The control of a single-phase grid-connected energy storage system (ESS) requires a very fast and accurate estimation of grid voltage frequency and phase angle. A phase-locked loop (PLL) based synchronization algorithm usually extracts this information. The operation and control of the entire system are directly affected by the performance of PLL. In this article, a novel advanced single-phase PLL (ASϕPLL) technique with reduced complexity is proposed for the fast and accurate extraction of grid information in an ESS under distorted and abnormal grid conditions, including harmonics, interharmonics, dc offset, and grid faults. The proposed method provides a faster dynamic response, lower frequency overshoot, and accurate estimation under off-nominal grid frequencies with reduced computational complexity in comparison with the existing method. The advanced performance of the proposed ASϕPLL is verified through the simulation and experimental results.

The continuous deployment of distributed energy sources and the increase in the adoption of electric vehicles (EVs) require smart charging algorithms. The existing EV chargers offer limited flexibility and controllability and do not fully consider factors (such as EV user waiting time and the length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy, and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions, and high charging cost. In this article, a neuro-fuzzy particle swarm optimization (PSO)-based novel and advanced smart charge controller is proposed, which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (photovoltaic) output, and battery state of health. A rule-based fuzzy controller becomes complex as the number of inputs to the controller increases. In addition, it becomes difficult to achieve an optimum operation due to the conflicting nature of control requirements. To optimize the controller response, the PSO technique is proposed to provide a global optimum solution based on a predefined cost function, and to address the implementation complexity, PSO is combined with a neural network. The proposed neuro-fuzzy PSO control algorithm meets EV user requirements, works within technical constraints, and is simple to implement in real time (and requires less processing time). Simulation using MATLAB and experimental results using a dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller.

Phasor measurement units (PMUs) are a key part of electrical power systems, providing the dynamic monitoring and control of electrical units and impacting overall operation and synchronization of a network if not properly designed. This paper investigates the use of a phase-locked loop (PLL)-based algorithm for PMUs (to accurately find the magnitude, phase, and frequency) in a three-phase system. Various PLLs are reported in the literature, ranging from the very basic to advanced, capable of dealing with normal and abnormal grid behavior and mainly used for the control of grid-connected converters. In this paper, a number of PLLs were utilized to perform PMU functions, and a benchmarking study has been investigated to analyze the developed PLL-driven PMUs under various grid conditions (such as unbalanced faults, harmonics, and frequency variations). The simulation and experimental results were provided to support the performance capabilities and suggested limitations. In addition, the best PMU in benchmarking was used in the Kundur’s two-area system to show the significance of PMUs in a power system.

In critical healthcare units, such as operation theaters and intensive care units, healthcare workers require specific temperature environments at different stages of an operation, which depends upon the condition of the patient and the requirements of the surgical procedures. Therefore, the need for a dynamically controlled temperature environment and the availability of the required heating/cooling electric power is relatively more necessary for the provision of a better healthcare environment as compared to other commercial and residential buildings, where only comfortable room temperature is required. In order to establish a dynamic temperature zone, a setpoint regulator is required that can control the zone temperature with a fast dynamic response, little overshoot, and a low settling time. Thus, two zone temperature regulators have been proposed in this article, including double integral sliding mode control (DISMC) and integral terminal sliding mode control (ITSMC). A realistic scenario of a hospital operation theater is considered for evaluating their responses and performance to desired temperature setpoints. The performance analysis and superiority of the proposed controllers have been established by comparison with an already installed Johnson temperature controller (JTC) for various time spans and specific environmental conditions that require setpoints based on doctors’ and patients’ desires. The proposed controllers showed minimal overshoot and a fast settling response, making them ideal controllers for operation theater (OT) zone temperature control.