Water pumping system (WPS) plays a key role in human civilization, including agriculture, drinking, and industrial usage. To decrease the load demand on grid and provide electrical supply to WPS in rural areas, a locally placed standalone photovoltaic (PV)-based WPS is one of the best feasible solutions. Among many motors, brushless direct current motors (BLDCs) are attractive for WPS due to many advantages. However, integrating batteries to WPS will increase the cost and required maintenance, which will not be affordable for farmers. Hence, PV-fed BLDC motor-based WPS without battery is considered and implemented sensorless speed controller with sliding mode controller (SMC) in this article. Generally, power generated by PV systems will be affected by partial shading condition (PSC) and unable to produce maximum availability power due to failure of the conventional perturbed and observe (P&O) algorithm. In order to overcome this condition, a whale optimization algorithm (WOA) has been integrated to the conventional P&O algorithm in this article. A hybrid whale optimization–P&O (WOPO) algorithm can effectively work to extract the maximum possible power from PV during both PSC and normal condition. The results are compared with genetic algorithm (GA), particle swarm optimization (PSO), and gray wolf optimization (GWO) for maximum power point tracking (MPPT) under PSC. In order to reduce the cost of system, the inverter is also working as an MPPT device for the PV system by integrating the WOPO algorithm with SMC of inverter. Hence, an extra dc–dc converter will not be required for MPPT. Extensive results are presented with OPAL-RT to validate the proposed system under both steady-state and transient conditions. In addition to this, dSPACE-based hardware results are also presented in this article to validate the proposed system.

Whale Optimization Algorithm for PV Based Water Pumping System Driven by BLDC Motor Using Sliding Mode Controller

The analysis of different components of a grid-linked hybrid energy system (HES) comprising a photovoltaic (PV) system is presented in this work. Due to the increase of the population and industries, power consumption is increasing every day. Due to environmental issues, traditional power plants alone are insufficient to supply customer demand. In this case, the most important thing is to discover another approach to meet customer demands. Most wealthy countries are now concentrating their efforts on developing sustainable materials and investing considerable amounts of money in product development. Wind, solar, fuel cells, and hydro/water resources are among the most environmentally benign renewable sources. To control the variability of PV generation, this sort of application necessitates the usage of energy storage systems (ESSs). Lithium-ion (Li-ion) batteries are the most often used ESSs; however, they have a short lifespan due to the applied stress. Hybrid energy storage systems (HESSs) started to evolve as a way to decrease the pressure on Li-ion batteries and increase their lifetime. This study represents a great power management technique for a PV system with Li-ion batteries and supercapacitor (SC) HESS based on an artificial neural network. The effectiveness of the suggested power management technique is demonstrated and validated using a conventional PV system. Computational models with short-term and long-term durations were used to illustrate their effectiveness. The findings reveal that Li-ion battery dynamical stress and peak value are reduced, resulting in longer battery life.

/pdf/artificial-deep-neural-network-in-hybrid-pv-system-for-1ms3q4xk.pdf

Artificial Deep Neural Network in Hybrid PV System for Controlling the Power Management

Of all the renewable energy sources, solar photovoltaic (PV) power is estimated to be a popular source due to several advantages such as its free availability, absence of rotating parts, integration to building such as rooftops, and less maintenance cost. The nonlinear current-voltage (I–V) characteristics and power generated from a PV array primarily depend on solar insolation/irradiation and panel temperature. The extracted PV output power is influenced by the accuracy with which the nonlinear power–voltage (P–V) characteristic curve is traced by the maximum power point tracking (MPPT) controller. In this paper, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). To validate the usefulness of the RIO MPPT algorithm, MATLAB/Simulink simulations are performed under varying environmental conditions, for example, step changes in solar irradiance, and partial shading of the PV array. Furthermore, the search performance of the RIO algorithm is examined on different unconstrained benchmark functions, and it is that realized that the RIO algorithm has improved convergence characteristics in terms of finding the optimal solution than Particle swarm optimization (PSO). The results demonstrated that the RIO-based MPPT performs remarkably in tracking with high accuracy as the PSO-based MPPT.

https://www.preprints.org/manuscript/202202.0126/v1/download

Roach Infestation Optimization MPPT Algorithm for Solar Photovoltaic System

Multi-objective collaborative optimization in cement calcination process: A time domain rolling optimization method based on Jaya algorithm

The stability control of nominal frequency and terminal voltage in an interconnected power system (IPS) is always a challenging task for researchers. The load variation or any disturbance affects the active and reactive power demands, which badly influence the normal working of IPS. In order to maintain frequency and terminal voltage at rated values, controllers are installed at generating stations to keep these parameters within the prescribed limits by varying the active and reactive power demands. This is accomplished by load frequency control (LFC) and automatic voltage regulator (AVR) loops, which are coupled to each other. Due to the complexity of the combined AVR-LFC model, the simultaneous control of frequency and terminal voltage in an IPS requires an intelligent control strategy. The performance of IPS solely depends upon the working of the controllers. This work presents the exploration of control methodology based on a proportional integral–proportional derivative (PI-PD) controller with combined LFC-AVR in a multi-area IPS. The PI-PD controller was tuned with recently developed nature-inspired computation algorithms including the Archimedes optimization algorithm (AOA), learner performance-based behavior optimization (LPBO), and modified particle swarm optimization (MPSO). In the earlier part of this work, the proposed methodology was applied to a two-area IPS, and the output responses of LPBO-PI-PD, AOA-PI-PD, and MPSO-PI-PD control schemes were compared with an existing nonlinear threshold-accepting algorithm-based PID (NLTA-PID) controller. After achieving satisfactory results in the two-area IPS, the proposed scheme was examined in a three-area IPS with combined AVR and LFC. Finally, the reliability and efficacy of the proposed methodology was investigated on a three-area IPS with LFC-AVR with variations in the system parameters over a range of Â ± 50%. The simulation results and a comprehensive comparison between the controllers clearly demonstrates that the proposed control schemes including LPBO-PI-PD, AOA-PI-PD, and MPSO-PI-PD are very reliable, and they can effectively stabilize the frequency and terminal voltage in a multi-area IPS with combined LFC and AVR.

/pdf/load-frequency-control-and-automatic-voltage-regulation-in-a-lmhry65c.pdf

Load Frequency Control and Automatic Voltage Regulation in a Multi-Area Interconnected Power System Using Nature-Inspired Computation-Based Control Methodology

To augment the photovoltaic (PV) power generation conversion, a maximum power point tracking (MPPT) technique plays a very significant role. This article introduces a hybrid MPPT algorithm integrating modified invasive weed optimization (MIWO) and perturb and observe (P&O) technique under the rapid weather change and partial shading scenarios for the efficient extraction of the maximum power from the standalone PV-based hybrid system. MIWO handles the initial stages of MPPT followed by the application of the P&O algorithm at the final stages in view of acquiring the rapid global peak and maximal PV power. The studied microgrid comprises of the PV system, battery, electrolyzer, fuel cell, and load. A coordinated dc-voltage regulation and power management strategy between each subsystem of the hybrid microgrid is implemented to save the battery from the undesirable charging/discharging operation. Additionally, with the monitoring of dc voltage, the dc/dc converter associated with the battery and dc link plays as an MPPT circuit of the PV without the requirement of an extra dedicated circuit. Takagi–Sugeno (TS) fuzzy controller is adopted for suppressing/mitigating the voltage oscillations of the microgrid during the variations in the solar irradiance/temperature and power demand. The results clearly exhibit the superior performance of the proposed methodology compared with some of the existing techniques.

/pdf/coordinated-power-management-and-control-of-standalone-pv-2se24hfv4a.pdf

Coordinated Power Management and Control of Standalone PV-Hybrid System With Modified IWO-Based MPPT

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rpg.2019.1128

Modified demagnetisation control strategy for low-voltage ride-through enhancement in DFIG-based wind systems

Bio‐inspired hybrid BFOA‐PSO algorithm‐based reactive power controller in a standalone wind‐diesel power system

This paper proposes an adaptive Jaya optimization algorithm (AJOA) scheme for optimizing the cascade PI-PD controller gains in Automatic Generation Control (AGC) of the power system to further enhance the load frequency control (LFC) performance. Jaya algorithm is a more recent and powerful heuristic search process. In spite of this, the original Jaya algorithm (OJA) may degrade its search performance by reason of the absence of algorithm-specific parameters. A maiden effort has been applied to consider an algorithm-specific variable (i.e., weight parameter) in the OJA for augmenting its search process towards the optimal result. The search performance of AJOA based on different unconstrained benchmark problems realized to be more appropriate than Particle swarm optimization (PSO), Gravity search algorithm (GSA), Ant lion optimization (ALO), and the original Jaya algorithm. Illustration of convergence characteristics of the above-mentioned algorithms, the AJOA is quite faster in contributing to the optimal solution and leading to a reduction in computational burden. In addition, the investigations reveal that AJOA with PI-PD cascaded controller suppressing the dynamics of the frequency oscillations in an interconnected two-area power system during the system events such as variations in parameters and load perturbations.

Adaptive Jaya Algorithm for Optimized PI-PD Cascade Controller of Load Frequency Control in Interconnected Two-Area Power System

This paper presents a -fuzzy adaptive Jaya optimization algorithm (FAJOA) with integrating an algorithm-specific parameter in the original Jaya algorithm (OJA). A Takagi-Sugeno (TS)-fuzzy is implemented for dynamically adapting the weight parameter (w) of the FAOJA for improving the search performances such as faster convergence and realization of the optimal solution. Different benchmark functions are chosen as the test functions to examine the proficiency of the FAJOA. The same FAJOA has applied to accomplish the optimum values of the cascaded PI-PD controller parameters for augmenting the load frequency control (LFC) performance. In this paper, an interconnected two-area thermal-hydro based power system is investigated for the analysis. It is observed that the suggested FAJOA improves the dynamics of the LFC in each area and tie-line power flow of the power system. As well, the results demonstrate that the proposed FAJOA is comparatively superior to some of the existing optimization algorithms.

Terje Gjengedal

Papers

Coordinated Power Management and Control of Standalone PV-Hybrid System With Modified IWO-Based MPPT

Modified demagnetisation control strategy for low-voltage ride-through enhancement in DFIG-based wind systems

Bio‐inspired hybrid BFOA‐PSO algorithm‐based reactive power controller in a standalone wind‐diesel power system

Adaptive Jaya Algorithm for Optimized PI-PD Cascade Controller of Load Frequency Control in Interconnected Two-Area Power System

A Novel Fuzzy Adaptive Jaya Optimization for Automatic Generation Control in Multi-Area Power System