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To enhance the stability of power system, a power system stabilizer (PSS) is one of the control mechanisms installed in the power plants. A suitable PSS parameters set can help in reducing the power swing. In general, the design of PSS parameters is performed based on a nominal operating condition of the power system. In this paper, a multi‐objective optimization algorithm based on pole placement technique is proposed to find a robust PSS parameters set. The proposed strategy searches for the optimal PSS parameters under multiple operating conditions of the generator. Preliminary simulation results on a single machine infinite bus (SMIB) model show that the proposed PSS parameters design method can further improve the robustness of the overall power systems performance under different types of disturbance. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Vehicle routing problem (VRP) is one of the classic combinatorial optimization problems where an optimal tour to visit customers is required with a minimum total cost in the presence of some constraints. Recently, VRP is being solved with the use of deep reinforcement learning (DRL), with node sets considered (represented) as a graph structure. Existing Transformer based DRL solutions for VRP rely only on node information ignoring the role of information on the edges between nodes in the graph structure. In this paper, we proposed an attention‐based end‐to‐end DRL model to solve VRP which embeds edge information between nodes for rich graph representation learning. We used Transformer based encoder‐decoder framework with an edge information embedded multi‐head attention (EEMHA) layer in the encoder. The EEMHA‐based encoder learns underlying structure of the graph and generates an expressive graph topology representation by merging node and edge information. We trained our model using proximal policy optimization (PPO) with some code‐level optimization techniques. We conducted an experiment on randomly generated instances and on a real‐world data generated from road networks to verify the performance of our proposed model. The result from all experiments show that our model performs better than the existing DRL methods and most of the conventional heuristics with good generalizability from random instance training to real‐world instance testing of different problem size. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a popular optimization technique known for its simplicity and effectiveness. This paper introduces a variant that achieves a better balance between exploration and exploitation, named DiPSO. DiPSO incorporates a novel strategy based on trends in mean distance between individuals for local exploitation control. Experiments on 29 benchmark functions demonstrate that DiPSO consistently outperforms the state‐of‐the‐art variant of PSO. Convergence analysis reveals that DiPSO achieves faster convergence and superior solutions. These results highlight the effectiveness of DiPSO in solving optimization problems. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Energy‐efficient train operation (EETO) in high‐speed railways (HSRs) is an extra cost‐effective and flexible means to promote energy‐saving. This paper first examines the energy consumption sources and energy‐saving measures of high‐speed trains (HSTs). Then presents the EETO in HSRs, including three categories: energy‐efficient train control, energy‐efficient train timetabling, and EETO considering train timetabling and driving strategy. Next, the current research status and progress on three aspects of EETO in HSRs, namely optimization algorithm, constraint condition, and oriented scenario, are sorted out. Finally, the research on EETO in HSRs has prospected and proposed directions that can study in depth in the future. By summarizing and reviewing the research on EETO in HSRs, it is hoped to furnish a reference for the railway sector to enhance quality, efficiency, and green, low‐carbon development. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The rapid growth of distributed energy resources integrated in distribution systems leads to an increasing need of continuously and automatically changing the system topology to realize the economic operation of distribution networks. This paper proposes an optimization model of dynamic reconfiguration for distribution networks based on a new method of time series analysis. Equivalent daily curve considering time‐varying nature of distributed generator and load demands is divided by an improved fuzzy C‐means clustering algorithm, where the indicator of section function is set to find the optimal reconfiguration time intervals. The uncertainty of distributed generator outputs and load demands is described by the interval algorithm. Then the affine Taylor expansion is adopted to solve the interval power flow equations. The reconfiguration optimization model is solved with decimal particle swarm optimization algorithm based on loop search. The optimal dynamic reconfiguration of a modified 70‐bus test system with distributed generators is carried out and the simulation results demonstrate the effectiveness and superiority of the proposed method. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Aiming at the problem of unnecessary waste of power resources in the power system caused by the uncertainty of wind farm output, and then affecting the economics of the system. This paper proposes a power system scheduling model based on wind power output forecasting errors. In the forecasting stage, in view of the defect that the Fruit Fly Optimization Algorithm (FOA) is easy to fall into the local optimal value, an Adaptive Mutation Fruit Fly Optimization Algorithm (AMFOA) based on the flavor concentration variance is proposed. And then, the parameters of Extreme Learning Machine (ELM) are optimized by AMFOA to predict the wind power output. In the scheduling phase, a limit scenario is established based on the obtained prediction error to reduce the fluctuation of the wind farm output. When modeling, the influence of the prediction error is considered in the power balance equation, and it is constructed to maximize the probability of its establishment. And then, incorporate it into the objective function. Based on this, a scheduling model is established and solved by AMFOA. Finally, an example is used to verify the accuracy of the proposed forecasting model and the economics of the scheduling model. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Evolutionary algorithm (EA) is a generic term for optimization algorithms inspired by biological optimization processes in the natural world. Although EAs are widely applied to complex real‐world problems because they do not require mathematical expression of target problems, theoretical design methods of EAs have not been established. To solve this issue, this paper proposes an approach of designing EAs within an algorithm framework in which mathematical characteristics are derived, and it also presents a concrete framework for that purpose. The presented framework, Regulated Evolution Strategies (RES) provides a stability analysis result that contributes to designing algorithms with expected behavior. The RES framework has a high degree of freedom in designing algorithms, so that it is possible to incorporate various contrivances such as local improvement of samples and reduction of constraint violations in RES‐based algorithms while maintaining the stability analysis result. Numerical experiments prove that the RES framework has a capability for designing high‐performance EAs. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Power transformers are important pieces of equipment for the operation of power systems. Accurate diagnosis of their fault is closely related to the stable operation of the entire power grid. In order to improve the diagnostic accuracy of transformer fault, the grey wolf optimization (GWO) algorithm is introduced, and the differential evolution mechanism is integrated into the algorithm. Therefore, this paper proposes a transformer fault diagnosis method based on the modified grey wolf optimization algorithm (MGWO) and support vector machine (SVM), so that the application method realizes optimization of the penalty factor and the kernel parameter in SVM. Through the analysis of existing data examples, the SVM model optimized by the MGWO algorithm has the advantages of good generalization and strong predictive ability, and its fault diagnostic accuracy is higher than those of the genetic algorithm, particle swarm optimization algorithm, and GWO algorithm. This method has practical application significance. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the properties of the superior solution set search (SSSS) problem and point out the structural similarities between it and the multiobjective optimization problem. Based on the analyzed properties, we propose a new Firefly algorithm (FA) based on superior relations, which can search for the superior solution set by using the user's quantitative desire level as a search strategy. By introducing the distance of the problem space into the moving mechanism, FA makes it possible to search for multiple local optimal solutions by dividing the solution set into multiple groups. By analyzing this property of FA, we clarify the affinity between FA and the SSSS problem. We subsequently analyze the SSSS problem and FA, and discuss their properties together in a similar problem setting. We propose an FA based on superior relations as a new optimization technique for the superior solution set problem based on these analyses. Numerical experiments are then conducted using the SSSS problem and demonstrate the usefulness of the proposed method by comparing the performance of the proposed method with the conventional FA. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Large‐scale grid‐connected electric vehicles (EVs) and wind power generation have brought new challenges to economical optimal scheduling of power system because of their uncertainties. It is essential to establish accurate scheduling models and arrive at the economical optimal scheduling strategy. This paper develops a probability model of wind power output, which adopts the normal distribution to depict the randomness of wind speed, considering turbulence. The probability model of vehicle‐to‐grid (V2G) is also established, and the corresponding probability distribution is used to describe the initial charge/discharge time, daily mileage, and charge state of the EV. A collaborative scheduling model is built for the grid and consumers, taking account of the fuel cost of thermal power unit, the uncertainty cost of wind power output, and the cost of V2G service. The scheduling model is solved by an improved particle swarm optimization algorithm, which applies the inertia weight obeying the standard normal distribution and the nonlinear anticosine learning factors to avoid premature convergence and the loss of population space diversity. Finally, the validity and practicability of the proposed method are verified by the simulation of a ten‐machine power system integrated with wind power and EVs. Simulation results show that V2G can optimize the output of thermal power units and eliminate the surplus output of wind power. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Abstract: Recently, wind power generation are installed as a countermeasure for the environmental issue in Japan. However, wind power generation output is fluctuated due to wind condition. Moreover, it is some negative impact for system operation such as the economic load dispatching control and the load frequency control. In Japan, NEDO has started the project as a countermeasure for massive penetration of wind power generation in 2014, called “R&D Project on Grid Integration of Variable Renewable Energy.” In this paper, we proposed the output fluctuation suppression control method of wind power generation using the battery and forecast data of wind generation output. Specifically, the target value of wind power generation output is calculated to decrease the deviation output from fluctuation suppression code for ramp event using deterministic optimization method. The effectiveness of proposed method is evaluated through the computational simulation using forecast data and actual wind power generation output in Tohoku area. [ABSTRACT FROM AUTHOR]

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner. [ABSTRACT FROM AUTHOR]

The Spiral Optimization Algorithm (SOA) is an optimization technique developed recently (2011) by K. Tamura and K. Yasuda at Tokyo Metropolitan University-Japan. SOA is a metaheuristic based on an analogy of spiral phenomena in nature and it is simple in concept, few in parameters and easy in implementation. In this paper, SOA is proposed for solving the Combined Economic and Emission Dispatch (CEED) problem. It is aimed, in the CEED problem, that scheduling of generators should operate with both minimum fuel costs and emission levels, simultaneously, while satisfying the load demand and operational constraints. The CEED problem is formulated as a multi-objective problem by considering the fuel cost and emission objectives of generating units. The bi-objective optimization problem is converted into a single objective function using a price penalty factor. The proposed algorithm has been implemented on three test systems with 3, 6, and 40 generating units, with different constraints and various cost curve nature. In order to see the effectiveness of the proposed algorithm, its results are compared to those reported in the recent literature. Those results are quite encouraging showing the good applicability of SOA for CEED problem. [ABSTRACT FROM AUTHOR]