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Perturb and observe (P&O) is a well-known maximum power point tracking (MPPT) algorithm that is used in solar photovoltaic (PV) systems to increase its efficiency. However, as the PV system uses solar irradiance and temperature for making electric power, the fast change of these two affects the performance of P&O and the efficiency of the PV system. Thus, the P&O algorithm fails to detect maximum power point (MPP) if temperature and irradiance change quickly. Therefore, this paper presents an adaptive neuro-fuzzy inference system (ANFIS) based P&O algorithm of MPPT controller for a solar PV system to solve the issues mentioned earlier. The utilization of the proposed ANFIS in the P&O algorithm can track the fast changes in solar irradiance and temperature to extract the maximum power from the solar PV panel. Comparative analysis has been done on MATLAB/Simulink software for both the traditional P&O and the proposed ANFIS-based P&O algorithm to show the effectiveness of the proposed MPPT controller. &nbsp

Sustainable energy resources are essential to meet the world's growing population and extending energy demands. Among the potential solutions, incorporating renewable energy sources into hybrid energy systems holds a lot of opportunities. This paper presents a design and economic analysis for an off-grid microgrid intending to power agricultural loads. Solar resources and PV-inverter system were modeled using pvlib -python, while the remainder of the microgrid, including the battery energy storage system (BESS) and biogas-based generator (BGG), was modeled and simulated using a custom dispatch method. The same system was modeled in Homer as well, and the outcomes of the designed microgrid were compared. When compared to Homer, the proposed approach reduced life cycle cost (LCC), and leveliz ed cost of energy (LCOE) by 25%, and 20%, and emissions by 85%. In terms of generation, the proposed strategy reduced PV production by 20%, BGG output by 85%, and unmet load and surplus energy by 14% and 65%, respectively. The study additionally addressed an in-depth approach to modeling PV using various data sources and the associated modules and functionalities.

The growing popularity of electric vehicles on a worldwide scale leads to further research to monitor their performance. The use of Internet of Things (IoT) technology will make it easier to integrate the automated real-time monitoring system with the current electric vehicle technology. The great majority of electric vehicles use rechargeable lithium-ion batteries. Use of lithium-ion batteries creates an overcharging situation in the battery, which significantly decreases battery life. It also increases the possibility of disastrous safety risks due to fire. This paper develops an IoT-based battery management system to minimize hazardous situations. The battery monitoring system (BMS) notifies the user about the condition of the battery in real time.

An essential instrument for the operation of a power system is to monitor and analyze the data to find the fault and rectify it before the system collapses completely. This paper intents to utilize the idea to create a control system that will fulfill objectives like monitoring vital parameters controlling the power distribution, outage management by fault detection based on the variation of Voltage, Frequency, and Current & protection of the Circuit against any significant incidents by isolating the load from utility and flagging the information through feedback to the utility authority. The Project implements the high-end technology for IoT applications that will connect with the Microcontroller to receive the data at regular intervals and post the timestamped data in a cloud platform for remote monitoring and archiving. This innovation can reduce human dependency and overcome the whole outage situation by building a two-way communication that means electricity and information are traded between consumers and utilities to capitalize on the efficiency of a grid system. The proposed model offers intelligent monitoring of the timestamped data with remote access & historical data archiving for a better load profile. An outage management system is designed for the proposed system by intellectual fault detection through Voltage, Frequency & current variation, and isolation of the faulty part from the entire system, maintaining an auto circuit recloser application before the permanent isolation for reducing the human interaction & flagging the outage information & supply emergency backup during the shutdown period, and in the process, helping the power grid from total power outage or blackout.

Due to the extensive integration of renewable energy sources (RESs), i.e., photovoltaic (PV) system, the future power system is developing into an inverter-based system from a dominated alternator-based power system. This massive penetration of inverter-based PV system reduced the system inertia and damping characteristics of the power grid, impacting the fault ride-through (FRT) capability and causes frequency instability. Modern grid codes require that PV systems should work in the same way as conventional power plants and assist the system during transient state. However, most of the conventional inverter control mechanisms failed to fulfill the requirements of grid codes, especially when the penetration ratio of the PV system is close to the conventional unit. Therefore, this paper proposes a virtual synchronous generator (VSG) control mechanism of PV system inverter to augment FRT competency and frequency stability. The proposed VSG control system mimics the behavior of conventional power plants. To observe and evaluate the proposed controller behavior, simulation analyses were executed in the PSCAD/EMTDC software for both proposed and conventional controllers. The simulation results clearly indicate that the proposed VSG control system has sufficient damping characteristics to ensure FRT capability and frequency stability.      &nbsp

Solar power stations worldwide have been rising every year as well as the usage of sustainable power has increased. The addition into the traditional electricity grid networks of renewable sources such as the photovoltaic (PV) system is an essential challenge, given its erratic power generation. The major issue is to detach PV from the faulty grid, which leads to fluctuation in the system's interconnected PV system and system-wide energy interruptions. The low voltage ride through (LVRT) enables the PV scheme should stay linked to the grid even under fault situations in short periods and must assist the grid return to regular situations according to the modern grid codes. Hence, this paper develops and analyzes five aspects of control strategy to increase PV station's LVRT capability. The in-depth simulation of the independent units of the grid-connected large-scale PV farm is accomplished using PSCAD/EMTDC to assess the feasibility of the suggested control tactic for the modified IEEE nine-bus system.

With the rising penetration of modern converter-based wind farm (WF) into the existing grid system deteriorates system inertia due to reduction of the capacity of conventional power stations which may lead to the frequency instability as well as power system transient instability. In order to solve this concern, this paper presents a robust virtual inertia control approach for battery storage system (BSS) to enhance the frequency stability of the grid system after the generation failure owing to severe grid disruption. The control approach integrated inertial controller based on the rate of change of frequency (ROCOF) and droop controller according to frequency deviation. The impacts of the proposed virtual inertia controller (VIC) is confirmed through simulation analysis on a multi-machine power system with conventional power stations, permanent magnet synchronous generator (PMSG) with full converter based WF and squirrel cage induction generator (SCIG) based WF. Simulation study clearly demonstrates that by adopting both strategies, the BSS can effectively minimize the frequency nadir and steady-state error.             &nbsp

Recently, permanent magnet synchronous generator (PMSG) is one of the most familiar type of generator for wind power plant (WPP). Generally, PMSG is connected to gird using back to back converter. During fault period, power imbalance situation is happened between machine side and gird converter. As a result, the DC-link voltage can be rise significantly which can damage the whole converter system. In this paper, a novel DC-Link protection system of buck converter based on fuzzy logic is designed in order to augment the transient stability of the PMSG system. The new buck converter along with its control system is designed to manage the supplied voltage of the braking resistor during fault period. For investigating the performance of the proposed system, fault analysis is performed on different case scenarios PSCAD/EMTDC software.

Fixed speed wind turbine-squirrel cage induction generator (FSWT-SCIG) based wind farm (WF) is increasing significantly. However, FSWT-SCIG have no frequency control capability, which creates a significant problem on power system steady-state stability. This paper represents a new operational strategy to control frequency of the entire power system including large-scale FSWT-SCIG based WF by using battery storage system (BSS). The proposed cascaded control of BSS is designed to provide effective amount of real power during steady-state period to damp frequency fluctuations. To evaluate the validity of the proposed system, simulation studies are executed on a reformed IEEE nine-bus power system with three synchronous generators (SGs) and SCIG-based WF along with BSS. The simulation results indicate that the proposed system can be an effective solution to reduce frequency fluctuations of the hybrid power system during steady-state condition.