Comparative Performance of UPQC Control System Based on PI-GWO, Fractional Order Controllers, and Reinforcement Learning Agent

Nicola, Marcel; Nicola, Claudiu-Ionel; Sacerdoțianu, Dumitru; Vintilă, Adrian

doi:10.3390/electronics12030494

Cited by 11 publications

(11 citation statements)

References 31 publications

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“…Te goal of the optimization is to minimize error by optimally selecting the MSF parameters a i , b i , and c i in the ANFIS. Te harmony ftness function (F), which is derived from the goal as the selected problem as shown in equation (15), evaluates the quality of each harmony. As stated in equation ( 23), an improvised harmony…”

Section: Ppfamentioning

confidence: 99%

“…In the meantime, a thorough examination of various phased synchronization approaches that work well for SHAPF control and operation was conducted [14]. To minimize THD, the GWO-based PIC was recommended for diferent UPQC loading circumstances [15]. A novel approach was put forth to UPQC to stabilize DLCV oscillations and enable prompt, accurate fault restructuring [16].…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective Neuro-Fuzzy Controller Design and Selection of Filter Parameters of UPQC Using Predator Prey Firefly and Enhanced Harmony Search Optimization

Srilakshmi,

Rao,

Swarnasri

et al. 2024

International Transactions on Electrical Energy Systems

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This research introduces a unified power quality conditioner (UPQC) that integrates solar photovoltaic (PV) system and battery energy systems (SBES) to address power quality (PQ) issues. The reference signals for voltage source converters of UPQC are produced by the Levenberg–Marquardt back propagation (LMBP) trained artificial neural network control (ANNC). This method removes the necessity for conventional dq0, abc complex shifting. Moreover, the optimal choice of parameters for the adaptive neuro-fuzzy inference system (ANFIS) was achieved through the integration of the enhanced harmony search algorithm (EHSA) and the predator-prey-based firefly algorithm (PPFA) in the form of the hybrid metaheuristic algorithm (PPF-EHSA). In addition, the algorithm is employed to optimize the selection of resistance and inductance values for the filters in UPQC. The primary objective of the ANNC with predator-prey-based firefly algorithm and enhanced harmony search algorithm (PPF-EHSA) is to enhance the stability of the DC-link capacitor voltage (DLCV) with reduced settling time amid changes in load, solar irradiation (G), and temperature (T). Moreover, the algorithm seeks to achieve a reduction in total harmonic distortion (THD) and enhance power factor (PF). The method also focuses on mitigating fluctuations such as swell, harmonics, and sag and also unbalances at the grid voltage. The proposed approach is examined through four distinct cases involving various permutations of loads and sun irradiation (G). However, in order to demonstrate the performance of the suggested approach, a comparison is conducted with the ant colony and genetic algorithms, i.e., (ACA) (GA), as well as the standard methods of synchronous reference frame (SRF) and instantaneous active and reactive power theory (p-q). The results clearly demonstrate that the proposed method exhibits a reduced mean square error (MSE) of 0.02107 and a lower total harmonic distortion (THD) of 2.06% compared to alternative methods.

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Section: Ppfamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiobjective Neuro-Fuzzy Controller Design and Selection of Filter Parameters of UPQC Using Predator Prey Firefly and Enhanced Harmony Search Optimization

Srilakshmi,

Rao,

Swarnasri

et al. 2024

International Transactions on Electrical Energy Systems

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“…To compare the performance of the proposed control systems, the DF calculation was also taken into account. It can be specified that the box-counting method is used to calculate DF according to [11,38,39].…”

Section: Numerical Simulationsmentioning

confidence: 99%

Improved Performance for PMSM Sensorless Control Based on the LADRC Controller, ESO-Type Observer, DO-Type Observer, and RL-TD3 Agent

Nicola,

Nicola

2023

Mathematics

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Starting from the fact that in sensorless control systems of the Permanent Magnet Synchronous Motor (PMSM), the load torque can have short and significant variations, this paper presents the sensorless control of a PMSM based on a Linear Adaptive Disturbance Rejection Controller (LADRC) type controller. Essentially, the successful operation of the LADRC controller to achieve PMSM rotor speed control performance depends on a good estimation of the disturbances acting on the system. Traditionally, an Extended State Observer (ESO) is used to make such an estimate. In this paper, it is proposed to use a Disturbance Observer (DO) to estimate the external disturbances, and after their rejection, the LADRC controller ensures an equivalent global behavior of the control system with an ideal double integrator, thus increasing ease in achieving the desired control performance. Control structures and Matlab/Simulink implementation of the PMSM sensorless control system based on the LADRC controller with an ESO-/DO-type observer are presented, as is its use in tandem with a Reinforcement Learning Twin-Delayed Deep Deterministic Policy Gradient (RL-TD3) specially trained agent that provides correction signals for more accurate estimation of external disturbances and hence improved control performance. To optimize the gain value of the DO-type observer, a computational intelligence algorithm such as the Ant Colony Algorithm (ACO) is used. Qualitatively superior performance is achieved by using LADRC with the RL-TD3 agent control structure in terms of parametric robustness, response time, and steady-state error. In addition, by calculating the fractal dimension (DF) of the controlled signal and the PMSM rotor speed, it is found that the higher the DF, the better the performance of the control system. The validation of the superiority of the proposed control structures is carried out by means of numerical simulations in the Matlab/Simulink environment.

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“…In addition to the performance indicators described above, an additional one can be introduced, namely the FD of the controlled signal, in this case the FD of the PMSM rotor speed signal. In the papers [25,26], a hypothesis verified by numerical simulations is introduced, namely that more efficient control systems are also characterised by a higher value of the FD [27,28] of the controlled signal.…”

Section: Introductionmentioning

confidence: 99%

Improved Performance of the PMSM Sensorless Control System Based on DTC Strategy and SMC Using Fractional Order and Fractal Dimension Calculus

Nicola,

Selișteanu

et al. 2024

Preprint

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Starting from the fact that one of the global control strategies of the Permanent Magnet Synchronous Motor (PMSM), namely the Direct Torque Control (DTC) type control strategy, is characterised by the fact that the inner flux and torque control loop usually uses hysteresis ON-OFF type controllers and the oscillations introduced by them have to be cancelled out by the outer speed loop controller (which is usually a PI speed controller and whose performance is good around the global operating points and for relatively small variations of the external parameters and disturbances caused in particular by the load torque variation), while retaining the advantages of the DTC strategy, this paper presents the improvement of the performance of the PMSM sensorless control system using Proportional Integrator (PI), PI Equilibrium Optimizer Algorithm (EOA), Fractional Order (FO) PI, Tilt Integral Derivative (TID) and FO Lead-Lag for constant flux, and Sliding Mode Control (SMC) and FOSMC for variable flux. The performance indicators of the control system are the usual ones: response time, settling time, overshoot, steady-state error and speed ripple, plus another one given by the fractal dimension (FD) of the PMSM rotor speed signal, and the hypothesis that the FD of the controlled signal is higher when the control system provides better performance is verified. The paper also presents the basic equations of the PMSM, based on which the synthesis of integer and fractional controllers, the synthesis of an observer for estimating the PMSM rotor speed, electromagnetic torque and stator flux are presented. The comparison of the performance of the proposed control systems and the demonstration of the parametric robustness are performed by numerical simulations in Matlab/Simulink using Simscape Electrical and Fractional Order Modelling and Control (FOMCON). Real-time control based on an embedded system using a TMS320F28379D controller demonstrates the good performance of the PMSM sensorless control system based on the DTC strategy in a complete hardware-in-the-loop (HIL) implementation.

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Comparative Performance of UPQC Control System Based on PI-GWO, Fractional Order Controllers, and Reinforcement Learning Agent

Cited by 11 publications

References 31 publications

Multiobjective Neuro-Fuzzy Controller Design and Selection of Filter Parameters of UPQC Using Predator Prey Firefly and Enhanced Harmony Search Optimization

Multiobjective Neuro-Fuzzy Controller Design and Selection of Filter Parameters of UPQC Using Predator Prey Firefly and Enhanced Harmony Search Optimization

Improved Performance for PMSM Sensorless Control Based on the LADRC Controller, ESO-Type Observer, DO-Type Observer, and RL-TD3 Agent

Improved Performance of the PMSM Sensorless Control System Based on DTC Strategy and SMC Using Fractional Order and Fractal Dimension Calculus

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