Explore the vast range of titles available.

Saidi Hassan was a teacher, but also a lifelong student, completing a doctorate in surgery from the University of Nairobi just last year. Colleagues said he was not pursuing education for its own sake, but for the opportunity to improve his skills and to acquire knowledge he could share. “He had an idea of impacting others”, said Daniel Ojuka, a lecturer in the University of Nairobi's Department of Surgery. “His interest was in developing people.”

This research investigates the impact of solar renewable energy source (RES) penetration on power system reliability indices. The increasing integration of solar RES presents challenges to grid stability due to their intermittent nature, necessitating advanced methodologies for reliability assessment. This study focuses on three key indices: System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), and Expected Energy Not Supplied (EENS). A Bayesian approach using Hamiltonian Monte Carlo (HMC) sampling within Stan utilizing rstanarm package in R was employed to analyze real-world data from the CENPELCO San Carlos 20MVA substation in the Philippines. Gamma regression models were selected as superior to Gaussian and Weibull models based on model fitting scores of WAIC and LOOIC with − 4158.4 and − 4265.4 expected log predictive density scores respectively. The models incorporated solar RES penetration levels, load profiles, and average daily-hourly solar irradiance as predictors. Results indicate no statistically significant relationship between solar RES penetration and SAIDI or SAIFI, with zero Gamma model coefficients, potentially due to the masking effect of solar irradiance variability, diverse load profiles, and the conventional substation-level reliability index measurements. However, EENS showed Gamma model coefficient of 0.1, a small non-significant positive relationship with solar RES penetration. The study highlights the need for more sophisticated models to fully capture the dynamic relationships between solar RES integration and reliability indices. This study indicates that increasing solar RES penetration alone may not guarantee improved system-level reliability indices for distribution utilities. Grid operators should prioritize investments in reliability-enhancing measures and sophisticated grid management strategies to address solar intermittency. The findings emphasize the importance of customer-level impact assessments and suggest that policy frameworks should incentivize grid modernization and standardized customer-level reliability assessment methods. Future research should explore higher solar RES penetration scenarios and incorporate additional factors influencing reliability. This research offers a robust Bayesian framework for assessing the impact of solar RES penetration on power system reliability.

The rapid growth of distributed photovoltaic (PV) resources is transforming distribution networks into active systems with highly variable net loads, while the rising frequency and severity of extreme weather events is increasing outage risk and restoration challenges. In this context, utilities require reliability assessment tools that jointly represent operational variability and climate-driven stressors beyond stationary assumptions. This paper presents a weather-aware probabilistic framework to quantify the reliability of active distribution networks with high PV penetration. The approach synthesizes realistic residential demand and PV time series at 15-min resolution, models extreme weather as a low-probability/high-impact escalation of component failure rates and restoration uncertainty, and computes IEEE Std 1366–2022 indices (SAIFI, SAIDI, ENS) through Monte Carlo simulation. The methodology is validated on a modified IEEE 33-bus feeder with parameter values representative of urban/suburban overhead networks. Compared with classical reliability modeling, the proposed framework captures in a unified pipeline the joint effects of load/PV stochasticity, weather-dependent failure escalation, and repair-time dispersion, providing a consistent statistical interpretation supported by kernel density estimation and convergence diagnostics. The results show that (i) extreme weather shifts the distributions of SAIFI, SAIDI and ENS to the right and thickens upper tails (higher exceedance probabilities); (ii) PV penetration yields a non-monotonic response with measurable improvements up to intermediate levels and saturation/partial degradation at very high penetrations; and (iii) compound risk is nonlinear, as the mean ENS surface over (r[sub.PV],P[sub.ext]) exhibits a valley at moderate PV and a ridge for large storm probability. A tornado analysis identifies the base failure rate, storm escalation factor and storm exposure as dominant drivers, in line with resilience literature. Overall, the framework provides an auditable, scenario-based tool to co-design DER hosting and resilience investments.

The existing literature on power grid reliability extensively examines the effects of individual automation technologies, such as Smart Grids, IoT, and AI, on reducing SAIDI (System Average Interruption Duration Index) and SAIFI (System Average Interruption Frequency Index) indices. However, previous studies have largely focused on partial analyses, often limited to specific aspects of grid operation or isolated case studies. As a result, there is a lack of a comprehensive and integrated theoretical approach that considers the interdependencies between different automation technologies, their impact on various levels of grid management and the economic consequences of their deployment. This study presents a novel theoretical framework aimed at providing a holistic perspective on power grid automation and its impact on energy supply reliability. The key elements of this approach include developing a multidimensional mathematical model that integrates the impact of key automation technologies on SAIDI and SAIFI, allowing for a quantitative assessment of different implementation strategies and applying a probabilistic approach to predict the likelihood of power outages based on the level of automation and real-time grid conditions. This proposed framework offers a holistic view of power grid automation, integrating technical, economic and operational dimensions. It serves as a foundation for further empirical research and the implementation of intelligent grid modernisation strategies, aiming to enhance power supply stability and increase the resilience of distribution networks against outages. The introduced concept aligns with the current challenges of the energy transition, providing utilities and policymakers with analytical tools for making optimal decisions regarding the adoption of digitalisation and automation technologies in the power sector.

The impact of natural hazards on the Puerto Rican electric system significantly affects the well-being of its population, particularly affecting populations in remote areas. The present study aims to identify the most affected geographic regions using a complementary approach that leverages open source governmental data. By harnessing the power of comprehensive data and utilizing a graphical analysis tool—a set of open access dashboards with maps and graphs—positive change can be fostered, providing stakeholders with comprehensive data for informed decision-making. The dashboards facilitate the identification, association, and analysis of different metrics such as customers without services, SAIDI (System Average Interruption Duration Index), SAIFI (System Average Interruption Frequency Index), and CHoLES (Customers Hours of Lost Electric Service). Additionally, the dashboard provides information on the progress of the net metering, demonstrating the rapid integration of PV (photovoltaic) and BES (battery energy storage) systems. Interactive dashboards for stakeholders to analyze key metrics such as SAIDI, SAIFI and CHoLES were designed and built using an interactive data visualization platform.

The studies on strategies for improving restoration times in electrical distribution systems are extensive. They have theoretically explored the application of mathematical models, the implementation of remotely controlled systems, and the use of digital simulators. This research aims to connect conceptual studies and the implementation of improvements and impact assessment in electrical distribution systems in developing countries, where distribution technologies vary widely, by employing a comprehensive methodology. The proposed research examines the restoration times for faults in substations within general distribution networks in the central–western region of Mexico. The study comprises these stages: (a) diagnosing the electrical supply, demand, and infrastructure; (b) analyzing the electrical restoration time and the restoration index of the substations; and (c) providing recommendations and implementing pilot tests for improvements in the identified critical substations. The results revealed 12 analysis zones, including 120 distribution substations, 150 power transformers, and 751 medium voltage circuits. Among the substations, 73% have ring connections, 15% have TAP connections, and 12% have radial connections. Additionally, 27% of the substations rely on only a single distribution line. The study identified areas with significant challenges in restoring electricity supply, particularly focusing on power transformers: 32 transformers with permanent power line failures requiring load transfer via medium voltage; 67 transformers requiring optimized restoration maneuvers due to specific characteristics; and 4 areas with opportunities to enhance the reliability of the power supply through remote-controlled link systems. The analysis resulted in the installation of 145 remote link systems, which improved restoration rates by over 40%. This approach is expected to be replicated throughout Mexico to identify improvements needed in the national distribution system.

In order to ensure continuous energy supply, Distribution System Operators (DSOs) have to monitor and analyze the condition of the power grid, especially checking for random events, such as breakdowns or other disturbances. Still, relatively little information is available on the operation of the Low Voltage (LV) grid. This can be improved thanks to digital tools, offering online processing of data, which ultimately increases effectiveness of the power grid. Among those tools, the use of the Advanced Metering Infrastructure (AMI) is especially conducive for improving reliability. AMI is one of the elements of the system Supervisory Control and Data Acquisition (SCADA) for the LV grid. Exact knowledge of the reliability conditions of a power grid is also indispensable for optimizing investment. AMI is also key in providing operational capacity for carrying out energy balance in virtual power plants (VPPs). This paper deals with methodology of identification and location of faults in the AMI-supervised LV grid and with calculating the System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) on the basis of the recorded events. The results presented in the paper are based on data obtained from seven MV/LV transformer stations that supply over 2000 customers.

The development and use of PV (Photovoltaic), Wind, and Hydro-based Distributed Generation (DG) is presently on the rise worldwide for improving stability and reliability, and reducing the power loss in the distribution system with reduced emission of harmful gases. A crucial issue addressed in this article, due to the increased penetration of DGs, is islanding operations. The detection of islanding is performed by a proposed v&f (voltage and frequency) index method. The reliability indices of the IEEE-33 and 118 radial bus distribution system after the detection of islanding by the proposed method is evaluated by considering the islanding issue as customer interruption. To mitigate the islanding, a reconfiguration strategy using Particle Swarm Optimization (PSO) is also performed and the proposed strategy is also evaluated with the conventional reconfiguration strategy of the distribution system.

Background Saidi sheep are the most abundant ruminant livestock species in Upper Egypt, especially in the Assiut governorate. Sheep are one of the most abundant animals raised for food in Egypt. They can convert low-quality roughages into meat and milk in addition to producing fiber and hides therefore; great opportunity exists to enhance their reproduction. Saidi breed is poorly known in terms of reproduction. So this work was done to give more information on some hormonal, oxidative, and blood metabolites parameters in addition to histological, histochemical and immunohistochemical investigations of the ovary during follicular phase of estrous cycle. The present study was conducted on 25 healthy Saidi ewes for serum analysis and 10 healthy ewes for histological assessment aged 2 to 5 years and weighted (38.5 ± 2.03 kg). Results The follicular phase of estrous cycle in Saidi sheep was characterized by the presence of ovarian follicles in different stages of development and atresia in addition to regressed corpus luteum. Interestingly, apoptosis and tissue oxidative markers play a crucial role in follicular and corpus luteum regression. The most prominent features of the follicular phase were the presence of mature antral (Graafian) and preovulatory follicles as well as increased level of some blood metabolites and oxidative markers. Here we give a new schematic sequence of ovarian follicles in Saidi sheep and describing the features of different types. We also clarified that these histological pictures of the ovary was influenced by hormonal, oxidative and blood metabolites factors that characterizes the follicular phase of estrous cycle in Saidi sheep. Conclusion This work helps to understanding the reproduction in Saidi sheep which assist in improving the reproductive outcome of this breed of sheep. These findings are increasingly important for implementation of a genetic improvement program and utilizing the advanced reproductive techniques as estrous synchronization, artificial insemination and embryo transfer.

This paper introduces an innovative approach that significantly enhances the reliability indices of the radial distribution system, thereby effectively upgrading its overall performance. The optimal location and number of isolators in the electrical distribution network are determined using a metaheuristic algorithm, such as the genetic algorithm and particle swarm optimization approaches. The product of the system's annual outage length and the total number of consumers affected is used to frame the problem within the restrictions of the ideal number of switches and the need for a power supply that can meet consumer requests. The cost of energy not served (CENS) and system average interruption duration index (SAIDI) are reliability indicators that have been used to identify the root of the issue. The method has been put to the test on 59 and 34 load point systems, and the results demonstrate that the suggested method in the study is accepted. The GA and PSO are compared on 34 and 59 bus radial network and found that GA provide better result. The ENS and profit have been compared, and it is found GA provides superb result in both cases. It is also observed that the optimal number of isolators required in 59 bus system is 31 and 42 for GA and PSO, respectively. The application of the suggested technique for isolator placement in 59 bus radial network provides a reduction in SAIDI 45.6% and 43.97% from GA and PSO, respectively, it is also observed that reduction in ENS is 59.9% and 58.45% from GA and PSO, respectively. Diminution in ENS indicates that the final group of clients is happy with the quality of the service being rendered in their location. Graphical abstract