Purpose This paper aims to examine the design and control of a symmetric multilevel inverter (MLI) using grey wolf optimization and differential evolution algorithms. Design/methodology/approach The optimal modulation index along with the switching angles are calculated for an 11 level inverter. Harmonics are used to estimate the quality of output voltage and measuring the improvement of the power quality. Findings The simulation is carried out in MATLAB/Simulink for 11 levels of symmetric MLI and compared with the conventional inverter design. A solar photovoltaic array-based experimental setup is considered to provide the input for symmetric MLI. Field Programmable Gate Array (FPGA) based controller is used to provide the switching pulses for the inverter switches. Originality/value Attempted to develop a system with different optimization techniques.
The increasing demand for electricity and global attention has led energy planners and developers to explore and develop clean energy. In this case, Renewable Energy Source (RES) has become an alternative source of energy generation. Due to the infrequent nature of renewable energy, interrupted power availability cannot be directly used by the load system. To overcome these issues, a DC-DC converter will implement and compensate the source power, but every source needs a Multi-Port Converter (MPC) in the hybrid system. This work aims to develop a multi-port DC-DC converter for integrates multiple Renewable Energy Sources (RES) with variable input voltage and load characteristics. The proposed circuit will absorb maximum power from various renewable resources while maintaining a high output load power adjustment, transfer efficiency and reliability function using Assimilate Power Flow Control (APFC) technique control schemes. The converter topology is utilized for high-power applications with hybrid energy storage system is proposed. The new topology assimilates multiple renewable energy and power multiple loads with changed output levels. Therefore, the controller circuit automatically adjusts the duty cycle value to obtain a desired constant output voltage value, despite all the source voltage and load output changes. In order to achieve this goal, an appropriate feedback controller can adjust the output voltage and the reference value by automatically adjusting the input voltage’s fast response to changes in the duty cycle and output load and low noise sensitivity with low overshoot and zero steady-state error. The proposed multi-port DC-DC converter topology is established in MATLAB 2017b software; the performance of the proposed APFC techniques based MPC converter operation are determined by the steady-state energy flow under various load varying condition. The proposed APFC techniques’ effectiveness is evaluated for each of the different parameters like steady-state error, THD, and the system’s efficiency.
The most significant limitation of stand-alone microgrid systems is the challenge of meeting unexpected additional demands. If demand exceeds the capacity of a standalone system, the system may be unable to satisfy demand. This issue is alleviated in grid-connected technology since the utility system will provide more power if it is demanded. As a result, load scheduling is an integral element of the demand response of a standalone system. There are two components to this problem. If the capacity of a battery-supported power system is restricted, for the period of time that the source is available, it will not be able to meet the entire demand. Appropriately the demand is dispersed across a period of time until the next charge becomes available. Some demands may be disregarded in order to accomplish peak load trimming or if the system is incapable of meeting demand without compromising other important technical and consumer objectives. This is a challenging assignment. This article aims to develop an Adaptive Demand Response Management System (ADRMS) capable of load scheduling and load shedding using an interwoven multidimensional Bayesian inference supported by multiple mathematical models. A two-stage hardware architecture is being developed, with the first hardware measuring demand and source capacity before sending the data to the second hardware via LPWAN for mathematical analysis. In the first phase, two approaches are used to forecast demand: Gaussian Naive Bayes Model (GNBM) and Bayesian Structural Time Series analysis. GNBM is rapid but fails to properly forecast the output when there is zero frequency error whereas BSTS can offer more precise results than GNBM but is slower. Hence two approaches are employed in tandem. The next stage is to assign demand source integration. This is accomplished using Bayesian Reinforcement Learning (BRL), which is based on a number of incentives, including anomaly, cost factors, usefulness, reliability, and size. All Bayesian models are subjected to much of the common Bayes rule, resulting in the formulation of a blended polymorphism model that reduces computing time and memory allocation, and improves processing reliability. The Isolation Forest (IF) method is used to identify and avoid vulnerable loads by determining demand anomalies. The last step employs a Dynamic Preemptive Priority Round Robin (DPPRR) algorithm for preemptive priority based load scheduling based on forecasted data to allocate the next loads to be added.
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