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To properly control the network of the power system and ensure its protection, Phasor measurement units (PMUs) must be used to monitor the network's operation. PMUs can provide synchronized real-time measurements. These measurements can be used for state estimation, fault detection and diagnosis, and other grid control applications. Conventional state estimation methods use weighting factors to balance the different types of measurements, and zero injection measurements can lead to large weighting factors that can introduce computational errors. The offered methods are designed to ensure that these zero injection criteria can be strictly satisfied while calculating the voltage profile and observability of the various distribution networks without sacrificing computing efficiency. The proposed method's viability is assessed using standard IEEE distribution networks. MATLAB coding is used to simulate the case analyses. Overall, the study provides a valuable contribution to the field of power distribution system monitoring and control by simplifying the process of determining the optimal locations for PMUs in a distribution network and assessing the impact of ZI buses on the voltage profile of the system.

Due to their simplicity, cheapness, and ease of maintenance, induction motors (IMs) are the most widely used motors in the industry. However, if they are not properly controlled, the load torque and motor speed will fluctuate in an unsatisfactory fashion. To effectively control the load torque and speed of these IMs, it is necessary to use specialized drives. The entire system (IMs + Drives) will experience uncertainty, nonlinearities, and disruptions, which calls for an outstanding performance control structure. The sensorless sliding mode predictive torque control (SSM-PTC) for both AC-DC converter and DC-AC inverter, which are utilized for feeding the IM, is investigated in this work. The AC-DC converter is controlled using the SSM-PTC method in order to follow the DC-link reference voltage throughout any changes in the operating point of the IM. While the DC-AC inverter is controlled using a sensorless predictive power control (SPPC). Within a unity power factor, this SPPC regulates the reactive power flow between the motor and the supply to account for the undesirable harmonic components of the grid current. In addition, an experimental performance improvement of SSM-PTC of IM supplied by a 5-leg AC-DC-AC power converter using extended Kalman filter (EKF) without weighting factor (WF) is also studied in this work. Design and implantation of the suggested control systems are performed using a dSPACE 1104 card. The experimental results of the proposed converter control demonstrate that the suggested approach effectively regulated the DC link, reducing load torque and speed fluctuations. In the context of inverter control, a prompt active power response yields a motor current waveform that resembles a sinusoidal pattern, exhibiting minimal levels of harmonic distortion.

Solar energy, a prominent renewable resource, relies on photovoltaic systems (PVS) to capture energy efficiently. The challenge lies in maximizing power generation, which fluctuates due to changing environmental conditions like irradiance and temperature. Maximum Power Point Tracking (MPPT) techniques have been developed to optimize PVS output. Among these, the incremental conductance (INC) method is widely recognized. However, adapting INC to varying environmental conditions remains a challenge. This study introduces an innovative approach to adaptive MPPT for grid-connected PVS, enhancing classical INC by integrating a PID controller updated through a fuzzy self-tuning controller (INC-FST). INC-FST dynamically regulates the boost converter signal, connecting the PVS's DC output to the grid-connected inverter. A comprehensive evaluation, comparing the proposed adaptive MPPT technique (INC-FST) with conventional MPPT methods such as INC, Perturb & Observe (P&O), and INC Fuzzy Logic (INC-FL), was conducted. Metrics assessed include current, voltage, efficiency, power, and DC bus voltage under different climate scenarios. The proposed MPPT-INC-FST algorithm demonstrated superior efficiency, achieving 99.80%, 99.76%, and 99.73% for three distinct climate scenarios. Furthermore, the comparative analysis highlighted its precision in terms of control indices, minimizing overshoot, reducing rise time, and maximizing PVS power output.

The integration of renewable sources (RSs) and the widespread deployment of electric vehicles (EVs) has transitioned from a luxury to a necessity in modern power systems. This results from the sharp increase in electric power demand and public awareness of switching to green energy. However, in addition to load fluctuations and changes in system parameters, these RSs and EVs negatively impact the load frequency (LF). This work presents a LF control for a modern multi-area power system incorporating photovoltaic (PV) and EV chargers. The proposed controller primarily utilizes EV chargers within modern power systems. This approach offers the advantage of using the already present components instead of introducing new ones. The proposed controller comprises the ecological optimization approach (ECO) and the integral controller (I). Both of these components are designed for autonomous vehicle-to-grid (V2G) devices. The proposed control technique is applied to a three-area power system, where the V2G scheme is located in Area-1. Variations in the load, PV power generated, and system parameters are considered to evaluate the effectiveness of the proposed (I+ECO+V2G) controller for controlling the LF. To assess the performance of the proposed I+ECO+V2G system, a comparative analysis is conducted to compare its performance with both the I+ECO system and the standard I-controller. The simulation findings demonstrate that implementing the I+ECO and the proposed I+ECO+V2G strategies results in enhanced system stability and decreased LF fluctuations compared to the conventional I-control approach. Furthermore, while comparing the I+ECO control technique to the suggested control strategy I+ECO+V2G, it was seen that the latter reaches steady state values more quickly. The results validate the robustness and effectiveness of the proposed controller in mitigating the impacts of load disturbances, uncertainties, and nonlinearities within the system. These simulations were performed using MATLAB/SIMULINK. To validate the outcomes of the simulation results, an experimental setup consisting of a real-time dSPACE DS1103 connected to another PC via QUARC pid_e data acquisition card was used. The experimental findings have substantiated the accuracy of the simulation findings about the superiority of the I+ECO+V2G methodology compared to both the I+ECO and I-control methodologies concerning system performance and LF control.