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This paper is concerned with the design of a dual-loop control system for permanent magnet synchronous motor (PMSM). An improved linear extended state observer (LESO) with excellent estimation capability is employed to develop an improved linear active disturbance rejection control (LADRC) suitable for PMSM speed regulation, achieving outstanding disturbance suppression in PMSM speed control. The use of an internal model control scheme to initialize the parameters of the proportional-integral- (PI-) based current controller simplifies the search space of the control system parameter optimization. An improved particle swarm optimization (PSO) algorithm is applied to optimize the controller parameters, thereby enhancing the overall system performance. Finally, through a series of simulations and experiments, we validate that our proposed controller exhibits superior performance compared to some other control methods. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid economic development, the development speed of all walks of life has entered a new level, and the power industry has also developed rapidly. Driven by market demand, China's power transmission range and power transmission capacity will enter a new level. At the same time, the problems brought about by the development of the power system are equally severe. Due to the large load density in individual areas, the detection of short-circuit current must be improved as an important issue. The purpose of this paper is to study how to improve the practical model of short-circuit current calculation of photovoltaic power plants, so that it can be well applied to the current high-density current detection in China. Therefore, this paper improves the recursive least squares (RLS) algorithm and applies it to the practical model of short-circuit current calculation of photovoltaic power plants and describes the improvement process of the algorithm in detail. At the same time, this paper designs relevant experiments and analysis to count the data of the improved RLS algorithm in the short-circuit current calculation of the actual photovoltaic power station and combines the data of this part to test and analyze the ability of the algorithm. The experimental results in this paper show that the improved RLS algorithm has a very good improvement in the calculation accuracy of the short-circuit current calculation of photovoltaic power plants in the actual model calculation. At the same time, the calculation efficiency is also improved, and the current tracking effect is also improved by 7%. [ABSTRACT FROM AUTHOR]

In order to solve the problem of accurately mining the power consumption characteristic data of power users, this paper proposes a research on power user network data mining algorithms based on multi-information fusion. In the past, the mining methods based on neural networks and cure algorithms were affected by noise data, resulting in low mining accuracy. To solve this problem, this paper proposes a user-complex power consumption feature mining method based on the K-means clustering algorithm. In the K-means clustering algorithm, the principle of mining complex power consumption characteristics of users is studied, and the data are cleaned, integrated, and preprocessed by protocol transformation to avoid noise interference. The information entropy principle is used to cluster the matrix to regularize the feature points. According to the complex power consumption characteristics, the power consumption feature points are determined through the cluster, the distance between the clusters is calculated, the power consumption feature information gain is obtained, and the user complex power consumption feature mining is completed. The experimental results show that the mining accuracy of this method is up to 99%, providing users with better quality services. Conclusion. The automatic control performance of the system is good, and the acquisition results are accurate and reliable. [ABSTRACT FROM AUTHOR]

English translation, also a kind of language conversion, refers to the activities of expressing each other in English to express another language or another language to express English. This paper aimed to evaluate and research methods of English translation based on the improved Gaussian mixture model (GMM). The paper proposed a simple evaluation and analysis of English translation using a discriminative training algorithm. In the experimental part, on the one hand, taking students, teachers, and translators as case study objects, through a questionnaire survey, it can be known that the number of students who scored more than 80 points in the English translation test is increasing every year, from 11.30% in the first year to 21.13% in the fourth year, an increase of nearly 10%. On the other hand, it can be seen from the speech translation experiment that the correct translation rate was 46.18% by using intelligent technology to distinguish translation. The experimental results showed that the discriminative training algorithm under the improved GMM algorithm is effective in the research of English translation evaluation. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of power load forecasting, this paper proposes a neural network-based short-term monitoring method. First, the original energy load signal is decomposed by the CEEMDAN algorithm to obtain several eigenmode function components and residual components; several eigenmode function components and residual functions are fed into the NARX neural network for computational purposes. The partial hypothesis is superimposed in the following part to obtain the final short-term forecast. According to the test results, the MAPE of the CEEMDAN-NARX model is 4.753%, 3.540%, and 0.343% lower than the SVM, RNN, and NARX models, respectively, and 3.741% and 2.682% lower than CEEMDAN-SVM and CEEMDAN-RNN, respectively. The MAPE and RMSE of the CEEMDAN-NARX model are 0.765% and 101.7 MW, respectively, which are 0.468% and 45.2 MW lower than NARX models, respectively. Compared to CEEMDAN-SVM, the MAPE of CEEMDAN-NARX and CEEMDAN-RNN decreased by 0.986% and 0.692%, respectively, and the RMSE of CEEMDAN-NARX decreased by 111.5 and 65.7 MW, respectively, compared to CEEMDAN-SVM. Conclusion is that the load forecasting model based on the combination of CEEMDAN algorithm and NARX neural network can effectively connect, reduce the negative impact of noise on forecasting results, and improve forecasting accuracy. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is a crucial issue to maintain the reliable operation of power systems. However, achieving this objective is not easy, especially when renewable energy sources (RESs) are penetrated into the power system due to their uncertainty nature. This paper provides an optimal solution for the power flow problem including two different types of RESs based on a marine predator algorithm (MPA). The OPF model used in this paper has three different types of energy resources (thermal, wind, and solar). The output power from wind or solar generator has two probabilities either underestimation or overestimation consequently. These two probabilities have been translated into the objective function by two extra costs, penalty cost, and reserve cost, respectively. To check the validity of the proposed algorithm, it is applied to a modified IEEE-30 and IEEE-57 bus systems. The obtained results are compared with some recent optimization methods. The results show the effectiveness of marine predator algorithm in providing the optimal solution for the power flow problem with maintaining the power system constraints inviolate. [ABSTRACT FROM AUTHOR]

The photovoltaic(PV) system's efficiency can be improved with the aid of effective solar PV cell modeling. However, flawed solar cell parameters affect PV cell modeling incorrectly. The manufacturers are usually not providing all the necessary data for the exact modeling of PV cells. It is therefore necessary to efficiently predict the parameters of the PV cell. The literature discusses various optimization algorithms, but the suboptimal outcomes are obtained by most of the algorithms due to their convergence toward local minima. This paper introduces a new stochastic optimization algorithm for the estimation of solar PV cell parameters. Therefore, for the evaluation of the solar cell, a new novel chaotic algorithm is introduced in this paper called the chaotic tunicate swarm algorithm (CTSA). The proposed algorithm has a new function called an outstanding mathematical model of adaptive weights to stimulate negative and positive inputs from the spreading wave in order to find the best way to connect food with an excellent exploitation tendency and exploratory ability. For the double‐diode model, the computation time of the chaotic variants (CTSA1‐1.1885, CTSA2‐1.2502, CTSA3‐1.2962, CTSA4‐1.1204, and CTSA5‐1.2214) is better than the rest of the compared algorithms (particle swarm optimization [PSO]‐17.4532, tunicate swarm algorithm (TSA)‐2.5278, Harris hawks optimization [HHO]‐10.8942, ant lion optimizer [ALO]‐15.0214, and atom search optimization [ASO]‐9.5214). For the triple‐diode model, the computation time of the chaotic variants (CTSA1‐1.2658, CTSA2‐1.3520, CTSA3‐1.3056, CTSA4‐1.2290, and CTSA5‐1.3859) is better than the rest of the compared algorithms (PSO‐18.5421, TSA‐3.2568, HHO‐9.8754, ALO‐13.5841, and ASO‐11.8412). The superiority of proposed algorithm is established using ranking test, statistical error analysis, and sensitivity temperature variation. [ABSTRACT FROM AUTHOR]

Summary: The fault detection in power converters is a meaningful aspect to ensure the system's power quality. In this context, the open circuit fault (OCF) in power switches is increasingly discussed. This paper proposes an OCF detection method that triggers the power switches based on the slope of the output predicted voltage. The proposed algorithm allows a sensorless detection of the OCF. The regulator synthesis uses the transfer function for a small signal model. The efficiency of the prediction method is validated by an estimation of the output voltage for several duty cycles offering a good accuracy, in which the error does not exceed 5%. The validity of the proposed method is demonstrated by MATLAB/Simulink tools and a comparative analysis is given at the end of this paper. Compared to other works in the literature, the major advantage of the proposed method is the simplicity of implementation, lower cost, and a better OCF detection time, which does not exceed 15% of the switching period in the test conditions. [ABSTRACT FROM AUTHOR]

Summary: Automatic generation control (AGC) plays a crucial role in modern power system to control the frequency with change in power demand. A novel fractional order (FO) hybrid PID‐fuzzy‐PID (FO‐hPID‐FPID) controller is proposed in this paper as the secondary controller for AGC of a three unequal area interconnected nonlinear reheat thermal power system considering generation rate constraint. This paper also deals with the implementation of FO fuzzy PID (FOFPID) and FO PID (FOPID) controllers in addition to the proposed controller along with their counterpart integer order controllers to establish the dominance of FO controllers over integer order controllers. Various parameters of the controllers are optimized by hybridizing two global optimization techniques: particle swarm optimization and modified sine cosine algorithm (MSCA) and the optimization techniques so obtained is called as hybrid PSO‐MSCA (hPSO‐MSCA) technique. A step load perturbation of 0.01 p.u. is injected in area‐1, and the system response is investigated independently with different controllers. It is revealed that the proposed FO‐hPID‐FPID delivers superior performance than the other controllers. Sensitivity analysis for parametric variations is carried out, and random load test is performed to validate the robustness of the proposed controller. Further, it is observed that the controllers deliver result in an ascending order of control performance as PID, FOPID, FPID, FOFPID, hPID‐FPID and FO‐hPID‐FPID controllers with FO‐hPID‐FPID controller delivering the best result and the FO controllers deliver better control performance than their integer order counterparts. [ABSTRACT FROM AUTHOR]

Absrtact: Background: Nowadays, numerical relays configured through power swing blocking function are vulnerable to faults with high impedance owing to nonlinearities present in the faulty path. Identification of symmetrical high impedance faults incepting during the power swing scenario is of prime importance for protection engineers to avoid cascade tripping. It is analyzed that the classical approaches for symmetrical fault detection during power swing can mal‐operate owing to high level of uncertainty arise during validation of high impedance fault, symmetrical faults and cross country faults during power swing with varied level of compensation which has been explored in this research. Aims: This paper presents an innovative scheme for series compensated line by utilizing the capability of Relevance Vector Machine (RVM) as a disturbance classifier taking into consideration of cross country faults, CT saturation; power swings prone to symmetrical faults along with high impedance faults in comparison with prevailing methods. Materials & Methods: In order to validate the proposed RVM based disturbance classifier algorithm, different kinds of faults and power swing situations have been applied in IEEE 9 bus test system using PSCAD/EMTDC software followed by validation in MATLAB. The features of sequence components of currents are estimated using Modified Full Cycle Discrete Fourier Transform (MFCDFT). The extracted features are used as control inputs to RVM for discernment among power swing and fault under the influence of wide range of system and fault dynamics. Results: The developed RVM based disturbance classifier scheme generates a tripping signal in the event of high impedance fault during power swing and remains stable during swing conditions. It can be used in conjunction with an adaptive numerical distance relaying scheme for realizing complete protection of the power grid for all zones of protection. A comparative analysis with an existing disturbance classifier algorithm indicates relative superiority with much improved fault classification accuracy. Discussion: In order to validate the proposed algorithm, different kinds of fault and power swing scenario are realized in IEEE 9 bus test system using PSCAD and current samples are acquired at sampling frequency of 4 kHz. The phasor estimation of sampled signals has been performed using MFCDFT which is used to derive feature vector extraction from symmetrical components. The extracted features are applied to RVM based fault classifier algorithm for classification of disturbance. The obtained results are also compared with existing schemes of protections. It has been proved that developed algorithm outperforms with much improved fault classification accuracy. Conclusion: This paper discovers relevance vector machine based adaptive learning approach for dynamic disturbance classification in complex power grid network. The developed algorithm has been validated on IEEE 9 bus test system modelled in PSCAD. The fault and system dynamics are varied over a wider range which includes variation in fault type, fault impedance, fault inception angle, power flow angle, fault location and degree of line compensation level. Moreover, the validation of scripted algorithm has been performed for very complex power grid scenarios like cross‐country faults, high impedance faults with varying degree of compensation, power swing and saturation of instrument transformers etc. The feature vectors are extracted using MFCDFT and applied to supervised RVM based disturbance classifier and outperforms with classification accuracy of 99.74% which indicates it's superiority over existing classifier approaches based on SVM & ANN. [ABSTRACT FROM AUTHOR]

Summary: Identifying maximum penetration level (MPL) of Solar Photo‐Voltaic Generation (SPVG) in the distribution system is crucial for system planners and network operators to keep the system parameters within the acceptable limits. This paper provides a systematic and extensive procedure for evaluation of MPL of SPVG and its enhancement methods in distribution systems. More importantly, it illustrates a novel algorithm for affixing the penetration cap of Solar PV. The methodology initially derives a new stability index for locating SPVGs in the system, including both active and reactive power support from SPVGs. The study shows the utility of redefining MPL of SPVGs by considering the reactive power injection through solar inverters also. Deviating from the practice of considering the conventional constraints of voltage rise and maximum loading for MPL estimation, the paper considers loss minimization as the major constraint. The algorithm has potential to identify distributed integration scenario of SPVGs compared to the limited number of placements in the extant algorithms. This paper provides a novel algorithm to evaluate of maximum penetration level (MPL) of solar photo voltaic generation (SPVG) and its enhancement methods in distribution systems. The utility of redefining the MPL of SPVG by considering the reactive power injection through solar inverter is illustrated. The algorithm has distinct advantages over the extant algorithms in identifying a distributed integration scenario. [ABSTRACT FROM AUTHOR]

Summary: The continuous increase of the distributed energy resources (DERs) penetration levels leads to voltage stability problems in the distribution system. One of the approaches for the mentioned emerging challenge is the proper placement of automatic voltage regulators (AVRs). This paper investigates the optimal placement and sizing of AVRs in a distribution network by presenting a new modification of the teaching‐learning‐based optimization (TLBO) algorithm. The objective functions consist of minimizing the distribution system voltage deviation, energy generation cost, and electrical losses. The modification improves the convergence velocity and accuracy of the TLBO algorithm using the combination of mutation technique and quasi‐opposition‐based‐learning concept. This paper compares the performance of the proposed algorithm with other famous evolutionary algorithms. The test distribution system contains installed DERs that work more efficiently after the placement of AVRs based on the mentioned objective functions by the proposed optimization algorithm. The simulation results display the best optimization algorithms for AVRs placement with a significant level of less than 0.10 (ie, probability‐value). The proposed multiobjective optimization algorithm's considerable merit is the accuracy and convergence velocity in solving this specific optimization problem. [ABSTRACT FROM AUTHOR]

The continuous expansion of the international market makes more people pay attention to learning different languages. As one of the most popular languages, German is favored by scholars. The use of intelligent translation systems to learn German has become the main way of learning. The increasing development of science and technology is accompanied by the emergence of a large number of new German words. The traditional German intelligent translation system has a slow update speed of words and cannot recognize new words, which seriously affects the accuracy of the translation. According to these questions, in this paper, the data mining algorithm is introduced into the German machine intelligent translation system. By evaluating the German machine intelligent translation system using a data mining algorithm in four aspects: translation speed, accuracy rate, recall rate, and user satisfaction, the evaluation results found that the translation speed of the system increased by 4.68%, the accuracy rate increased by 4.2, the recall rate has increased by 3.8%, and the user satisfaction score has increased by 0.825 points. The data mining algorithm can improve the German machine intelligent translation system and users' interest in learning and promote German education. [ABSTRACT FROM AUTHOR]

Summary: Energy management of the grid connected microgrid (MG) systems with low cost using a novel hybrid algorithm is implemented in this paper. The novel proposed hybrid technique is the combined performance of both the Random Forest (RF) and Coral Reefs Optimization (CRO) algorithm and in this way it is named as RFCRO. Optimum operation of micro‐sources for decreasing the electricity production cost by hourly day‐ahead and real‐time scheduling is the main objective of the paper. The proposed hybrid technique is to manage the power flows between the energy sources and the grid. To achieve this point, demand response and minimum cost of energy are determined. This technique is involved with the mathematical optimization problems that necessitate more than one fitness function to be optimized simultaneously. By using the inputs of MG‐like wind turbine, photovoltaic array, battery, and fuel cell with corresponding cost functions, the RF is employed to predict the load demand. CRO clarifies the squirrel in optimizing the configuration of MG based on the load demand. The proposed hybrid technique is implemented in MATLAB/Simulink working platform and compared with other solution techniques like BFOANN, ALO, GOAPSNN, and RBFNN‐SSA technique. The comparison result reveals that the superiority of the proposed technique confirms its ability to solve the problem. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a fractional‐order PI controller‐based direct torque control (DTC) scheme for an induction motor (IM). The IM drive is fed with multipoint clamped (MPC) inverter to realize the five‐level operation. The DTC scheme is implemented with the help of stator‐flux coordinates. The DTC scheme estimates the reference stator voltages from the reference speed and torque commands and space vector modulator synthesizes the reference stator voltages to generate the switching states of the inverter. The paper begins by featuring the theoretical concepts related to five‐level MPC inverter and compares it with conventional multilevel inverters in terms of total number of components required, conduction and switching power loss. In this paper, space vector modulation (SVM) algorithm for the generation of switching pulses to the five‐level MPC inverter based on fractal structure has been proposed with optimal switching sequence which further reduces the switching power loss, total inverter system loss, and phase‐current harmonic distortion. The design procedure and tuning of PI controllers (using fractional‐order system) for the proposed DTC‐SVM scheme are also presented. The proposed drive system allows full‐load torque control with fast response and reduced torque ripple for wide range of speed variations. The proposed DTC‐SVM fed IM drive is implemented in the MATLAB/Simulink software. The simulation results are verified with experimentation. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses a novel formulation and solution for the security constrained reserve management (SCRM) problem in both normal and N − 1 contingency conditions. Indeed, the SCRM problem is a modified version of the security constraint unit commitment problem which is updated with the operational reserve management and frequency stability constraints in both normal and N − 1 contingency conditions. The objective of this problem is to determine the optimal amounts of generation associated with different units and frequency regulation reserve (FRR) and allocate the FRR among the generation units. Therefore, the SCRM problem is a mixed integer nonlinear problem which is solved using the genetic algorithm in this paper. The powerful PLEXOS software is also utilized to solved and evaluate the results of the SCRM problem. To determine the effectiveness of the proposed method, some test systems, that is, IEEE 24‐bus, IEEE 39‐bus, and IEEE 118‐bus test systems are considered. Results confirm the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Summary: The purpose of this paper is to present a model for optimal placement of distributed generation units and sectionalizing switches (relocation of the existing switches and installation of new ones) to increase distribution network resilience against the threat of transmission system outage. The proposed model is formulated as a multi‐objective optimization problem with the objectives of minimizing investment costs and maximizing load restoration. In this regard, distribution system operation during normal conditions as well as transmission system outages is considered. In addition, the unique characteristics of the real power grid in Khuzestan province in southwestern of Iran are addressed in the proposed model which makes the model applicable to the real networks with similar characteristics in many parts of the Middle East. This grid has two completely different load patterns (in terms of load curve shape and peak load) corresponding to two hot and temperate periods, which causes completely different concerns for the network's operator. The proposed model is solved using multi‐objective genetic algorithm (NSGA‐II), and it is implemented on the 33‐node test system and the results are analyzed. Moreover, it is applied to the 327‐node real‐world test system in Khuzestan province to demonstrate its applicability to large networks. In both cases, the results confirm the effectiveness of the proposed optimization model. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses the allocation of a hybrid system that includes PV‐DG and DSTATCOM. The planning problem considers the variations of load demand and solar irradiance under deterministic and probabilistic conditions. An efficient optimization algorithm called MPA is implemented to assign the optimal placement and ratings of the hybrid PV‐DG and DSTATCOM. The considered objective function is a multi‐objective function that includes the annual cost reduction, improvement of voltage profiles, and system stability improvement. The assessment is accomplished with the inclusion of a single and two‐hybrid system on a large 94‐bus system. For validating the effectiveness of the MPA, the yielded results are compared with the PSO, which is considered a commonly used algorithm. In deterministic conditions, the hourly variations of the load demand and solar radiation are considered in four yearly seasons, while in probabilistic conditions, 3 years of hourly historical data of solar irradiance and load demand are utilized to describe the uncertainties of the load demand and solar irradiance. The simulation results demonstrate that the optimal inclusion of a single‐hybrid PV‐DG and DSTATCOM system can enhance the system's technical performance (the voltage profile, the voltage stability) and enhance the economic scheme (total annual cost reduction). In addition, the inclusion of two‐hybrid systems is superior compared with the inclusion of a single‐hybrid system in terms of the considered objective functions, as well as the proposed technique is more efficient for solving the allocation problem of the hybrid system. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an efficient three‐stage algorithm for identifying the turbine‐governor (TG) and synchronous generator (SG) parameters is proposed. Instead of considering approximate models, precise models of the TG and SG are used. In the proposed approach, d‐q axes parameters of SG, mechanical parameters of SG, and TG model parameters are estimated. For the TG model, all the main control loops are carefully modeled with respect to the real‐world system. Moreover, the robustness of the governor is enhanced by employing an anti‐windup configuration for the load‐speed controller. The well‐known seventh‐order SG model is also used. The accuracy of the model and effectiveness of the parameter identification procedure is verified by experimental data. The results show satisfactory numbers for the selected performance index in various operating points when different controllers are in effect. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a simple parameter independent speed estimation algorithm for vector‐controlled permanent magnet synchronous motor (PMSM) drive. The speed estimation algorithm is formed using modified X‐MRAS (modified fictitious quantity model reference adaptive system), independent of machine parameter, integrator, and differentiator terms. Any variation in the machine parameters will not affect the presented speed/position estimation algorithm. The speed sensorless method makes the drive more compact, reliable, reduces the cost, and mitigates environmental issues. The functional quantity of modified X‐MRAS is developed using rotor reference frame voltages and currents. The proposed speed estimator is found to be stable in all the four quadrants of operation. This algorithm performs satisfactorily for variable speed and load operations. The proposed algorithm is verified through computer‐based simulation using the MATLAB/SIMULINK platform and supported with experimental validation through the PMSM laboratory‐developed prototype using the dSPACE‐1104. [ABSTRACT FROM AUTHOR]

Summary: Quasi Z‐source inverter (qZSI) is a single‐stage high gain buck‐boost converter suitable for various applications like renewable energy sources, power electronic drives, and power supply systems. Controller design for qZSI should ensure simultaneous control of multiple objectives such as the DC capacitor voltage, source inductor current, and load current. While controllers dealing with multiple objectives, obtaining a good steady‐state and transient performance with low control complexity is essential. Fortunately, Model Predictive Control (MPC) is emerging as an effective alternative that can address all the above‐said issues simultaneously. However, some of the severe problems to be addressed with the conventional MPC algorithms developed for qZSI are applying a single vector per sampling duration, higher computational requirements, and lack of analytic procedure to handle weight factors. This paper presents a control algorithm with features of weight factor elimination and AC load current ripple reduction by using a modified double vector predictive control approach with fewer computational requirements. The principle of inclusion of zero vector, weight factor elimination, and a detailed calculation for duty ratio are included. As the converter operates at a fixed frequency, significant ripple reduction for DC current, link voltage, and load current ripple have been achieved. The proposed MPC requires only ≈81% of computations needed for the conventional MPC. Methodological comparison has been provided to present the superiority of the proposed MPC compared to appropriate conventional MPCs available in the literature. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a modified sine cosine algorithm (MSCA) is proposed and is applied to design the power system stabilizer (PSS) and lead lag compensator‐based static synchronous series compensator (SSSC) controller to enhance the power system stability. Two test systems: single machine infinite bus (SMIB) and multi‐machine power system (MMPS) working under various loading conditions are taken as case studies. The objective of the work is to enhance the stability of the system by minimizing the rotor speed deviation of the generator. For this, the optimal controller parameters of PSS and SSSC are determined by using the MSCA technique. In the proposed MSCA method, two newly proposed search agent updating schemes (ie, SCA/rand‐target and SCA/best‐target) with parallel use of sine, cosine operators have been ensemble to generate the new search agents. The proposed MSCA method combines the exploration property of SCA/rand‐target search agent updating scheme with exploitation property of SCA/best‐target search agent updating scheme, which enhances the chances of attaining the near optimal solutions in fewer generations. The simulation results obtained from the proposed MSCA method have been applied on the two test system configurations of power system, which shows significant reduction in oscillations as compared with some well‐known heuristic algorithms. The mean rotor speed deviation achieved using the proposed method for SMIB system and MMPS is 0.0013858 and 0.00046857 pu, respectively. Extensive statistical analysis on obtained results using Wilcoxon signed rank test, and Friedman and Nemenyi hypothesis test ensure the reliability and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: Electric power systems try to maximize resilience by various enhancement strategies as preventive and corrective actions against extreme weather events. This paper presents an operational network reconfiguration strategy during a high wind event to strengthen the resiliency of distribution networks. In the proposed resilience enhancement strategy, a bi‐level optimization problem is formulated with two conflicting objectives (i) maximizing grid resilience and (ii) realize the primary objective by a minimum number of out‐of‐service lines (OSLs) switching operations. Furthermore, the algorithm considers the priority of loads, which is an important characteristic of modern distribution grids. The optimization problem is solved by the bi‐level genetic algorithm (BiGA). The IEEE 33‐bus network is utilized to demonstrate the application of the developed algorithm. The simulation results show a significant improvement in the resiliency of the tested network and reveal that switching operations can be used effectively to increase the resiliency of distribution networks against natural disasters. Furthermore, sensitivity analysis is performed to show the effectiveness of the proposed approach for finding the global optimal point. The proposed network reconfiguration strategy helps to prepare for severe weather conditions. [ABSTRACT FROM AUTHOR]

Summary: The losses and efficiency of a modular multilevel converter (MMC) are strongly linked to the switching frequency, component selection, and the choice of internal control structures such as the circulating current control and sub‐module (SM) capacitor voltage balancing algorithm. Equally, the structure of the SM impacts the overall converter operation and the efficiency of the conversion system. However, consideration for the impact of converter operating points on the overall system efficiency is required in order to derive a complete picture. Thus, this paper presents a comprehensive evaluation of the impact of inverter/rectifier operation and power factor on the losses of the different SMs in an MMC with a particular focus on SMs and operational limits for high‐voltage DC transmission systems. Considering 27 different SM topologies, it is demonstrated that due to the voltage balancing requirements, the inverter or rectifier operation modes of the converter and the power factor, influence the average losses of SMs and also the maximum and minimum losses within a fundamental period. [ABSTRACT FROM AUTHOR]

Summary: Parallel transmission lines are widely used in modern power systems due to their ability to increase power transmission and reliability of the power network. However, due to these networks' complexity, fault locating is a potential problem in these types of systems. Besides, when the wind farms enter these systems, a new challenge will be created for the fault location (FL) techniques; and the reason is that the impedance continuously varies in wind power plants. This paper proposes a novel FL algorithm for parallel transmission lines using the distributed parameter line model. This method uses long short‐term memory networks for estimating source impedance and other network parameters. The algorithm shows accurate performance during noisy conditions and precisely determines the fault distance during both three‐phase and single‐phase faults. The presented method is implemented using PSCAD and Python software. The simulation results illustrate the applicability of the proposed method to estimate FL in the presence of wind farms in the system. [ABSTRACT FROM AUTHOR]

Summary: It is known that the demand for renewable energy systems during recent years has increased significantly, especially for wind and photovoltaic systems. The continuous improvement of the efficiency and performance of these systems is important to their growth, particularly in terms of harvesting the maximum power. Many types of methods and techniques are available for tracking the maximum power point, the most important of which is the concept of artificial intelligence. Artificial intelligence (eg, neural networks and fuzzy neural networks) has great potential to handle complex systems or those that have non‐linear parameters and are influenced by environmental conditions. In this paper, artificial intelligence is used to predict the optimal reference values for wind turbines and photovoltaic panels used in maximum power point tracking (MPPT). Neural networks were used in the wind turbine system, while fuzzy neural networks were used in the photovoltaic systems. The networks were trained in real‐time using an incremental training mode based on the back‐propagation algorithm. The proposed system was designed practically based on the microcontroller and has been tested, and the results are extracted in real‐time. The results have proven their effectiveness and conformity with the required optimal values. [ABSTRACT FROM AUTHOR]

Summary: A hybrid AC/DC microgrid takes advantages of both DC and AC microgrids with distributed generation (DG) units in both the respective grids connected through a bidirectional interlinking converter (BIC). Control of hybrid AC/DC microgrid presents a significant research and engineering challenge and hence needs increased research efforts. Existing control algorithms restrict individual controllers to control only designated parameters. This paper proposes MPC‐based distributed control algorithm that will analyze grid conditions, and it would designate BIC to regulate the highest deviated parameter (AC/DC voltage or current) in all nonlinear conditions by performing multivariable optimization using variable control scheme in MPC. Other parameters would be controlled by individual controllers as per their capability. MPC being a nonlinear control technique having an excellent dynamic response and constraint inclusion feature has advantage as compared to other linear and nonlinear techniques. The performance of the proposed algorithm has been compared against other techniques and proved in MATLAB/SIMULINK simulation. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an auto‐tuned Incremental Conductance (Inc) maximum power point tracking (MPPT) algorithm for a photovoltaic (PV) system. Proposed MPPT algorithm is adaptive in nature in terms of both MPPT perturbation frequency and step‐size based on the change in PV voltage. To verify the functionality of the proposed adaptive frequency adaptive step‐size Inc MPPT method, a boost converter based PV setup is developed and the algorithm is implemented in Arduino Mega‐2560 micro‐controller. An extensive comparative result analysis of the proposed technique is done with the conventional fixed frequency variable step‐size Inc MPPT algorithms which are based on power‐voltage slope and change in power. The proposed change in PV voltage based adaptive frequency adaptive step‐size algorithm improves both steady‐state and dynamic performance compared to the conventional fixed frequency variable step‐size Inc algorithm. The steady‐state oscillation is low with a faster tracking response. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a phase‐lock loop control algorithm based on enhanced complex coefficient filter (ECCF‐PLL) is proposed to estimate the fundamental frequency positive‐sequence components (FFPSCs) of current and voltage signals. To mitigate power quality (PQ) problems, suitable gating pulses required for the unified power quality conditioner (UPQC) are followed by the reference signal generation and extraction of FFPSC. The power angle control (PAC) concept is used here to perform the functionality of the UPQC‐S controller for mitigation of PQ problems and sharing load reactive power between both VSCs as UPQC. The proposed ECCF‐PLL can quickly estimate the properties of the signals (grid frequency, phase angle and fundamental amplitude) under adverse grid conditions. Its sequence‐selective filtering ability enables the ECCF‐PLL to eliminate/extract disturbances before the signal enters the PLL control loop structure, while ECCF‐PLL manages an acceptable dynamic response. In addition to the ECCF‐PLL‐based algorithm, a particle swarm optimization (PSO) technique is implemented to estimate the value of the proportional–integral (PI) gains. The numerical values acquired by the PSO algorithm are chosen as PI gains, thereby boosting the DC and AC bus voltages at reference level at nearly six iterations within a few cycles. Detailed analysis of UPQC‐S is done through MATLAB and its interface with a Xilinx Spartan‐3 FPGA XC3S5000 board under OP‐5142 system for compensation of various current‐ and voltage‐based PQ issues through ECCF‐PLL with optimized PI controller gains. The performance of developed system is tested under transient and steady‐state conditions. [ABSTRACT FROM AUTHOR]

Summary: Provisional microgrids create by the connection of two beside microgrids to achieve economic benefits. So far, there have been many reviews on AC microgrids. However, due to the many advantages of DC microgrids, they cannot be ignored. To achieve the advantages of both AC and DC microgrids, hybrid structures were considered. The subject of controlling hybrid microgrids is relatively more complex. Due to the variable nature of the distributed generation resources, this complexity in islanding conditions is even greater than normal conditions. This paper proposes a robust frequency control for islanding provisional microgrid including hybrid AC/DC part as a master microgrid and AC conventional part as a slave microgrid. As authors are aware, there are no scientific reports for frequency control of the proposed microgrid. One of the issues affecting the performance of this controller is the choice of weighting functions. To achieve optimal controller, an algorithm is used to determine the best weighting functions according to the way the controller works, and by examining the sensitivity functions. Finally, simulations were performed to investigate the performance of the controller and the results of using this controller for this microgrid were examined. [ABSTRACT FROM AUTHOR]

Summary: This paper describes the performance of a dynamic voltage restorer (DVR) using various adaptive controls. The implemented control algorithms are; the amplitude adaptive notch filter (AANF), the quasi newton (QN), and the sliding mode observer (SMO) based control algorithms equipped with optimized PI tuning for three leg voltage sources converter‐based topology. These control algorithms are assessed and compared using key factors including computational parameters complexity, system response, dynamic and steady state performance etc. The control of a DVR is mainly dependent on the error minimization of the DC voltage and the load terminal voltages, respectively, which directly affects the generation reference load voltage for a DVR. Accordingly, optimization algorithms have been implemented for estimating the PI controllers' gains along with the control algorithms under investigation. The optimization method Harris Hawks Optimization algorithm (HOA) has been implemented for PI tuning for all aforementioned control algorithms. The estimation technique of the PI controller gains using the aforementioned optimization algorithm is elaborated mathematically and pictorially described. A comparative analysis of the performance of the HOA optimization method in estimating the gains of PI controllers of the adaptive control algorithms for three phase DVR are simulated using MATLAB. Moreover, all control algorithms have been developed and tested on the Micro Lab Box based experimental setup of DVR in the laboratory and their performance were compared while considering unbalance for compensation and satisfactory results were obtained. [ABSTRACT FROM AUTHOR]

Summary: The performance of the Photovoltaic (PV) system is highly affected by fluctuating climatic conditions. Due to these changing climatic conditions, the irradiance diminishes, and it results in the non‐linearity in voltage‐power (P‐V) curves of the PV system. Therefore, it is one of the reasons for the occurrence of the multiple peaks on P‐V curves instead of a single peak. Therefore, it is challenging to track the global maximum power point tracking (MPPT) under these circumstances with traditional MPPT techniques viz. perturb and observe (P&O) and fuzzy logic control (FLC). Concerning global MPP tracking, this paper proposes a novel MPPT method based on a Rao‐1 optimization algorithm. The proposed algorithm is used to overcome the drawbacks of the classical P&O, FLC, and PSO based MPPT techniques under partical shading conditions (PSCs) and rapidly changing atmospheric conditions viz. slow convergence speed and oscillation around the MPP. The proposed MPPT tracks the global MPP under any abnormal conditions like PSCs. To show the efficiency of the proposed MPPT a comparative assessment is performed with well‐established MPPT methods in the literature. The simulation results witnessed that the proposed MPPT is more superior in improving the PV system performance under normal as well as under PSCs. The proposed MPPT over the PSO technique decreases the tracking time by 72.7% for pattern 1, 29.8% for pattern 2, 66.7% for pattern 3, 61.9% for pattern 5, 27.8% for pattern 6, and improves the tracking efficiency in all shading patterns. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a novel valley V2 control scheme for interleaved boost converter is presented to improve the transient response performance of system and realize the current sensorless control, and its stability is analyzed by a nonlinear analysis technique. In this proposed methodology, the current sensor is saved by the proposed control signals sharing algorithm, and the nonlinear dynamics is investigated by employing a nonlinear analysis method based on the Monodromy matrix. Meanwhile, the information of the converter parameters' influence on the system stability can be obtained by calculating the Monodromy matrix of switching event. It is worth mentioning that the location of eigenvalues of this matrix can be utilized to analyze the relationship between converter parameters and stability margin. Furthermore, by adopting the knowledge gained from this analysis, an auto‐tuning resonant parametric perturbation is proposed to ensure an excellent performance of converter with an extended range of stable operation. Finally, the simulation and experimental results are presented to verify the effectiveness and theoretical analysis of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an unsupervised clustering algorithm, called modified Iterative Self‐Organizing Data Analysis Technique Algorithm (m‐ISODATA), to capture representative nonchronological scenarios for representing short‐term uncertainties in power system models. The proposed approach is suitable to automatically obtain the number of scenarios required to fully capture the variability of historical series, avoiding the need of adjusting the number of clusters as in techniques commonly used in the literature. The performance of the m‐ISODATA is discussed and compared with Monte Carlo simulation, the well‐known k‐means, and hierarchical agglomerate clustering algorithms. In addition, the obtained scenarios are applied to a wind‐solar‐thermal power system generation expansion planning and to a probabilistic optimal power flow, considering uncertainties over wind and load demand. Finally, the source codes are provided with the best parameters as default. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a novel 13‐level multilevel inverter topology. This topology structure synthesizes a reduced number of components to produce the desired output levels; only a pair of DC sources, pair of switched capacitors, and 10 power switches are considered in this configuration. This structure employs the switched capacitors to compensate the DC sources for generating the desired output voltage levels. This reduction has successfully reduced the system cost, and the inverter's output voltage waveform reflects low THD and the low level of electromagnetic interference (EMI). Those switched capacitors have an online method for charging and balancing the capacitors' voltages without any use of auxiliary charging circuits; this ensures a compact size for the proposed topology. The system's controlling scheme provides a simple algorithm with fewer commutations for the switches per cycle. This reduction ensures a long lifetime with enhanced performance and high‐efficiency operation. The proposed D‐type supports the modularity process through the cascaded connection between two or more units in series; this will increase the system's power capability and reduce the THD level while increasing the output levels in the output voltage waveform. The proposed D‐type MLI's validity has been verified through the computer simulations using MATLAB Simulink based on applying two different controlling techniques with high and low switching frequencies. To support the simulation results and increase the level of reliability for the proposed topology, the experimental prototype hardware controlled with dSPACE (DS11103) unit has been investigated. [ABSTRACT FROM AUTHOR]

Summary: With the grid's evolution, the end‐users demand becomes more vital for demand side management (DSM). Accurate load forecasting (LF) is critical for power system planning and using advanced demand response (DR) strategies. To design efficient and precise LF, information about various factors that influence end‐users demand is required. In this paper, the impact of different factors on electrical demand and capacity of climatic factors existence and their variation is discussed and analysed. The Pearson correlation coefficient (PCC) is utilized to express the degree of electric demand correlation with metrological and calendar factors. Then, the optimal‐Bayesian regularization algorithm (BRA) based on ANN for LF is presented. The effect of the number of neurons in hidden layers on output is observed to select the most appropriate option. Additionally, heating degree days (HDDs) and cooling degree days (CDDs) indices are investigated to consider the impact of air conditioners' (ACs) loads in different seasons. Case studies on data from Dallas, Texas, USA, are used to demonstrate the influence of various factors on electrical demand. The proposed algorithm's effectiveness for LF and error formulations shows that optimal‐BRA‐enabled LF presents better accuracy than state‐of‐the‐art approaches. Thus, the proposed electric demand prediction strategy could help the system operator know DR potential at different times better, leading to optimal system resources dispatching through DR actions. [ABSTRACT FROM AUTHOR]

Summary: Enhancing distribution system operation accomplished with the integration of renewable energy resources (RERs) has several technical, economical, and environmental dimensions. In this regard, this paper presents an optimal integration procedure of Distributed Generation (DG) based on Photovoltaic panel (PV) and Distribution Static Compensator (DSTATCOM) in Electrical Distribution System (EDS). The proposed procedure is formulated as a multi‐objective function (MOF). The considered objectives that reflect the technical, economic, and environmental issues, are Active Power Loss Level (APLL), Short Circuit Level (SCL), Voltage Deviation Level (VDL), Net Saving Level (NSL), and Environmental Pollution Reduction Level (EPRL). The proposed procedure investigates several hybrid optimization methods that combine the firefly algorithm (FA) with various acceleration coefficients PSO algorithms to improve the overall solution quality of the hybrid algorithms compared with the individual algorithms. To prove the capability of the proposed procedure, four different cases are tested on IEEE 33‐bus and 69‐bus EDSs. Added to that, the proposed algorithms are extended to practical Algerian EDS in Adrar City 205‐bus. Results obtained by the hybrid FA‐SCAC‐PSO algorithm showed that the simultaneous allocation of multiple DG and DSTATCOM in all standard and practical test systems significantly reduces the loss and enhances the voltage profile. An energy‐efficient analysis to proceed for different cases studied based on the best hybrid FA‐SCAC‐PSO algorithm to reach the best value of MOF compared to other algorithms, moreover the capability to achieve the optimal allocation of DG and DSTATCOM by maintaining the voltages profile within the permissible limit, whatever the variation of load. Significant technical economic and environmental achievements are found for different case studies especially in the existence of DGs and DSTATCOM devices. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a new fault‐tolerant multilevel inverter (FTMLI) configuration, which can withstand both the single‐switch and multi‐switch faults by maintaining rated voltage and power. A new control algorithm is developed to compensate the switch fault, with the generation of appropriate control signals to the corresponding switches. Sine‐carrier level‐shifted pulse width modulation is used to generate control signals to the switches. The detailed loss analysis for all the switches is performed by considering variations in the junction temperature. It helps in the efficient design of heat sinks for the switches. The comparative analysis is performed for the proposed topology with existing topologies in terms of reliability, efficiency, and mean time to failure. The proposed topology is simulated in MATLAB/Simulink platform and these results are validated through a laboratory prototype. The performance of the proposed FTMLI topology is found satisfactory. [ABSTRACT FROM AUTHOR]

Summary: The International Space Station (ISS), launched on 20 November 1998, is the largest manmade structure orbiting the Earth. It is one of the most complicated systems providing a scientific laboratory environment to conduct scientific experiments within the space environment for further space explorations. It also provides a comfortable and safe place for astronauts to operate the station. In this study, the modeling and simulation of the ISS's electrical power system have been conducted in MATLAB/Simulink. It provides a virtual environment to emulate the ISS electric power system. This virtual environment allows the evaluation of different load management algorithms for ISS to enhance its energy sustainability. In addition, the paper presents a load management algorithm for managing ISS operations and its impact on the battery state of charge (SoC) at various photovoltaic (PV) generation percent factors, that is, 50%, 75%, and 100%. The simulation results indicate that the proposed load management algorithm can improve the performance of battery SoC up to 13%. [ABSTRACT FROM AUTHOR]

Summary: The generation of charging current scenario is an important step in the operation and planning of power systems with high electric vehicle (EV) penetrations. With the development of the modeling method, a number of methods based on probabilistic models are applied to generate scenarios. Model‐based methods are often difficult to scale or sample. Data‐driven technologies use a large number of data to mine the mapping relationships, instead of explicitly specifying a model. In this paper, we proposed a data‐driven approach to generate scenarios using generative adversarial networks (GANs), which can learn the distribution of the charging current of EVs and obtain more abundant scenarios. The proposed method is applied to time‐series data from the charging current dataset of EVs. Firstly, the K‐Means clustering algorithm is used to preprocess the data to divide the distribution of charging current into four areas. Then, aiming to improve the training speed, GANs with gradient penalty (GP) is used for the generation of EV scenarios, which can use the GP term to optimize the Lipschitz limit. Finally, statistical methods are applied to estimate the quality of the generated data. Results show that the proposed method can effectively extend the historical data for the operation and planning of EVs in the future compared with the traditional GANs. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a novel methodology for installing a converter station at optimal AC bus in a hybrid AC‐DC system using a multi‐objective unified optimal power flow (MO‐UOPF). The MO‐UOPF model is developed here for the voltage source converter‐high voltage direct current (VSC‐HVDC) network. The various techno‐economic objectives are considered for the OPF problem: minimizing total generation cost, AC/DC transmission line losses with converter station losses, enhancement of voltage stability improvement, and reduction in CO2 gas emissions. The particle swarm optimization (PSO) algorithm has proven its superiority over many meta‐heuristic algorithms for solving the multi‐objective optimization problem. Also, various improved versions of PSO have been developed since its inception. Here, the MO‐UOPF problem is solved using a modified multi‐objective particle swarm optimization (MO‐PSO) technique with a simultaneous approach. The proposed methodology is analyzed with modified 5‐bus and IEEE‐30 bus test systems with different configurations for two‐terminal point‐to‐point transmission. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a novel fractional‐order PID plus derivative with filter coefficient (FOPID‐DN) controller for automatic voltage regulator (AVR) system using the MATLAB/Simulink environment. An AVR system is employed in the power system to maintain the terminal voltage at the desired level. Parameters of the proposed FOPID‐DN controller are optimally tuned by the recently developed Equilibrium Optimizer (EO) algorithm. Performance of the designed FOPID‐DN controller for an AVR system is compared with various controllers which are optimally tuned by different optimization algorithms. Comparative transient response analysis of the proposed technique has been carried out by considering vital transient response indicators like maximum overshoot, rise time, and settling time. Performance analysis of the proposed technique for an AVR system has been done by considering several input conditions. Robustness analysis of the EO algorithm tuned FOPID‐DN controller has been performed by varying the time constants of different components in an AVR system. The capability of the FOPID‐DN controller to handle nonlinearities of an AVR system has been investigated. The stability of the FOPID‐DN controlled AVR system has been analyzed. In addition, the disturbance rejection capability of the FOPID‐DN controlled AVR system has also been investigated. From the various simulation results obtained using the MATLAB/Simulink environment, it has been observed that the proposed FOPID‐DN‐based AVR system provided better performance as compared to different existing controllers. [ABSTRACT FROM AUTHOR]

The vigorous development of renewable energy has induced multiple sources connected to regional distribution networks. However, the high impedance ground fault that appeared in the distribution network can induce an intermittent arc accompanied by extinction and reignition, which may damage renewable energy generation devices severely. Therefore, the detection of arcing faults is profoundly necessary for practice, but the high impedance arcing fault is easily confused with switches of motor or capacitor bank. In order to solve high impedance arcing fault detection, this paper proposes a high impedance arcing fault detection method that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Teager energy operator (TEO). Firstly, CEEMDAN algorithm is used to extract the intrinsic mode function (IMF) of transient zero‐sequence current (TZSC). According to the correlation coefficient and waveform similarity, which can select maximum correlation coefficient value as the characteristic component of TZSC; Then, TEO is utilized to track the change of signal in real‐time, meanwhile, wavelet singular entropy (WSE) is applied to calculate the characteristic component of TZSC. In particular, high impedance arcing fault (HIAF), motor switching (MS), and capacitor bank switching (CBS) can be effectively identified by comparing WSE. The effectiveness of CEEMDAN‐TEO detection method is verified by simulation, which owns advantages in extraction accuracy, calculation efficiency, and detection accuracy of feature components. [ABSTRACT FROM AUTHOR]

Summary This paper proposes a new management algorithm and operation of a hybrid renewable energy system (HRES) connected to the power system. The whole hybrid system is managed in such a manner that it can produce as much needed by the grid system. The proposed algorithm is used to distribute proportionally the active and reactive power references to the PV source and wind generators according to their ability contribution. Based on the available active powers and the ability on reactive power of each sub‐system, the references are calculated using a proportional distribution algorithm and sent individually by the principal controller to each auxiliary controller. The analysis of the simulation results obtained under Matlab/Simulink shows the effectiveness of the proposed management algorithm and the flexibility of the hybrid renewable energy system studied. [ABSTRACT FROM AUTHOR]

Summary: Many issues are arising during the operation like interruption, disorder, and uncertainty in energy systems. One of the situations is islanding and that is required to protect humans and converter devices, those are working in interconnected electrical energy system environment. Variable converter pulse (VCP) method is proposed to identify the islanding detection (ISD) with rate of change of under/over voltage and rate of change of under/over frequency. Adaptive ISD algorithm is developed for proper tuning of VCP and used to identify ISD events in integrated utility grid. When a network is extended by means of new small‐scale energy systems that is, distributed generation (DG) and operating with main grid network, the possibility of pre‐existence of ISD data is also reconsidered for generating trip signal to isolate DG. In this paper, updating VCP‐based control as function of change in voltage and frequency at point of common coupling (PCC) with island events and non‐island events. Number of pulses and those are variable during island conditions and updated control vector to meet an objective of proposed ISD method. Especially multiple DG connected to PCC is also addressed in proposed method. [ABSTRACT FROM AUTHOR]

Summary: Mathematical model parameter estimation of solar photovoltaic (PV) cell, module and array represent a prodigious challenge in recent researches, where analytical, metaheuristic and hybrid techniques play a vital role for parameter extraction by using manufacturer's datasheet and experimental data, although the precise and highly reliable solution identification is still a complex problem to be solved. In this paper, a new Emperor Penguin Optimisation‐ (EPO) based parameter estimation approach for a single‐diode Rp solar PV model is presented that is cohesively analysed under different sets of temperature and irradiance (G). Validation of the proposed technique is perceived on the analysis of performance characteristics, that is, current‐voltage (I‐V) and power‐voltage (P‐V) of KC200GT, PWP201, and STP6 120‐36 PV modules under various simulation conditions. Moreover, estimated results are compared with the experimental data and several established estimation techniques in the literature for validation and demonstrate the proposed technique with highly precise outcomes having reduced computational cost for the PV model parameter extraction problem. [ABSTRACT FROM AUTHOR]

Summary: The present paper describes a new optimal design for an autonomous renewable energy‐based CHP system for a remote area in Zhidoi county, China. The configuration contains different parts of the electric heater (EH), photovoltaic‐thermal (PV/T), wind turbines (WTs), thermal energy storage (TES), and electrical energy storage (EES). The total annual cost (TAC) is utilized as a cost function of the system configuration and the idea is to minimize this function to access an optimal configuration. Due to the complicated nonlinear nature of this system, a metaheuristic‐based method, called Improved Marine Predators Algorithm (IMPA) has been introduced and designed. The reason for using this new algorithm is to cover the main drawbacks of most metaheuristics like better accuracy and higher convergence speed. To show the capability of the designed IMPA, it is validated by some different new metaheuristics from the literature. Afterward, the algorithm is used for system configuration optimization. Some sensitivity analysis is also investigated to shoe the method capability and the final results confirm the high ability of the proposed method for providing an optimal renewable energy‐based CHP system. The final results show a $56 307.74 value of TAC. The total efficiency of the system for the winter and the summers are 66% and 61%, respectively and the minimum total annual cost happens at AD = 0.8983. Finally, the minimum value of the total annual cost is achieved by the proposed method with 922.4 kWh. [ABSTRACT FROM AUTHOR]

Summary: Most of the proposed algorithms for synchrophasor estimation are based on the Discrete Fourier Transform (DFT). However, this technique has well‐known limitations, such as the leakage effect. Alternatively, this paper presents a positive‐sequence synchrophasor measurement algorithm based on a Synchronous Reference Frame Phase‐Locked Loop (SRF‐PLL). The algorithm is modular and consists of four stages. The first one is the abc‐dq transform that, with the aid of the second stage, a Finite Impulse Response (FIR) provides the positive‐sequence of the grid voltages. The filter also helps to improve the signal‐to‐noise ratio. This filter class was chosen due to its characteristic of linear phase and constant group delay to avoid unwanted phase distortions at the measurements. The third stage is an SRF‐PLL based on a PID controller that estimates the amplitude and phase of the grid voltage's positive‐sequence component and the grid frequency. By assessing the dynamic condition tests, it was observed that the PLL could not comply with all the IEEE standard requirements. Therefore, a fourth stage was included to calculate the frequency, and Rate of Change of Frequency (ROCOF) based on the estimated positive‐sequence phase. Experimental results obtained with a test platform shown that the proposed algorithm complies with all IEEE C37.118.1‐2011 and C37.118.1a‐2014 standard requirements. [ABSTRACT FROM AUTHOR]

Summary: In line with the accomplishment of PQ standards, the search for methodologies to estimate harmonic distortions in large electrical networks, using a small number of meters, emerges as a challenging subject. In this way, this paper proposes a new methodology for HSE in underdetermined power systems monitored with a small number of meters. The HSE approach is modeled as an optimization problem, for which, the solution is achieved using a new strategy, called HA, obtained from the union of the ES algorithm based on CMA with the SADE algorithm. To highlight the approach application, simulations studies are carried out using two transmission systems, and the results enhance the estimation accuracy offered by the HA in comparison to two other HSE methods. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an innovative hybrid algorithm (ihPSODE) for economic load dispatch (ELD) problems with or without valve‐point effects. It integrated with suggested novel PSO (nPSO) and DE (nDE). Where in nPSO, a new, inertia weight and acceleration coefficient as well as position update equation are familiarized, to escape stagnation. And in nDE, a new, mutation strategy and crossover rate is introduced, to avoid premature convergence. In order to balance between global and local search capability, after calculation of ihPSODE population, best half member has been identified and rest members discarded. Further, in current population nPSO is employed to maintain exploration and exploitation, then nDE is used to enhance convergence accuracy. The proposed ihPSODE and its suggested component nPSO and nDE have been tested over IEEE CEC2006 (it comprising linear, nonlinear, polynomial, quadratic, and cubic functions), IEEE CEC2014, and IEEE CEC2017 (these test suites consist of unimodal, multimodal, hybrid, and composition functions) well‐known benchmark functions. Then, the efficacy of the proposed algorithms has been demonstrated on nonconvex economic load dispatch problems which consist of 3‐, 6‐, 15‐, 40‐, and 140‐unit test systems. The comparative results with some of the state‐of‐the‐art algorithms confirm the effectiveness of the proposed algorithms in both aspects of better quality solutions and higher efficiency. [ABSTRACT FROM AUTHOR]