This work discusses the simulation and experimental demonstration of a genetic algorithm hybrid fuzzy-fuzzy controller (GA-HFFC) system to achieve speed control of a variable-speed induction motor (IM) drive based on a space vector pulse width modulation (SVPWM) technique by means of an eZdspF28335 digital signal processing (DSP) experiment board. Two features of field-oriented control (FOC) were used to design the GA-HFFC, namely, the current and frequency. To overcome the limitations of the FOC technique, the principles of the GA-HFFC were introduced through the acceleration-deceleration stages to regulate the speed of the rotor with the help of a fuzzy frequency controller, while a fuzzy stator current amplitude controller was involved during the steady-state stage. The results revealed that the proposed control approach could deliver a practical control solution in the presence of diverse operating conditions.

The growing tendency of wind energy technology usage has induced researchers to seek more studies on the improvement of wind energy harvesting systems with highly reliable power injected into the electric grid. The amount of extracted wind energy by a wind turbine depends on the accuracy of the maximum power point tracking (MPPT) algorithm associated with the control scheme of the wind energy conversion system (WECS). This paper aims to present a novel development of MPPT algorithm based on parabolic prediction techniques (PPT) for variable speed wind turbines (VSWT) so as to achieve maximum wind power extraction and efficiency enhancement. The developed PPT-MPPT algorithm uses parabolic convex functions to estimate the parabola wind turbine output curve and locate the maximum power point on that curve. Simultaneously the algorithm establishes a systematic procedure for adjusting the concavity and the optimal part of the estimated parabola curve to make sure that the proposed technique is in continuous convergence. The feasibility of the developed PPT-MPPT algorithm has been validated under rapid and random changing wind speed profiles via the MATLAB/Simulink environment. The performance of the proposed PPT-MMPT algorithm is evaluated by comparing the simulation finding to those obtained using the benchmark perturb and observe(P&O) algorithms. The simulation outcomes affirm that the proposed PPT-MPPT algorithm outperformed the other algorithms in terms of tracking efficiency and extracted power which can be regarded as an efficacious manner to improve the VSWT system.

The expanding trend of wind power technology motivates scholars to pursue more investigation on optimising energy extraction from the wind, and integrating high-quality power into the utility grid. This paper is aimed at introducing a novel application of the sine cosine algorithm (SCA) which attempts to find the optimal gains of proportional-integral (PI) controllers used to control the power electronic converters (PEC) equipped with the variable speed wind turbine (VSWT) such that a maximum power extraction and performance enhancement can be realized. The PEC equipped with the VSWT combines a machine side converter (MSC) and a grid-side inverter (GSI). Both the MSC and GSI are controlled by the proposed SCA-based PI controllers through cascaded vector control schemes. The MSC is responsible for controlling the wind generator’s rotational speed, active power, and reactive power. The GSI is used to regulate the direct current (DC)-link voltage and to keep the terminal voltage at the desired frame set by the operator. To obtain the optimum PI gains, the SCA is applied to minimize the sum of the integral squared error (ISE) of twelve PI controllers error inputs in the control schemes simultaneously. Performances of the proposed SCA-PI control schemes are assessed under severe grid disturbance and random wind speed variation to mimic more realistic conditions. The effectiveness of the proposed SCA-PI is verified in the MATLAB/Simulink environment, and the results are compared to those obtained using a grey wolf optimizer and particle swarm algorithm-based optimal PI controller. The simulation findings confirm the SCA-PI can be regarded as an efficacious way to enhance the performance of the VSWT.

The research work on camera placement has focused on maximizing the coverage or minimizing the installation cost of video surveillance systems. Typical placement schemes mount surveillance cameras with no emphasis on the coverage demand divergences, which impacts the system’s cost and efficiency. This paper addresses the camera placement problem based on an inverse modeling taxonomy. Thus, rather than performing the optimization on uniformly distributed grids, the paper introduces an underlying mechanism to elaborate the security sensitive zones prior to the coverage optimization. The outcome of the prioritization process is termed as Risk Maps. Obtained empirical results show the reliability of the placement using inverse modeling. Finally, the validation of the proposed placement scheme is carried out in a constraint environment.

Wind power’s increasing use and its integration into the utility grid have prompted scholars to focus more on the refinement of wind power harvesting systems, injecting stable power and providing ancillary services to the utility grid. This article introduces a novel hybrid approach termed as sine cosine algorithm and transient search optimization (HSCATSO) optimizer of better exploration and exploitation phases for optimal designing the power electronic converter control schemes (PECCS) of the grid-tied variable speed wind turbine (VSWT) system. The PECCS is simultaneously coordinated with a robust maximum power extraction algorithm (MPEA) to form enhanced control systems for achieving the best wind harnessing, improving the VSWT system performance, and supporting the utility grid stability. In this context, the HSCATSO optimally designs the PECCS parameters based on minimizing the summation of integral squared error (ISE) of multiple error signals in the developed control schemes in coordination with the MPEA. The superiority of the HSCATSO optimizer is validated using twenty benchmark functions and statistically analysed against four well-known optimization algorithms. Meanwhile, the effectiveness of the optimally designed PECCS using the HSCATSO is verified by the extensive simulation analysis in MATLAB/Simulink considering severe grid disturbance and real wind speed data taken from Kudat, Sabah, Malaysia to mimic realistic circumstances. The obtained results have been compared with that realized using the other algorithms-based design PECCS. The simulation outcomes affirmed that the PECCS designed by the HSCATSO and coordinated with MPEA resulted in higher power harvesting and enhanced the grid-tied VSWT system stability better than the competitive control schemes.