^{2} =98.8$ %, Error = 0.060) and other PSO-ANN Model ( $\text{R}^{2} \,\,=96.3$ %, Error = 0.025) and GA-ANN model ( $\text{R}^{2} =98.7$ %, Error = 0.008). This research exemplifies the involvement of superior nature-inspired intelligent techniques in this modern era to further enhance the savings in production time, energy, and investment of desalination plants.