Development Of Grid Research Articles

Evaluation of a Point-of-Care Test for Bilirubin in Malawi.

BiliSpec is a low-cost spectrophotometric reader and disposable paper-based strip to quantify total serum bilirubin from several blood drops. This study was a prospective evaluation of BiliSpec in 2 neonatal wards in Malawi compared with a reference standard bilirubinometer over a large range of bilirubin and hematocrit levels. The accuracy of BiliSpec and a transcutaneous bilirubinometer were compared with the reference standard of spectrophotometry for 475 blood samples collected from 375 subjects across a range of total serum bilirubin concentrations from 0.0 to 33.7 mg/dL. The development of error grids to assess the clinical effects of measurement differences is reported. BiliSpec was found to have a mean bias of -0.48 mg/dL and 95% limits of agreement of -5.09 mg/dL to +4.12 mg/dL. Results show 90.7% of BiliSpec measurements would have resulted in the same clinical decision as the reference standard, whereas 55.0% of transcutaneous bilirubin measurements would have resulted in the same clinical decision as the reference standard. This evaluation supports use of BiliSpec to provide accurate, low-cost, point-of-care bilirubin measurements in low-resource hospitals. Future work is needed to evaluate BiliSpec among a larger number of users.

Organic Redox Flow Batteries: Insights from Experimental and Numerical Study

Secondary batteries are the key technology for the sustainable development of electrical grids with a high share of renewable power generation. Among different battery technologies, Organic Redox Flow Batteries (ORFBs) are considered one of the most promising solutions for low-cost large-scale energy storage systems thus avoiding the social and environmental issues associated with extraction of raw materials for large batteries. Mathematical modeling of ORFBs, that complements experimental testing, is a key tool that helps to understand and analyze the behavior of particular candidates, including electrolytes, membranes and geometrical parameters, on a battery level at operating conditions that are close to operational.In this study, we present a single-cell zero-dimensional model based on our previous works [1,2] extended for simulation of redox flow batteries with different organic redox-couples. The model is based on the general mass and charge conservation principles. Hence, it can simulate a wide range of RFB systems with different scales and electrolytes. The key feature of the model is the approach for the determination of integral mass-transfer coefficient on the electrode-electrolyte interface that allows describing non-linear polarization behavior at high current densities. Such approach makes the model very flexible and esilty extadable even to the other types of batteries with hybrid configurations (e.g. solid boosters). The kinetic parameters were also fitted for the effective single-step heterogeneous redox mechanisms at positive and negative electrodes in the case when activation losses treatment was needed to reproduce the experimental polarization curves. In addition, the model simulates the dynamic behavior of the cells working with different redox-couples in the wide range of loading currents and electrolyte flow rate and thus allowing to explore their performance at different operating conditions. Validation of the model showed a good agreement with experimental data (average error less than 5%) in the range of current decities of 20 – 150 mA cm-2.The model was also used for investigation of scalability of the considered ORFBs analyzing their performance on the level of industial-scale systems.The influence of internal processes on the battery behavior has been studied and the effect of mass-transport limitations has been estimated. The key parameters (coulombic, voltage, energy efficiencies and electrolyte utilization) of the batteries have been computed for the wide range of current densities and flow rates. The obtained results provide important insights for reserachers and ingeneers to develop reliable and efficient organic RFB systems applicable for grid-scale energy storage systems.

Automatic Calculation and Simulation of Time-Varying Failure Rate of Digital Relay Protection Device

Efficiency is an important indicator of risk assessment, but the failure rate changes with time, which makes risk assessment very difficult. Traditional methods often only measure the steady-state and static indicators of relay protection. The traditional method often only measures the steady-state and static indicators of the relay protection, or directly uses the RiskWeibull analysis system to perform reliability analysis on the relevant data. However, this method can only perform quantitative static analysis on the data. Once the system is disturbed, its reliability will be affected. Analysis is not possible. Since the relay protection device has the possibility of malfunction and refusal at the same time, in order to distinguish, the risk is introduced into the reliability evaluation of the relay protection device to achieve the purpose of considering the possibility and consequences of its failure at the same time. Based on the traditional method, this paper proposes a method to calculate the time-varying failure rate of the relay protection device by using the support vector machine model, which improves the influence of hidden risks on the power grid relay protection, and not only realizes the scientific hidden danger evaluation, risk rating. It is of great significance for the development of power grids to evaluate the consequences of failures and to make the transmission lines operate safely and stably.

Rhythm Analysis: A Heuristic Tool for Historical Infrastructure Research.

Infrastructures enable circulation, but the tools to analyze it are lacking. This essay presents rhythm analysis as a heuristic device: a way to research movements in infrastructure. Thus far, infrastructure research has paid little attention to movement and rhythm and instead privileged system building, grid development, and integration. The article proposes putting movement-or rhythm-at the center of infrastructure research by considering three ideal types of rhythm: episodic, cyclical, and linear. The notion of rhythm enables historians to distinguish movements and their impact on infrastructure usage. To operate effectively, infrastructures need to integrate technological, material, and social aspects by shaping and synchronizing different rhythms. Discussing premodern and industrial examples of water, transportation and energy, the article shows how infrastructures transformed natural rhythms. It elucidates the social preference for cyclical rhythms, the impact of the grid's materiality on rhythm formation, and the significance of political and social ordering schemes.

A Price-Based Grid Resources Pricing Approach for NonStorable Real Assets

A grid-computing paradigm delivers the processing power of massively parallel computation to all subscribed users. Current trends, research, and developments in grid computing show that the available grid resources exist as non-storable compute commodities and are distributed geographically – the grid problem. To solve the grid problem, several initiatives have developed frameworks for grid economy and have proposed several algorithms towards an optimized resources scheduling in a grid environment. However, since the grid resources availability depends on the time of usage and are transient, such generic approaches lack the ability to capture the realistic valuation of the resources and fail to guarantee the certainty in their availability measured as Quality of Service (QoS). Uncertainties in grid resource availability do not guarantee a user expected QoS without over committing (e.g., storing the non-storable resources) resources to the users. To guarantee QoS (satisfy a users’ computing needs), we propose a price-based and quality-aware model that captures the realistic value of the grid compute commodities. We use the financial option theory from a real option perspective to value grid resources by treating them as real assets. We discuss a set of results on pricing grid compute cycles. Our results are based on the compute cycle usage obtained from the WestGrid node at the University of Manitoba. We extend and generalize our study to any grid in general but with specific reference to the WestGrid. Keywords: Financial Options, Price Stochasticity, Compute Cycles, Real Options, Quality of Service. Allenotor, D. & Oyemade, D. A. (2022): A Price-Based Grid Resources Pricing Approach for Non-Storable Real AssetsJournal of Advances in Mathematical & Computational Science. Vol. 10, No. 2. Pp 1-18 DOI: dx.doi.org/10.22624/AIMS/MATHS/V10N2P1. Available online at www.isteams.net/mathematics-computationaljournal.

Research on the Transformer Intelligent Operation and Maintenance System Based on the Graph Neural Network

With the development of the smart grid and energy Internet, the power industry generates huge, multi-source, heterogeneous, and highly coupled data, which are difficult to utilize. The intelligent operation and maintenance system of the power transformer based on the knowledge graph and graph neural network is developed in this article. The multi-source heterogeneous data are structured and modeled by the constructed knowledge graph, and it presents the correlation among data more intuitively. On this basis, the graph neural network is designed to achieve the prediction and excavate the deep information hidden in the data. The testing results show that the system has fully used the multi-dimensional and interrelated heterogeneous data, achieving a deep information mine. It benefits the management and strategy implementation for the system scientifically and guides the operation and maintenance of the transformer. The system is of great significance on improving the efficiency of the transformer maintenance and safe operation.

It’s not all about the money—landowner motivation and high voltage grid development

ABSTRACT The transition to a renewable energy future requires the extensive expansion of current high voltage grids. Due to the amount of land needed for expansion, issues related to land use have led to increased grid development opposition among landowners which in turn leads to significant project planning and budget overruns. Yet knowledge about why landowners support or object to high voltage grid development is limited. In this study, we use a theory on pluralism to uncover and categorize the multiplicity of motivations of 200 individual landowners in the Netherlands. Our results indicate that only a small number of landowners who oppose grid development focus on individual monetary gain through compensation for limits on their land use. Furthermore, most landowners find the fair and equal distribution of both the advantages and disadvantages of such limits more important than individual financial compensation. As such, overcoming contentious land use issues related to high voltage grid development by way of high individual financial compensation isn’t the only solution. Highlights Land use conflicts affect expansions of high voltage grids crucial for meeting CO2 objectives Motivations of landowners are unevenly divided among different rationalities Most individual landowners do support high voltage grid developments Individual financial compensation isn’t the only solution

Guest Editorial: Situational awareness of integrated energy systems

Guest Editorial: Situational awareness of integrated energy systems

Scalability and Replicability for Smart Grid Innovation Projects and the Improvement of Renewable Energy Sources Exploitation: The FLEXITRANSTORE Case

In this paper, detailed scalability and replicability plans have been developed to facilitate the adoption of innovation technologies in the pan-EU market. Smart grid development must enable both information and power exchange between suppliers and customers, thanks to the enormous innovation in intelligent communication, monitoring, and management systems. Implementing physical infrastructure alone is not enough, but a smart grid must include new business models and new regulations. In recent years, the number, participants, and scope of smart grid initiatives have increased, with different goals and results. FLEXITRANSTORE project integrates hardware and software solutions in all areas of the transmission system and wholesale markets, unleashing the potential for full flexibility of power systems and promoting the penetration of renewable energy sources and pan-EU markets. Full deployment of these demonstrated solutions requires a reasonable level of scalability and replicability to prevent project demonstrators from continuing local experimental exercises. Scalability and replicability are fundamental requirements for successful scaling-up and replication. Therefore, scalability and replicability enable or at least reduce barriers to the growth and reuse of project demonstrator results.

Development of smart grid for the power sector in India

Technological advancements in the energy industry have expedited the growth of the smart grid, necessitating multidisciplinary study in power systems and management. India, the world's third-largest producer and consumer of electricity, has various power-related issues, including significant transmission and distribution losses, electricity theft, and environmental concerns. As a result of these issues, the energy industry is exploring new technologies to improve the grid's efficiency, sustainability, and security. This study tries to uncover elements that might be facilitators for India's smart grid development. To analyse the various components of the smart grid, questionnaire surveys, interviews, and workshops were conducted with eminent academicians, researchers, and industry experts working in the power sector. An integrated approach of soft system methodology (SSM), thematic analysis and fuzzy cognitive mapping have been used in this study to better comprehend the intricate interactions between stakeholders. This research findings reveal that technical development along with consumer acceptance is a crucial factor for the efficacious implementation of smart grid.

Measurement of Power Grid Resilience Based on a Dynamic Inoperability Input–Output Model

The increase in global natural disasters, emergencies, and terrorist attacks brings great challenges to the flexible operation of power grids. The research on the flexibility of power grids becomes more and more important. At the same time, the rapid development of renewable energy and smart grids also provides new opportunities for research of flexible power grids. Based on the dynamic inoperability input–output model (DIIM), this study creatively uses the node transmission power to represent the relationship between nodes in the power grid and then studies the resilience measurement of the power grid. First, the significance of studying power grid resilience is discussed, and the absorption and recovery capacity of power grid resilience is described. Second, the power grid resilience measurement model based on the DIIM is established, and a theoretical analysis is carried out. Using the characteristics of inoperability, the power grid resilience is measured from the time aspect, and the characteristics of the model are analyzed at the same time. On the basis of theoretical analysis, this study also provides a control strategy to improve the grid resilience, which has good convenience and applicability. Finally, the theoretical analysis method is applied to a power grid example, and the correctness and effectiveness are verified. The measurement model can intuitively display the resilience characteristics of power grids and provide theoretical support for management and emergency response.

Data encryption based on a 9D complex chaotic system with quaternion for smart grid

With the development of smart grid, operation and control of a power system can be realized through the power communication network, especially the power production and enterprise management business involve a large amount of sensitive information, and the requirements for data security and real-time transmission are gradually improved. In this paper, a new 9-dimensional (9D) complex chaotic system with quaternion is proposed for the encryption of smart grid data. Firstly, we present the mathematical model of the system, and analyze its attractors, bifurcation diagram, complexity, and 0–1 test. Secondly, the pseudo-random sequences are generated by the new chaotic system to encrypt power data. Finally, the proposed encryption algorithm is verified with power data and images in the smart grid, which can ensure the encryption security and real time. The verification results show that the proposed encryption scheme is technically feasible and available for power data and image encryption in smart grid.

Study on the smart grid vulnerability index based on the moment-generating function and distortion function

Although smart grids are characterized by self-healing, economy, high efficiency, and security, many hidden dangers exist in the development of smart grids due to a gradually expanding power grid and the continuous access of new energy to the power grid. Therefore, the development of smart grids, especially their reliability, security, and vulnerability, warrants further investigation. In this study, the vulnerability of smart grids is identified, and the vulnerability elements of smart grids are selected. Based on relevant theories, such as credibility and the combination of the credibility-based moment-generating function and the distortion function, a calculation model and framework of the vulnerability index of a smart grid are constructed. An empirical analysis is also conducted. This study provides a scientific basis for analyzing the vulnerability of smart grids and suggesting reasonable preventive measures and auxiliary decision-making information for relevant planning, design, and operation personnel, which contributes to the sustainable and healthy development of smart grids.

Quantum computing in power systems

Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems. However, today's power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources, as well as managing huge volumes of data accordingly. These unprecedented challenges call for transformative analytics to support the resilient operations of power systems. Recently, the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system computations. Quantum computing harnesses quantum mechanisms to solve traditionally intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics. This paper reviews the newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations, hardware implementations, and recommended practices. We also review the quantum networking techniques to ensure secure communication of power systems in the quantum era. Finally, we discuss challenges and future research directions. This paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids.

Synchrophasor Data Analytics: Transmission Line Parameters Online Estimation for Energy Management

Transmission line parameters are fundamental parameters of power system models that influence the safety, stability, and economy of power utility grids. Transmission line parameter estimation is a basic and necessary function and module of a power grid energy management system. However, traditional offline parameter estimation methods cannot cope well with parameter uncertainties caused by transmission line aging, environmental factors, and modern power grid developments. The widespread deployment of phasor measurement units (PMUs) in power networks has paved the way for accurate real-time-measurement-based transmission line parameter tracking. This article proposes PMU-based parameter estimation models considering series and shunt compensators. An online parameter estimation method is developed based on recursive least squares with a variable forgetting factor, which can adapt to various parameter mutations and has strong robustness to outlier measurements. This article also analyzes the sensitivity of parameters with respect to synchrophasor data. The simulation results under different scenarios verify the effectiveness, robustness, and adaptivity of the proposed method, which exhibits significant advantages over other existing parameter estimation methods.

Effect of Impeller’s Blade Number on The Performance of Mixing Flow in Stirred Tank using CFD Simulation Method

The component of an impeller in stirred tank plays an important role in the mixing process for a wide range of industries. The present study conducted the analysis of impeller design specifically on the number of blades to investigate the flow performance in the stirred tank. The analysis was also performed to identify the location of the particle flow dead zone at the bottom of the tank. The grid development and fluid flow study were accomplished using the CFD simulation method via the Ansys Fluent software package. The impeller design with the arrangement of 3,4 and 5 blades number was involved in this study. The rotational speed of the blade was set constant at 90 rpm. The flow characteristic at the vertical position of a tank at 0.01, 0.1, 0.2 and 0.3 m has been investigated. The general governing equation and solution approach used in this simulation were also presented in this paper. The results showed that 5 blades impeller produced a broader region of high-velocity magnitude distribution compared to the 3 and 4 blades. Hence, 5 blades impeller also relatively produced a higher distribution of velocity magnitude compared to the 3 and 4 blades impeller, typically in the upper region of the tank. However, the effect of a number of blades seemed not significant in the region close to the rotating impeller.

A Method of Power Flow Database Generation Base on Weighted Sample Elimination Algorithm

With the rapid development of urban power grids, distributed renewable energy sources and adjustable loads have increased significantly, resulting in more complex operation conditions, increasing the difficulty of power flow calculations. The usage of artificial intelligence technology to assist in calculating power flows for large-scale urban grids has a wide range of application prospects. It is currently difficult to generate enough grid operation database with controlled distribution for artificial intelligence (AI) method research. Data is one of the important factors affecting the performance of deep learning algorithms, and the lack of research on data distribution characteristics also hinders the performance of deep learning algorithms. The distributional characteristics of data sets in high-dimensional feature spaces are difficult to represent and measure, and the algorithm design process is prone to encounter curse of dimensionality. This paper proposed a novel method for generating databases to improve the solving efficiency of data-driven power flow calculation problems. The proposed method removes samples based on the characteristics of data distribution. It constructs two databases, namely the blue noise distribution database and the variable density boundary enhanced distribution database. Compared with the classical stochastic sampling database, the proposed boundary-enhanced variable density (BEVD) database has significantly improved the judgment accuracy of power flow convergence. Finally, the China Electric Power Research Institute-36 (CEPRI-36) bus system is used to verify the effectiveness of the proposed method. The judgment accuracy was improved by 2.91%–9.5%.

Multi-Objective Optimal Source–Load Interaction Scheduling of Combined Heat and Power Microgrid Considering Stable Supply and Demand

With the development of smart grids, it has become possible to take demand-side resource utilization into account to improve the comprehensive benefits of combined heat and power microgrids (CHP-MGs). In order to improve the benign interaction between the source and the load of the system, the source side decouples the thermoelectric linkage through energy storage devices and improves the system multi-energy supply capacity by introducing various energy flow forms of energy devices. On the demand side, considering the elasticity of electric heating load and the diversity of heating mode, an integrated demand response (IDR) model is established, and a flexible IDR price compensation mechanism is introduced. On this basis, aiming at the optimal stability of supply and demand and the minimum operating cost of the system, a multi-objective optimal operation model of combined heat and power source–load interaction is constructed, taking into account the user satisfaction with energy consumption and the internal equipment load constraints of the system. Finally, an improved multi-objective optimization algorithm is used to solve the model. The analysis of the algorithm shows that the source–load interaction multi-objective optimal scheduling of the cogeneration microgrid considering the stability of supply and demand can effectively improve the stability of supply and demand and the economy of the system.

Cross‐layered distributed data‐driven framework for enhanced smart grid cyber‐physical security

Smart Grid (SG) research and development has drawn much attention from academia, industry and government due to the great impact it will have on society, economics and the environment. Securing the SG is a considerably significant challenge due the increased dependency on communication networks to assist in physical process control, exposing them to various cyber-threats. In addition to attacks that change measurement values using False Data Injection (FDI) techniques, attacks on the communication network may disrupt the power system's real-time operation by intercepting messages, or by flooding the communication channels with unnecessary data. Addressing these attacks requires a cross-layer approach. In this paper a cross-layered strategy is presented, called Cross-Layer Ensemble CorrDet with Adaptive Statistics(CECD-AS), which integrates the detection of faulty SG measurement data as well as inconsistent network inter-arrival times and transmission delays for more reliable and accurate anomaly detection and attack interpretation. Numerical results show that CECD-AS can detect multiple False Data Injections, Denial of Service (DoS) and Man In The Middle (MITM) attacks with a high F1-score compared to current approaches that only use SG measurement data for detection such as the traditional physics-based State Estimation, ECD-AS strategy and other machine learning classification-based detection schemes.

Key target and defect detection of high-voltage power transmission lines with deep learning

Automatic visual detection of key targets and defects for power transmission lines based on power transmission line inspection robots (PTLIR) and unmanned aerial vehicles (UAVs) is an ongoing trend in the smart grid development. The advancement of deep learning has accelerated the intelligence of power grid inspection. In terms of the power transmission line fittings detection application, improving the detection accuracy of small targets and defects using a deep learning detection network is challenging, owing to the complex background lighting characteristics of high-voltage power transmission lines in the wild. To address this problem, we present an inspection method for key targets and defects in high-voltage power transmission lines based on a deep learning object detection network. First, we collected sample images of key targets under different backgrounds, lighting conditions, and postures. Further, data augmentation was performed to solve the problem of imbalance in the number of target categories, and a large standard dataset was constructed. Second, we improved the extraction ability of small object features by optimizing the detection network. The precision and recall rate of the optimized detection network were 93.5% and 96.2%, respectively. Furthermore, small targets and defects in a complex environment could be successfully detected. Additionally, the detection of targets and defects in the inspection videos recorded by the PTLIR and UAVs were realized. Experimental results demonstrated that the proposed method performed well in the detection accuracy of key targets and defects in similar high-voltage power transmission line environments. It can realize remote, automatic inspection of high-voltage power transmission lines in the field.