Optimal Control Parameters Research Articles

Frequency regulation of interconnected hybrid power system with Assimilation of electrical vehicles

Recent widespread connections of renewable energy resource (RESs) in place of fossil fuel supplies and the adoption of electrical vehicles in place of gasoline-powered vehicles have given birth to a number of new concerns. The control architecture of linked power networks now faces an increasingly pressing challenge: tie-line power fluctuations and reducing frequency deviations. Because of their nature and dependence on external circumstances, RESs are analogous to continually fluctuating power generators. Using a fractional order-based frequency regulator, this work presents a new method for improving the frequency regulation in a two-area interconnected power system. In order to deal with the frequency regulation difficulties of the hybrid system integrated with RES, the suggested controller utilizes the modified form of fractional order proportional integral derivative (FOPID) controller known as FOI-PDN controller. The new proposed controllers are designed using the white shark optimizer (WSO), a current powerful bioinspired meta heuristic algorithm which has been motivated by the learning abilities of white sharks when actively hunting in the environment. The suggested FOI-PDN controller's performance was compared to that of various control methodologies such as FOPID, and PID. Furthermore, the WSO findings are compared to those of other techniques such as the salp swarm algorithm, sine cosine algorithm and fitness dependent optimizer. The recommended controller and design approach have been tested and validated at different loading conditions and different circumstances, as well as their robustness against system parameter suspicions. The simulation outcomes demonstrate that the WSO-based tuned FOI-PDN controller successfully reduces peak overshoot by 73.33%, 91.03%, and 77.21% for region-2, region-1, and link power variation respectively, and delivers minimum undershoot of 89.12%, 83.11%, and 78.10% for both regions and tie-line. The obtained findings demonstrate the new proposed controller's stable function and frequency controlling performance with optimal controller parameters and without the requirement for a sophisticated design process.

Series and shunt FACTS controllers based optimal reactive power dispatch

Optimal reactive power dispatch involves the determination and management of reactive power resources in a power system to maintain voltage stability, improve power transfer capability, and minimize system losses. Reactive power is essential for maintaining voltage levels within acceptable limits and ensuring the reliable operation of electrical networks. The whale optimization algorithm (WOA) has been proposed to obtain the optimal location of flexible alternating current transmission system (FACTS) components. The efficacy of WOA is tested using conventional IEEE 14 and 30 bus test systems. Static var compensator (SVC) is used as shunt and the thyristor-controlled series capacitor (TCSC) as a series FACTS controller. The analysis is carried out for both the systems with and without FACTS controllers. Optimization techniques are applied to select the optimal control parameters. The suggested strategy is compared to other contemporary techniques such as particle swarm optimization (PSO) and grey wolf optimization (GWO). At various loading situations, the WOA-based technique outperforms other two techniques.

Dead-time compensation and single-loop control strategy for ground power unit

Instead of the auxiliary power system, the ground power unit supplies to airplanes during stopovers in airports, which is an important means of reducing carbon emissions of airport. However, under the restraint of switching frequency and high dynamic response speed, a single control loop is usually implemented in 400 Hz voltage-source inverter, the sampling and control of the inductor current in the LC filter are avoided, result in the design of the control system being more difficult, and the direction of the dead-time compensation can not be obtained due to the lack of the current polarity. In view of the above problem, this paper firstly analyzes the influence on the output voltage of inverter due to the dead-time, including the errors of fundamental amplitude and phase, and the low-order harmonic distortion. Then, according to the state equation of the inductor current, a current observation model without zero drift is proposed to obtain the polarity of the inductor current. On this basis, feedforward compensation is carried out for the voltage error generated by the dead-time. Combined with the amplitude-frequency characteristic curve, the parameter optimization design method of the single closed-loop proportional-resonant controller is presented. Finally, a simulation model and an experimental platform are established to verify the feasibility and effectiveness of the current observation model, dead-time compensation method and controller parameter optimization design method proposed in this paper.

Integrated Optimization of Structure and Control for Fast Steering Mirrors.

This study concerns the problem of integrated optimization of structure and control based on a fast steering mirror, aiming to achieve simultaneous optimization of the mechanical structure and control system. The traditional research and development path of the fast steering mirror involves a lengthy process from the initial design to the final physical manufacture. In the prior process, it was necessary to produce physical prototypes for repeated debugging and iterative optimization to achieve design requirements, but this approach consumes a significant amount of time and cost. To expedite this process and reduce unnecessary experimental costs, this study proposes an integrated optimization of structure and control (IOSC) method. With the use of IOSC, it is possible to achieve simultaneous optimization of structure and control. Specifically, the use of non-dominated sorting genetic algorithm II (NSGA-II) obtains globally optimal controller parameters and mechanical structure parameters under certain performance indices. This achieves an effective balance between the resonance frequency generated by the system and the working bandwidth, providing a high-precision reference for the research and development of fast steering mirrors.

Design optimization of Solar Power Inverter using the GRA Method

Among the most crucial components of a solar-powered system is an inverter. It is an apparatus that transforms the direct current (DC) produced by solar panels into the alternating current (AC) required by the electric system. Flashing hybrid solar inverter is the best solar inverter for home which offers the following features: - Its operating voltage is between 100-290V. - It has 700 VA of power. 5kW solar inverter price budget starts at $2,000 for excellent single-phase devices and $1,000 for existing datasets (like Sungrow) (eg Fronius or SMA). The most popular size, 5kW, can accommodate arrays up to 6.6kW in power. A solar charge controller can only function effectively during the day, because when the ultraviolet irradiance is strong and, the system's Voltage rating must reach the inverter at a moderate DC voltage level in order for it to function. Your photovoltaic systems and inverter operate at night power will be off. The inverter doesn't run overnight because it doesn't want to draw electricity. Instead, it rises again in the morning when the sun shines. Your home and solar system are connected to the utility grid. By switching to solar PV-based power generation from the airport's current conventional energy source, the carbon footprint of the facility can be decreased. Power distribution solar PV power facilities may be built in regions that are required to be broad and free of obstructions around runways. Based on first year operating data, the current study seeks to evaluate the operational efficiency of a 12 MWp solar-powered airport at Cochin Airport Limited (CIAL), India. With the aid of the most well-liked PV simulation programmes, such as Deterioration in quality and Solar Gis, the plant effectiveness is precisely rendered. The software's performance metrics were discovered to perfectly resemble the observed values. Economic and ecological studies of Kochi airports powered by solar energy attest to its efficiency in lowering carbon footprint, resulting in an airport with almost no emissions that is clean, green, and sustainable.Gray correlation analysis is widely used to measure the degree of relationship between sequences through the gray correlation coefficient. Gray relational analysis has been used by many researchers to optimize control parameters with multiple responses through gray relational grading. The fundamental tenet of the GRA approach is that the chosen option must have the "greatest degree of Green relation" to the positive-ideal solution and the "minimum of grey relation" to the negative-best answer. A technique for determining whether or not variables are connected and how much they are correlated is called grey correlation coefficient analysis. The primary method of determining these curves' geometric similarity is by the construction of characteristic series curves. Type of Solar Module, No of modules per string, No of string, No of inverters and No of transformers Short circuit current (ISC), Open circuit voltage (VOC), Operating Temperature and Dimension. Stress Management in Healthcare Institutions in Work overload is got the first rank whereas is the Overtime is having the lowest rank.

Flutter performance simulation on streamlined bridge deck with active aerodynamic flaps

AbstractActive aerodynamic flaps can effectively improve the aerodynamic stability of bridges; however, the determination of optimal control parameters often requires a large number of experiments. This study proposes a method for determining the optimal control parameters of active flaps based on the surrogate model and computational fluid dynamics (CFD) simulation technology. The computational fluid dynamics method is used to calculate the flutter derivatives of the deck–flap system under a small number of control parameters, and then the surrogate model is used to extend the flutter derivatives to the design domain of entire control parameters. Furthermore, the changing trend of flutter performance under flap control parameters can be obtained. This method can avoid complex and expensive active control wind tunnel tests and obtain more accurate optimal control parameters at less cost, which is helpful for the convenient application of active control technology in actual engineering.

System Identification and Fractional-Order Proportional–Integral–Derivative Control of a Distributed Piping System

The vibration of piping systems is one of the most important causes of accelerated equipment wear and reduced work efficiency and safety. In this study, an active vibration control method based on a fractional-order proportional–integral–derivative (PID) controller was proposed to suppress pipeline vibration and reduce pipeline damage. First, a mathematical model of the distributed piping system was established using the finite element analysis method, and the characteristics of the distributed piping system were studied effectively. Further, the time-frequency domain parameter identification method was used to realise the system identification of the cross-point vibration transfer function between the brake and sensor, and the particle swarm optimisation algorithm was utilised to further optimise the transfer function parameters to improve the system identification accuracy. Therefore, a fractional-order PID controller was designed using the D-decomposition method, and the optimal controller parameters were obtained. The experimental and numerical simulation results show that the improved system identification algorithm can significantly improve modelling accuracy. In addition, the designed fractional-order PID controller can effectively reduce the system’s overshoot, oscillation time, and adjustment time, thereby reducing the vibration response of piping systems.

Quantum control and quantum speed limits of single-well systems

Achieving precise control and efficient manipulation of different quantum states is one of the most important goals of quantum technology. We study the quantum control and quantum speed limits of single-well systems by taking a harmonic oscillator whose frequency is a power function of time as an example when the transitionless quantum driving is imposed. For different eigenstates, we reveal the effects of control time and power exponent on Bures angle, fidelity, time-averaged cost, and quantum-speed-limit time. We find that the shortest control time for any eigenstate is proportional to the control parameter (i.e., power exponent). For different eigenstates meeting the same high fidelity requirements, it is shown that an optimal control parameter can always be obtained. Our findings provide theoretical support for the rapid and efficient manipulation of quantum states in single-well systems by using shortcut-to-adiabaticity technique experimentally.

Directions for modifying the artificial bee colony algorithm to optimize control parameters for complex systems

In recent years, bioinspired algorithms based on the use of a population approach and a probabilistic search strategy have become especially popular among researchers involved in multidimensional and multicriteria optimization. Such algorithms are based on the principles of cooperative behavior of a decentralized self-organizing colony of living organisms (bees, ants, birds, etc.) to achieve certain goals (for example, to meet nutritional needs). However, their practical application encounters a number of difficulties leading to a decrease in convergence. This article discusses the possibility of modifying the artificial bee colony algorithm by using a hybridization strategy with various data mining methods. One of these difficulties is the lack of a reasonable approach to determining initial search positions. As a solution, it is proposed to divide the population into clusters, the centers of which will be the initial positions. The need for interaction between individuals makes it advisable to use fuzzy clustering, which allows the formation of intersecting clusters. Another difficulty is associated with the choice of “free” parameters, for which the authors have not developed recommendations for choosing their optimal values. To solve this problem, it is proposed to use the idea of coevolution, which consists in the parallel launch of several interacting subpopulations, for each of which different “settings” are applied. The proposed algorithm is applicable to multidimensional optimization tasks, in which it is necessary to find such a combination of different types of elements belonging to some “large” population that will ensure the achievement of the maximum effect under given restrictions. Examples of such tasks are determining the species and quantitative composition of plants to form the terrestrial ecosystem of a carbon farm or mass recruiting, which consists of selecting a large number of personnel for the same positions.

Control parameter optimization of underwater gliders with uncertainty using hierarchical optimization method and interval analysis theory

This article attempts to solve the engineering optimization problem for control parameter configuration of underwater gliders with parameter uncertainty. The optimization objectives are maximizing the glider energy utilization rate and average voyage velocity; design variables include the net buoyancy adjustment amount and movable mass block translation amount; and uncertain parameters include the glider control parameter errors, manufacturing errors and sensor measurement errors. First, a dynamic simulation-based performance evaluation model of the glider is established, and interval number order theory is used to quantify the uncertainty. Then, surrogate models of the performance evaluation model are established, and the Sobol’ method-based sensitivity analysis is executed to identify the key uncertain parameters. Thus, a hierarchical optimization framework is introduced, and its system level and sub-level are used for determining optimal control parameter values and performance evaluation parameter intervals, respectively. Finally, optimization calculation is executed under different parameter configurations, and some rules are summarized.

Gliding strategy analysis and optimisation of underwater gliders balancing energy consumption, motion accuracy and voyage velocity

ABSTRACT This paper presents a novel gliding strategy for underwater gliders, which can better balance the glider energy consumption, motion accuracy and voyage velocity. This strategy is designed according to the performance analysis results of the glider under the conventional gliding strategy. In the shallow water areas with the higher current intensity, the glider adjusts the net buoyancy to a larger value, which can ensure the higher voyage velocity and motion accuracy. In the deep water areas with the smaller current intensity, the glider adjusts the net buoyancy to a smaller value, which can ensure the higher energy utilisation rate. In the different water areas, the movable mass block translation amount will also be adjusted. To improve the performance analysis efficiency, the radial basis function (RBF) neural network is employed to establish surrogate models of the glider dynamic model. Then, we carry out the glider control parameter optimisation using the surrogate models and non-dominated sorting genetic algorithm II. The comparison research between the conventional gliding strategy and the novel gliding strategy shows that the novel gliding strategy can make the glider have the higher motion accuracy under the specific energy utilisation rate. Besides, the novel gliding strategy will not affect the optimal solution of the glider voyage velocity and energy utilisation rate, and it improves the glider motion accuracy by mainly reducing the voyage velocity. The advantage of the novel strategy is mainly to improve the glider motion accuracy, which may provide certain guidance for the actual application of underwater gliders.

Time Domain Analysis of Ride Comfort and Energy Dissipation Characteristics of Automotive Vibration Proportional–Integral–Derivative Control

<div>A time domain analysis method of ride comfort and energy dissipation characteristics is proposed for automotive vibration proportional–integral–derivative (PID) control. A two-degrees-of-freedom single wheel model for automotive vibration control is established, and the conventional vibration response variables for ride comfort evaluation and the energy consumption vibration response variables for energy dissipation characteristics evaluation are determined, and the Routh stability criterion method was introduced to assess the impact of PID control on vehicle stability. The PID control parameters are tuned using the differential evolution algorithm, and to improve the algorithm’s adaptive ability, an adaptive operator is introduced, so that the mutation factor of differential evolution algorithm can change with the number of iterations. The PID control parameter optimization method presented in this article is versatile and can be used to optimize PID control parameters under different conditions. This article provides the results of PID control parameter optimization under different conditions. Based on PID control and its parameter tuning, a time domain solution method for two types of vibration response variables, their root mean square values, and the average power of energy consumption vibration of automotive vibration PID control is proposed. Two kinds of simulation test schemes are designed under urban driving conditions, which are commonly used Class B road and pulse road, with a commonly used vehicle speed of 60 km/h. The ride comfort and energy dissipation characteristics of passive suspension and PID control are compared. The results show that introducing energy consumption vibration response variables as a supplement to the conventional vibration response variables is beneficial for expanding the research and application scope of the automotive vibration and its control; with the sprung mass acceleration deviation as the control input, PID control can effectively improve the vehicle ride comfort, but it makes the vibration energy dissipation characteristics worse.</div>

A hybrid technique linked FOPID for a nonlinear system based on closed-loop settling time of plant

Wind and hydroelectric systems are more cost-effective and environmentally beneficial. A hybrid technique is proposed for the fractional-order proportional-integral-derivative (FOPID) controller to regulate the wind and hydro system. The proposed hybrid technique combines the feedback-artificial-tree (FAT), and atomic-orbital-search (AOS); together known as FAT-AOS approach. The proposed technique is utilized to decide the optimum controller parameters, and it guarantees system constancy in large disturbances using less computation and overshoot by restraining the parameter variation. The FAT is used to predict the optimum gain parameter of FOPID, and minimizing the system error is accomplished with the AOS approach. The performance metrics are peak time, rise time, settling time, and peak overshoot, are analyzed. The performance of the proposed method is done in the MATLAB platform. The simulation result of proposed approach for the rise time as 0.001 sec, settling time is 0.012 sec, and the overshoot percentage is 0.02 %. By comparing the existing methods, like Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Particle swarm optimization (PSO), the proposed approach rise time and settling time overshoot, is less. The comparison proves that the proposed system delivers improved outcome than existing systems.

Motion-pressure coupled control and simulation of long-endurance capability for multicapsule stratospheric airships

The current study focuses on the motion-pressure coupled control for a multicapsule stratospheric airship and transforms the path-tracking and heading-hold control of airships into guidance tracking with a time-varying weighted sum of longitudinal and lateral velocities by the definition of compound speed. Herein, an improved nonlinear predictive control method is provided to reduce the control energy consumption by the rolling optimization of controller parameters based on finite time intervals, ensuring infinite-time path-tracking tasks. Simultaneously, combined with the proposed cyclic regulation process of safe pressure between internal and external capsules, this study can fully reflect the force-thermal coupled rule of airships under the actions of atmospheric environment and maneuvering force, while evaluating the long-endurance capability of airships under the conditions of safe superheating and overpressure. The effectiveness of the motion-pressure coupled controller was verified through numerical simulations, which can overcome the influence of environmental wind and achieve a tracking effect for the desired cruise path and compound speed. The airspeed provided during the cyclic circadian time caused the maximum superheating of the helium controlled within 30 °C. The helium in the internal and external capsules achieved circadian regulation. The equivalent micropore diameter of the capsule of 5 mm can achieve 55 days of long-endurance flight. The controller satisfies the requirements of cruise-flight application modes for multicapsule stratospheric airships with important engineering value.

Fractional-Order Fuzzy PID Controller with Evolutionary Computation for an Effective Synchronized Gantry System

Gantry-type dual-axis platforms can be used to move heavy loads or perform precision CNC work. Such gantry systems drive a single axis with two linear motors, and under heavy loads, a high driving force is required. This can generate a pulling force between the drive shafts in the coupling mechanism. In these situations, when a synchronization error becomes too large, mechanisms can become deformed or damaged, leading to damaged equipment, or in industrial settings, an additional power consumption. Effectively and accurately acquiring the synchronized movement of the platform is important to reduce energy consumption and optimize the system. In this study, a fractional-order fuzzy PID controller (FOFPID) using Oustaloup’s recursive filter is used to control a synchronous X–Y gantry-type platform. The optimized controller parameters are obtained by the measurement of control errors in a simulated environment. Four optimization methods are tested and compared: particle swarm optimization, invasive weed optimization, a gray wolf optimizer, and biogeography-based optimization. The systems were tested and compared in order to optimize the control parameters. Each of the four algorithms is simulated on four contour shapes: a circle, bow, heart, and star. The simulations and control scheme of the experiments are implemented using MATLAB, and the reference paths were planned using non-uniform rational B-splines (NURBS). After running the simulations to determine the optimal control parameters, each set of acquired control parameters is also tested and compared in the experiments and the results are recorded. Both the simulations and experiments show good results, and the tracking of the X–Y platform showed improved performance. Two performance indices are used to determine and validate the relative performance of the models and results.

Integrated Simulation and Calibration Framework for Heating System Optimization

In a time where sustainability and CO2 efficiency are of ever-increasing importance, heating systems deserve special considerations. Despite well-functioning hardware, inefficiencies may arise when controller parameters are not well chosen. While monitoring systems could help to identify such issues, they lack improvement suggestions. One possible solution would be the use of digital twins; however, critical values such as the water consumption of the residents can often not be acquired for accurate models. To address this issue, coarse models can be employed to generate quantitative predictions, which can then be interpreted qualitatively to assess “better or worse” system behavior. In this paper, we present a simulation and calibration framework as well as a preprocessing module. These components can be run locally or deployed as containerized microservices and are easy to interface with existing data acquisition infrastructure. We evaluate the two main operating modes, namely automatic model calibration, using measured data, and the optimization of controller parameters. Our results show that using a coarse model of a real heating system and data augmentation through preprocessing, it is possible to achieve an acceptable fit of partially incomplete measured data, and that the calibrated model can subsequently be used to perform an optimization of the controller parameters in regard to the simulated boiler gas consumption.

A discrete event simulation model assessing the impact of using new packaging in an agri-food supply chain

Fresh product loss is a global issue having negative impact on both the economy and the environment. The perishability of fruits and vegetables can lead to post-harvest losses in the supply chain as well as at consumer’s homes. Packaging plays an important role in preventing such losses. This paper focuses on the impact of new packaging on reducing product waste and other logistics costs. The objective is to evaluate the performance of an inventory control policy that uses new packaging to extend the shelf life of strawberries while maintaining their quality. We have developed a discrete event simulation model, that represents store inventory, by considering random consumer demand and random product shelf life, to assess various packaging types leading to different post-harvest lives of strawberries. We then use a simulation-optimisation approach to determine optimal inventory control parameters and conduct extensive numerical experimentations to evaluate the impact of the new packaging, under three distribution strategies (FIFO, FEFO, and LEFO). Results indicate that the new packaging has a significant impact: extending the shelf life of strawberries can reduce the total logistics cost by 0.66% (when using LEFO strategy) to 6.37% (FIFO strategy) and improve sales by 0.67% (FEFO) to 10.07% (FIFO).

Optimization of the energy management system in hybrid electric vehicles considering cabin temperature

The air conditioning (AC) system provides passengers with a comfortable temperature environment and is one of the main energy consumers in modern plug-in hybrid electric vehicles (PHEV). However, the AC system is often overlooked when designing the energy management system (EMS) for PHEVs. To improve the energy efficiency of PHEVs, this paper integrates the Genetic Simulated Annealing Algorithm (GASA) with fuzzy control to propose an energy management system for PHEVs that considers the internal temperature of the vehicle. First, considering the uncertainty of the vehicle's driving environment, an optimized Interval Type-2 Fuzzy Controller (IT2-FC) was designed for real-time torque distribution. Second, an iteratively modified genetic simulated annealing algorithm was used to optimize control parameters, correcting the defect of genetic algorithms tending to get trapped in local optima and lingering around optimal positions. Then, considering the energy consumption of the AC system, a vehicle-integrated thermal management model oriented towards control was established. Lastly, the performance of the proposed EMS was discussed. The results show that, compared to the rule-based methods, the proposed EMS reduces fuel consumption by 17.77% and 17.727% in cooling and heating modes, respectively, and compared to the A-ECMS methods, it reduces fuel consumption by 7.38% and 6.31% in cooling and heating modes, respectively. At the same time, the proposed EMS ensures thermal comfort in the cabin. In cooling mode, the traditional rule-based strategy failed to maintain the cabin temperature within the preset range, while the proposed EMS showed significant improvement. In heating mode, the EMS reaches and maintains the cabin's set temperature 35% faster than the rule-based strategy, and 8.75% faster than the A-ECMS strategy.

Stability of hydropower units under full operating conditions considering nonlinear coupling of turbine characteristics

The nonlinearity of the hydro-turbine governing system (HTGS) will affect the regulation stability. This paper investigates the stability of HTGS considering the nonlinear coupling of hydro-turbine characteristics (NCTC) under full operating conditions. Firstly, the HTGS models considering the NCTC and power grid conditions are established. Then, the Hopf bifurcation analysis is carried out to comprehensively explain the mechanism of HTGS stability. Finally, the effect regularity of the NCTC on the stability of HTGS is summarized under the full operating conditions. The results indicate that the NCTC includes the coupling of nonlinear speed characteristic and linear head characteristic (NSHC), the coupling of nonlinear head characteristic and linear speed characteristic (NHSC), and the coupling of nonlinear head characteristic and linear opening characteristic (NHOC). The effect of NCTC on stability and dynamic response is mainly realized through the NHOC. The NHOC is favorable for stability under negative load disturbances and unfavorable for stability under positive load disturbances. A smaller unit inertia time constant or a greater flow inertia reduce the effect of NHOC on stable domain. The NHOC has different effects on stability under different operating conditions with a certain regularity. This paper provides potential support for optimizing control parameters of the HTGS.

Robust self‐adaptive fuzzy controller for load‐frequency control of islanded airport microgrids considering electric aircraft energy storage and demand response

AbstractThe rise of the renewable energy sources (RES) in microgrids has increased the impact of damping and low inertia on network stability. This gives rise to several issues in the power systems, including power fluctuations from variable RES, degradation of the frequency regulation, voltage surges, and oversupply from grid‐connected distributed generators. This paper presents a cascaded hybrid load‐frequency controller for an airport microgrid that addresses frequency fluctuations caused by load changes. It is combining a self‐adaptive fuzzy controller (SAF) and a classical two‐degree‐of‐freedom proportional integral derivative (PID) controller (2DOFPID) called CSAF‐2DOFPID. It is applied to the control section of a diesel generator, an electric aircraft energy storage system, and a demand response program. The controller parameters are optimized using a suggested hybrid differential artificial hummingbird algorithm (D‐AHA) to minimize frequency deviation. The proposed control framework is simulated on an airport microgrid comprising various components, and the impact of time delay on the controller's signals, system uncertainty is also examined. The simulation results demonstrate that the proposed self‐adaptive cascaded fuzzy controller outperforms other controllers such as PID, CF‐PID, and CF‐2DOFPID in reducing frequency fluctuations, and the D‐AHA algorithm achieves favourable results compared to other algorithms in the optimal tuning controller parameters.