Institute Of Electrical And Electronics Engineers Research Articles

Systematic Review of AI-Assisted MRI in Prostate Cancer Diagnosis: Enhancing Accuracy Through Second Opinion Tools

Background: Prostate cancer is a leading cause of cancer-related deaths in men worldwide, making accurate diagnosis critical for effective treatment. Recent advancements in artificial intelligence (AI) and machine learning (ML) have shown promise in improving the diagnostic accuracy of prostate cancer. Objectives: This systematic review aims to evaluate the effectiveness of AI-based tools in diagnosing prostate cancer using MRI, with a focus on accuracy, specificity, sensitivity, and clinical utility compared to conventional diagnostic methods. Methods: A comprehensive search was conducted across PubMed, Embase, Ovid MEDLINE, Web of Science, Cochrane Library, and Institute of Electrical and Electronics Engineers (IEEE) Xplore for studies published between 2019 and 2024. Inclusion criteria focused on full-text, English-language studies involving AI for Magnetic Resonance Imaging (MRI) -based prostate cancer diagnosis. Diagnostic performance metrics such as area under curve (AUC), sensitivity, and specificity were analyzed, with risk of bias assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Results: Seven studies met the inclusion criteria, employing various AI techniques, including deep learning and machine learning. These studies reported improved diagnostic accuracy (with AUC scores of up to 97%) and moderate sensitivity, with performance varying based on training data quality and lesion characteristics like Prostate Imaging Reporting and Data System (PI-RADS) scores. Conclusions: AI has significant potential to enhance prostate cancer diagnosis, particularly when used for second opinions in MRI interpretations. While these results are promising, further validation in diverse populations and clinical settings is necessary to fully integrate AI into standard practice.

Exosomes: A Fluid Biopsy Source for Clinical Interventions of Noncommunicable Diseases Treatment: A Review

Introduction: The beginning of exosome biosynthesis is marked by the emergence of the initial endosomes through the inward splitting of the plasma cell membrane. This process is facilitated through the endosomal categorization complex essential for transport, which is also involved in the production of different extracellular vesicles. Exosomes are naturally occurring nanosized vesicles found in all bodily fluids and can be successfully extracted from preserved biological materials, while maintaining their structural integrity. Methods: The articles published recently in high-quality journals (Science Direct, Springer, Institute of Electrical and Electronics Engineers (IEEE), and Taylor & Francis) indexed in various indexing sources such as Scopus, Web of Science, PubMed, Google Scholar, and so on were collected using keywords such as fluid biopsy (FB) exosomes, endosomes, and noncommunicable diseases (NCDs) treatment, new biomarkers, and treatment. Discussion: In recent years, exosomes have emerged as an exciting option for “FB” that has demonstrated significant potential in the areas of noninvasive medical testing, predictions, as well as tracking responses to therapy for noncommunicable diseases. Nevertheless, specific constraints need to be addressed to expand the application of exosome-based FB as a widely accepted and reliable testing method in typical medical facilities. Conclusion: This review provides a comprehensive overview of our present understanding of exosomes to be an FB method for diagnosing, predicting outcomes, and tracking treatment responses in NCDs. It also discusses the main constraints, innovations in technology, as well as future possibilities of using this application in medical treatment.

Understanding AI's Role in Endometriosis Patient Education and Evaluating Its Information and Accuracy: Systematic Review.

Endometriosis is a chronic gynecological condition that affects a significant portion of women of reproductive age, leading to debilitating symptoms such as chronic pelvic pain and infertility. Despite advancements in diagnosis and management, patient education remains a critical challenge. With the rapid growth of digital platforms, artificial intelligence (AI) has emerged as a potential tool to enhance patient education and access to information. This systematic review aims to explore the role of AI in facilitating education and improving information accessibility for individuals with endometriosis. This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to ensure rigorous and transparent reporting. We conducted a comprehensive search of PubMed; Embase; the Regional Online Information System for Scientific Journals of Latin America, the Caribbean, Spain and Portugal (LATINDEX); Latin American and Caribbean Literature in Health Sciences (LILACS); Institute of Electrical and Electronics Engineers (IEEE) Xplore, and the Cochrane Central Register of Controlled Trials using the terms "endometriosis" and "artificial intelligence." Studies were selected based on their focus on AI applications in patient education or information dissemination regarding endometriosis. We included studies that evaluated AI-driven tools for assessing patient knowledge and addressed frequently asked questions related to endometriosis. Data extraction and quality assessment were conducted independently by 2 authors, with discrepancies resolved through consensus. Out of 400 initial search results, 11 studies met the inclusion criteria and were fully reviewed. We ultimately included 3 studies, 1 of which was an abstract. The studies examined the use of AI models, such as ChatGPT (OpenAI), machine learning, and natural language processing, in providing educational resources and answering common questions about endometriosis. The findings indicated that AI tools, particularly large language models, offer accurate responses to frequently asked questions with varying degrees of sufficiency across different categories. AI's integration with social media platforms also highlights its potential to identify patients' needs and enhance information dissemination. AI holds promise in advancing patient education and information access for endometriosis, providing accurate and comprehensive answers to common queries, and facilitating a better understanding of the condition. However, challenges remain in ensuring ethical use, equitable access, and maintaining accuracy across diverse patient populations. Future research should focus on developing standardized approaches for evaluating AI's impact on patient education and exploring its integration into clinical practice to enhance support for individuals with endometriosis.

Digital Traffic Lights: UAS Collision Avoidance Strategy for Advanced Air Mobility Services

With the advancing development of Advanced Air Mobility (AAM), there is a collaborative effort to increase safety in the airspace. AAM is an advancing field of aviation that aims to contribute to the safe transportation of goods and people using aerial vehicles. When aerial vehicles are operating in high-density locations such as urban areas, it can become crucial to incorporate collision avoidance systems. Currently, there are available pilot advisory systems such as Traffic Collision and Avoidance Systems (TCAS) providing assistance to manned aircraft, although there are currently no collision avoidance systems for autonomous flights. Standards Organizations such as the Institute of Electrical and Electronics Engineers (IEEE), Radio Technical Commission for Aeronautics (RTCA), and General Aviation Manufacturers Association (GAMA) are working to develop cooperative autonomous flights using UAS-to-UAS Communication in structured and unstructured airspaces. This paper presents a new approach for collision avoidance strategies within structured airspace known as “digital traffic lights”. The digital traffic lights are deployed over an area of land, controlling all UAVs that enter a potential collision zone and providing specific directions to mitigate a collision in the airspace. This strategy is proven through the results demonstrated through simulation in a Cesium Environment. With the deployment of the system, collision avoidance can be achieved for autonomous flights in all airspaces.

Edge Computing Method for False Data Injection Attack Detection in Electromechanical Transient Simulation Grid

Because edge computing is close to the terminal, it has significant advantages of low latency and high-security realtime control and has huge application prospects in the current popular smart grid. Because its edge side is close to the terminal, it can also effectively avoid the risk of information leakage when control instructions or communication data are transmitted to remote cloud center servers over a long distance. However, edge devices have a greater probability of encountering false data injection attacks (FDIAs) from illegal terminals because of their proximity to terminals. The transient electromechanical situation of the smart grid due to FDIAs is analyzed under edge computing. Due to the characteristics that the status values of grid nodes have temporal correlation before and after and spatial correlation between nodes, the Long Short-Term Memory (LSTM) for training time series data is selected to predict the status values of grid nodes in advance. The FDIA detection method is also proposed based on the LSTM network. By calculating the predicted value at each historical moment and the root means square error (RMSE) of the system state estimation at that moment, the detection threshold of the scheme is calculated through the cumulative distribution function of RMSE. Simulation experiments are conducted on the Institute of Electrical and Electronics Engineers (IEEE)-14 node standard system. The detection rate is as high as 99.71%, which verifies the effectiveness of the proposed FDIA detection scheme. This paper studies the security threat of FDIA to the stable power system operation and provides theoretical analysis and practical reference for other power grid security protection strategies.

Analysis on key technologies of autonomous vehicle sensing system

Autonomous driving technology is an inevitable result of the combination of intelligence and network technology, and is an important direction for the future development of the automotive industry. According to the American Institute of Electrical and Electronics Engineering (IEEE), autonomous vehicle will occupy 75% of the market share by 2040, which is to promote the key technology for the fourth round of artificial intelligence technology development will reshape the "pyramid" of the traditional automotive industry [1], leading a new transportation and travel ecosystem. the progress of automatic driving is the key to the further development and maturity of technology, and also the key to the industrialization of autonomous vehicle.

Non-intrusive demand response management strategy to mitigate the impacts of residential electric vehicle charging on distribution systems

The proliferation of electric vehicles (EVs) is expected to significantly increase electricity demand. However, if left uncontrolled, the aggregated demands from EVs have the potential to affect the power balance and quality of distribution systems adversely. Given this complex framework, this work presents a non-intrusive demand response management (DRM) approach to mitigate the impacts of residential EV charging on distribution systems. After analyzing a database comprising 157,250 operations, 74,316 charging patterns described by 17 attributes are obtained. These patterns are used for sampling EV fleets distributed in the Institute of Electrical and Electronics Engineers (IEEE) 33-bus test system. From scenarios with different levels of EV penetration, critical regions in the demand curve are identified, while actions for demand response (DR) are taken. Despite the increase in total energy consumption, larger EV fleets have greater potential for participation in DR. At a penetration level of 5%, it is not possible to shift excess energy consumption during the peak period, even with the participation of about 47% of the fleet, which corresponds to all users involved in EV charging during the peak period. At a penetration level of 50%, the energy goal can be achieved with the participation of about 15% of the fleet. In this latter scenario, the proposed management strategy can achieve a peak demand reduction of 9–11%.

A Comparative Analysis of European and American Reglementations Used in Power Quality Investigations

Abstract In the realm of electrical engineering, standards play a pivotal role in ensuring the quality and reliability of electrical systems. This paper conducts a comprehensive comparative analysis of the Institute of Electrical and Electronics Engineers (IEEE), International Electrotechnical Commission (IEC), and European Committee for Electrotechnical Standardization (CENELEC) standards, with a particular focus on power quality engineering. Specifically, it focused on key parameters such as the K-factor, the factor K, the Harmonic Loss Factor FHL and the unbalanced factor. By elucidating the differences and similarities between these standards, this study aims to provide insights into their respective impacts on electrical infrastructure, guiding engineering practices, and facilitating global trade. Understanding the regional applicability and industry impact of IEEE, IEC, and CENELEC standards is essential for engineers, manufacturers, and policymakers to navigate the complexities of electrical quality engineering in a diverse and interconnected world.

Space Division and WGAN-GP Based Fast Generation Method of Practical Dynamic Security Region Boundary

Fast and accurate transient stability analysis is crucial to power system operation. With high penetration level of wind power resources, practical dynamic security region (PDSR) with hyper plane expression has outstanding advantages in situational awareness and series of optimization problems. The precondition of obtaining accurate PDSR boundary is to locate sufficient points around the boundary (critical points). Therefore, this paper proposes a space division and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based fast generation method of PDSR boundary. First, the typical differential topological characterizations of dynamic security region (DSR) provide strong theoretical foundation that the interior of DSR is hole-free and the boundaries of DSR are tight and knot-free. Then, the space division method is proposed to calculate critical operation area where the PDSR boundary is located, tremendously compressing the search space to locate critical points and improving the confidence level of boundary fitting result. Furthermore, the WGAN-GP model is utilized to fast obtain large number of critical points based on learning the data distribution of the small training set aforementioned. Finally, the PDSR boundary with hyperplanes is fitted by the least square method. The case study is tested on the Institute of Electrical and Electronics Engineers (IEEE) 39-bus system and the results verify the accuracy and efficiency of the proposed method.

Fuzzy Classification Approach to Select Learning Objects Based on Learning Styles in Intelligent E-Learning Systems

In e-learning systems, even though the automatic detection of learning styles is considered the key element in the adaptation process, it does not represent the main goal of this process at all. Indeed, to accomplish the task of adaptation, it is also necessary to be able to automatically select the learning objects according to the detected styles. The classification techniques are the most used techniques to automatically select the learning objects by processing data derived from learning object metadata. By using these classification techniques, considerable results are obtained via several approaches and consist of mapping the learning objects into different teaching strategies and then mapping these strategies into the identified learning styles. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they do not directly map learning object metadata elements to learning style dimensions. Moreover, they do not consider the fuzzy nature of learning objects. Indeed, any learning object can be suitable for different learning styles at varying degrees of suitability. This highlights the need to find a way to remedy this shortcoming. Our work is part of the automatic selection of learning objects. So, we will propose an approach that uses the fuzzy classification technique to select learning objects based on learning styles. In this approach, the metadata of each learning object that complies with the Institute of Electrical and Electronics Engineers (IEEE) standard are stored in a database as an Extensible Markup Language (XML) file. The Fuzzy C Means algorithm is used, on one hand, to assign fuzzy suitability rates to the stored learning objects and, on the other hand, to cluster them into the Felder and Silverman learning styles model categories. The experiment results show the performance of our approach.

Enhancing Ethiopian power distribution with novel hybrid renewable energy systems for sustainable reliability and cost efficiency

Economic development relies on access to electrical energy, which is crucial for society’s growth. However, power shortages are challenging due to non-renewable energy depletion, unregulated use, and a lack of new energy sources. Ethiopia’s Debre Markos distribution network experiences over 800 h of power outages annually, causing financial losses and resource waste on diesel generators (DGs) for backup use. To tackle these concerns, the present study suggests a hybrid power generation system, which combines solar and biogas resources, and integrates Superconducting Magnetic Energy Storage (SMES) and Pumped Hydro Energy Storage (PHES) technologies into the system. The study also thoroughly analyzes the current and anticipated demand connected to the distribution network using a backward/forward sweep load flow analysis method. The results indicate that the total power loss has reached its absolute maximum, and the voltage profiles of the networks have dropped below the minimal numerical values recommended by the Institute of Electrical and Electronics Engineers (IEEE) standards (i.e., 0.95–1.025 p.u.). After reviewing the current distribution network’s operation, additional steps were taken to improve its effectiveness, using metaheuristic optimization techniques to account for various objective functions and constraints. In the results section, it is demonstrated that the whale optimization algorithm (WOA) outperforms other metaheuristic optimization techniques across three important objective functions: financial, reliability, and greenhouse gas (GHG) emissions. This comparison is based on the capability of the natural selection whale optimization algorithm (NSWOA) to achieve the best possible values for four significant metrics: Cost of Energy (COE), Net Present Cost (NPC), Loss of Power Supply Probability (LPSP), and GHG Emissions. The NSWOA achieved optimal values for these metrics, namely 0.0812 €/kWh, 3.0017 × 106 €, 0.00875, and 7.3679 × 106 kg reduced, respectively. This is attributable to their thorough economic, reliability, and environmental evaluation. Finally, the forward/backward sweep load flow analysis employed during the proposed system’s integration significantly reduced the impact of new energy resources on the distribution network. This was evident in the reduction of total power losses from 470.78 to 18.54 kW and voltage deviation from 6.95 to 0.35 p.u., as well as the voltage profile of the distribution system being swung between 1 and 1.0234 p.u., which now comply with the standards set by the IEEE. Besides, a comparison of the cost and GHG emission efficiency of the proposed hybrid system with existing (grid + DGs) and alternative (only DGs) scenarios was done. The findings showed that, among the scenarios examined, the proposed system is the most economical and produces the least amount of GHG emissions.

Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges

Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers’ experiences, tend to be error-prone and lack precision across vast farming areas, especially in data-scarce regions. Recent advancements in data collection, notably through high-resolution sensors and the use of deep learning (DL), have significantly increased the accuracy and breadth of agricultural data, providing better support for policymakers and administrators. In our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting, underscoring its growing significance in enhancing yield predictions. Our approach enabled us to identify 92 relevant studies across four major scientific databases: the Directory of Open Access Journals (DOAJ), the Institute of Electrical and Electronics Engineers (IEEE), the Multidisciplinary Digital Publishing Institute (MDPI), and ScienceDirect. These studies, all empirical research published in the last eight years, met stringent selection criteria, including empirical validity, methodological clarity, and a minimum quality score, ensuring their rigorous research standards and relevance. Our in-depth analysis of these papers aimed to synthesize insights on the crops studied, DL models utilized, key input data types, and the specific challenges and prerequisites for accurate DL-based yield forecasting. Our findings reveal that convolutional neural networks and Long Short-Term Memory are the dominant deep learning architectures in crop yield prediction, with a focus on cereals like wheat (Triticum aestivum) and corn (Zea mays). Many studies leverage satellite imagery, but there is a growing trend towards using Unmanned Aerial Vehicles (UAVs) for data collection. Our review synthesizes global research, suggests future directions, and highlights key studies, acknowledging that results may vary across different databases and emphasizing the need for continual updates due to the evolving nature of the field.

Multi-Objective Stochastic Paint Optimizer for Solving Dynamic Economic Emission Dispatch with Transmission Loss Prediction Using Random Forest Machine Learning Model

Dynamic economic emission dispatch problems are complex optimization tasks in power systems that aim to simultaneously minimize both fuel costs and pollutant emissions while satisfying various system constraints. Traditional methods often involve solving intricate nonlinear load flow equations or employing approximate loss formulas to account for transmission losses. These methods can be computationally expensive and may not accurately represent the actual transmission losses, affecting the overall optimization results. To address these limitations, this study proposes a novel approach that integrates transmission loss prediction into the dynamic economic emission dispatch (DEED) problem. A Random Forest machine learning model was offline-trained to predict transmission losses accurately, eliminating the need for repeated calculations during each iteration of the optimization process. This significantly reduced the computational burden of the algorithm and improved its efficiency. The proposed method utilizes a powerful multi-objective stochastic paint optimizer to solve the highly constrained and complex dynamic economic emission dispatch problem integrated with random forest-based loss prediction. A fuzzy membership-based approach was employed to determine the best compromise Pareto-optimal solution. The proposed algorithm integrated with loss prediction was validated on widely used five and ten-unit power systems with B-loss coefficients. The results obtained using the proposed algorithm were compared with seventeen algorithms available in the literature, demonstrating that the multi-objective stochastic paint optimizer (MOSPO) outperforms most existing algorithms. Notably, for the Institute of Electrical and Electronics Engineers (IEEE) thirty bus system, the proposed algorithm achieves yearly fuel cost savings of USD 37,339.5 and USD 3423.7 compared to the existing group search optimizer algorithm with multiple producers (GSOMP) and multi-objective multi-verse optimization (MOMVO) algorithms.

Metoda Sederhana untuk Memperbaiki Kinerja Motor Listrik 3 Fasa berdasarkan Insulation Resistance Test

In the electrical energy conversion laboratory, the stable operation of the induction motor is one of the most important factors. Failure of induction motor operation can be fatal in research activities in energy conversion laboratories. Prevention is needed so that the performance of induction motors remains optimal. this paper was created as an alternative for laboratory users of electrical energy conversion in maintaining the performance of induction motors. Based on a survey conducted by the Institute of Electrical and Electronics Engineers (IEEE), the main cause of electric motor operation failure is generally caused by insulation failure, so electric motor testing is generally dominated by insulation testing. Many insulation testing methods such as Insulation Resistance (IR) testing, Polarization Index (PI), Delta tangent, and other tests. In this paper, IR-based testing is proposed, because it is relatively cheap and easy to implement. The IR testing process is carried out using direct voltage from the Insulation Tester to the sample to be tested using the IEEE 43TM-2013 Standard. The test results show the IR value of the sample motor is good. This condition is categorized as safe to operate, so research activities that use induction motors can run well and safely.

Personal exposure to radiofrequency electromagnetic fields: A comparative analysis of international, national, and regional guidelines

A worldwide overview and analysis for the existing limits of human exposure to Radiofrequency Electromagnetic Fields (RF-EMF) is given in this paper. These reference levels have been established by different national and even regional governments, which can be based on the guidelines provided by the recommendations of the International Commission on Non-Ionizing Radiation Protection (ICNIRP), the International Committee on Electromagnetic Safety of the Institute of Electrical and Electronics Engineers (IEEE), and even in the United States of the Federal Communications Commission (FCC), as well as, are based on the so-called precautionary principle. Explicit reference is made to the exposure limits adopted in countries or regions, such as Canada, Italy, Poland, Switzerland, China, Russia, France, and regions of Belgium (Brussels, Flanders, Wallonia), where the limits are much lower than the international standards.The limits are compared to a selected set of in-situ measurements. This clearly shows that the measured values are typically very small compared to the international standards but could be somewhat higher compared to the reduced limits. Based on this observation and the reasonable assumption that the sensitivity of people to Electromagnetic Fields (EMF) is the same everywhere (whole-body), we propose the idea to establish a worldwide reference limit for the general public, thus applicable in all countries, if the ICNIRP considers it appropriate. Research must continue to generate measurement data that demonstrate the levels of exposure to which we are really exposed, and with this, provide arguments to the organizations that established the guidelines, especially the ICNIRP, to evaluate whether the current limits are too much. High and can be modified when considered pertinent. To the best of our knowledge, at no time has the reference level for the general public been exceeded.

Detecting Invasive Alien Plant Species Using Remote Sensing, Machine Learning and Deep Learning

Invasive alien plants (IAPs) are nonnative species that pose significant threats to the environment by outcompeting native vegetation and disrupting ecosystem functions. Efforts to monitor and eradicate IAPs have been limited due to the challenges in accurately identifying these plants using traditional remote sensing (RS) methods. This paper reviews the literature to identify the most accurate and reliable plant detection methods for IAPs. Advanced searches were conducted on ScienceDirect, Scopus and Institute of Electrical and Electronics Engineers (IEEE) Xplore databases using keywords such as ‘Remote Sensing (RS)’, ‘Machine Learning (ML)’, ‘Deep Learning (DL)’, ‘Invasive Alien Plant (IAP)’ and ‘detection’. The search yielded 1689 articles: 1129 focused on the RS methodologies, 303 on ML, 142 on DL and 115 combining all three approaches. The review found that the RF and support vector machine (SVM) algorithms are the most effective for detecting IAPs. This suggests that future research should prioritize the application of ML and DL techniques, particularly RF and SVM, due to their high potential for improving IAP identification and aiding in their eradication. These advancements align with Sustainable Development Goal (SDG) 15, which emphasizes the protection, restoration and sustainable use of terrestrial ecosystems, and SDG 8, which promotes sustained, inclusive and sustainable economic growth. Effective IAP management not only preserves biodiversity but also creates economic opportunities by improving land value and usability. This review underscores the importance of integrating advanced technological methods in environmental management to support both ecological and economic objectives.

Use of Artificial Intelligence in the Identification and Management of Frailty: A Scoping Review Protocol

IntroductionRapid population ageing and associated health issues such as frailty are a growing public health concern. While early identification and management of frailty may limit adverse health outcomes, the complex...

Control tuning methodology for modular multilevel converter‐based STATCOM

SummaryThe application of modular multilevel converters (MMC) in medium voltage static synchronous compensators (STATCOMs) has been investigated in recent years. A huge challenge for MMC‐STATCOM is to compute the gain of the controllers. Therefore, the contribution of this paper is the proposal of a control tuning methodology for MMC‐STATCOM. For this purpose, the control and tuning strategies are presented. Since MMC‐STATCOM can employ different modulation strategies, two distinct modulation strategies are considered: nearest‐level control, with variable switching frequency; and phase‐shifted carrier pulse‐width modulation, with fixed switching frequency. Moreover, the proposal was validated through simulation and an experimental platform. The results indicate that the power response follows the reference, besides supplying the internal losses of the converter. The 10% ripple tolerance of the submodule voltages is respected in steady state for both modulation strategies. Moreover, the 5% total demand distortion (TDD) limit recommended by the Institute of Electrical and Electronics Engineers (IEEE) standard is attended. These results demonstrate the effectiveness of the controls employed.

A resilient self‐healing approach for active distribution networks considering dynamic microgrid formation

AbstractA large number of renewable distributed generation (DG) systems connect to the distribution network, affecting the structure of the traditional distribution network and forming an active distribution network (ADN). Although the accelerating grid penetration of DGs brings significant challenges to the distribution network operation, the islanded operation capability of DGs provides a flexible solution to the self‐healing of ADNs from faults. To ensure that ADN can quickly recover and reconfigure in the event of a fault and continue to maintain safe, economical, and reliable operation, this paper proposes a dynamic microgrid formation method for ADNs combined with the dynamic network reconfiguration and intentional islanding operation of DGs. An optimization model is designed to represent the proposed self‐healing method, maximizing the load restoration and minimizing the DG cost, line network loss, and voltage excursion simultaneously. A binary hybrid optimization solver is applied to pursue the optimal self‐healing schedules from the optimization model. The self‐healing method is evaluated on the Institute of Electrical and Electronics Engineers (IEEE) 33‐node system and the IEEE 123‐node system, which indicate its rationality and effectiveness fully verified. By optimizing the on and off states of the normally open switches and the on‐grid and off‐grid operation states of DGs, ADNs not only get healed with minimum load curtailment, but also achieve minimal DG generation cost, network loss, and node voltage deviation. In addition, compared with traditional solvers, the proposed solver has a slightly higher computational time than the corresponding solver.

Walrus optimizer: A novel nature-inspired metaheuristic algorithm

Metaheuristic algorithms are intelligent optimization approaches that lead the searching procedure through utilizing exploitation and exploration. The increasing complexity of real-world optimization problem has prompted the development of more metaheuristic algorithms. Hence, this work proposes a novel swarm intelligence algorithm, Walrus optimizer (WO). It is inspired by the behaviors of walruses that choose to migrate, breed, roost, feed, gather and escape by receiving key signals (danger signals and safety signals). To test the capability of the proposed algorithm, 23 standard functions and the benchmark suite from the IEEE (Institute of Electrical and Electronics Engineers) Congress on Evolutionary Computation (CEC) 2021 are used. In addition, to evaluate the practicability of the proposed algorithm to solve various real-world optimization problems, 6 standard classical engineering optimization problems are examined and compared. For statistical purposes, 100 independent optimization runs are conducted to determine the statistical measurements, including the mean, standard deviation, and the computation time of the program, by considering a predefined stopping criterion. Some well-known statistical analyses are also used for comparative purposes, including the Friedman and Wilcoxon analysis. The results demonstrate that the proposed algorithm can provide special stability features and very competitive performance in dealing with high-dimensional benchmarks and real-world problems. The proposal of WO promotes the continuous development and application expansion of artificial intelligence, improves the efficiency of optimization calculation, and provides powerful tools for solving complex problems in the real world. The source code of WO is publicly availabe at https://ww2.mathworks.cn/matlabcentral/fileexchange/154702-walrus-optimizer-wo.