Big data applications such as Artificial Intelligence (AI) and Internet of Things (IoT) have in recent years been leading to many technological breakthroughs in system modeling. However, these applications are typically data intensive, thus requiring an increasing cost of resources. In this paper, a first-of-its-kind comprehensive review of data selection methods across different engineering disciplines is given in order to analyze the effectiveness of these methods in improving the data efficiency of mathematical modeling algorithms. Eight distinct selection methods have been identified and subsequently analyzed and discussed on the basis of the relevant literature. In addition, the selection methods have been classified according to three dichotomies established by the survey. A comparative analysis of these methods was conducted along with a discussion of potentials, challenges, and future research directions for the research area. Data selection was found to be widely used in many engineering applications and has the potential to play an important role in making more sustainable Big Data applications, especially those in which transmission of data across large distances is required. Furthermore, making resource-aware decisions about the use of data has been shown to be highly effective in reducing energy costs while ensuring high performance of the model.

Demand response programs have been receiving more serious attention as alternatives for participating in load frequency control. Inverter air conditioners (IAC) are acknowledged as suitable devices for demand response due to their increasing contribution to network consumption. Despite their potential, their use presents challenges, including delayed responses, variable interference, and the absence of coordination with traditional generation units, which may affect control performance. Also, existing control strategies fail to consider operational and physical constraints, resulting in possible model mismatches. In this paper, a model predictive control with feedback correction (MPCFC) is proposed to dispatch control signals to the IACs so they can effectively participate in the frequency control of an interconnected power system. The feedback correction method is presented to enhance prediction accuracy in the MPC and weaken the influence of model parameter mismatches and external disturbances. Furthermore, to minimize the impacts of communication delays on frequency overshoot/undershoot, this study introduces an intelligent supervisory coordinator based on an artificial neural network to coordinate the reaction of traditional generation units and IACs to correct significant frequency variations brought on by the time delays. The effectiveness of the developed control scheme is verified through numerical studies by comparing it with the IAC with PI and MPC controllers (without coordinator) and the system without IACs. Case studies are investigated on a two-area power system in MATLAB/Simulink environment, and the OPAL-RT real-time simulator is used to validate the results.

This article introduces an adaptive constrained generalized predictive controller (GPC) applied to a float interleaved boost converter (FIBC) with a low output current. FIBCs face uncertainties due to thermal effects in switches and diodes, variable operating frequencies, inductor losses, measurement errors, and load changes. The controller uses FIBC's direct duty cycle/output voltage model. It features constraints on the control signal and its variations, helping to regulate the input peak current. The controller's stability and performance under uncertainties are enhanced by three mechanisms: adaptive adjustment of control signal's upper constraint and the output error weight coefficient, and an online model estimation mechanism for the GPC. This includes constraining estimated model poles for assured closed-loop stability. Experimental results with 20% real uncertainties and load changes demonstrate the controller's effectiveness: it reduces input peak current, and output voltage oscillations, and overshoots by 3.5 A, 1 V, and 7.5%, respectively, compared to a basic constrained GPC.

This paper proposes an intelligent primary control strategy for voltage source converter (VSC)-based ac microgrid (MG). This is implemented by using a proportional resonant (PR) regulator adopted in the inner level of primary control of VSCs. An approach based on brain emotional learning (BEL) is proposed to provide an online and adaptive tuning of control coefficients of the PR regulator. The proposed BEL approach is fully model-free, indicating that the coefficients are regulated without previous knowledge of the system model and parameters. The outer level of primary control employs a droop control loop to regulate power-sharing among different distributed generators. Unlike the conventional control methods with constant coefficients, which are typically designed for a specified operating condition, the proposed approach avoids the dependency of the converter control system on the operating conditions and accommodates varying loading conditions. A sensitivity analysis is also performed to investigate the effects of PR coefficients on the system stability. Moreover, a Mesh analysis is carried out to examine the stability of dominant frequency modes of the whole AC-MG using the proposed control scheme. Simulations are provided to demonstrate the performance of the proposed control scheme.

In this article, a robust model predictive control (MPC) is proposed for the gate-controlled series capacitor (GCSC) to contribute to load frequency control. The proposed control model consists of a two-layer MPC in which a nominal MPC produces an initial control signal together with a prediction of system frequency response in a nominal system without uncertainty. An ancillary MPC then produces a control command for the actual system with uncertainty based on measurements, and signals provided by the nominal system. The control signals are provided in such a way that the error in the frequency response of the actual system is minimized with respect to that of the nominal system. Optimization procedures are also conducted to attain optimal values of weighting coefficients associated with the input, and output in the objective functions. Constraints associated with system, and GCSC control are also taken in providing the control signals into consideration. A linear model is presented to formulate the contribution of GCSC control to the power flow of tie-line. The effectiveness of the proposed method in dealing with uncertainties is compared with a conventional MPC, a scheme with proportional-integral controllers, and a system without GCSC. The method robustness is also illustrated by case studies with load uncertainty, and wind power fluctuations, and also uncertainty of parameters. The impact of the delay in communication links on the performance of the control scheme is also evaluated.