In recent years, photovoltaic (PV) systems have emerged as economical solutions for irrigation systems in rural areas. However, they are characterized by low voltage output and less reliable configurations. To address this issue in this paper, a promising inverter configuration called Impedance (Z)-source inverter (ZSI) is designed and implemented to obtain high voltage output with single-stage power conversion, particularly suitable for irrigation application. An improved and efficient modulation scheme and design specifications of the network parameters are derived. Additionally, a suitable fault-tolerant strategy is developed and implemented to improve reliability and efficiency. It incorporates an additional redundant leg with an improved control strategy to facilitate the fault-tolerant operation. The proposed fault-tolerant circuit is designed to handle switch failures of the inverter modules due to the open-circuit and short-circuit faults. The relevant simulation and experimental results under normal, faulty and post-fault operation are presented. The post-fault operation characteristics are identical to the normal operation. The motor performance characteristics such as load current, torque, harmonic spectrum, and efficiency are thoroughly analysed to prove the suitability of the proposed system for irrigation applications. This study provides an efficient and economical solution for rural irrigation utilized in developing countries, for example, India.

The increasing penetration of photovoltaic (PV) systems in low-voltage residential feeders has elevated the need for grid support at the distribution level to prevent violations of local voltage constraints. In this article, a coordinated reactive power support (RPS) methodology is presented that utilizes the demand-side flexibilities of the end user to keep local voltage levels within allowed levels. A cloud-based architecture is implemented to optimally coordinate consumers’ reactive power capable demand-side resources such as electric vehicles, solar PV systems, flexible home appliances, etc., considering their varying characteristics, ratings, and purposes. An optimization-based two-stage device scheduling and management model is presented for the cloud server that schedules consumers’ devices in day ahead for cost minimization, and optimally allocates the required RPS in real time among the candidate devices based on priority. Two device prioritization strategies are proposed that consider the reliability of reactive power capable consumer devices and management complexity, thereby, allowing consumers to either enhance the candidate devices’ lifetime or reduce the management complexity while participating in grid support. The proposed RPS methodology is validated using simulation studies, and an experimental setup is established to verify the viability of the proposed cloud-based coordination system for RPS. Case studies indicate that the proposed method can effectively prevent overvoltage situations by using coordinated RPS from consumers’ devices while maximizing their reliability. Results also indicate that the proposed methodology is economically more viable than state-of-the-art voltage control strategies.

Automotive applications often experience conflicting-objective optimization problems focusing on performance parameters that are catered through precisely developed cost functions. Two such conflicting objectives which substantially affect the working of traction machine drive are maximizing its speed performance and minimizing its energy consumption. In case of an electric vehicle (EV) powertrain, drive energy is bounded by battery dynamics (charging and capacity) which depend on the consumption of drive voltage and current caused by driving cycle schedules, traffic state, EV loading, and drive temperature. In other words, battery consumption of an EV depends upon its drive energy consumption. A conventional control technique improves the speed performance of EV at the cost of its drive energy consumption. However, the proposed optimized energy control (OEC) scheme optimizes this energy consumption by using robust linear parameter varying (LPV) control tuned by genetic algorithms which significantly improves the EV powertrain performance. The analysis of OEC scheme is conducted on the developed vehicle simulator through MATLAB/Simulink based simulations as well as on an induction machine drive platform. The accuracy of the proposed OEC is quantitatively assessed to be 99.3% regarding speed performance which is elaborated by the drive speed, voltage, and current results against standard driving cycles.