Innovative AVR-LFC design for a multi-area power system using hybrid fractional-order PI and PIDD2 controllers based on dandelion optimizer

In this article, the problem of voltage and frequency stability in a hybrid multi-area power system including renewable energy sources (RES) and electric vehicles has been investigated. Fractional order systems have been used to design innovative controllers for both load frequency control (LFC) and automatic voltage regulator (AVR) based on the combination of fractional order proportional-integral and proportional-integral-derivative plus double derivative (FOPI–PIDD2). Here, the dandelion optimizer (DO) algorithm is used to optimize the proposed FOPI–PIDD2 controller to stabilize the voltage and frequency of the system. Finally, the results of simulations performed on MATLAB/Simulink show fast, stable, and robust performance based on sensitivity analysis, as well as the superiority of the proposed optimal control strategy in damping frequency fluctuations and active power, exchanged between areas when faced with step changes in load, the changes in the generation rate of units, and the uncertainties caused by the wide changes of dynamic values

A novel ultra local based-fuzzy PIDF controller for frequency regulation of a hybrid microgrid system with high renewable energy penetration and storage devices

A new ultra-local control (ULC) model and two marine predator algorithm (MPA)-based controllers; MPA-based proportional-integral-derivative with filter (PIDF) and MPA-based Fuzzy PIDF (FPIDF) controllers; are combined to enhance the frequency response of a hybrid microgrid system. The input scaling factors, boundaries of membership functions, and gains of the FPIDF con-troller are all optimized using the MPA. In order to further enhance the frequency response, the alpha parameter of the proposed ULC model is optimized using MPA. The performance of the pro-posed controller is evaluated in the microgrid system with different renewable energy sources and energy storage devices. Furthermore, a comparison of the proposed MPA-based ULC-PIDF and ULC-FPIDF controllers against the previously designed controllers is presented. Moreover, a vari-ety of scenarios are studied to determine the proposed controller’s sensitivity and robustness to changes in wind speed, step loads, solar irradiance, and system parameter changes. The results of time-domain simulations performed in MATLAB/SIMULINK are shown. Finally, the results demonstrate that under all examined conditions, the new ULC-based controllers tend to further enhance the hybrid microgrid system’s frequency time response

A novel primary and backup relaying scheme considering internal and external faults in HVDC transmission lines

Discrimination of different DC faults near a converter end of a DC section consisting of a filter, a smoothing reactor, and a transmission line is not an easy task. The faults occurring in the AC section can be easily distinguished, but the internal and near-side external faults in the DC section are very similar, and the relay may cause false tripping. This work proposes a method to distinguish external and internal faults occurring in the DC section. The inputs are the voltage signals at the start of the transmission line and the end of the converter filter. The difference in voltage signals is calculated and given to an intelligent controller to detect and discriminate the faults. The intelligent controller is designed using machine learning (ML) and deep learning (DL) techniques for fault detection. The long short-term memory (LSTM-) based relay gives better results than other ML methods. The proposed method can distinguish internal from external faults with 100% accuracy. Another advantage is that a primary relay is suggested that detects faults quickly within a fraction of milliseconds. Nevertheless, another advantage is that a backup relay has been designed in case the primary relay cannot operate. Results show that the LSTM-based protection scheme provides higher sensitivity and reliability under different operation modes than the conventional traveling wave-based relay

A resilience-oriented bidirectional anfis framework for networked microgrid management

This study implemented a bidirectional artificial neuro-fuzzy inference system (ANFIS) to solve the problem of system resilience in synchronized and islanded grid mode/operation (during normal operation and in the event of a catastrophic disaster, respectively). Included in this setup are photovoltaics, wind turbines, batteries, and smart load management. Solar panels, wind turbines, and battery-charging supercapacitors are just a few of the sustainable energy sources ANFIS coordinates. The first step in the process was the development of a mode-specific control algorithm to address the system’s current behavior. Relative ANFIS will take over to greatly boost resilience during times of crisis, power savings, and routine operations. A bidirectional converter connects the battery in order to keep the DC link stable and allow energy displacement due to changes in generation and consumption. When combined with the ANFIS algorithm, PV can be used to meet precise power needs. This means it can safeguard the battery from extreme conditions such as overcharging or discharging. The wind system is optimized for an island environment and will perform as designed. The efficiency of the system and the life of the batteries both improve. Improvements to the inverter’s functionality can be attributed to the use of synchronous reference frame transformation for control. Based on the available solar power, wind power, and system state of charge (SOC), the anticipated fuzzy rule-based ANFIS will take over. Furthermore, the synchronized grid was compared to ANFIS. The study uses MATLAB/Simulink to demonstrate the robustness of the system under test

A modified multi-level inverter system for grid-tied DES applications

Energy harvesting from renewable energy sources is trending in the world due to inventions in modern technology. This paper proposes a grid-tied single-phase modified W-type 81-level inverter. Inverter design equations to calculate various parameters, such as the number of voltage levels and the number of DC power sources, along with the feedback controller equations, are developed to integrate the proposed topology with an electric power grid. The modeling of the control system for the proposed topology is carried out in the synchronously rotating reference frame for single-phase systems. The PWM generation part of the proposed inverter system makes use of the binary search nearest level algorithm to efficiently track the grid voltage signal. The proposed system integrates the inverter with the grid without the need for an output filter. The efficiency analysis shows that the proposed system delivers active and reactive power to the grid with an efficiency of around 90% and a THD of 1.04%. The voltage and current waveforms for the dynamic active and reactive power flow reveal that the proposed system exhibits a good transient and steady-state response. The overall system is simulated in MATLAB/Simulink and the results are verified using a hardware implementation of the prototype circuit.Web of Science1424art. no. 1654

Review on unidirectional non-isolated high gain DC-DC converters for EV sustainable DC fast charging applications

Modern electrical transportation systems require eco-friendly refueling stations worldwide. This has attracted the interest of researchers toward a feasible optimal solution for electric vehicle (EV) charging stations. EV charging can be simply classified as Slow charging (domestic use), Fast charging and Ultrafast charging (commercial use). This study highlights recent advancements in commercial DC charging. The battery voltage varies widely from 36V to 900V according to the EVs. This study focuses on non-isolated unidirectional converters for off-board charging. Various standards and references for fast off-board charging have been proposed. Complete transportation is changed to EVs, which are charged by the grid supply obtained by burning natural fuels, contributing to environmental concerns. Sustainable charging from sustainable energy sources will make future EV completely eco-friendly transportation. The research gap in complete eco-friendly transit is located in interfacing sustainable energy sources and fast DC EV charging. The first step towards clean, eco-friendly transportation is identifying a suitable converter for bridging the research gap in this locality. A simple approach has been made to identify the suitable DC-DC converter for DC fast-charging EVs. This article carefully selected suitable topologies derived from Boost, SEPIC, Cuk, Luo, and Zeta converters for clean EV charging applications. A detailed study on the components count, voltage stress on the controlled and uncontrolled switches, voltage gain obtained, output voltage, power rating of the converters, switching frequency, efficiency obtained, and issues associated with the selected topologies are presented. The outcome of this study is presented as the research challenges or expectations of future converter topologies for charging

A novel method for life estimation of power transformers using fuzzy logic systems: An intelligent predictive maintenance approach

Power transformers are a fundamental component of the modern power distribution network. The fault-free operation of step-up and step-down transformers is of prime importance to the continuous supply of electrical energy to the consumers. To ensure such efficient operation, power distribution companies carry out routine maintenance of distribution transformers through preplanned schedules. The efficacy of such maintenance depends on a proper understanding of the transformer and its components and efficient prediction of faults in these components. There are several components whose condition can be studied to predict transformer failures and therefore the overall health of a transformer. These include transformer windings, insulations, transformer oil, core insulations, and ferromagnetic cores. This work develops a new, simplified fuzzy logic-based method to predict the health of a transformer by taking into account the state of several individual components. Case studies are used to demonstrate the efficacy of the developed method

A new-fangled connection of UPQC tailored power device from wind farm to weak-grid

A significant portion of wind power conversion systems worldwide comprise wind farms (WFs) that use Squirrel Cage Induction Generator (SCIG) and are directly linked to the power grid. In facilities that generate electrical energy at a moderate level, WFs are connected by means of distribution systems that use medium voltage (MV). It is not uncommon for such a system to produce a scenario in which the amount of electricity generated corresponds to the grid’s transit volume. When a wind farm’s wind power generation system is connected to a weak grid, the lack of potential control of the Point of Common Coupling (PCC) is a primary issue. This strategy is called a “Wind Farm with Weak Grid Connection.” Therefore, the amalgamation of weak grids, fluctuating electricity from wind, and variations in load on the system cause disruptions in the PCC voltage, further degrading the Power Quality (PQ) and the WF stability. Either the control method at the production level or the compensating strategies at the PCC level can improve this situation. If wind farms are built on SCIG and are directly linked to the grid, it is essential to utilise the last substitute. The technology known as Custom Power Devices (CUPS), proved extremely helpful for this type of application. This study presents a compensation technique based on a specific CUPS device, known as the Unified Power Quality Compensator (UPQC), as a possible solution. The potential terminals of WF needed to be regulated, and the voltage fluctuations on the grid side required to be reduced, so a custom-made control strategy for the UPQC device was designed internally. The control of power, such as active and reactive in the UPQC’s series and shunt converters, as well as the transmission of power via the UPQC DC-Link between converters, are the foundation of the internal control strategy that has been developed. Compared to other bespoke tactics that use reactive power, this strategy increases the UPQC’s capability to provide compensation. The suggested study calculates THD using a FUZZY controller. The results are compared to PI controller results. Simulation findings show how the suggested compensating strategy can minimise THD values and improve wind farm power and stability. The simulations suggest that the proposed compensating strategy enhances WF power and stability

First-of-Its-Kind Frequency Enhancement Methodology Based on an Optimized Combination of FLC and TFOIDFF Controllers Evaluated on EVs, SMES, and UPFC-Integrated Smart Grid

The most recent advancements in renewable energy resources, as well as their broad acceptance in power sectors, have created substantial operational, security, and management concerns. As a result of the continual decrease in power system inertia, it is critical to maintain the normal operating frequency and reduce tie-line power changes. The preceding issues sparked this research, which proposes the Fuzzy Tilted Fractional Order Integral Derivative with Fractional Filter (FTFOIDFF), a unique load frequency controller. The FTFOIDFF controller described here combines the benefits of tilt, fuzzy logic, FOPID, and fractional filter controllers. Furthermore, the prairie dog optimizer (PDO), a newly developed metaheuristic optimization approach, is shown to efficiently tune the suggested controller settings as well as the forms of the fuzzy logic membership functions in the two-area hybrid power grid investigated in this paper. When the PDO results are compared to those of the Seagull Optimization Algorithm, the Runge Kutta optimizer, and the Chaos Game Optimizer for the same hybrid power system, PDO prevails. The system model incorporates physical constraints such as communication time delays and generation rate constraints. In addition, a unified power flow controller (UPFC) is put in the tie-line, and SMES units have been planned in both regions. Furthermore, the contribution of electric vehicles (EVs) is considered in both sections. The proposed PDO-based FTFOIDFF controller outperformed many PDO-based traditional (such as proportional integral derivative (PID), proportional integral derivative acceleration (PIDA), and TFOIDFF) and intelligent (such as Fuzzy PID and Fuzzy PIDA) controllers from the literature. The suggested PDO-based FTFOIDFF controller has excellent performance due to the usage of various load patterns such as step load perturbation, multi-step load perturbation, random load perturbation, random sinusoidal load perturbation, and pulse load perturbation. Furthermore, a variety of scenarios have been implemented to demonstrate the advantageous effects that SMES, UPFC, and EV units have on the overall performance of the system. The sensitivity of a system is ascertained by modifying its parameters from their standard configurations. According to the simulation results, the suggested PDO-based FTFOIDFF controller can improve system stability despite the multiple difficult conditions indicated previously. According to the MATLAB/Simulink data, the proposed method decreased the total fitness function to 0.0875, representing a 97.35% improvement over PID, 95.84% improvement over PIDA, 92.45% improvement over TFOIDFF, 83.43% improvement over Fuzzy PID, and 37.9% improvement over Fuzzy PIDA

Voltage and frequency regulation in smart grids via a unique Fuzzy PIDD2 controller optimized by Gradient-Based Optimization algorithm

This paper proposes a maiden intelligent controller design that consists of a Fuzzy Proportional–Integral–Derivative–Double Derivative (FPIDD2) controller whose parameters are fine-tuned using the Gradient-Based Optimization algorithm (GBO). The proposed FPIDD2 regulator is employed as a secondary regulator for stabilizing the combined voltage and frequency loops in a two-area interconnected power system. It has been shown that the GBO optimization algorithm outperforms other optimization strategies such as the Chimp Optimization Algorithm (ChOA), the Whale Optimization Algorithm (WOA), and the Gorilla Troops Optimization algorithm (GTO). The proposed FPIDD2 controller is tested in a conventional two-area power system. Then, the investigation is expanded to a two-area hybrid system, with each area comprising a mix of traditional (thermal, gas, and hydraulic power plants) and renewable generation units (wind and solar power). Additionally, the proposed controller takes into account system nonlinearities (such as generation rate limitations, governor deadband, and communication time delays), system uncertainties, and load/renewables fluctuations. In the two tested systems, the dynamic responses of each system demonstrate that FPIDD2 has a superior ability to attenuate the deviations in voltage and frequency in both areas of the system. In the studied conventional system, the proposed FPIDD2 controller is compared with a PID controller tuned by the Multi-Objective Non-Linear Threshold Accepting Algorithm (MONLTA), which has been presented in the literature, and a Fuzzy PID (FPID) controller tuned by GBO. In the investigated hybrid system, the suggested FPIDD2 regulator is compared to a GBO-tuned Integral Derivative-Tilted (ID-T) controller and FPID controller. As a fitness function (FF) for the GBO, the criteria of minimizing the integral time absolute error (ITAE) are applied. The results are presented in the form of MATLAB/SIMULINK time-domain simulations