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Changes are emerging that will significantly alter structure and operation of this century's distribution networks, and photovoltaic (PV) systems will play greater role in energy sector, with implications for power system reliability. Considering uncertainties in solar irradiance and electrical loads and incorporating them into the optimization problem within an appropriate methodology is becoming increasingly important in reshaping distribution networks. In this paper, uncertainty scenarios are handled with Monte Carlo Simulation (MCS) under genetic algorithm (GA) and differential evolution- (DE-) based optimization, and probability distribution functions (pdf) of bus voltages and line current are obtained to be used in chance-constrained stochastic programming. This present study focuses on investigating impact of uncertainties in PV system operating under different irradiance scenarios on power loss with probabilistic constraints in distribution networks instead of precise deterministic limits to contribute more efficient and reliable use of energy. By combining meta-heuristic optimization and MCS technique under one framework, this paper contributes to knowledge base of how to allocate PV plants within distribution networks under chance-constrained strategy. In order to show the effectiveness of the proposed methodology, obtained optimization results are tested using MCS under set of uncertainty conditions and network constraints are evaluated for limit violation probabilities. The effectiveness of this method is investigated based on comparative results of two different optimization methods through probabilistic analysis and simulation. [ABSTRACT FROM AUTHOR]

To address the issue of multiobjective control in multienergy systems with diverse operational objectives, a two-stage optimization framework based on expected point tolerance has been proposed in this paper. In the first stage, a single objective function is used for optimization control to obtain the expected point of the multiobjective optimization problem. Then, in the second stage, by defining the allowable deviation between each optimization objective and the expected point, the original multiobjective optimization problem is transformed into a single objective optimization problem solution with tolerance measurement. Finally, in the simulation scene of a multienergy system, it is demonstrated that compared with the optimal results under each single objective method, the proposed method increases power line loss, maximum voltage deviation, new energy consumption, and economy by 2.22, 2.30, 1.02, and 2.45 times, respectively. Compared with the suboptimal results, the proposed method reduces power line loss by 22.26, 1.74, 1.09, and 0.97 times, respectively. Combining the shape of the Pareto frontier, it is demonstrated that the proposed method can comprehensively consider the needs of multiple power optimization objectives for forming a more reasonable and effective system optimization scheduling and also provide a new approach for solving multiobjective optimization problems. [ABSTRACT FROM AUTHOR]

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]

Accurate parameter identification plays an integral role in the modeling of an optimized solar module which in turn helps in the error-free prediction of its output. Five important parameters: the photoelectric current (Iph), ideality factor(α), saturation current (Io), series resistance (Rs), and shunt resistance (Rsh) are required for the accurate modeling of the PV cells/modules and need to be extracted as these parameters are not provided by the manufacturer in the datasheet. This paper proposes a new metaheuristic jellyfish optimization (JFO) algorithm for the parameter extraction of a solar module. The JFO algorithm achieves the optimal solution without being trapped in local solutions in less time. The parameter extraction using the JFO algorithm is done on two different solar modules i.e., Soltech-1STH-215P and PWP-201. The results are compared in terms of extracted parameters (Iph, α, Io, Rs, and Rsh) with the well-known optimization techniques like PSO, GA, and others available in the literature and with the manufacturer I–V and P–V characteristics. The proposed technique I–V and P–V characteristics are validated at different environmental conditions and are found to be similar to that of PSO and GA. It is also observed that the extracted parameters obtained using JFO are comparable with the other twenty-two techniques, and the proposed technique is one of the highly efficient techniques that can be utilized for parameter extraction of PV modules and to predict solar cell characteristics for all commercial modules without setting up any experimental measurements. MATLAB/simulation software is used for implementation and performance validation. [ABSTRACT FROM AUTHOR]

Power quality is the main problem with the power system network. Poor electricity quality may cause disruptions and financial challenges for consumers. Additionally, it could cause electronic gadgets to overheat, be damaged, or operate inadvertently. Transformers and other components of the power distribution system may also overheat and experience core saturation. This study investigates potential problems with the quality of the electricity in a photovoltaic linked power system. This paper suggests a new optimization method for day-ahead trading and control in DC microgrid power management (MG). The goal of the multiobjective optimization dispatch (MOOD) problem is to lower overall operational costs as well as the costs associated with power loss in efficient conservation systems and exhaust emission quantities such as nitrogen oxides, sulphur dioxide, and carbon dioxide. Using the weighted sum approach, the multiobjective optimization problem is reduced to a single optimization problem. The analytical hierarchy process (AHP) method is then used to calculate the weight coefficients while accounting for each objective function's preferences. Power balancing, high levels of renewable energy penetration, the most effective scheduling of battery charging and discharging, control of load curtailment, and the technical limitations of the system are all taken into account when evaluating the system's performance in both grid-connected and standalone operation modes. The ant lion optimizer (ALO) technique is taken into account to tackle MOOD, comparing the effectiveness of the proposed method to other well-known heuristic optimization techniques. The simulation's results demonstrate how effectively the proposed strategy can address the coordinated control and optimization dispatch difficulties. They also found that operating the MG system economically in grid-connected mode can save overall costs by roughly 4.70% compared to doing it in independent mode. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for the coordinated design of a power system stabilizer- (PSS-) and static VAR compensator- (SVC-) based stabilizer. For this purpose, the design problem is considered as an optimization problem, while the decision variables are the controllers' parameters. This paper proposes an effective optimization algorithm based on a rat swarm optimizer, namely, adaptive rat swarm optimization (ARSO), for solving complex optimization problems as well as coordinated design of controllers. In the proposed ARSO, instead of a random initial population, the algorithm starts the search process with fitter solutions using the concept of the opposite number. In addition, in each iteration of the optimization, the new algorithm replaces the worst solution with its opposite or a random part of the best solution to avoid getting trapped in local optima and increase the global search ability of the algorithm. The performance of the new ARSO is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed ARSO for coordinated design of controllers in a power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. The numerical investigations show that the new approach may provide better optimal damping and outperform previous methods. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for the coordinated design of a power system stabilizer- (PSS-) and static VAR compensator- (SVC-) based stabilizer. For this purpose, the design problem is considered as an optimization problem, while the decision variables are the controllers' parameters. This paper proposes an effective optimization algorithm based on a rat swarm optimizer, namely, adaptive rat swarm optimization (ARSO), for solving complex optimization problems as well as coordinated design of controllers. In the proposed ARSO, instead of a random initial population, the algorithm starts the search process with fitter solutions using the concept of the opposite number. In addition, in each iteration of the optimization, the new algorithm replaces the worst solution with its opposite or a random part of the best solution to avoid getting trapped in local optima and increase the global search ability of the algorithm. The performance of the new ARSO is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed ARSO for coordinated design of controllers in a power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. The numerical investigations show that the new approach may provide better optimal damping and outperform previous methods. [ABSTRACT FROM AUTHOR]

Code-switching is the choice of a language, a variant of using multiple languages in the same conversation. Broadly speaking, code-switching refers to adjusting one's language style, appearance, behavior, and expression in order to improve the comfort of others in exchange for fair treatment, quality service, and employment opportunities. "Besieged City" is considered a masterpiece of 20th-century China. From the data point of view, this work has a total of 110 code shifts, but there are many studies on this language phenomenon, but none of them involve the perspective of register. However, language translation research based on register theory is of great significance. It is generally believed that the human brain's thinking is divided into three basic ways: abstract (logical) thinking, image (intuitive) thinking, and inspiration (awareness) thinking. Artificial neural networks are the second way to simulate human thinking. Therefore, this paper proposes research on cognitive feature extraction of pun code-switching based on a neural network optimization algorithm. It mainly introduces code-switching under cognitive language and also briefly analyzes code-switching and speech feature extraction and uses a neural network optimization algorithm to conduct an in-depth analysis of code-switching. Finally, in the experimental part, the experimental analysis of the famous novel "Besieged City" is carried out, the application of 89 language code-switching in the text is deeply analyzed, and the data analysis of its three variables is carried out from the perspective of register. The experimental results show that: in novels, there are two types of code-switching: preparation and improvisation. 24 code-switches are prepared, accounting for 26.9%, and 10 code-switches are improvisation, accounting for 8.9%. As for the verbal code-change, there are both preparatory and impromptu ones. 36 code-switching cases were improvised, accounting for 40.4%, and 19 code-switching cases were prepared, accounting for 21.3%. The analysis also confirms that the more formal the text, the less linguistic transformation it contains. [ABSTRACT FROM AUTHOR]

This paper proposes parameter-free two best-worst optimizers (BWOs) that combine the searching capabilities of Jaya and Rao-1 algorithms to intensify their exploration and exploitation capabilities. For the proposed first optimizer, BWO-1, parallel taxonomy has been adopted for the Jaya and Rao-1 combination to obtain dual sets of updated solutions for a given current solution set. For the proposed second optimizer, BWO-2, along with the parallel taxonomy, a subloop of random scaling factors has been introduced in the solution updating mechanism of both standard Jaya and Rao-1 algorithms to generate multiple sets of updated solutions in a single iteration. The best solutions from obtained multiple solution-sets will survive only and lead to the next iteration. Hence, the proposed solution updating mechanisms for BWO-1 and BWO-2 increase the probability of getting a quality solution under set conditions. The ability and scalability of BWO-1 and BWO-2 were assessed, solving the optimization problem of power loss reduction and voltage deviation minimization in the IEEE 33-bus and 69-bus test systems. The results show that the BWO-2 exhibited a lead of 2.97%, 1.83%, and 0.72% over the Rao-1, Jaya, and BWO-1 techniques, respectively. Besides, the BWO-2 achieves up to 38.76% more reduction in power losses against the existing standard, improved, and hybrid optimization techniques. [ABSTRACT FROM AUTHOR]

The traditional fault location methods are not effective for the multisource active distribution network and are difficult to meet both requirements of timeliness and fault tolerance simultaneously. In this paper, a fault location method for active distribution networks which combines both improved matrix algorithm and optimization algorithm is proposed. The improved matrix algorithm is constructed to determine the fault section hypothesis by using the causal relationship between the alarm information and the fault section. Subsequently, network splitting is used for causal verification to check whether the alarm information is distorted. If the alarm information is normal, the fault sections can be quickly located by the above matrix algorithm. If the alarm information is distorted, the optimization model is constructed by using the fault section hypothesis selected by the matrix algorithm. The discrete particle swarm optimization (DPSO) algorithm is used to solve this optimization model, which can accurately locate the real fault sections. Hence, the advantages of both matrix algorithm and optimization algorithm are complementary. Simulation results show that the proposed method has the advantages of both high timeliness and fault tolerance in a complex active distribution network. [ABSTRACT FROM AUTHOR]

Improving efficiency with sustainable radial distribution networks (RDNs) is challenging for larger systems and small grid-connected RDNs. In this paper, the optimal placement of DGs with the Harris hawks optimizer (HHO) under seasonal load demands is proposed to simultaneously reduce total active and reactive power losses and minimize bus voltage drops with the consideration of operational constraints of RDNs. HHO is a newly inspired metaheuristic optimization algorithm primarily based on the Harris hawks' intelligent behaviors during the chasing of the prey. Furthermore, the authors have investigated four stages of DGs. The first stage involves the optimal allocation of one DG. The second stage includes an investigation with two DGs, the third stage considers three DGs, and the fourth stage investigates the integration of four DGs. The effectiveness of the applied HHO is validated on IEEE 33 and 69 bus RDNs, and results are analyzed by comparing with the standard optimization methods. The Big-O test is also executed for statistical analysis with standard algorithms. The simulation results reveal the better performance of the applied HHO under different circumstances than other algorithms. Furthermore, the total active and reactive power losses and bus voltage drops are improved by adding more DGs into IEEE 33 and 69 bus RDNs. [ABSTRACT FROM AUTHOR]

This paper considers the problem of tracking the global maximum power point (GMPP) in partially shaded conditions (PSCs) as a multiobjective optimization problem and solves it using a novel multiobjective optimization algorithm on the basis of Bayesian optimization formulation. Bayesian optimization is a metamodel-based global optimization method that is able to balance exploration and exploitation. The Pareto solutions are obtained by using a multiobjective Bayesian optimization algorithm. Also, a new acquisition function is proposed to improve the diversity and convergence of the Pareto solutions. Two objective functions are introduced to remove the large tracking errors and oscillations of the operating point around the GMPP. The suggested method is implemented online for GMPP tracking so that the suggested method monitors any change in environmental conditions and generates the optimal duty cycle for the DC-DC converter for the GMPP tracking (GMPPT) by the PV array. Several multipeak PSC scenarios are implemented and simulated to show efficiency of the suggested approach. The MATLAB/SIMULINK is employed to implement a photovoltaic (PV) system comprising a PV array, a boost converter, and the proposed multiobjective Bayesian optimization algorithm (MOBOA). The simulation results show a very satisfactory performance of the MOBOA in terms of transient state and steady-state oscillations and tracking speed. [ABSTRACT FROM AUTHOR]

In this paper, it is proposed to apply the mayfly optimization algorithm (MOA) to perform the coordinated and simultaneous tuning of the parameters of supplementary damping controllers, i.e., power system stabilizer (PSS) and power oscillation damping (POD), that actuate together with the automatic voltage regulators of the synchronous generators and the static synchronous series compensator (SSSC), respectively, for damping low-frequency oscillations in power systems. The performance of the MOA is compared with the performances of the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm for solving this problem. The dynamics of the power system is represented using the current sensitivity model, and, because of that, a current injections model is proposed for the SSSC, which uses proportional-integral (PI) controllers and the residues of the current injections at the buses, obtained from the Newton–Raphson method. Tests were carried out using the New England system and the two-area symmetrical system. Both static and dynamic analyses of the operation of the SSSC were performed. To validate the proposed optimization techniques, two sets of tests were conducted: first, with the purpose of verifying the performance of the most effective algorithm for tuning the parameters of PSSs, PI, and POD controllers, and second, with the purpose of performing studies focused on small-signal stability. The results have validated the current injections model for the SSSC, as well as have indicated the superior performance of the MOA for solving the problem, accrediting it as a powerful tool for small-signal stability studies in power systems. [ABSTRACT FROM AUTHOR]

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has attracted a lot of attention since it can handle multiobjective problems (MOP) with a complicated Pareto front. The procedure involves decomposing a MOP into single subproblems, which are eventually optimized simultaneously based on the MOP neighborhood information. However, the MOEA/D strategy tends to produce a distributed optimization that is not of good quality in some problems with complex Pareto optimal front, such as problems with a long tail and sharp peak, common in real-world situations. This paper proposes an improved MOEA/D to enhance the distributed optimization quality and minimize its complexity while accelerating the optimization to get a better solution. The improved method is achieved by incorporating a Hybrid Differential Evolution/Particle Swarm Optimization algorithm and a hybrid operator based on nondominated sorting and crowding distance algorithm. This incorporation takes place in the mutation generator and initial population part of the original MOEA/D algorithm. Simulations and comparisons are carried out based on some MOP benchmark functions to verify the proposed method's performance. The experimental results show that the proposed method achieves better performance compared to other algorithms. Furthermore, the proposed method is also applied to optimize the multiobjective wave energy converter model to maximize power per year and minimize cost per unit power. [ABSTRACT FROM AUTHOR]

Accurate analysis of load angle stability of the synchronous generator depends on considering mechanical parameters in addition to electrical parameters in synchronous generator modeling, which leads to increasing state-space variables and model complexity and ultimately reduces the accuracy and performance of conventional controllers. Therefore, an intelligent controller is required for the stable and reliable operation of the synchronous load angle generator. With this approach in this paper, first considering the electrical and mechanical parameters, a complete 14-order model of the synchronous generator is obtained. Then, a PID controller based on the Harris optimization algorithm is proposed to stabilize the load angle of the synchronous generator. The HHO optimization algorithm is a population-based optimization technique that optimizes PID controller coefficients. The effectiveness of the new HHO technique in optimizing the controller PID coefficients over known optimization techniques such as GA-PID and Modified GA-PID has been confirmed using simulation results. The simulation results show that the proposed controller has a higher performance in load angle stability compared to the sliding mode and fuzzy-PID controllers. [ABSTRACT FROM AUTHOR]