Enhancing Power Quality in Decentralized Hybrid Microgrids: Optimized DSTATCOM Performance Using Cascaded Fractional-Order Controllers and Hybrid Optimization Algorithms

The rapid integration of distributed generation sources and power electronic converters in modern smart grids has introduced significant power quality (PQ) challenges, including increased harmonic distortion, voltage instability, and power losses. Existing optimization techniques struggle with slow convergence, suboptimal tuning of control parameters, and limited PQ enhancement. To deal with these problems, this research proposes a novel Lightning Search Optimization–Quantum Neural Network (LSO-QNN) approach for optimizing the Distribution Static Compensator (DSTATCOM)-based Proportional–Integral–Derivative (PID) controller to enhance PQ in grid-connected renewable energy systems. The novelty of this work lies in the hybridization of LSO with QNN, which enables adaptive and dynamic tuning of PID controller gains, ensuring faster convergence, enhanced harmonic suppression, and improved voltage stability compared to conventional methods. Simulation findings indicates that the proposed LSO-QNN approach achieves a voltage Total Harmonic Distortion (THD) of 0.38% and Current THD of 3.4%, significantly outperforming Particle Swarm Optimization (PSO) (6.4%), Heap-Based Optimizer (HBO) (4.5%), and Nomadic People Optimizer (NPO) (5.5%). Moreover, it improves voltage stability (1.1s) and reduces computation time (34 ms), achieving an overall efficiency of 99.3%. These findings establish the superiority of LSO-QNN over traditional optimization techniques, making it a highly effective and practical solution for real-world PQ enhancement in smart grids.

Breast cancer is the second most frequent malignant tumor in the world. Early findings of breast cancer can significantly improve treatment effectiveness. Manual methods of breast cancer diagnosis are prone to human fault and inaccuracy, and they take time. A computer‐aided diagnosis can assist radiologists in making better choices by overcoming the disadvantages of manual methods. One of the significant steps in the breast cancer diagnosis process is feature selection. In recent decades, many studies have proposed numerous hybrid optimization methods to select the optimal features in the breast cancer detection system. However, many hybrid optimization algorithms are trapped in local optima and have slow convergence speed. Thus, it reduces the classification accuracy. For resolving these issues, this work proposes a hybrid optimization algorithm that combines the grasshopper optimization algorithm and the crow search algorithm for feature selection and classification of the breast mass with multilayer perceptron. The simulation is experimented with using MATLAB 2019a. The efficacy of the proposed hybrid grasshopper optimization‐crow search algorithm with multilayer perceptron system is compared to multilayer perceptron based algorithms of enhanced and adaptive genetic algorithm, teaching learning‐based whale optimization algorithm, butterfly optimization algorithm, whale optimization algorithm, and grasshopper optimization algorithm. From the results obtained, the proposed grasshopper optimization‐crow search algorithm with the multilayer perceptron method outperforms the comparative models in terms of classification accuracy (97.1%), sensitivity (98%), and specificity (95.4%) for the mammographic image analysis society dataset.

Proportional and Integral (PI) controllers are usually accustomed for the control operation of Distribution Static Compensator (DSTATCOM) for the distribution system. However, since the system is very nonlinear and subject to numerous disturbances, the PI controlled DSTATCOM cannot offer optimum performance for various operation points. Additional controllers like the Instant Power Theory (IPT) supported fuzzy logic approach is needed for the DSTATCOM to supply adequate dynamic current control and to enhance power quality and stability of the distribution system. This paper presents the planning of fuzzy logic Controllers (FLC) of a DSTATCOM for improving power quality for a distribution system. The IPT based FLC for the DSTATCOM is used for improving the power quality and the dynamic performance of a distribution system is compared with a DSTATCOM controlled IPT based PI controller. The performances of the DSTATCOM controllers are evaluated throughout grid-side currents with linear and non-linear loads of the distribution system. Simulation results show that the DSTATCOM with IPT based FLC will improve the power quality by reducing reactive currents and harmonic currents with higher system dynamic performance than the DSTATCOM with IPT based standard PI controller.

Large penetration of PV in a microgrid connected distribution system impacts the weak utility distribution system by creating various power quality (PQ) problems such as voltage sag or swell, unbalance, harmonics and DC current injection etc. These power quality (PQ) issues and they are broadly classified as current related PQ issues and voltage related PQ issues. The current related issues are mitigated by one of the custom power device connected in parallel namely distribution static compensator (DSTATCOM), by operating in current controlled mode (CCM). The dynamic voltage restorer (DVR) is another custom power device (CPD) for mitigating voltage related PQ problems. It is reported in literature that, a suitable modification of a DSTATCOM can achieve the capability to compensate voltage disturbances also. A multiple functions of DSTATCOM is thus achieved by operating the device so as to mitigate the current and voltage related PQ issues. Thus a multi-functional control strategy allow DSTATCOM to compensate voltage and current related PQ problems. This paper presents a hybrid DSTATCOM which can be used in highly PV penetrated distribution system to control the voltage and current related PQ issues. The topology uses a suitably designed foreign inductor, connected between the grid and load. A dynamic reference load voltage generation algorithm using instantaneous symmetric component theory (ISCT) is presented. The simulation is done with real time voltage level using MATLAB/SIMULINK.

Power quality improvement in distribution network using DSTATCOM with battery energy storage system

Integrations of renewable energy (RE) sources like wind energy and solar energy to utility grid affects quality of electrical power. Utility operators have to ensure a good quality power supply and their services to retain the existing consumers or attract the new consumers. An effective system for monitoring and improvement of power quality (PQ) can help to achieve this task. The improvement of PQ can be achieved by using the distribution static compensator (DSTATCOM) in utility grid. This research work presents a control design using synchronous reference frame (SRF) theory to provide efficient control to the DSTATCOM for PQ improvement in utility grid in the presence of solar PV energy. A capacitor and battery bank is used as the DC bus in the design of DSTATCOM. The DSTATCOM has been implemented for improvement of PQ with solar PV energy during various operational events. The events considered include the tripping/reclosing of feeder, switching ON/OFF of capacitor bank, switching ON/OFF of inductive-resistive (LR) load. Proposed study is carried out using MATLAB/Simulink software with the help of an IEEE-13 bus test system. Proposed study is performed using a test system comprising of voltage source and load integrated on point of common coupling (PCC).

In the proposed paper, Distribution Static Compensator (DSTATCOM) is used as a voltage regulator in Distribution Grid. The power quality of the Distribution Grid can be improved by voltage regulation using different control techniques. In the proposed paper, control techniques, including the PI control, DQ control and Voltage control using Minimum Power Point Tracking (mPPT) are analyzed and simulated. The simulation is done for distribution grid with and without DSTATCOM during fault. Simulation with DSTATCOM is done for three different control techniques and its performances are compared. The mPPT avoids the circulation of unnecessary reactive compensation for voltage regulation by operating at the Minimum Power Point (mPP). Comparative analysis is made for DSTATCOM with the different control techniques during fault condition. The simulation of distribution Grid for voltage relation using DSATCOM is carried out using MATLAB Simulink.

PurposeSea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions.Design/methodology/approachCloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance.FindingsThe performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively.Originality/valueTo the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.

Distribution static compensator (DSTATCOM) is a shunt compensation device that is generally used to solve power quality problems in distribution systems. In an all-electric ship power system, power quality issues arise due to high-energy demand loads such as pulse loads. This paper presents the application of a DSTATCOM to improve the power quality in a ship power system during and after pulse loads. The control strategy of the DSTATCOM plays an important role in maintaining the voltage at the point of common coupling. A novel adaptive control strategy for the DSTATCOM based on artificial immune system (AIS) is presented in this paper. The optimal parameters of the controller are first obtained by using the particle swarm optimization algorithm. This provides a sort of innate immunity (robustness) to common system disturbances. For unknown and random system disturbances, the controller parameters are modified online, thus providing adaptive immunity to the control system. The performance of the DSTATCOM and the AIS-based adaptive control strategy is first investigated in MATLAB-/Simulink-based simulation platform. It is verified through a real-time ship power system implementation on a real-time digital simulator and the control algorithm on a digital signal processor.

Abstract. This paper focuses on the 4-PUU parallel mechanism with Schönflies motion, investigating its kinematic analysis and utilizing intelligent algorithms to solve its forward kinematics equations. Using screw theory as the mathematical framework, the number and nature of the degrees of freedom of the 4-PUU parallel mechanism are analyzed, proving that the mechanism can achieve 3T1R motion. The forward and inverse position kinematics equations are established with link lengths as constraints, and the velocity and acceleration equations of the mechanism are derived using the vector method. The forward kinematics equations of the mechanism are transformed into an unconstrained optimization problem, which is solved using an improved beluga whale optimization (BWO). To enhance the uniformity of the initial population distribution, a chaotic-opposition-based learning initialization strategy is introduced. Additionally, an elite strategy and a golden-sine-based position update mechanism are incorporated to improve the optimization capability of BWO. The integration of these strategies results in an enhanced optimization algorithm with superior global search ability. Given the mechanism's dimensional parameters, theoretical and motion simulations are conducted using MATLAB and Adams. The results indicate that the motion simulation curves from Adams closely match the numerical simulation curves from MATLAB, validating the theoretical derivation. Building on this, the improved beluga whale optimization (IBWO) algorithm, along with comparison algorithms (DEb1, ABC, PSO), is applied to solve the forward kinematics equations for interpolation points along the simulated trajectory. Numerical experiments demonstrate that IBWO outperforms the other comparison algorithms in solving the problem efficiently and accurately.

A Distribution static compensator (DSTATCOM) has become a potential choice in alleviating power quality(PQ) issues in distribution network with non-linear loads. DSTATCOM essentially maintains the PCC voltage and inverter DC link voltage by using two AC and DC PI controllers. Conventionally the computation of these gain values of PI controllers are done based on the model based or trial and error method. This paper presents comparitive study of optimisation based approach for obtaining the k p and k i values of the pi controller. Particle swarm optimization(PSO) and Ant colony optimization(ACO) based PI controller tuning is used in DSTATCOM for improved dynamic response and also the power quality improvement. The DSTATCOM is modeled and simulated in MATLAB/SIMULINK. The optimization techinques based PI controller tuning have been compared with conventional approach and thier performances are verified.

Wireless Sensor Networks (WSN) utilizes more nodes to forward messages and information from source node to destination node and these nodes are battery-powered devices. Thus, Energy saving is always vital role of Routing Protocols which need to maximize the lifetime of WSN. Mobile ad hoc networks (MANET) are self-starting and self-constructing multichip wireless networks in which, the assembly of the system deviates vigorously. This is mainly due to the mobility of the nodes. The nodes in the network not only act as hosts but also as routers that route data from other nodes in network. MANETs require an efficient routing protocol that achieves the quality of service (QoS) mechanism. They are not considering the mobility as one constraint for those problems. Because, mobility of nodes causes frequent link failures and high error rates, so it makes difficult to maintain the desired QoS in the network. To overcome those problems, we propose mobility aware routing protocol for MANET using hybrid optimization (MARP-HO) algorithm, which maximizes the QoS in data transmission. The proposed routing protocol comprises of two phases. The clustering process consists of cluster formation and cluster head (CH) selection. First, the energy efficient clustering is done by improved animal migration optimization (IAMO) algorithm. Then, we gather multiple constraints are energy consumption, received signal strength, mobility and cooperative rate from each cluster member. The gathered constraints used to compute the node cost of each cluster member, here the highest cost node act as CH of cluster. Second, we compute routing path between source–destination using improved ant colony optimization (IACO) algorithm. The proposed MARP-HO routing protocol is compared with existing state-of-art routing protocols in terms of energy consumption, delivery ratio, loss ratio, link stability, delay, network lifetime, number of dead nodes and throughput.

An increasing growth of non-linear load in the three-phase four-wire distribution system, results in various power quality problems in the distribution system. Custom power devices (CPD) have been evolved to solve the power quality problems in the distribution system. Among the various custom power devices, Distribution Static Compensator (DSTATCOM) is one of the effective solutions for solving the power quality problems. This paper reveals about Distribution static compensator (DSTATCOM) for balancing of source currents, harmonic mitigation and neutral current compensation in three-phase four-wire distribution system. A synchronous reference frame (SRF) theory is projected for the control of DSTATCOM. Four-leg voltage source converter (VSC) with a dc capacitor is used as four-wire DSTATCOM. Here DSTATCOM is operated in load compensation mode. The first three-legs are used for balancing of source current and harmonic mitigation and the fourth leg is used for neutral current compensation. The compensation performance of DSTATCOM using the proposed control strategy is demonstrated using simulation results obtained from MATLAB/SIMULINK.

Enhancing Available Transfer Capability (ATC) is crucial in today’s deregulated power systems. Power can be transferred from the sending end to the receiving end only if adequate ATC is available. Operators are encouraged to make the best use of existing infrastructure to improve ATC margins. Flexible AC Transmission Systems (FACTS) devices help by regulating phase angle, voltage, and reactance, which changes load flow and controls bus voltages. In this study, the Whale Optimization Algorithm (WOA) is used to find the optimal location and control settings of a Distribution Static Compensator (DSTATCOM). ATC forecasting under 3 and 6% load growth is done using linear regression, with the inclusion of DSTATCOM. Equations linking load growth to ATC for various sinking buses are developed. Forecast accuracy is improved using quadratic regression in which the errors are under 1% and sometimes as low as 0.01%. This approach helps operators and planners prepare for future transmission needs under growing demand.

Modern utility systems are being heavily strained by rising energy consumption and dynamic load variations, which have an impact on the quality and reliability of the supply. Harmonic injection and reactive power imbalance are caused by the widespread divergence. Power quality (PQ) issues are mostly caused by renewable energy powered by power electronic converters that are integrated into the utility grid, despite the fact that a range of industries require high-quality power to function properly at all times. Several solutions have been created, but continuing efforts and newly improved solutions are needed to solve these problems by operating according to various international standards. Distributed Static Compensator (DSTATCOM) was created in the proposed model to enhance PQ in a standard bus system. A standard bus system using the DSTATCOM model was initially developed. A real-time dataset was gathered while applying various PQ disturbance conditions. A deep learning controller was created using this generated dataset, which examined the bus voltages to generate the DSTATCOM pulse signal. Two case studies, the IEEE 13 bus and the IEEE 33 bus system, were used to analyze the proposed work. Performance of the proposed deep learning controller was verified in various situations, including interruption, swell, harmonics, and sag. The outcome of THD in the IEEE 13 bus is 0.09% at the sag period, 0.08% at the swell period, 0.01% at the interruption period, and in the IEEE 33 bus was 1.99% at the sag period, 0.44% at the swell period, and 0.01% at the interruption period. Also, the effectiveness of the proposed deep learning controller was examined and contrasted with current methods like K-Nearest Neighbor (KNN) and Feed Forward Neural Network (FFNN). The validated results show that the suggested method provides an efficient mitigation mechanism, making it suitable for all cases involving PQ issues.