Broadcasting, radar, sonar and space telecommunication systems use phased arrays to produce directed signals to be transmitted at the desired angle. This system requires a large number of antenna elements. The presence of faulty element(s) in an array causes asymmetry, which results in a deformed radiation pattern with higher sidelobe levels. Higher sidelobe levels indicate waste of energy by transmitting and receiving signals in unwanted directions. Hence, it is important to develop a method that detects faulty elements and corrects the radiation pattern. To correct the failed radiation pattern, failed elements in an array must be identified first. There have been various studies conducted on linear array failed radiation pattern correction and the finding of faulty elements, but investigation on the planar array is limited. Further, the optimization suggested for linear arrays does not necessarily work for the planar array. In this study, planar array faulty antenna detection was developed with pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO) methods by reducing the Signal to Noise Ratio (SNR) as the objective function. The analysis was varied for 8 × 8 and 6 × 6 planar arrays with different types of failures. The results were compared to find the best method to identify the faulty element’s location in a planar array. The pattern search method produced outstanding results in finding the faulty element’s locations by providing 100% accuracy for all types of failure, while other methods failed to do the same.

Phased arrays are widely used in different fields, such as broadcasting, radar, optics, and space communications. The principle of phased arrays is to generate a directed signal from a large number of antennas to be steered at any desired angle. This, however, increases the probability of defective elements in an array. Faulty elements in an array cause asymmetry and result in increased sidelobe levels which rigorously distort the radiation pattern. Increased sidelobe radiation wastes energy and can cause interference by radiating and receiving signals in unintended directions. Therefore, it is necessary to find a method that can provide accuracy in the radiation pattern transmitted or received in the presence of failed element(s) in an array. This paper compares the few available optimization methods, namely, simulated annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Pattern Search (PS) methods. For each method, various types of failures were examined, and the most suitable techniques to recover the far-field radiation are recommended. The optimization is then carried out by selecting the optimal weights of the remaining working elements in the planar array. The optimized radiation pattern’s efficiency was evaluated by comparing the Signal to Noise Ratio (SNR) value of the optimized radiation with reference and failed radiation patterns. The PSO method showed a better performance compared to all the other methods in reducing the failed radiation pattern’s SNR value. In various types of failure tests, this method reduced the failed radiation pattern’s SNR from 1 to 10 dB. This method also successfully produced a radiation pattern that closely matches the reference pattern before any failed element(s) are presented in the array. The life cycle of a planar array system with faulty elements can be increased by optimizing the remaining active elements in the array with the PSO method. It also reduces the cost of restoring and replacing the failed elements in an array regularly. This approach also prevents near-field measurement that requires complicated processes using costly equipment.

Renewable energy sources and EV growth brings new challenges for grid stabilization. Smart grid techniques are required to reconfigure and compensate for load fluctuation and stabilize power losses and voltage fluctuation. Numerical tools are available to equip the smart grid to deal with such challenges. Distribution Feeder reconfiguration and reactive voltage injection to the disturbed grid are some of the techniques employed for the purpose. However, either reconfiguration or injection alone is used commonly for this purpose. In this study, both techniques are applied to EV penetration as load and compared. A balanced IEEE 33 Radial network is used in this study and selected branches with high power losses are targeted for the reactive voltage injection and Minimum Spanning tree techniques (MST). EV charging loads are usually modelled with time base distribution which requires times base power flow analysis for reactive power injection. A comparison between coordinated, reconfiguration, and reactive voltage injection shows differences in power losses, voltage distortion, and cost saving. The analysis is carried out with an integer linear programming technique for coordinated charging, a minimum spanning tree for network reconfiguration, and genetic optimization for reactive power injection. Besides, all power flow analyses are carried out with the Backward/Forward sweep method. The information would help lowering power losses, grid stabilization, and charging station infrastructure planning.

Active magnetic bearings, which are open-loop and unstable, require a feedback control system to ensure stable operation of the rotating machines that they support. Proportional-integral-derivative (PID) controllers are widely used in field applications of these bearings for this purpose. PID controllers are designed to work effectively within the linear region of operation of the rotating machines. Due to the inherent nonlinearity of the active magnetic bearings, large unbalance forces that may occur in these machines result in nonlinear vibration responses. Therefore, the PID controller’s effectiveness to control the vibration of the rotating machines is considerably reduced when the unbalance forces in these machines become large. Other control strategies, such as the fuzzy logic and the sliding mode control schemes, are more apt to deal with the nonlinear responses of the rotating machines supported by active magnetic bearings. The present work proposes an integrated fuzzy bang-bang relay controller for a rigid rotor mounted on active magnetic bearings. The effectiveness of this controller to suppress rotor vibrations is examined numerically. Performance comparison of this controller with the conventional fuzzy logic and PD controllers are made for different initial conditions, rotor imbalance magnitudes, and rotor angular speeds. At extreme operating conditions due to large rotor unbalance forces, where the magnetic bearings are highly nonlinear, the proposed integrated fuzzy bang-bang relay controller proved to be more superior over the conventional fuzzy logic and PD controllers.