Present-day power systems are mostly equipped with conventional meters and intended for the installation of highly accurate phasor measurement units (PMUs) to ensure better protection, monitoring and control of the network. PMU is a deliberate choice due to its unique capacity in providing accurate phasor readings of bus voltages and currents. However, due to the high expense and a requirement for communication facilities, the installation of a limited number of PMUs in a network is common practice. This paper presents an optimal approach to selecting the locations of PMUs to be installed with the objective of ensuring maximum accuracy of the state estimation (SE). The optimization technique ensures that the critical locations of the system will be covered by PMU meters which lower the negative impact of bad data on state-estimation performance. One of the well-known intelligent optimization techniques, the genetic algorithm (GA), is used to search for the optimal set of PMUs. The proposed technique is compared with a heuristic approach of PMU placement. The weighted least square (WLS), with a modified Jacobian to deal with the phasor quantities, is used to compute the estimation accuracy. IEEE 30-bus and 118-bus systems are used to demonstrate the suggested technique.

In this paper, a small-scale PV/Wind/Diesel Hybrid Microgrid System (HMS) for the city of Yanbu, Saudi Arabia is optimally designed, considering the uncertainties of renewable energy resources and battery degradation. The optimization problem is formulated as a multi-objective one with two objective functions: the Loss of Power Supply Probability (LPSP) and the Cost of Electricity (COE). An Improved Decomposition Multi-Objective Evolutionary Algorithm (IMOEAD) is proposed and applied to solve this problem. In this approach, different decomposition schemes are combined effectively to achieve better results than the classical MOEA/D approach. Twelve case studies are investigated based on different scenarios and different numbers of houses (5 and 10 houses). Each time, the suggested approach produced a set of solutions that formed a Pareto front (PF). Considering a variety of parameters, the optimal compromise option can be selected by the designer from the PF.

Livestock management is an emerging area of application of the quadrotor, especially for monitoring, counting, detecting, recognizing, and tracking animals through image or video footage. The autonomous operation of the quadrotor requires the development of an obstacle avoidance scheme to avoid collisions. This research develops an obstacle avoidance-based autonomous navigation of a quadrotor suitable for outdoor applications in livestock management. A Simulink model of the UAV is developed to achieve this, and its transient and steady-state performances are measured. Two genetic algorithm-based PID controllers for the quadrotor altitude and attitude control were designed, and an obstacle avoidance algorithm was applied to ensure the autonomous navigation of the quadrotor. The simulation results show that the quadrotor flies to the desired altitude with a settling time of 6.51 s, an overshoot of 2.65%, and a steady-state error of 0.0011 m. At the same time, the attitude controller records a settling time of 0.43 s, an overshoot of 2.50%, and a zero steady-state error. The implementation of the obstacle avoidance scheme shows that the distance threshold of 1 m is sufficient for the autonomous navigation of the quadrotor. Hence, the developed method is suitable for managing livestock with the average size of an adult sheep.