Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty
تجزیه و تحلیل جایگزین قدرت تطبیقی مبتنی بر یادگیری تقویتی برای مدیریت انرژی سیستم های ذخیره سازی انرژی ترکیبی مستقل با توجه به عدم اطمینان-2020
Hybrid energy storage systems (HESS) involve synergies between multiple energy storage technologies with complementary operating features aimed at enhancing the reliability of intermittent renewable energy sources (RES). Nevertheless, coordinating HESS through optimized energy management strategies (EMS) introduces complexity. The latter has been previously addressed by the authors through a systems-level graphical EMS via Power Pinch Analysis (PoPA). Although of proven efficiency, accounting for uncertainty with PoPA has been an issue, due to the assumption of a perfect day ahead (DA) generation and load profiles forecast. This paper proposes three adaptive PoPA-based EMS, aimed at negating load demand and RES stochastic variability. Each method has its own merits such as; reduced computational complexity and improved accuracy depending on the probability density function of uncertainty. The first and simplest adaptive scheme is based on a receding horizon model predictive control framework. The second employs a Kalman filter, whereas the third is based on a machine learning algorithm. The three methods are assessed on a real isolated HESS microgrid built in Greece. In validating the proposed methods against the DA PoPA, the proposed methods all performed better with regards to violation of the energy storage operating constraints and plummeting carbon emission footprint.
Keywords: Hybrid energy storage systems | Energy management strategies | Model predictive control | Kalman filter | Reinforcement learning
Scaling laws and similarity models for the preliminary design of multirotor drones
مقیاس بندی قوانین و مدل های شباهت برای طراحی اولیه هواپیماهای بدون سرنشین چند منظوره-2020
Multirotor drones modelling and parameter estimation have gained great interest because of their vast application for civil, industrial, military and agricultural purposes. At the preliminary design level the challenge is to develop lightweight models which remain representative of the physical laws and the system interdependencies. Based on the dimensional analysis, this paper presents a variety of modelling approaches for the estimation of the functional parameters and characteristics of the key components of the system. Through this work a solid framework is presented for helping bridge the gaps between optimizing idealized models and selecting existing components from a database. Special interest is given to the models in terms of reliability and error. The results are compared for various existing drone platforms with different requirements and their differences discussed.
Keywords: Multirotor drones | Scaling laws | Dimensional analysis | Surrogate models | Propulsion system | Landing gear
Zero-net energy management for the monitoring and control of dynamically-partitioned smart water systems
مدیریت انرژی صفر خالص برای نظارت و کنترل سیستم های اب هوشمند تقسیم شده -2020
The optimal and sustainable management of water distribution systems still represent an arduous task. In many instances, especially in aging water net-works, pressure management is imperative for reducing breakages and leakages. Therefore, optimal District Metered Areas represent an effective solution to decreasing the overall energy input without performance compromise. Within this context, this paper proposes a novel adaptive management framework for water distribution systems by reconfiguring the original network layout into (dynamic) district metered areas. It utilises a multiscale clustering algorithm to schedule district aggregation/desegregation, whilst delivering energy and supply management goals. The resulting framework was tested in a water utility network for the simultaneously production of energy during the day (by means of the installation of micro-hydropower systems) and for the reduction of water leakage during the night. From computational viewpoint, this was found to significantly reduce the time and complexity during the clustering and the dividing phase. In addition, in this case, a recovered energy potential of 19 MWh per year and leakage reduction of up to 16% was found. The addition of pump-as-turbines was also found to reduce investment and maintenance costs, giving improved reliability to the monitoring stations. The financial analyses to define the optimal period in which to invest also showed the economic feasibility of the proposed solution, which assures, in the analysed case study, a positive annual net income in just five years. This study demonstrates that the combined optimisation, energy recovery and creation of optimized multiple-task district stations lead to an efficient, resilient, sustainable, and low-cost management strategy for water distribution networks.
Keywords: Water distribution systems | Micro-hydropower systems | Sustainable and smart cities | Water-energy nexus | Water leakage reduction | Financial return-on-investment
Reliability assessment of measurement accuracy for FBG sensors used in structural tests of the wind turbine blades based on strain transfer laws
ارزیابی قابلیت اطمینان از دقت اندازه گیری سنسورهای FBG مورد استفاده در تست های ساختاری تیغه های توربین بادی بر اساس قوانین انتقال فشار-2020
FBG sensors are often packaged within composites before they are pasted on the blade surface, and many studies have shown that the materials, fatigue properties, geometric parameters, etc. of intermediate layer have influences on the measuring accuracy of the FBG sensors. Thus, this paper established an reliability calculation model based on strain transfer efficiency for the measuring accuracy of FBG sensors packaged by composites, analyzed the influences of material properties and geometric parameters of the adhesive layer on the performance of FBG sensors based on finite element analysis (FEA) method, and then compared the differences of strain transfer efficiency and reliability of the FBG sensors under different load conditions. The results show that the bond length and the bond thickness of the adhesive layer have greater influences on the performance of the FBG sensors compared with other parameters, both the strain transfer efficiency and the reliability of the FBG sensors will reduce over time under suddenly applied load and increase with increasing frequency of the alternating load.
Keywords: FBG sensors | Reliability assessment | Strain transfer law | Static load | Suddenly applied load | Alternating load
Juror appraisals of forensic evidence: Effects of blind proficiency and cross-examination
ارزیابی صلاحیت شواهد پزشکی قانونی: تأثیر مهارت کور و معاینه متقابل-2020
Forensic testimony plays a crucial role in many criminal cases, with requests to crime laboratories steadily increasing. As part of efforts to improve the reliability of forensic evidence, scientific and policy groups increasingly recommend routine and blind proficiency tests of practitioners. What is not known is how doing so affects how lay jurors assess testimony by forensic practitioners in court. In Study 1, we recruited 1398 lay participants, recruited online using Qualtrics to create a sample representative of the U.S. population with respect to age, gender, income, race/ethnicity, and geographic region. Each read a mock criminal trial transcript in which a forensic examiner presented the central evidence. The low- proficiency forensic examiner elicited a lower conviction rate and less favorable impressions than the control, an examiner for which no proficiency information was disclosed. However, the high-proficiency examiner did not correspondingly elicit a higher conviction rate or more favorable impressions than the control. In Study 2, 1420 participants, similarly recruited using Qualtrics, received the same testimony, but for some conditions the examiner was cross-examined by a defense attorney. We find crossexamination significantly reduced guilty votes and examiner ratings for low-proficiency examiners. These results suggest that disclosing results of blind proficiency testing can inform jury decision-making, and further, that defense lawyering can make proficiency information particularly salient at a criminal trial.
Keywords: Forensic science | Proficiency testing | Expert testimony | Cross-examination | Jury decision-making
Improving robot dual-system motor learning with intrinsically motivated meta-control and latent-space experience imagination
بهبود یادگیری حرکتی سیستم دوگانه ربات با انگیزه ذاتی متا کنترل و تجربه فضای پنهان تخیلی-2020
Combining model-based and model-free learning systems has been shown to improve the sample efficiency of learning to perform complex robotic tasks. However, dual-system approaches fail to consider the reliability of the learned model when it is applied to make multiple-step predictions, resulting in a compounding of prediction errors and performance degradation. In this paper, we present a novel dual-system motor learning approach where a meta-controller arbitrates online between model-based and model-free decisions based on an estimate of the local reliability of the learned model. The reliability estimate is used in computing an intrinsic feedback signal, encouraging actions that lead to data that improves the model. Our approach also integrates arbitration with imagination where a learned latent-space model generates imagined experiences, based on its local reliability, to be used as additional training data. We evaluate our approach against baseline and state-of-the-art methods on learning vision-based robotic grasping in simulation and real world. The results show that our approach outperforms the compared methods and learns near-optimal grasping policies in dense- and sparse-reward environments.
Keywords: Meta-control | Arbitration | Experience imagination | Intrinsic motivation | Reinforcement learning | Robotic grasping
Factors influencing the adoption of mHealth services in a developing country: A patient-centric study
عوامل مؤثر بر اتخاذ خدمات بهداشتی و درمانی در یک کشور در حال توسعه: یک مطالعه بیمار محور-2020
mHealth under the umbrella of eHealth has become an essential tool for providing quality, accessible and equal health care services at an affordable cost. Despite the potential benefits of mHealth, its adoption remains a big challenge in developing countries such as Bangladesh. This study aims to examine the factors affecting the adoption of mHealth services in Bangladesh by using the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model with perceived reliability and price value factors. It also examines the moderating effect of gender on the intention to use and on the actual usage behavior of users of mHealth services. A wellstructured face-to-face survey was employed to collect the data. Structural equation modeling (SEM) with a partial least squares method was used to analyze the data collected from 296 generation Y participants. The results confirmed that performance expectancy, social influence, facilitating conditions and perceived reliability positively influence the behavioral intention to adopt mHealth services. However, effort expectancy and price value did not have a significance influence on the behavioral intention. Moreover, Gender has a significant moderating effect on mHealth services adoption in certain cases. Finally, the theoretical and practical implications of this study are also discussed.
Keywords: mHealth | Developing countries | UTAUT model | Generation Y | Bangladesh
Data-driven automated discovery of variational laws hidden in physical systems
کشف خودکار داده های محور از قوانین تغییر یافته پنهان در سیستم های فیزیکی-2020
The automated discovery of physical laws from discrete noisy data is significant for eval- uating the response, stability, and reliability of dynamic systems. In contract to the exist- ing work on the discovery of differential laws, this paper presents a data-driven method to discover the variational laws of physical systems. The effectiveness and robustness to measurement noise are demonstrated with five physical cases. Two features of variational laws, the compact form and holistic viewpoint, lead to two intrinsic advantages in the data-driven discovery of variational laws, namely, reduced data requirement and robust- ness to noise. The presented data-driven method can be applied to discover variational laws in real time for physical fields or more complicated social sciences, with or without prior knowledge.
Online adaptive water management fault diagnosis of PEMFC based on orthogonal linear discriminant analysis and relevance vector machine
تشخیص خطای مدیریت آب انطباقی آنلاین PEMFC بر اساس تجزیه و تحلیل تمایز خطی متعامد و دستگاه بردار ارتباط-2020
A data-driven strategy for characterizing the water management failure in a Proton Exchange Membrane Fuel Cell (PEMFC) is presented in this paper. To carry out the diagnosis of water management failure, first the original single cell voltages are projected into lowerdimension features by applying orthogonal linear discriminant analysis (OLDA). Then, a classification methodology termed relevance vector machine (RVM) is employed to classify the lower-dimension features into different categories that indicate the respective health states of the system. The initially trained projecting vectors and classifiers lose their efficiency gradually the characteristics of PEMFC system change, such as the cell voltages decaying with time due to the normal degradation due to aging. An online adaptive diagnostic strategy based on the posterior probability of RVM is proposed, so as to keep the diagnostic accuracy over time. The efficiency and reliability of this online adaptive diagnostic strategy is validated using an experimental database from a 90-cell PEMFC stack.
Keywords: Proton exchange membrane fuel cell | (PEMFC) | Orthogonal linear discriminant | analysis (OLDA) | Relevance vector machine (RVM) | Water management failure | Online adaptive diagnostics
A contingency based energy management strategy for multi-microgrids considering battery energy storage systems and electric vehicles
یک استراتژی مدیریت انرژی مبتنی بر شرایط احتمالی برای چند میکروگرید با توجه به سیستم های ذخیره انرژی باتری و وسایل نقلیه الکتریکی-2020
The emergence of microgrids along with extending the use of new energy resources, energy storage systems and electric vehicles at distribution level has changed traditional distribution systems into multi-microgrids (MMGs) which are usually more stable and reliable. For an MMG system, the probability of a fault occurrence at each time period makes the system operation process more complex. From this point of view, this paper aims at proposing a coordinated energy management strategy for optimal operation of MMG systems using a variable weighted multi-objective function. Based on this method, in the case of occurrence of a contingency problem, multiple operators are able to change the weight of functions depending on contingencies and are responsible for the proper use of energy storage systems and other distributed energy resources. Moreover, an efficient optimization algorithm called targeted search shuffled complex evolution is proposed to quickly optimize decision parameters during faulted and normal operation modes. Finally, a unified framework is presented to implement the proposed energy management strategy along with the reliability study of the intended test system, and the ability of the proposed approach is investigated in a modified reliability-based case study by considering different scenarios
Keywords: Energy management strategy | Energy storage systems | Electric vehicles (EVs) | Multi-microgrid (MMG) | Optimization | Shuffled complex evolution