An IOT-Enabled Generator for Power Monitoring and Load Management with Power Factor Improvement

The quality of power supply and reliability play a vital role in the smooth operation and maintenance of commercial use. These requirements have significant applications when dealing with residential areas, hospitals, industries, educational sectors, banks and airports, etc. In this regard, backup diesel generators are considered the most important source for an uninterrupted supply of electricity. However, there is an emergent need to avoid sudden shutdown of generators in the events of overload, shortage of fuel flow, service interval and lagging of power factor. These common problems can be addressed through monitoring of power generator parameters, for instance, real time remote monitoring to measure the health of the generator, the problem of load management due to high demand of power during peak hours and power factor improvement due to exceeding inductive load. In this paper, our proposed architecture—based on an IOT solution—consists of different sensors, namely a current transformer for measuring load, fuel gauge for fuel level monitoring, and temperature measurement with the energy module to determine the power factor of the system. Our proposed system is operated and tested on a real trolley-mounted 25 KVA generator.

Comparison of Flexibility Factors and Introduction of A Flexibility Classification Using Advanced Heat Pump Control

With the increasing use of renewable energy, the energy flexibility of buildings becomes increasingly important regarding grid support. Therefore, there is a need to describe this flexibility in a concise manner. For the characterization of building energy flexibility, flexibility factors can be used. The comparison of a selection of existing flexibility factors shows that they are not easy to use or understand for designers and users. A simplification is necessary. The aim of this study is to introduce a flexibility classification that is easy to understand and shows in an easy way if a building already uses the lowest energy cost level or if further improvement is possible. The classification expresses the annual energy costs in colored classes: green (class A) for lowest up to red (class D) for highest level. Basically, the flexibility classes can be derived for any metric of interest, in this paper examples are shown for energy costs and CO2eq emissions. The results given are based on the simulation of load management scenarios with different penalty signals applied for the heat pump operation of a residential building.

Does Menopausal Hormone Therapy, Exercise, or Both Improve Pain and Function in Postmenopausal Women With Greater Trochanteric Pain Syndrome? A 2 × 2 Factorial Randomized Clinical Trial

Background: Greater trochanteric pain syndrome (GTPS) is a debilitating chronic condition, most prevalent in postmenopausal women. A positive association between high estrogen levels and tendon health may exist, and postmenopausal women have reduced estrogen. Menopausal hormone therapy (MHT) may reduce the incidence of tendon abnormality, particularly when combined with exercise. Purpose: To determine the effect of MHT and exercise on tendon pain and function in postmenopausal women with GTPS. Study Design: Randomized controlled clinical trial; Level of evidence, 1. Methods: Postmenopausal women (N = 132; n = 12, lost to follow-up) with GTPS were randomized into MHT and placebo transdermal cream groups combined with tendon-specific or sham exercise. All groups received education about avoiding gluteal tendon compression and load management throughout 12 weeks of intervention. The primary outcome was the Victorian Institute of Sport Assessment for gluteal tendinopathy (VISA-G), and secondary outcomes were measured at baseline and at 12 and 52 weeks. The Global Rating of Change was assessed at 12 and 52 weeks. A linear mixed-effects model was used to assess differences. Body mass index (BMI) was included as a covariate. Results: All participant groups improved over time (baseline vs 12 weeks, P < .001; baseline vs 52 weeks, P < .001). There was no difference among exercise groups measured by all outcomes (VISA-G: baseline, P = .97, mean difference [MD] = 0.10; 12 weeks, P = .49, MD = 2.15; 52 weeks, P = .32, MD = −3.08). There was a significant interaction effect between cream and BMI; therefore, the population was stratified by BMI levels (<25, <30, ≥30). The MHT groups (with exercise and education) had significantly better VISA-G outcomes (baseline, P = .04, MD = −11.20, 95% CI = −21.70 to −0.70; 12 weeks, P < .001, MD = −20.72, 95% CI = −31.22 to −10.22; 52 weeks, P = .002, MD = −16.71, 95% CI = −27.21 to −6.22) and secondary measure scores as compared with placebo at all time points when BMI was <25. Conclusion: MHT or placebo combined with tendon-specific or sham exercise plus education reduced pain and increased function for this population. For women with a BMI <25, MHT with any exercise plus education was better than placebo. A targeted exercise or sham exercise strategy is effective when prescribed with education about avoiding gluteal tendon compression and load management. Registration: ACTRN12614001157662 (Australian New Zealand Clinical Trials Registry).

Impact of geomagnetic disturbances on power transformers: risk assessment of extreme events and data availability

Certain rare events can have a drastic impact on power systems. Such events are generally known as high-impact low-probability (HILP) events. It is challenging to predict the occurrence of a HILP event mainly due to lack of data or sparsity and scarcity of data points. Yet, it is essential to implement an evidence-driven asset management strategy. In this paper, event tree analysis is used to assess the risk of power transformer failure due to a geomagnetically induced currents (GIC). Those currents are caused by geomagnetic disturbances in Earth’s magnetic field due to solar activity. To assess the impact on power transformers, an understanding of the mechanism and sequence of sub-events that lead to failure is required to be able to construct an event tree. Based on the constructed event tree, mitigation actions can be derived. GIC blockers or reducers can be used. However, that would require extensive installation and maintenance efforts, and the impact on system reliability has to be studied. Also, such technology is still in its infancy and needs extensive validation. A suggested alternative is to combine early warning data from solar observatories with a load management plan to keep transformers below their rated operation point such that a DC offset due to GIC would not cause magnetic core saturation and overheating. Load management and the risk of early warning false positives can incur a negative effect on reliability. Nevertheless, the risk assessment performed in this paper show that incorporating load management in asset planning is a viable measure that would offset the probability of catastrophic failure.

Application of Demand-side Technology in Power System Intelligent Regulation

To reduce peak demand for electricity, smooth load curve shape, improve power system safety and efficiency, this paper, by using intelligent home appliance user operation comfort model is set up to quantify the acceptance, this paper proposes a maximum minimum load management algorithm based on optimization strategy to change electric power use time and power consumption mode.The results show that the proposed model and algorithm can forecast and manage the power load well, and can reduce the peak to average ratio by 14.3% and the total expenditure by 15.3% while maintaining the operating comfort of power users to the maximum.The load management problem of multiple power users can reach Nash equilibrium in a finite number of iterations, and this Nash equilibrium point is also the global optimal point.