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Grain-oriented silicon steel (GO FeSi) laminations are vital components for efficient energy conversion in electromagnetic devices. While traditionally optimized for power frequencies of 50/60 Hz, the pursuit of higher frequency operation (f ≥ 200 Hz) promises enhanced power density. This paper introduces a model for estimating GO FeSi laminations’ magnetic behavior under these elevated operational frequencies. The proposed model combines the Maxwell diffusion equation and a material law derived from a fractional differential equation, capturing the viscoelastic characteristics of the magnetization process. Remarkably, the model’s dynamical contribution, characterized by only two parameters, achieves a notable 4.8% Euclidean relative distance error across the frequency spectrum from 50 Hz to 1 kHz. The paper’s initial section offers an exhaustive description of the model, featuring comprehensive comparisons between simulated and measured data. Subsequently, a methodology is presented for the localized segregation of magnetic losses into three conventional categories: hysteresis, classical, and excess, delineated across various tested frequencies. Further leveraging the model’s predictive capabilities, the study extends to investigating the very high-frequency regime, elucidating the spatial distribution of loss contributions. The application of proportional–iterative learning control facilitates the model’s adaptation to standard characterization conditions, employing sinusoidal imposed flux density. The paper deliberates on the implications of GO FeSi behavior under extreme operational conditions, offering insights and reflections essential for understanding and optimizing magnetic core performance in high-frequency applications.

Non-technical loss of electricity (comprising theft, fraud, non-payment and billing irregularities) is a significant issue, particularly in developing countries, and represents a large financial burden on utility companies, governments and society as a whole. This paper takes a wholistic and global view of the challenge and provides a broad perspective of the interrelated issues. Media reports and public perception of non-technical losses tend to focus on residential consumers, particularly those with limited financial resources, whereas review of more robust literature indicates that the largest proportion of non-technical losses is often due to industry, state-owned enterprises and relatively well-off residential consumers. Measures to reduce non-technical losses focusing on average residential consumers, such as pre-paid metering, therefore have limited effect on overall losses. Strengthening of legal and regulatory frameworks, particularly with regard to those larger users, and installing high security tamper-resistant metering systems for commercial consumers may have more effect. The reasons for non-technical losses, especially theft, are complex, but the customer–utility relationship is a key determinant. Improvement of this relationship through local participation in development of renewable energy schemes, such as rooftop solar photovoltaics, could bring benefit if challenges such as financing, design of the distribution system, utility company codes and standards and competence in post installation maintenance can be overcome.

Many banks use the loss distribution approach in their advanced measurement models to estimate regulatory or economic capital. This boils down to estimating the 99.9% value-at-risk of the aggregate loss distribution and is notoriously difficult to do accurately. Also, it is well-known that the accuracy with which the tail of the loss severity distribution is estimated is the most important driver in determining a reasonable estimate of regulatory capital. To this end, banks use internal data and external data (jointly referred to as historical data) as well as scenario assessments in their endeavor to improve the accuracy with which they estimate the severity distribution. In this paper, we propose a simple new method whereby the severity distribution may be estimated using both historical data and experts' scenario assessments. The way in which historical data and scenario assessments are integrated incorporates measures of agreement between these data sources, which can be used to evaluate the quality of both. In particular, we show that the procedure has definite advantages over traditional methods in which the severity distribution is modeled and fitted separately for the body and tail parts, with the body part based only on historical data and the tail part based on scenario assessments

This study introduces an enhanced version of quadratic interpolation optimization (QIO) merged with Gaussian mutation (GM) operator for optimizing photovoltaic (PV) units and capacitors within distribution systems, addressing practical considerations and discrete nature of capacitors. In this regard, the variations in power loading and power productions from PV sources are taken into consideration. The QIO is inspired by the generalized quadratic interpolation (GQI) method in mathematics and is enhanced with GM operator that introduces randomness into the solution to explore the search space and avoid premature convergence. The proposed QIO‐GM is tested on practical Egyptian and standard IEEE distribution systems, demonstrating its effectiveness in minimizing energy losses. Comparative studies against standard QIO, northern goshawk optimization (NGO), and optical microscope algorithm (OMA), as well as other reported algorithms, validate QIO‐GM’s superior performance. Numerically, in the first system, the designed QIO‐GM algorithm achieves 2.5% improvement over QIO, a 4.4% improvement over NGO, and a 9.2% improvement over OMA, leading to a substantial reduction in carbon dioxide (Co 2 ) emissions from 110,823.886 to 79,402.82 kg, reflecting a commendable 28.35% decrease. Similarly, in the second system, QIO demonstrates a significant reduction in Co 2 emissions from 72,283.328 to 54,627.65 kg, with a commendable 28.3% decrease. These results underscore QIO‐GM’s effectiveness in not only optimizing energy losses but also contributing to substantial environmental benefits through reduced emissions.

This work presents an optimal methodology based on an augmented, improved, subtraction-average-based technique (ASABT) which is developed to minimize the energy-dissipated losses that occur during electrical power supply. It includes a way of collaborative learning that utilizes the most effective response with the goal of improving the ability to search. Two different scenarios are investigated. First, the suggested ASABT is used considering the shunt capacitors only to minimize the power losses. Second, simultaneous placement and sizing of both PV units and capacitors are handled. Applications of the suggested ASAB methodology are performed on two distribution systems. First, a practical Egyptian distribution system is considered. The results of the simulation show that the suggested ASABT has a significant 56.4% decrease in power losses over the original scenario using the capacitors only. By incorporating PV units in addition to the capacitors, the energy losses are reduced from 26,227.31 to 10,554 kW/day with a high reduction of 59.75% and 4.26% compared to the initial case and the SABT alone, respectively. Also, the emissions produced from the substation are greatly reduced from 110,823.88 kgCO2 to 79,189 kgCO2, with a reduction of 28.54% compared to the initial case. Second, the standard IEEE 69-node system is added to the application. Comparable results indicate that ASABT significantly reduces power losses (5.61%) as compared to SABT and enhances the minimum voltage (2.38%) with a substantial reduction in energy losses (64.07%) compared to the initial case. For both investigated systems, the proposed ASABT outcomes are compared with the Coati optimization algorithm, the Osprey optimization algorithm (OOA), the dragonfly algorithm (DA), and SABT methods; the proposed ASABT shows superior outcomes, especially in the standard deviation of the obtained losses.

Purpose This paper aims to assess the impact of infrastructure development on Ghana’s economic growth. Design/methodology/approach Using data obtained from the World Bank’s World Development Indicators, the United States’ (US) International Energy Statistics and the Central Intelligence Agency’s (CIA) Factbooks from 1980 to 2016, an autoregressive distributed lag (ARDL) framework is used to determine the long- and short-run impact of the selected infrastructure stock and quality indices on Ghana’s economic growth. Findings Findings indicate a statistically significant relationship between infrastructure development and economic growth. Additionally, electricity-generating capacity is identified as the infrastructure stock index that has the greatest positive impact on Ghana’s economic growth. The study reveals that electricity-distribution loss has a significant negative effect over both long- and short-run periods. Research limitations/implications Commercial petroleum export from Ghana since 2010 has been a key contributor to economic growth. Although its aggregate effect is included in the annual GDP figures adopted for the study, the authors would have wished to assess its impact on GDP as an independent standard growth determinant. However, because of a lack of available data over this study period, petroleum exports could not be adopted as an independent standard growth determinant. Additionally, an aggregated index of infrastructure stock and quality could not be derived because of the small size of data available. Hence, this study did not assess its impact on Ghana’s economic growth. Practical implications The research provides pragmatic guidance to policymakers to focus their efforts on expanding electricity-generating capacity while simultaneously taking steps to curb electricity transmission and distribution losses. These two related actions offer the greatest positive impact on infrastructure development and, as a consequence, Ghana’s economic growth. Originality/value This paper represents the first attempt to empirically study the relationship between infrastructure development and Ghana’s economic growth. A key contribution to the existing body of knowledge includes strong evidence of a positive effect of infrastructure development upon Ghana’s economic growth. Results also reveal that the greatest positive impact on economic growth is derived from electricity-generation capacity. However, the study also uncovers a negative, but statistically significant, relationship between road and economic growth.

In this paper, we focus on modeling and predicting the loss distribution for credit risky assets such as bonds and loans. We model the probability of default and the recovery rate given default based on shared covariates. We develop a new class of default models that explicitly accounts for sector specific and regime dependent unobservable heterogeneity in firm characteristics. Based on the analysis of a large default and recovery data set over the horizon 1980-2008, we document that the specification of the default model has a major impact on the predicted loss distribution, whereas the specification of the recovery model is less important. In particular, we find evidence that industry factors and regime dynamics affect the performance of default models, implying that the appropriate choice of default models for loss prediction will depend on the credit cycle and on portfolio characteristics. Finally, we show that default probabilities and recovery rates predicted out of sample are negatively correlated and that the magnitude of the correlation varies with seniority class, industry, and credit cycle. This paper was accepted by Wei Xiong, finance.

Spurred by the simultaneous need for data privacy protection and data sharing, federated learning (FL) has been proposed. However, it still poses a risk of privacy leakage in it. This paper, an improved Differential Privacy (DP) algorithm to protect the federated learning model. Additionally, the Fast Fourier Transform (FFT) is used in the computation of the privacy budget , to minimize the impact of limited arithmetic resources and numerous users on the effectiveness of training model. Moreover, instead of direct analyses of the privacy budget through various methods, Privacy Loss Distribution (PLD) and privacy curves are adopted, while the number of artificial assignments hyperparameters is reduced, and the grid parameters delineated for FFT use are improved. The improved algorithm tightens parameter bounds and minimizes human factors’ influence with minimal efficiency impact. It decreases the errors caused by truncation and discreteness of PLDs while expanding the discreteness interval to reduce the calculation workload. Furthermore, an improved activation function using a temper sigmoid with only one parameter , smooths the accuracy curve and mitigates drastically fluctuating scenarios during model training. Finally, simulation results on real datasets show that our improved DP algorithm, which accounts for long trailing, facilitates a better balance between privacy and utility in federated learning models.

Increased performance requirements in new power electronics areas of application, such as electric aircraft, make innovations on different design levels necessary. In order to quickly compare different topologies, analytical loss equations provide a fast and straightforward way to narrow down the possible solution space. The approach widely used in the literature results in long and complex terms, which can only be compared between different literature sources with great effort. Moreover, the literature lacks a detailed summarizing description of these analytical equations and their derivation, starting from the standard two-level VSI up to three-level midpoint clamped inverter topologies, such as the ANPC topology in its different modulation schemes. The application of such higher-level inverter topologies allows hybrid device configurations to become performant solutions. This work aims to give a closed-form description of the analytical loss modeling and the theoretical background and provide an implementation approach for a wide span of inverter topologies and for different modulation methods.

Due to the increasing use of renewable, non-controllable energy generation systems energy storage systems (ESS) are seen as a necessary part of future power delivery systems. ESS have gained research interest and practical implementation over the past decade and this is expected to continue into the future. This is due to the economic and operational benefits for both network operators and customers, battery energy storage system (BESS) is used as the main focus of this research paper. This paper presents an analytical study of the benefits of deploying distributed BESS in an electrical distribution network (DN). The work explores the optimum location of installing BESS and its impact on the DN performance and possible future investment. This study provides a comparison between bulk energy storage installed at three different locations; medium voltage (MV) side and low voltage (LV) side of the distribution transformer (DT) and distributed energy storage at customers’ feeders. The performance of a typical UK DN is examined under different penetration levels of wind energy generation units and BESS. The results show that the minimum storage size is obtained when BESS is installed next to the DT. However, the power loss is reduced to its minimum when BESS and wind energy are both distributed at load busbars. The study demonstrates that BESS installation has improved the loss of life factor of the distribution transformer.