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This paper proposes an advanced Internet of Things (IoT) system for measuring, monitoring, and recording some power quality (PQ) parameters. The proposed system is designed and developed for both hardware and software. For the hardware unit, three PZEM-004T modules with non-invasive current transformer (CT) sensors are used to measure the PQ parameters and an Arduino WeMos D1 R1 ESP8266 microcontroller is used to receive data from the sensors and send this data to the server via the internet. For the software unit, an algorithm using Matlab software is developed to send measurement data to the ThingSpeak cloud. The proposed system can monitor and analyse the PQ parameters including frequency, root mean square (RMS) voltage, RMS current, active power, and the power factor of a low-voltage load in real-time. These PQ parameters can be stored on the ThingSpeak cloud during the monitoring period; hence the standard deviation in statistics of the voltage and frequency is applied to analyse and evaluate PQ at the monitoring point. The experimental tests are carried out on low-voltage networks 380/220 V. The obtained results show that the proposed system can be usefully applied for monitoring and analysing chosen PQ parameters in micro-grid solutions.

This paper proposes an improved coyote optimization algorithm (ICOA) for optimizing the location and sizing of solar photovoltaic distribution generation units (PVDGUs) in radial distribution systems. In the considered problem, four single objectives consisting of total power losses, capacity of all PVDGUs, voltage profile index, and harmonic distortions are minimized independently while satisfying branch current limits, voltage limits, and harmonic distortion limits exactly and simultaneously. The performance of the proposed ICOA method has been improved significantly since two improvements were carried out on the two new solution generations of the conventional coyote optimization algorithm (COA). By finding four single objectives from two IEEE distribution power systems with 33 buses and 69 buses, the impact of each proposed improvement and two proposed improvements on the real performance of ICOA has been investigated. ICOA was superior to COA in terms of capability of finding higher quality solutions, more stable search ability, and faster convergence speed. Furthermore, we have also applied five other metaheuristic algorithms consisting of biogeography-based optimization (BBO), genetic algorithm (GA), particle swarm optimization algorithm (PSO), sunflower optimization (SFO), and salp swarm algorithm (SSA) for dealing with the same problem and evaluating further performance of ICOA. The result comparisons have also indicated the outstanding performance of ICOA because it could find much better results than these methods, especially SFO, SSA, and GA. Consequently, the proposed ICOA is a very effective method for finding the optimal location and capacity of PVDGUs in radial distribution power systems.

With the penetration of distributed generation (DG) units, the power systems will face insecurity problems and voltage stability issues. This paper proposes an innovatory method by modifying the conventional continuation power flow (CCPF) method. The proposed method is realized on two prediction and correction steps to find successive load flow solutions according to a specific load scenario. Firstly, the tangent predictor is proposed to estimate the next predicted solution from two previous corrected solutions. And then, the corrector step is proposed to determine the next corrected solution on the exact solution. This corrected solution is constrained to lie in the hyperplane running through the predicted solution orthogonal to the line from the two previous corrected solutions. Besides, once the convergence criterion is reached, the procedure for cutting the step length control down to a smaller one is proposed to be implemented. The effectiveness of the proposed method is verified via numerical simulations on three standard test systems, namely, IEEE 14-bus, 57-bus, and 118-bus, and compared to the CCPF method.

The detection, mitigation, and classification of power quality (PQ) disturbances have been issues of interest in the power system field. This paper proposes an approach to detect and classify various types of PQ disturbances based on the Stockwell transform (ST) and decision tree (DT) methods. At first, the ST is developed based on the moving, localizing, and scalable Gaussian window to detect five statistical features of PQ disturbances such as the high frequency of oscillatory transient, distinction between stationary and non-stationary, the voltage amplitude oscillation around an average value, the existence of harmonics in a disturbance signal, and the root mean square voltage at the internal period of sag, swell or interruption. Then, these features are classified into nine types, such as normal, sag, swell, interruption, harmonic, flicker, oscillatory transient, harmonic voltage sag, and harmonic voltage swell by using the DT algorithm that is based on a set of rules with the structure “if…then’’. This proposed study is simulated using MATLAB simulation. The IEEE 13-bus system, the recorded real data based on PQube, and the experiment based on the laboratory environment are applied to verify the effectiveness.

The subsynchronous resonance (SSR) is an important problem in the power system, and especially the series compensated transmission lines may cause SSR in the turbine generators, such that it leads to the electrical instability at subsynchronous frequencies and potential turbine-generator shaft failures. Taking the Vietnamese Vungang thermal plants as an example, a shaft failure of Vungang I thermal power unit has occurred on November 24, 2015, due to SSR. The main cause for this failure is a resonance caused by the series capacitors on the 500 kV grid. This paper analyzes the SSR based on the location of shaft cracks and turbine generator mode shape for Vungang I and II thermal power plants. On the basis of that, it develops a novel control strategy for each Flexible AC Transmission system (FACTS) device as the thyristor controlled series compensator (TCSC), static VAR compensator (SVC), and static synchronous compensator (STATCOM). Then they are comparable to one another in order to choose a feasible solution for mitigating the SSR. The effectiveness of the proposed control strategy is verified via time domain simulation of the Vietnamese 500/220 kV transmission system using EMTP-RV and PSS/E programs. The obtained results show that the proposed strategy for SVC can be applied to immediately solve the difficulties encountering in the Vietnamese power system.

This paper applies a proposed modified stochastic fractal search algorithm (MSFS) for dealing with all constraints of optimal reactive power dispatch (ORPD) and finding optimal solutions for three different cases including power loss optimization, voltage deviation optimization, and L-index optimization. The proposed MSFS method is newly constructed in the paper by modifying three new solution update mechanisms on standard stochastic fractal search algorithm (SSFS). The first modification is to keep only one formula and abandon one formula in the diffusion process while the second modification and the third modification are used in the first update and the second update. In two updates of SSFS, solutions with low quality are updated with high probability while other solutions with high quality do not get chances to be updated. This manner results in the fact that some promising solutions around the high quality solutions can be missed. In order to tackle this restriction, the second modification of MSFS is to newly update the worst solutions in the first update and the best solutions in the second update. In the third modification, all existing formulas of SSFS in the two updates are abandoned and the same new proposed technique is used for updating such solutions in two updates. Compared to SSFS, the three modifications can bring advantages to MSFS such as using smaller number of produced solutions per iteration, spending shorter execution time, finding better optimal solutions, and owning more stable search ability. Furthermore, the proposed method also sees its effectiveness and robustness over SSFS by testing on IEEE 30-bus system and IEEE 118-bus system with three different single objectives for each system. The proposed method can find less minimum, average, and maximum for all the cases in addition to faster search speed. Besides, the proposed method is also compared to other methods such as PSO-based method group, GA-based method group, DE-based method group, and other recent methods. Result comparisons also indicate that the proposed method can be more efficient than almost all these methods with respect to less minimum and smaller values of control parameters. As a result, evaluation of the performance of the proposed method is that it should be used for seeking solutions of ORPD problem.

This paper presents a novel improved firefly algorithm (IFA) to deal the problem of the optimal operation of thermal generating units (OOTGU) with the purpose of reducing the total electricity generation fuel cost. The proposed IFA is developed based on combining three improvements. The first is to be based on the radius between two solutions, the second is updated step size for each considered solution based on different new equations, and the third is to slightly modify a formula producing new solutions by using normally distributed random numbers and canceling uniform random numbers of conventional firefly algorithm (FA). The effect of each proposed improvement on IFA is investigated by executing five benchmark functions and two different systems. The performance of IFA is investigated on six other study cases consisting of different types of objective function and complex level of constraints. The objective function considers single fuel with quadratic form and nonconvex form, and multifuels with the sum of several quadratic and nonconvex functions while a set of constraints taken into account are power loss, prohibited zone, ramp rate limit, spinning reserve, and all constraints in transmission power networks. The obtained results indicate the proposed improvements in terms of high optimal solution quality, stabilization of search ability, and fast convergence compared with FA. In addition, the comparisons with other methods also lead to a conclusion that the proposed method is a very promising optimization tool for systems with quadratic fuel cost function and with complicated constraints.

In this paper, a novel improved Antlion optimization algorithm (IALO) has been proposed for solving three different IEEE power systems of optimal reactive power dispatch (ORPD) problem. Such three power systems with a set of constraints in transmission power networks such as voltage limitation of all buses, limitations of tap of all transformers, maximum power transmission limitation of all conductors and limitations of all capacitor banks have given a big challenge for global optimal solution search ability of the proposed method. The proposed IALO method has been developed by modifying new solution generation technique of standard antlion optimization algorithm (ALO). By optimizing three single objective functions of systems with 30, 57 and 118 buses, the proposed method has been demonstrated to be more effective than ALO in terms of the most optimal solution search ability, solution search speed and search stabilization. In addition, the proposed method has also been compared to other existing methods and it has obtained better results than approximately all compared ones. Consequently, the proposed IALO method is deserving of a potential optimization tool for solving ORPD problem and other optimization problems in power system optimization fields.

The Vietnamese power system has experienced instabilities due to the effect of increase in peak load demand or contingency grid faults; hence, using flexible alternating-current transmission systems (FACTS) devices is a best choice for improving the stability margins. Among the FACTS devices, the thyristor-controlled series capacitor (TCSC) is a series connected FACTS device widely used in power systems. However, in practice, its influence and ability depend on setting. For solving the problem, this paper proposes a relevant method for optimal setting of a single TCSC for the purpose of damping the power system oscillations. This proposed method is developed from the combination between the energy method and Hankel-norm approximation approach based on the controllability Gramian matrix considering the Lyapunov equation to search for a number of feasible locations on the small-signal stability analysis. The transient stability analysis is used to compare and determine appropriate settings through various simulation cases. The effectiveness of the proposed method is confirmed by the simulation results based on the power system simulation engineering (PSS/E) and MATLAB programs. The obtained results show that the proposed method can apply to immediately solve the difficulties encountering in the Vietnamese power system.

The paper proposes a modified Bat algorithm (MBA) for searching optimal solutions of Economic dispatch of combined heat and power generation (CHPGED) with the heat and power generation from three different types of units consisting of pure power generation units, pure heat generation units and cogeneration units. The CHPGED problem becomes complicated and big challenge to optimization tools since it considers both heat and power generation from cogeneration units. Thus, we apply MBA method with the purpose of enhancing high quality solution search ability as well as search speed of conventional Bat algorithm (BA). This proposed approach is established based on three modifications on BA. The first is the adaptive frequency adjustment, the second is the optimal range of updated velocity, and the third is the retained condition of a good solution with objective to ameliorate the search performance of traditional BA. The effectiveness of the proposed approach is evaluated by testing on 7, 24, and 48 units systems and IEEE 14-bus system and comparing results with BA together with other existing methods. As a result, it can conclude that the proposed MBA method is a favorable meta-heuristic algorithm for solving CHPGED problem.

The identification and localization of a fault are a basic requirement for optimal operation of a modern power system. An effective fault identification method significantly reduces outage time, improves the electrical supply reliability, and enhances the speed of protection control. This paper proposes a novel method based on the theory of the two-terminal traveling wave range to identify the fault location in a voltage source converter based high voltage direct current (VSC-HVDC) system containing mixed cable and overhead line segments. It uses variational mode decomposition (VMD) and the Teager energy operator (TEO) as a new method to detect the traveling wave fault through a fault signal. The effectiveness of the proposed method is verified via time domain simulation of the hybrid VSC-HVDC transmission system using PSCAD/EMTDC and MATLAB software. Simulation results show that the proposed method demonstrates high fault location accuracy and excellent robustness with a slight effect on transient resistance and fault types, and that it performs better than the existing transient detection techniques, such as wavelet transform and ensemble empirical mode decomposition.

Economic load dispatch (ELD) is an important optimization problem for operating and controlling modern power systems, and if ELD is effectively executed, power systems work stably and economically. The main objective of this paper is to develop a novel method to solve the ELD with the purpose of minimizing the total fuel cost of all available generating units while requirements are to satisfy all constraints regarding thermal units, generators, and transmission power networks. The proposed high performance cuckoo search algorithm (HPCSA) is developed from the efficient technique for the second new solution generation of conventional cuckoo search algorithm (CCSA), called adaptive mutation technique. This proposed technique diversifies the local search ability based on a new comparison criterion. The HPCSA is verified on difference systems under special conditions, namely the 10-unit system with multi fuels, 15-unit system considering prohibited operating zones, and three IEEE systems with 30, 57, and 118 buses considering transmission power network constraints. The specific evaluation of the HPCSA is compared to that of Lagrange optimization-based methods (LMS), neural network-based methods (NNMS), CCSA, and other popular methods such as Particle swarm optimization (PSO) variants, Differential evolution (DE) variants, Genetic Algorithm (GA) variants, and state-of-the-art methods. In comparison with CCSA, the proposed method is always more effective and more robust since the proposed method can find most solutions with better quality and faster convergence speed. In comparison with LMS and NNMS, the proposed method can also find solutions with approximate or equal quality. In comparison with popular methods and state-of-the-art methods, the proposed method has more potential since it can reach faster convergence to valid solutions with approximate or better quality. Consequently, it can be concluded that the proposed HPCSA is an effective optimization tool for dealing with ELD problems.

This paper proposes an effective novel cuckoo search algorithm (ENCSA) in order to enhance the operation capacity of hydrothermal power systems, considering the constraints in the transmission network, and especially to overcome optimal power flow (OPF) problems. This proposed algorithm is developed on the basis of the conventional cuckoo search algorithm (CSA) by two modified techniques: the first is the self-adaptive technique for generating the second new solutions via discovery of alien eggs, and the second is the high-quality solutions based on a selection technique to keep the best solutions among all new and old solutions. These techniques are able to expand the search zone to overcome the local optimum trap and are able to improve the optimal solution quality and convergence speed as well. Therefore, the proposed method has significant impacts on the searching performances. The efficacy of the proposed method is investigated and verified using IEEE 30 and 118 buses systems via numerical simulation. The obtained results are compared with the conventional cuckoo search algorithm (CCSA) and the modified cuckoo search algorithm (MCSA). As a result, the proposed method can overcome the OPF of hydrothermal power systems better than the conventional ones in terms of the optimal solution quality, convergence speed, and high success rate.

A feasible control strategy is proposed to control a doubly fed induction generator based on the wind energy converter system (DFIG-WECS). The main aim is to enhance the steady state and dynamic performance under the condition of the parameter perturbations and external disturbances and to satisfy the stator power response of the system. Within the proposed control method, the control scheme for the rotor side converter (RSC) is developed on the model predictive control. Firstly, the self-adaptive reference trajectory is established from the deduced discrete state-space equation of the generator. Then, the rotor voltage is calculated by minimizing the global performance index under the current prediction steps at the sampling instant. Through the control scheme for the grid side converter (GSC) and wind turbine, we have re-applied the conventional control. The effectiveness of the proposed control strategy is verified via time domain simulation of a 150 kW-575 V DFIG-WECS using Matlab/Simulink. The simulation result shows that the control of the DFIG with the proposed control method can enhance the steady and dynamic response capability better than the conventional ones when the system faces errors due to the parameter perturbations, external disturbances and the rotor speed.

This paper proposes a novel control structure for permanent magnet synchronous motors (PMSMs), integrating a fuzzy logic-enhanced PI controller (FPI) with maximum torque per ampere (MTPA) and field weakening (FW) strategies. The primary objective is to address the nonlinear behavior of PMSMs, particularly the complex relationship between reference speed and torque, while simultaneously minimizing copper losses, total harmonic distortion (THD), and thermal losses. The FPI controller enhances the performance of traditional PI control by dynamically adjusting parameters, improving control accuracy under nonlinear conditions, and mitigating crosscoupling effects between the d- and q-axis. The combination of MTPA and FW allows the motor to achieve optimal efficiency at both low and high speeds. At the same time, FPI is crucial in improving control quality, reducing THD, and minimizing energy losses. Compared to advanced control methods such as artificial neural networks (ANN) or model predictive control (MPC), the FPI controller provides a practical, resource-efficient solution. Its simplicity and lower computational demand make it ideal for real-time applications, while still delivering robust performance. Simulation results using MATLAB/Simulink with a salient-pole PMSM demonstrate the effectiveness of the proposed approach. The FPI controller outperforms the traditional PI controller in reducing %THD, with a reduction of 8.96% in MTPA mode (PI: 146.12%, FPI: 137.16%) and 61.26% in MTPA-FW mode (PI: 709.03%, FPI: 647.77%). Furthermore, the FPI controller increases motor efficiency by 0.35% in MTPA mode and 0.26% in MTPA-FW mode. These improvements contribute to better current waveform quality, reduced energy losses, and thermal losses, thus extending the lifespan of components and enhancing system stability. The findings underscore the FPI controller’s capability to optimize PMSM performance, particularly in applications demanding high energy efficiency, power stability, and precision speed control. The results show that the FPI controller combined with the MTPA-FW strategy provides a high-performance, reliable solution for PMSM systems. Furthermore, these findings indicate that the MTPA-FW strategy has strong potential in applications such as electric vehicles, crewless aerial vehicles, and systems requiring precise control at high speeds, opening up promising research and development directions for the future. [ABSTRACT FROM AUTHOR]

The primary goal of this paper is to develop and apply an adaptive fuzzy (AF) based on the proportional integral derivative (PID) controller for the automatic voltage regular (AVR) system in the synchronous generator (SG) to improve output voltage stability. This proposed AF-PID is applied to ameliorate the shortcomings of the traditional PID controller by determining the optimal coefficients for the controller. Firstly, the initial coefficients of the traditional PID controller are calculated by using the Ziegler-Nichols method. After that, it develops the AF algorithm based on the fuzzification, rules of the fuzzy controller, and defuzzification of the voltage signals for self-tuning the coefficients to add the traditional PID controller. The transient response of the system is simulated and analyzed by considering the different step changes in reference voltage. The efficacy of the proposed AF-PID controller for the AVR system in the SG is illustrated by comparing it with traditional PID and without the controller based on the obtained results from the case studies. It can be concluded that when applying the proposed method, the transient response characteristics of the system from the perspective of simulating via the time domain under any sudden load changes in system operation, such as the rise time, steady-state error, max peak overshoot, and settling time are greatly improved. The simulation has been performed using the MATLAB/Simulink program. [ABSTRACT FROM AUTHOR]

One of the significant obstacles in electrical power transmission is the incident of failures, most notably the fault in transmission lines. It can influence the quality of the supplied electrical power, and actually, the country’s security and economy are threatened. Researching an intelligent solution to diagnose the fault is the done thing. This paper integrates deep learning methods, namely the discrete wavelet transform (DWT), GoogleNet, and probabilistic neural network (PNN) approaches, to detect, classify, and locate the fault. The DWT-based multiresolution analysis (MRA) is used for fault identification to process energy distribution during the transient states. After feature extraction, the obtained energy of the subband signal is used as the feature vector. It is employed as an input of GoogleNet for the fault type classification. The effectiveness of the proposed method has been demonstrated on the 220kV transmission line between the Hoa Khanh and Hue substations of Vietnam via time-domain simulation using EMTP-RV and MATLAB softwares. The simulated results are compared to the accuracy of the line distance protection REL521 for considering even cases, including the normal case and ten different fault causes, and the conditions of the pre-fault load changes. As a result, it concludes that the proposed method is a favourable method applied to the problem of analyzing the power system stability in the field of power transmission. [ABSTRACT FROM AUTHOR]

This paper presents an entirely new subject to prevent SSR in thermal power plants. The SSR phenomenon is firstly analyzed by using the frequency scanning method in the frequency domain under considering other operating conditions for operating grid-configurations, and then the SSR due to the self-excitation and the transient phenomenon is analyzed to provide the inputs for working out an effective solution. Three devices, namely the thyristor controlled series capacitor (TCSC), static synchronous compensator (STATCOM), and blocking filter (BL) are considered as a proposed choice, and the control strategy for each mentioned device is developed to mitigate the SSR. For TCSC, the control strategy is designed based on two PI controllers for the voltage and current regulator, respectively; besides, the auxiliary subsynchronous damping controller is proposed to provide a supplementary signal of dynamic current on the input signal that is chosen the generator rotor speed deviation. For STATCOM, two PI controllers, using for the DC and connected point voltage regulator, proposed; besides, the auxiliary sub-synchronous damping controller is proposed to provide a supplementary signal of dynamic current on the input signal that is chosen the generator rotor speed deviation. For BL, the natural torsional vibration frequency of models to ensure that SSR due to torsional interaction (TI), torque amplification (TA), and induction generator effect (IEG) are prevented. The effectiveness of this study is verified via a time-domain simulation of the Vietnam Quang-Tri electric power system connected to the 500/220 kV transmission system using EMTP-RV and PSS/E programs. The obtained results show that the frequency scanning method is chosen as a useful method to analyze SSR and the effective solution can be applied to immediately solve the difficulties encountering in the Vietnamese power system to be BL. [ABSTRACT FROM AUTHOR]

A number of techniques have been proposed to dampen the power system oscillations in the electric power systems. Flexible alternating current transmission system (FACTS) devices are becoming one of them. Among the FACTS family, the static synchronous compensator (STATCOM), a shunt connected FACTS device, has been widely used to provide smooth and rapid steady state, limit transient voltage, and improve the power system stability and performance by absorbing or injecting reactive power. However, the influence ability depends on its placement, control signal, and place of receiving-signal in the network. In order to satisfy these issues, this paper proposes a method for optimal setting and signal position of the STATCOM into the multi-machine power systems with the aim for damping the electromechanical oscillations. This method is developed from the energy approach based on Gramian matrices considering multiple tasks on the Lyapunov equation, in which the observability Gramian matrix is used to seek an optimal location for STATCOM placement. The another is the controllability one used to determine the best local input signal placement that is chosen as a feedback signal for the power oscillation damping (POD) of STATCOM. In addition, the Krylov-based model reduction method is introduced to shorten the calculation time. The proposed method has been verified on the IEEE 24-bus system by analyzing the small-signal stability to search several feasible placements, and then the transient stability is analyzed to compare and determine an optimal placement through testing various cases. The obtained result is also compared with other optimal method. [ABSTRACT FROM AUTHOR]