Optimization Design Method for IGCT Gate Pole Drive Based on Improved Grey Wolf Algorithm

Integrated gate commutated thyristor (IGCT) has low loss, low cost, and high reliability in the application of modular multilevel converter (MMC). However, due to the special turn-off process of IGCT, the commutation transient is very different for IGCT in MMC. This letter reveals a current oscillation phenomenon of MMC based on IGCT and fast recovery diode (FRD). Especially, this oscillation effect becomes apparent when the FRD with high surge current capability is employed in MMC because of high junction capacitance and high stray inductance, which will threaten the safe operation of the system. This letter analyzes the current oscillation mechanism and gives the current oscillation modeling. In addition, through detailed tests under various stray inductances, the specific stray inductance range that can cause large current oscillation is obtained and an optimized inductance parameter of IGCT-MMC is proposed. The experiments show that the safe operation area of IGCT in MMC can be guaranteed maximally with the optimized parameter.

In recent years, with the rapid development of renewable energies and their connection demand with AC grids, modular multilevel converter technology (MMC) has been studied a lot. And the integrated gate-commutated thyristor (IGCT) has great potential in MMC application because of low loss, low cost and high reliability. However, there exists current oscillation between IGCT and the anti-paralleled fast recovery diode (FRD), which is caused by the changes of IGCT's anode potential during IGCT's turning off period. To explore the current oscillation mechanism, this paper extracts the stray inductances of a designed IGCT-MMC half bridge module and builds an IGCT-FRD current oscillation analyzing model using the physics based compact model of IGCT and the dynamic model of diode. The results show that greater the reverse recovery softness will cause smaller the current oscillation peak value with nearly constant cycle. As the equivalent junction capacitance of FRD increases, the current oscillation cycle and peak value both increase, while as the stray inductance increases, the current oscillation cycle increases and the peak value decreases.

Unmanned aerial vehicles (UAVs) exemplify automation in avionics offering a versatile platform for carrying out a diverse range of applications such as security surveillance, logistics, and agriculture.Optimizing generation path planning is a crucial challenge in UAV operations as it requires rapidly identifying a feasible route within the complicated environment.Bio-inspired algorithms have advantages in generating secure path planning.However, it has convergence challenges when dealing with narrow passageways and complex environments.To improve drone flying safety, this paper proposed an enhanced chaos chicken swarm optimization algorithm (CCSO).The proposed algorithm improved the standard chicken swarm optimization algorithm to address the convergence and local optima problems during secure path planning in a complex 3D environment for UAVs.Firstly, enhance the initialization process using a three-dimensional hyperchaotic system to provide dynamic uniform distribution to the chicken positions.Secondly, provide a new strategy to determine the hierarchal order of population.Furthermore, all steps of CCSO are executed in parallel computation to speed up the algorithm performance.Lastly, compare the proposed algorithm with standard chicken swarm optimization (CSO), particle swarm optimization (PSO), hybrid artificial bee colony (HABC), improved grey wolf optimization (IGWO), chaotic PSO (CPSO) and an improved chaos sparrow search algorithm (ICSSA) in simple and complex environments.The experimental results demonstrated that the CCSO has a minimum length route, reduces the initialization time, accelerates convergence, speed up runtime and the path is smoother than the other algorithms.The proposed algorithm outperforms PSO, CSO, HABC, IGWO, CPSO and ICSSA algorithms in term of fitness values and times since it has ability to improve the fitness values in complex environment by about 24%, 9%, 22%, 9%, 3% and 5% and speed up the converges toward optimal routes 4.5x, 3.34x, 8.48x, 2.87x, 1.23x and 2.27x when compared to the PSO, CSO, HABC, IGWO, CPSO and ICSSA techniques respectively which proved the superiority of CCSO in generating optimal path planning for UAVs in complex 3D environment.

Output voltage control of double chambers microbial fuel cell using intelligence-based optimized adaptive neuro fuzzy inference controller

Performance enhancement of a tuned mass damper for vibration suppression of an offshore jacket platform utilizing a novel metaheuristic optimization technique

Improved grey wolf optimization technique for fuzzy aided PID controller design for power system frequency control

This paper focuses on the dynamic workforce scheduling and routing problem for the maintenance work of harvesters in a sugarcane harvesting operation. Technician teams categorized as mechanical, hydraulic, and electrical teams are assumed to have different skills at different levels to perform services. The jobs are skill-constrained and have time windows. During a working day, a repair request from a sugarcane harvester may arrive, and as time passes, the harvester’s position may shift to other sugarcane fields. We formulated this problem as a multi-visit and multi-period dynamic workforce scheduling and routing problem (MMDWSRP) and our study is the first to address the workforce scheduling and routing problem (WSRP). A mixed-integer programming formulation and a hybrid particle swarm and whale optimization algorithm (HPSWOA) were firstly developed to solve the problem, with the objective of minimizing the total cost, including technician labor cost, penalty for late service, overtime, travel, and subcontracting costs. The HPSWOA was developed for route planning and maintenance work for each mechanical harvester to be provided by technician teams. The proposed algorithm (HPSWOA) was validated against Lingo computational software using numerical experiments in respect of static problems. It was also tested against the current practice, the traditional whale optimization algorithm (WOA), and traditional particle swarm optimization (PSO) in respect of dynamic problems. The computational results show that the HPSWOA yielded a solution with significantly better quality. The HPSWO was also tested against the traditional genetic algorithm (GA), bat algorithm (BA), WOA, and PSO to solve the well-known CEC 2017 benchmark functions. The computational results show that the HPSWOA achieved more superior performance in most cases compared to the GA, BA, WOA, and PSO algorithms.

Purpose This work applies a computational framework for vibration attenuation in periodic structures by combining the established wave and finite element (WFE) method with nature-inspired optimization algorithms. The purpose of this work is to provide a systematic comparison of various nature-inspired algorithms across both low-cost analytical and high-fidelity simulation-driven design problems. Methodology Two numerical examples are investigated. The first considers a semi-infinite beam with periodic masses, where the dynamic stiffness matrix is obtained analytically. Two optimization scenarios are addressed within the first example. The second example involves a periodic beam with alternating thick and thin segments, modeled using WFE, where the objective is to minimize transmissibility over a lower frequency range. Five nature-inspired optimization algorithms: Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Improved Grey Wolf Optimizer (IGWO), and Particle Swarm Optimization (PSO) are compared. In the first example, a fixed number of function evaluations ensures fair comparison; in the second, equal runtime account for the high computational cost of each function evaluation. Results In the first example, all algorithms achieved similar optimal solutions in the two optimization scenarios, with differences arising primarily in computational efficiency. For the first optimization scenario, distribution-free analysis showed that at intermediate function evaluation budgets, detectable differences emerge among algorithms, whereas in the second scenario, these differences diminish at higher evaluation budgets (with no significant pairwise contrasts), indicating convergence. GA incurred the highest computational overhead; DE was fast but tended to plateau while GWO, IGWO, and PSO exhibited strong accuracy-efficiency trade-offs within the tested budgets. In the second example, which operated under fixed runtime budgets, Kruskal–Wallis tests indicated significant differences at all time budgets: PSO consistently attained the lowest transmissibility, with GWO and IGWO close behind, GA showing modest improvement with longer runtime, and DE stagnating early. Across both cases, the effect-size analysis (Cliff’s $$\delta$$ ) confirms that observed gaps are generally small to medium at moderate budgets and tend to narrow at higher budgets as solutions converge. This highlights that the efficiency with which evaluations are translated into meaningful search progress is just as critical as raw accuracy in expensive, simulation-driven problems. The WFE-based framework therefore provides a general and effective tool for optimizing periodic structures for targeted vibration attenuation.

The paper proposes to use an improved Grey Wolf Optimization (GWO) optimized Fractional Order (FO) based type-II fuzzy controller for frequency regulation of an AC microgrid in presence of plug-in electric vehicles. The AC microgrid comprises various renewable-based energy sources such as wind turbine generator (WTG), photovoltaic cell (PV), microturbine (MT), aqua electrolyzer based fuel cell (FC) and diesel engine generator (DEG) along with various storage devices (ESD) like battery energy storage (BES), flywheel energy storage (FES) and electric vehicles (EV). The uncertain nature of solar, wind technologies and load demand makes the system more complicated and introduces the frequency oscillations in the system. It is highly needed to establish power balance among generation by designing an appropriate controller for frequency regulation. This paper proposes a robust fractional order type-II fuzzy PID (FO-T2-FPID) where the parameters of the FO-T2-FPID controllers are optimized by suggesting an improved GWO (I-GWO) algorithm. To demonstrate the capability of proposed I-GWO algorithm few comparative analyses have been performed over particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithm. It is also observed that for the same FO-T2-FPID controller structure, the percentage improvement in the objective function (ITAE) under load uncertainty by I-GWO compared to GWO and PSO are 29.74% and 65.94% respectively. Finally, it is conferred that proposed I-GWO optimized FO-T2-FPID controller significantly improves microgrid dynamic performance under various operational conditions.

The proposed research article introduces a new hybrid heuristic algorithm based on Autonomous Groups Particle Swarm and Grey Wolf optimizers (AGPSO-GWO) via multi-objective function for the allocation of the Flexible Alternating Current Transmission Systems (FACTS) controllers in power grids to minimize the active power system losses, voltage deviation, and the system operational costs. In this research work, the Static Var Compensator (SVC), Thyristor-Controlled Series Compensator (TCSC), and Unified power flow controller (UPFC) are used as FACTS controllers. A comparative analysis is conducted with other proposed heuristic optimization algorithms such as Particle Swarm Optimization (PSO), Autonomous Group Particle Swarm Optimization (AGPSO) Grey Wolf optimizer (GWO), and improved grey wolf optimization (IGWO) algorithms to confirm and validate the superiority of the proposed technique. Moreover, the proposed AGPSO-GWO has been compared with the previous existing Simulated Annealing (SA) in the literature to prove its superiority and ensure its validity. The proposed scheme has been validated and applied on 30-bus and 118-bus IEEE electric power systems. The numerical results had been carried out using MATLAB and MATPOWER packages. The simulation results show that the proposed algorithm offers the best performance among all algorithms to achieve the optimum global minimum solutions with the highest convergence rate.

One of the primary concerns of distribution system operator (DSO) is minimization of power loss. This work investigates the coordinated planning of distributed generation (DG) and distribution static compensator (D-STATCOM) for reduction of distribution system power loss. To achieve the optimal siting and rating of the DG and D-STATCOM, an improved grey wolf optimization (IGWO) has been proposed. To reduce the computational burden and search space in IGWO, the active and reactive power loss sensitivity factors has been employed. Besides, the impact of polynomial loads and mesh distribution network have been considered. The proposed algorithm’s performance has been tested on IEEE 33 bus distribution systems for validation. The test results emphasize the importance of combined allocation of DG and D-STATCOM in minimization of power loss and voltage profile improvement. Further, IGWO produces superior results than the conventional GWO and particle swarm optimisation (PSO).KeywordsDistribution static compensatorDistributed GenerationPower losses

Existing machine learning-based predictive control systems for chamber pressure face limitations such as improper reference setting, weak dynamic adaptability, and suboptimal parameter tuning. This study proposes a predictive control framework that integrates an adaptively updated LightGBM (AU-LightGBM) with an improved grey wolf optimizer (IGWO). First, theoretical analysis combined with LightGBM is used to estimate the ultimate face support pressure, which serves as the target chamber pressure. Second, an adaptive updating strategy is embedded into LightGBM to maintain stable prediction accuracy during continuous tunneling. Third, a KD-tree is employed to retrieve high-quality historical parameter sets, which are then used to initialize the grey wolf optimizer and enhance iterative optimization. The case study results demonstrate that the proposed framework exhibits strong effectiveness and adaptability across the conventional loess stratum and water-rich sand layer of the Shaolingyuan Tunnel, as well as the complex stratigraphic conditions of the Tianjin metro project. The average coefficient of determination ([Formula: see text]), root mean square error (RMSE), and mean absolute error (MAE) for chamber pressure prediction are 0.943, 0.082, and 0.055, respectively. After optimization, the average [Formula: see text], RMSE, and MAE between the predicted and reference chamber pressures are 0.753, 0.143, and 0.116, respectively. Under cohesive and non-cohesive soil conditions, the MAE between the results obtained by the hybrid LightGBM model and the experimental values are 11.987 and 7.958, respectively, which are lower than those of most theoretical models. Compared with six baseline learners, AU-LightGBM achieves the highest prediction accuracy, with [Formula: see text] RMSE, and MAE of 0.984, 0.014, and 0.010, respectively, between the predicted and actual values on the test set. By leveraging clustered historical samples, IGWO strengthens the initial population and yields more robust optimized parameters than the canonical GWO. The [Formula: see text], RMSE, and MAE between the chamber pressure optimized by IGWO and the reference values are 0.944, 0.023, and 0.015, respectively, all outperforming GWO, Genetic Algorithm, and Particle Swarm Optimization, thereby validating the superiority of the proposed method.

Manycast routing and spectrum assignment (RSA) in elastic optical networks (EONs) has become a hot research field. In this paper, the mathematical model and high efficient algorithm to solve this challenging problem in EONs is investigated. First, a multi-objective optimization model, which minimizes network power consumption, the total occupied spectrum, and the maximum index of used frequency spectrum, is established. To handle this multi-objective optimization model, we integrate these three objectives into one by using a weighted sum strategy. To make the population distributed on the search domain uniformly, a uniform design method was developed. Based on this, an improved grey wolf optimization method (IGWO), which was inspired by PSO (Particle Swarm Optimization, PSO) and DE (Differential Evolution, DE), is proposed to solve the maximum model efficiently. To demonstrate high performance of the designed algorithm, a series of experiments are conducted using several different experimental scenes. Experimental results indicate that the proposed algorithm can obtain better results than the compared algorithm.

To enhance prediction reliability and accuracy, an Lstm model optimized by the improved grey wolf algorithm is introduced for daily air quality index forecasting. Firstly, the model preprocesses the collected data and divides the data into a training set and a testing set. Then, using Tent Chaotic Sequence to generate an initial population, which increases the diversity of individuals in the population; And aming at the shortage of the search ability of Grey Wolf Optimization (GWO), updating the parameters <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> . The improved GWO (IGWO) used to optimize the relevant hyperparameters in the long and short-term memory neural network. Finally, the IGWO-LSTM model constructed with excellent hyperparameters will use the test set to obtain the prediction results. The experimental results demonstrate the proposed method outperforms the other four model in AQI prediction.

Particle Swarm Optimization (PSO) is a swarm intelligence based and stochastic algorithm to solve the optimization problem. Nevertheless, the traditional PSO has disadvantage from the premature convergence when finding the global optimization. To prevent from falling into the local optimum, we propose the Radius particle swarm optimization (R-PSO) which extends the Particle Swarm Optimization by regrouping the agent particles within the given radius of the circle. It initializes the group of particles, calculates the fitness function, and finds the best particle in that group. The R-PSO employs the group-swarm to keep the swarm diversity and evolution by sharing information from the agent particles which successfully maintain the balance between the global exploration and the local exploitation. Therefore the agent particle guides the neighbour particles to jump out of the local optimum and achieve the global best. The proposed method is tested against the well-known benchmark dataset. The results show that the R-PSO performs better than the traditional PSO in solving the multimodal complex problems.