Hamed Mojallali

The integration of power electronic interface-based renewable energy systems, along with fast dynamic loads, has significantly changed the dynamics of microgrids. This shift has reduced rotational inertia while introducing virtual inertia and fast frequency regulation strategies, enabling faster responses to fluctuations and necessitating real-time operation. Real-time operation, supported by advanced information and communication technologies, forms the backbone of this framework. However, it also exposes microgrids to cyber threats, which can disrupt operations by introducing delays in transmitted measurements and reference signals. To maintain microgrid resilient operations, an innovative control strategy is proposed for robust frequency regulation in microgrids under uncertainty against time-varying delay attacks. The proposed method exploits the advantages of fractional-order calculus and model-following control to enhance system operation resilience. Furthermore, a finite-time robust tracking control mechanism is utilized to ensure rapid responses. Performance of the proposed method is ensured through MATLAB simulations of a typical microgrid. Experimental test results obtained from the Opal-RT testbed are also presented. Results demonstrate the method's effectiveness in real-time operation within renewable microgrids, showcasing a swift response and reduced fluctuations.

Selecting appropriate dynamics and developing accurate models for gas turbines is a complex yet essential task, as model accuracy directly affects the performance of model-based control strategies. To address this challenge, this article employs the Active Disturbance Rejection Control (ADRC) approach, a model-free control method, for the operation of gas turbines. In the proposed scheme, Linear ADRC (LADRC) is applied to regulate the fuel flow and exhaust temperature, while Nonlinear ADRC (NLADRC) is used for rotor speed control. Considering the inherent delays in gas turbine systems, a predictor observer is incorporated. Furthermore, the controller parameters are optimized using a type-2 fuzzy method, where a lookup table is implemented to accelerate the computation process compared to real-time fuzzy inferences significantly. Simulation results confirm the effectiveness of the proposed controller in handling uncertainties and external disturbances.

This paper investigates the application of reinforcement learning (RL) techniques for optimizing proportional–integral–derivative (PID) controller parameters in gas turbine speed control systems. The research employs the Rowen mathematical model as the foundational framework and introduces a novel approach utilizing twin‐delayed deep deterministic policy gradient (TD3) algorithms. The methodology integrates machine learning with classical control theory to address the persistent challenges of maintaining optimal turbine speed during both transient startup phases and steady‐state operations. Implementation was conducted using a simulation environment based on MATLAB/Simulink, with the General Electric 5001M heavy‐duty gas turbine serving as the reference system. The RL agent was designed to interact with the simulated environment, continuously refining controller parameters to minimize performance metrics including integral error values, rise time, and settling characteristics. Comparative analysis between the proposed TD3‐optimized PID controller and conventional tuning methods demonstrates significant performance enhancements across multiple control criteria. The optimized system achieved notable reductions in settling time, overshoot magnitude, and steady‐state error, while also demonstrating improved disturbance rejection capabilities under variable load conditions and sensor noise.

Nowadays, the participation of wind power plants in electricity markets has become a severe challenge due to their intermittent nature for decision makers of market. In the presence of uncertainties, some sellers and buyers experience a reduction in their satisfaction. This paper presents a new method for the participation of wind power plants and uncertain customers in a day-ahead electricity market based on the local marginal pricing mechanism to maximize the total profits of sellers and buyers considering their importance level through a two-level optimization problem. For this purpose, using the empirical cumulative distribution function and the Monte Carlo method, the uncertainties are modeled. Then, by defining some economic indices to evaluate participants' satisfaction and using the analytic hierarchy process, a new objective function is proposed to optimize the mentioned indices. Simulations are implemented on a realistic 8-bus sample system, and the results confirm the efficiency of the proposed method in significantly reducing the costs of producers and customers, and consequently their total profits. Based on the results obtained from the presented method, the expected ranges for total cost fall between 1,270.91$ and 1,719.50$, while the expected ranges for total payment range from 2,151.41$ to 2,192.58$.

The energy consumed by artificial lighting accounts for a significant part of the energy consumption in buildings. Thus, energy consumption can be significantly decreased using optimization techniques, such as the optimum placement of the lighting sensors. In this paper, an optimal placement method of the light sensors is presented to minimize the lighting system's equipment costs and energy consumption and also meet visual comfort under the requirements of the European standard EN12464–1 regarding illuminance-based criteria. In the beginning, based on the illumination measurement grid's mathematical model, the potential location of the sensors is determined. As an average of the dimming levels of the LED lights, the objective function has been introduced. Then, the illuminance and uniformity levels are defined as constraints for the optimization problem. The Battle Royale Optimization (BRO) algorithm is used to solve the optimization problem. Ultimately, based on the results of the BRO algorithm and the calculation of the illuminance deviation, the optimal location of the light sensors is determined. The proposed method is tested in an office room. A fuzzy logic controller is developed to regulate the lighting control system's dimming levels to evaluate the proposed method's performance with other approaches. The comparison results have shown that the proposed method is superior as regards the number of sensors and the optimal sensor position, significant savings in energy consumption of up to 30.8%, and satisfactory visual comfort per the requirements of the European standard EN12464–1.

Abstract This article studies the output feedback finite‐time boundary control for unstable heat systems with the spatially varying coefficient. First, a finite‐time observer with switched gains under a state‐dependent switching law is designed in order to estimate the states of the system in a finite‐time only exerting one displacement boundary measurement. Next, an observer‐based linear finite‐time control is planned. Namely, a linear switched control under a state‐dependent switching law is proposed to vanish every solution of an unstable heat partial differential equation with spatially varying coefficients in a finite time. We also present explicit forms for the proposed observer gains and output feedback finite‐time controller. Finally, some numerical simulations are provided to confirm the theoretical results.

Abstract In this study, a step‐up DC‐DC converter with a combination of a two‐phase interleaved structure and a quasi‐Z impedance network is proposed for fuel cell vehicle application. The operation of the converter in a small duty cycle (0 < D < 0.5) reduces the conductive losses of the switches and as a result increases the efficiency of this converter compared to conventional boost converters. Also, due to the common ground between the input and output, unlike the floating interleaved boost converter (FIBC) and the parallel input‐series output converter (PISO), it does not face the problem of imposing additional EMI on the converter circuit. This converter has a low input current ripple, suitable voltage gain for fuel cell vehicles system, as well as high reliability due to the ability to operate with one phase in case of failure in the other phase. It can be a suitable candidate for practical application in fuel cell vehicles. The principles of operation and the main characteristics of the converter such as voltage gain, input current ripple, and voltage and current stress of the elements are explained and also a 120‐W experimental prototype with 12‐V input voltage and 60‐V output voltage is made to validate the theoretical analysis results.

Recently, the participation of wind sources in electricity markets has become a severe challenge due to their intermittent nature. Reconfiguration of power systems can effectively reduce the negative effects of uncertainties. So, this article presents a new method for participating in wind power plants and uncertain customers in a day‐ahead electricity market considering the reconfiguration process. This method tries to maximize social welfare through a two‐level optimization problem. To this end, uncertainties are modeled using the empirical cumulative distribution function and the Monte–Carlo method, and a probabilistic analysis of the market is performed. Then, by defining some indices to evaluate the participants’ satisfaction and using the analytic hierarchy process method (AHP), a new objective function is proposed so that its minimization leads to planning the system configuration and market participants to optimize mentioned indices. The proposed methodology also assumes that the participation of uncertain participants in the spot market will eliminate the imbalances caused by uncertainties. The simulations are implemented using real data on an 8‐bus sample network. The results confirm the efficiency of the proposed method in significantly reducing power producers’ and customers’ costs along with increasing total income and profit from the sale of energy.

This paper represents a new generation of adaptive disturbance rejection control (ADRC) which is more robust against disturbances than conventional ADRC. The non-derivability of the fal function employed in the traditional ADRC has negative impacts on its operation, so alternate functions will be used which are derivable at all segments. In this regard, odd hyperbolic and trigonometric functions were employed. The performance of the proposed structure was investigated to control the doubly fed induction generator (DFIG) speed. To enhance the efficiency of the suggested ADRC, fractional-order calculations and fuzzy logic were utilized simultaneously. The results in MATLAB platform indicate that: 1) With constant wind speed and changing reference speed, using new fal functions in fuzzy fractional-order ADRC (FFOADRC) has improved the performance compared to the PI regulator and FFOADRC with default fal function. 2) During wind speed variations and using new fal functions, the DFIG speed reaches the final steady amount as over damping, while this condition is critical damping when using FFOADRC with default fal function. 3) During network voltage reduction, using new FFOADRCs leads to fewer oscillations in the stator flux, DC-bus voltage, and DFIG speed, which shows their better performance than PI regulator and traditional ADRC.

One of the significant problems in demand response (DR) participation in the smart power system is calculating how much electrical power is required to extract from DR ( ) to keep the power system frequency in the allowable range. Calculating the precise amount of this value is impossible due to the random nature of load disturbance (LoD). In this paper, a new method is presented to control the DR participation in the load frequency control considering communication time delay. Extended State Observer is used to estimate the magnitude of the LoD, considering the structural uncertainty of the power system parameters. Then, the DR program is implemented in load frequency control to compensate the whole or part of LoD considering the available electrical power at the aggregators (PAV‐AGG) and communication time delay. Afterward, Active Disturbance Rejection Control is modified and adopted to control the uncompensated LoD. The salp swarm algorithm is employed to design the parameters of the proposed method, which is verified in comparison with the previous methods. The results demonstrate that the presented approach has potentially significant performance, assisting the power system frequency to be damped immediately with the small overshoot and undershoot.

Abstract In this paper, an overview of several strategies for fault ride-through (FRT) capability improvement of a doubly-fed induction generator (DFIG)-based wind turbine is presented. Uncertainties and parameter variations have adverse effects on the performance of these strategies. It is desirable to use a control method that is robust to such disturbances. Auto disturbance rejection control (ADRC) is one of the most common methods for eliminating the effects of disturbances. To improve the performance of the conventional ADRC, a modified ADRC is introduced that is more robust to disturbances and offers better responses. The non-derivability of the fal function used in the conventional ADRC degrades its efficiency, so the modified ADRC uses alternative functions that are derivable at all points, i.e., the odd trigonometric and hyperbolic functions (arcsinh, arctan, and tanh). To improve the efficiency of the proposed ADRC, fuzzy logic and fractional-order functions are used simultaneously. In fuzzy fractional-order ADRC (FFOADRC), all disturbances are evaluated using a nonlinear fractional-order extended state observer (NFESO). The performance of the suggested structure is investigated in MATLAB/Simulink. The simulation results show that during disturbances such as network voltage sag/swell, using the modified ADRCs leads to smaller fluctuations in stator flux amplitude and DC-link voltage, lower variations in DFIG velocity, and lower total harmonic distortion (THD) of the stator current. This demonstrates the superiority over conventional ADRC and a proportional-integral (PI) controller. Also, by changing the crowbar resistance and using the modified ADRCs, the peak values of the waveforms (torque and currents) can be controlled at the moment of fault occurrence with no significant distortion.

Simultaneous investigation of demand response programs and false data injection cyber-attack are critical issues for the smart power system frequency regulation. To this purpose, in this paper, the output of the studied system is simultaneously divided into two subsystems: one part including false data injection cyder-attack and another part without cyder-attack. Then, false data injection cyber-attack and load disturbance are estimated by a non-linear sliding mode observer, simultaneously and separately. After that, demand response is incorporated in the uncertain power system to compensate the whole or a part of the load disturbance based on the available electrical power in the aggregators considering communication time delay. Finally, active disturbance rejection control is modified and introduced to remove the false data injection cyber-attack and control the uncompensated load disturbance. The salp swarm algorithm is used to design the parameters. The results of several simulation scenarios indicate the efficient performance of the proposed method.

This paper represents a novel sensorless method for the vector control of doubly fed induction generator (DFIG) in a wind turbine system. The proposed method is based on the fuzzy fractional order adaptive disturbance rejection control (FFOADRC) estimating the rotor velocity. In this new method, there is no need to calculate the coupling terms and eliminate them by feed-forward compensation. In addition, all disturbances (internal and external) are estimated by a fractional order extended state observer (FESO). The effects of these disturbances are then neutralized by generating a suitable control command. The operation of the proposed system has been simulated in Matlab/Simulink environment. The comparisons were made between FFOADRC, adaptive disturbance rejection control (ADRC), fuzzy ADRC (FADRC), and proportional-integral (PI) controller under different operating conditions. The results show that: (1) After DFIG starts and under similar conditions, using FFOADRC, FADRC, and ADRC, the velocity reaches the steady state with the overshoot values of 0%, 3.64%, and 8.03%, respectively. (2) In the steady state after wind velocity variation, the %THD values of the stator current using FFOADRC, FADRC, and ADRC are, respectively, 1.47, 1.54, and 2.79. In this case, utilizing the PI controller, the control circuit has a slower performance than three other controllers. (3) The comparison between the aforementioned controllers during DFIG velocity control shows that using FFOADRC, the values of settling time, rise time, peak time, and delay time are smaller, and we have better performance that indicates the superiority of FFOADRC over ADRC, FADRC, and PI controller. Therefore, FFOADRC improves the wind turbine performance in different conditions

In recent years, due to the strong need to control blood pressure in patients, especially patients with heart problems, several control structures have been designed and implemented to control their blood pressure. The reason for this is the instability of blood pressure in this group of patients, and nurses and doctors need to use methods to prevent this instability, which can be a fatal factor for patients. To achieve this goal, a fractional-order PID controller whose coefficients are calculated using a hybrid-evolutionary algorithm called BH-PSO has been used in this paper. This control combination can excessively improve the system’s output response. This control structure can significantly reduce the error rate and settling time. Also, for the objective function, three functions integral absolute error (IAE), integral squared error (ISE), and integral time absolute error (ITAE) have been used to compare and reduce the system’s error rate.

This paper introduces a novel approach to enhance the Energy Valley Optimization (EVO) algorithm by incorporating chaos theory. We propose the Chaotic Energy Valley Optimization (CEVO) algorithm, which integrates six different chaotic maps to adapt the parameters of the original EVO. We evaluate the performance of CEVO on a variety of test functions and parameter identification tasks for chaotic systems. Our results demonstrate that while the original EVO algorithm performs well, the CEVO algorithm exhibits great performance in all of the cases, particularly in identifying parameters of chaotic systems. We compare CEVO with other established metaheuristic algorithms such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Artificial Protozoa Optimizer (APO), and Artificial Gorilla Troops Optimizer (GTO). The study reveals that the computational efficiency of CEVO is influenced by the statistical properties of the chaotic sequences and the location of the global optimum in nonlinear functions.

This paper compares the performance of metaheuristic optimization algorithms for tuning a PID controller to control the speed of a DC motor. The Harris Hawk, Grey Wolf, Ant Lion, Artificial Bee Colony, and Energy Valley algorithms were implemented in MATLAB and tested on a DC motor model. Results show the algorithms have similar behavior, but Energy Valley gave minimum ITAE error at low iterations. For higher iterations, Grey Wolf and Ant Lion performed best in terms of ITAE error and rise time respectively. Ultimately, the findings indicate relatively minor distinctions between the techniques, suggesting they provide equivalent capabilities for PID optimization without a decisively superior method.