Network Loss Research Articles

PlaqueNet: deep learning enabled coronary artery plaque segmentation from coronary computed tomography angiography

Cardiovascular disease, primarily caused by atherosclerotic plaque formation, is a significant health concern. The early detection of these plaques is crucial for targeted therapies and reducing the risk of cardiovascular diseases. This study presents PlaqueNet, a solution for segmenting coronary artery plaques from coronary computed tomography angiography (CCTA) images. For feature extraction, the advanced residual net module was utilized, which integrates a deepwise residual optimization module into network branches, enhances feature extraction capabilities, avoiding information loss, and addresses gradient issues during training. To improve segmentation accuracy, a depthwise atrous spatial pyramid pooling based on bicubic efficient channel attention (DASPP-BICECA) module is introduced. The BICECA component amplifies the local feature sensitivity, whereas the DASPP component expands the network’s information-gathering scope, resulting in elevated segmentation accuracy. Additionally, BINet, a module for joint network loss evaluation, is proposed. It optimizes the segmentation model without affecting the segmentation results. When combined with the DASPP-BICECA module, BINet enhances overall efficiency. The CCTA segmentation algorithm proposed in this study outperformed the other three comparative algorithms, achieving an intersection over Union of 87.37%, Dice of 93.26%, accuracy of 93.12%, mean intersection over Union of 93.68%, mean Dice of 96.63%, and mean pixel accuracy value of 96.55%.

Multi-objective optimization scheduling method considering flexible load integration for distribution network

In response to a series of issues in the distribution network, such as inadequate and inflexible utilization of flexible loads, delayed response to demand participation, and the uncertainty of new energy source output, a differentiated objective-based method for optimizing distribution network operations is proposed. Firstly, flexible loads are categorized, and corresponding mathematical models are established. Secondly, by employing chance-constrained programming to account for the uncertainty in new energy source output, a multi-objective optimization model is developed to reduce distribution network economic costs, decrease network losses, and enhance power supply reliability. Subsequently, an improved NSGA-III algorithm is introduced to address the multi-objective model. Finally, an 11-node distribution network is used as a case study, and three different algorithms are comprehensively compared. This confirms the rationality of the optimized scheduling scheme proposed in this paper.

Design of a Method for Analyzing Power Systems: Mathematical Models of Electric Vehicles and Distributed Generators

Electric vehicles (EVs) have significantly increased in recent years due to a growing awareness of the effects of polluting gases produced by combustion engines. The use of electric vehicles produces no emissions, making them a much more environmentally friendly alternative to traditional combustion engines. In addition, the price of EVs has decreased significantly in recent years, making them more affordable for consumers. The use of distributed generation (DG), an alternative source of energy for power systems, should also be considered. This study proposes a methodology for incorporating DGs and EVs into existing power systems. Power can be generated by DG directly where needed, while excess energy can be stored by EVs. Existing power systems can be made more efficient and cost-effective by combining these technologies. The methodology was applied to a 6-node IEEE system and a 33-node IEEE distribution system. It was found that DG can reduce network losses and voltage drops associated with EV penetration and that DG improves voltage drops.

The Impact of Solar Distributed Generation on Distribution Networks: Case Study of Chipata Distribution Network

The installation of solar PV systems in distribution networks is slowly increasing. The study focused on identifying the challenges and benefits of installing Distributed Solar Generation in the Chipata 33kV and 11kV distribution Networks. Chipata Town is in the Eastern Part of Zambia, and it has a peak demand of about 23.3MW. The 2023 version Power Factory Dig Silent Software was used to model the Chipata 33kV and 11kV distribution network and different Solar penetration levels were used to understand the impact of Distributed Solar. Chipata Distribution Network has four major stations operating at 33kV and 11kV voltages and four main distribution lines. The main objective of the study was to determine the impact of distributed solar PV in the Chipata Distribution Network. One of the specific objectives was to determine the hosting capacity of the Chipata Distribution Network. The hosting capacity gives the maximum amount of PV power integrated in a power system network without violating the set operation limits. The study analysed the impact of solar Distributed Generation on network losses, fault levels, voltage stability and line loading. The study revealed that an increase in penetration level resulted in a general increase in the Bus Bar voltages on both 11kV and 33kV voltage levels. Different Penetration levels of solar ranging from 6.66MW to 132MW were studied. Simulations revealed that the hosting capacity for Chipata Distribution Network is 96MW (412% of Solar PV Penetration Level). The voltages in the distribution network at hosting capacity were ranged from 0.93p.u to 1.02p.u. Short Circuit analysis simulations showed negligible impact on the fault level after integration of solar in the distribution network. Some circuits showed both increased and reduced power losses with increased solar penetration. The obtained results indicated that increased installation of distributed Solar PV systems in the Distribution Network impacts voltage stability, line losses and line loading.

Grid interlocking fault prevention and control method considering safety and network loss

Grid interlocking fault prevention and control method considering safety and network loss

Calculation method of carbon flow distribution in load-intensive regional energy centers

With the development of the economy, people’s demand for green energy has increased significantly. However, the traditional single fossil energy supply system cannot meet the needs of low-carbon. Therefore, this study employs energy hub to establish a multi-energy flow network that enables the integration of carbon flow within the network. Additionally, by utilizing the multi-energy flow trend, a carbon flow tracking method is adopted to achieve real-time carbon flow calculation. Results show that this network calculates the electricity cost of 20043 yuan, gas cost of 67253 yuan, and carbon emission cost of 3152 yuan. Compared with the traditional energy flow system, gas cost is reduced by 4.3% and 1.7%, electricity cost by 21.3% and 15.0%, and carbon emission cost by 8.7% and 6.6%. The two-way sharing carbon flow calculation model calculates that the user side and power supply side of the node each bear half of the network loss, proving two-way sharing effectiveness. Test results on IEEE5 machine 14-node system show that the calculation method can accurately find high-emission and low-emission areas, making the carbon emission allocation between power generation and user more fair and reasonable. This research can effectively reduce emissions cost, accurately calculate emissions flow in real time, and facilitate reasonable emission reduction planning.

Decomposition of unbalanced operation incremented active power loss in distribution network

Decomposition of unbalanced operation incremented active power loss in distribution network

Network partition and distributed voltage coordination control strategy of active distribution network system considering photovoltaic uncertainty

Aiming at the voltage exceeding problem caused by the introduction of Photovoltaic (PV) in Active distribution network (ADN), a comprehensive electrical distance index was established considering active and reactive voltage sensitivity, and the uncertainty of traditional PV probability density function (PDF) was optimized by random simulation method. The electrical distance expectation matrix between nodes was established by discretization of PV output probability distribution characteristics, and the ADN system was partitioned by affinity propagation (AP) algorithm. Taking active power loss as the objective function, the non-convex reactive power optimization control problem is transformed into a convex quadratic programming problem by LinDistFlow equation. An improved alternating direction method of multipliers (ADMM) which adaptively adjusts the penalty parameters is used to convert the finite boundaries of adjacent regions into data. By coordinating on-load tap-changer (OLTC), capacitor banks (CBs), and PV inverters on different time scales, the fast optimal control of global voltage in ADN is realized. The proposed method is tested on IEEE 33-bus and IEEE 123-bus distribution systems. After the optimization control algorithm described in this paper, the system network loss was reduced by 3.88%. Compared with the semi-definite programming relaxation, the calculation speed of LinDistFlow equation is increased by 72.2%. The results fully verify the feasibility and high efficiency of the control strategy described in this paper.

Analysis of large scale distribution network using Whale Optimization Algorithm

In this study, we use a loop matrix to describe the reorganisation of the RDN's formulation. Calculation time is increased when an optimum reorganisation is determined analytically. More network buses means more time to calculate. Therefore, a technique of optimisation is required to determine the best reorganisation of the radial distribution system. The optimum reorganisation aims to reduce network losses to a minimum and the voltage profile is enhanced. Loop matrices are used to describe the re-formulation of the radial distribution system. The analytical technique of identifying optimum reconfiguration involves additional calculation time. The computational complexity grows as the system's bus count rises. Therefore, a search for the best possible radial distribution system reconfiguration necessitates an optimization technique. The ideal reconfiguration focuses mostly on reducing the system's overall loss. The genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA) are the optimization methods used in this paper. Two test systems consisting of 119 buses, and 135 buses are used to evaluate the effectiveness of various optimization strategies. The outcomes are then compared with one another.

Valparaíso: Touristification and displacement in a UNESCO city

ABSTRACT Tourism has become a sector that is transforming historic areas of the current city. Its impact on the quality of life of communities, local economy, access to affordable housing, urban landscape, is being analyzed from studies on gentrification and touristification. This article analyzes the case of Valparaíso (Chile) and how the declaration of its historic area as a UNESCO World Heritage site in 2003, triggered a process of touristification. Through public programmes and private investment, houses were transformed into tourist shops and hotels, shaping a tourist bubble in these residential areas. How inhabitants felt and experienced the tourist transformation of their neighborhoods was analyzed through the use of interviews. The residents reports material and symbolic changes in their living conditions, which are experienced as pressure by displacement from their neighborhood. A pressure expressed in the loss of privacy, depopulation and loss of community networks, the displacement of neighborhood shops, the increase in property taxes, and the absence of public subsidies to rehabilitate their homes. The results place pressure by displacement as one of the main mechanisms of dispossession in historic areas of Latin American cities and suggest that in cities configured from neoliberal urban policies the UNESCO designation acts as a device that strengthens this dispossession.

Phyto-pharmacological Potential of Aegle marmelos (L.) for Neurological Disorders: Progress and Prospects.

Aegle marmelos, an Indian plant, has been extensively utilized by the people of the Indian subcontinent over about 5000 years. The leaves, bark, roots, and fruits, including seeds, are widely used to cure a variety of diseases in the Indian traditional system of medicine, Ayurveda, along with numerous folk medicines. By revealing the existence of significant bioactive chemicals, modern research has effectively substantiated the therapeutic effects of bael. The objective of this study was to review the literature regarding A. marmelos geographical distribution, morphology, therapeutic benefits, and phytochemicals found in the bael leaves, fruits, and other parts of the plant that offer a wide range of pharmacological applications in neurological disorders. A thorough literature search was conducted using five computerized databases, such as PubMed, Google Scholar, ScienceDirect, Elsevier, and Wiley Online Library (WOL), by using standard keywords "A. marmelos," "Geographical distribution," "Morphological description," "Ethnobotanical Uses," "Phytoconstituents" and "Neuroprotective activities" for review papers published between 1975 and 2023. A small number of earlier review articles focused on phyto-pharmacological potential of Aegle marmelos (L.) for neurological disorders. According to some research, Aegle marmelos extracts potentially have neuroprotective benefits. This is due to its capacity to alter cellular mechanisms that cause neuronal damage. Neurodegenerative illnesses usually induce permanent neuronal network loss overall the brain along with the spinal cord (CNS), resulting in chronic functional impairments. The review summarizes the multiple aspects and processes of A. marmelos extract and its components in several models of neurodegenerative diseases such as anxiety, epilepsy, depression, Parkinson's disease, Alzheimer's disease, and others. MDA, nitrite, TNF-, and IL-6 levels were dramatically elevated, whereas glutathione levels were significantly lowered in the hippocampus of STZ-treated rats. Furthermore, STZ-treated rats showed a substantial drop in catalase activity and an increase in AChE activity, indicating cholinergic hypofunction and neuronal injury. The neuroprotective ability of A. marmelos against STZ-induced oxidative stress and cognitive loss in rats suggests that it has therapeutic relevance in Alzheimer's disease (AD).

Key factors contributing to postgraduate students’ success: Making a case for Coronavirus-19 and beyond

Coronavirus-19 (COVID-19) pandemic caused major disruptions to the education sector, with detrimental effects on the supervision of postgraduate students and their success. Higher education institutions (HEIs) shut down to observe COVID-19 protocols and minimise the spread of the virus. University closures forced postgraduate-supervision processes to be conducted online, resulting in students experiencing many challenges, such as, loss of social networks and social interaction, lack of access to university computer laboratories and software, inability to collect face-to-face data, and wellness and mental problems. Whereas abundant research exists on COVID-19, little is known about factors that contributed to postgraduate students’ success during the COVID-19 pandemic. This qualitative case study reports on the factors that promoted student success during the COVID-19 pandemic, from the perspective of 23 postgraduate students. Data were collected through an online questionnaire and analysed through colour-coding and thematic categorisation. Results revealed that both students and supervisors contributed to the success of postgraduate students. They also showed that some students possessed certain attributes that enhanced their success, as well as that some postgraduate supervisors employed novel approaches to students’ supervision that promoted students’ success. This article encourages HEIs to leverage these factors to promote postgraduate student success beyond the COVID-19 pandemic.

Multistrategy Fusion Particle Swarm for Dynamic Economic Dispatch Optimization of Renewable Energy Sources

This paper presents a multistrategy fusion particle swarm optimization model for dynamic economic dispatching of renewable energy in distribution networks. The objective is to minimize active network losses and system voltage deviation while considering the integration of distributed energy sources and static reactive power compensators. The algorithm incorporates specific strategies, including a particle position change strategy based on the midpipeline convergence approach, a strategy for generating exploding particles near the optimal particles, and a particle velocity update strategy relying on the global optimal particle position. The inertia weights and particle position update methods of the simplified particle swarm optimization algorithm are also utilized. Simulation experiments are conducted on an IEEE 33 bus radial distribution system, demonstrating the effective optimization of system losses while ensuring system voltage stability. This research contributes to the scientific understanding of renewable energy integration in distribution networks and its economic dispatching.

On the design of 5GDHC substation control systems

ABSTRACT This paper presents challenges in the control of 5GDHC networks and proposes an approach for the robust control design of these networks. Temperatures are low and temperature differences are small in 5GDHC networks, in order to minimise heat losses in the distribution network and directly utilise low-temperature heat sources. Therefore, the robust control design of substations is crucial for stable and efficient operation of 5GDHC networks. We proposed the key steps to obtain a consistent control design that can be verified and tested prior to commissioning of the building substations and network. The design approach was elaborated in a case study in an existing neighbourhood in Haarlem, the Netherlands, constructed in the 1930s to 1970s. This neighbourhood will become independent of natural gas using PVT-rooftop panels, individual heat pumps (HP), a 2-pipe, bi-directional energy flow network operating at ultra-low temperatures and ATES system as a seasonal and daily storage facility.

Coordinated optimization of source‐grid‐load‐storage for wind power grid‐connected and mobile energy storage characteristics of electric vehicles

AbstractThe rapid growth in the number of electric vehicles (EVs), driven by the ‘double‐carbon’ target, and the impact of uncontrolled charging and discharging behaviour and discharged battery losses severely limit electric vehicles’ low carbon characteristics. Existing research on systemic low‐carbon emissions and electric vehicle charging and discharging issues is usually determined by considering only carbon trading markets or charging and discharging management on the source side. In this regard, a coordinated and optimized operation model that considers the participation of electric vehicle clusters in deep peaking and the source network load and storage adjustable resources is proposed. The upper layer establishes a real‐time price‐based demand response mechanism for the load side with the minimum net load fluctuation as the objective function; the middle layer establishes a comprehensive operation mechanism for the source and storage side that includes an orderly charging and discharging peaking compensation mechanism for electric vehicles, and a deep peaking mechanism that takes into account clean emission, and constructs an optimal operation model with the minimum comprehensive operating cost as the objective function; the lower layer establishes a distribution network loss minimization model for the network side that takes into account the orderly charging and discharging of electric vehicle as the objective function. The optimal load model with the objective function of minimizing the distribution network loss is established at the lower level. Finally, the original problem is transformed into a mixed integer linear programming problem, and the model's effectiveness is verified by setting different scenarios. The model reduces the total cost by 22.22%, improves the wind power consumption rate by 19.55%, reduces the actual carbon emission by 16.66%, and reduces the distribution network loss by 13.91% compared to the basic model.

Recovery strategy of distribution network based on dynamic island rescue under extreme weather

AbstractThe frequent occurrence of various extreme weather has a great influence on the normal and stable operation of the distribution network. In order to minimise the large‐scale power loss of the distribution network caused by extreme weather, considering that most of the practical disasters are concurrent with various weather types, and each disaster is independent of each other. A unified failure rate calculation model is proposed, which includes line break, short circuit, tower fall, insulator flashover etc., to realise disaster scenario prediction. Secondly, a resilience evaluation index of island rescue based on mobile energy storage system (IR‐MESS) is proposed. Thirdly, considering the flexibility of MESS, the pre‐disaster scheduling of MESS is carried out according to the predicted disaster scenario. After the disaster occurs, a spatio‐temporal optimisation scheduling model based on the rescue state and charge state of MESS is proposed, and a dynamic IR‐MESS is formed to provide power supply for important loads in out‐of‐load areas of the distribution network. Finally, taking the actual ice disaster in northeast China as an example, the results show that the rescue strategy based on IR‐MESS proposed in this paper can effectively elevate the resilience of the distribution network.

Distributed Voltage Reactive Power Control of Distribution Network Considering the Coordination Between DPV and SVC

With the increasing permeability rate of DPV in the distribution network, the safe and economic operation of the distribution network is faced with many challenges, among which the problem of voltage exceeding the limit must be solved urgently. A method for distributed voltage reactive power control, which involves coordinating DPV and SVC, is suggested to enhance the voltage regulation capacity in high-permeability distribution networks. Firstly, considering the limited reactive power regulation capacity of DPV, a coordinated control strategy is proposed to enable SVC when the reactive power capacity of DPV is insufficient. Secondly, a voltage-reactive power control model for distribution networks is established, with a focus on minimizing node voltage deviations and network losses. An adaptive penalty factor adjustment strategy, based on the alternating direction multiplier method, is introduced to achieve a comprehensive solution for this model. Finally, the enhanced IEEE 33-node system is utilized for verification and analysis. The outcomes demonstrate that the suggested coordination strategy involving DPV and SVC, alongside the distributed optimization control method, offers economic advantages and effectively achieves the safe and cost-effective operation of the distribution network.

Regenerative Braking Energy Utilization Analysis in AC/DC Railway Power Supply System With Energy Feedback Systems

In the power supply system with energy feedback system (EFS)s, the energy from EFS (WF) will flow to rectifiers, which is circulation flow (CF). However, the efficiency of CF between substations has been rarely studied. Besides, the relationship between the train operation and regenerative braking energy (RBE) utilizing efficiency is not clear. In this paper, the energy flow structure of the AC/DC railway power supply system including CF is established. In the double-train system, promoting CF when the trains’ distance is long can be beneficial to power utilization efficiency. Chengdu Metro Line 9 is analyzed as the multi-train system based on the iterative AC/DC power flow algorithm. The key to energy saving includes reducing the energy consumption of on-board resistance and the DC traction network loss. Compared with the catenary and rail system, the fourth-rail system can promote system energy efficiency by 23.5% at most. The CF and energy fed to main substations counts for less than 2% and 8% of WF. When the headway time is short, RBE should be avoided feedback to EFS, while the feedback power of EFS should be advocated when the headway time is long. The results can help guide the operation of urban rail to save energy.

The water flow diagram

IntroductionThe Water Flow Diagram (WFD) is a novel advocacy and communication tool that presents urban water supply and management in a simple visualization. Rapid urbanization, growing populations, and the climate crisis increase the pressure on water resources, particularly in urbanized areas. The WFD aims to foster a dialogue around conflict of interests and opportunities among different stakeholders, and trigger actions toward more sustainable urban water management (UWM), as well as a water secure future.MethodThe WFD is produced from data on water abstraction, water use of different sectors, water treatment, water recycling and contamination risks. The data were obtained from government services, wastewater and water utilities, large industries, universities and reports of intergovernmental organizations. If these sources did not have data, reports from NGOs or consultants, comparable contexts, default values or expert judgements were considered. The annual water flows are presented in a Sankey Diagram. An intuitive color code highlights the flows as “problematic” or “appropriate” and points to areas where UWM practices should be improved.Results and conclusionsThe final diagrams are a concise instrument that identifies challenges of UWM in the four application cases presented in this article. Key challenges that became evident included: pollution from agricultural production, the lack of wastewater and sanitation infrastructure, high water losses in the distribution networks, water exports leading to a lack in local supply and sewer overflows during heavy rainfalls. Opportunities identified were the need to: invest in sanitation and wastewater to protect resources, create coordination bodies to align conflict of interests, and/or invest in blue-green infrastructure for rainwater retention. The WFD triggered local actions, such as in-depth discussions between relevant actors, the formation of integrated water use committees and the interest of the national ministry in Senegal to replicate the diagram for other locations. This article presents the methodology, discusses the four case studies and deliberates on the prospective use of the WFD.

Low-Voltage Distribution Network Loss-Reduction Method Based on Load-Timing Characteristics and Adjustment Capabilities

The primary contributors to elevated line losses in low-voltage distribution networks are three-phase load imbalances and variations in load peak–valley differentials. The conventional manual phase sequence adjustment fails to capitalize on the temporal characteristics of the load, and the proliferation of smart homes has opened up new scheduling possibilities for managing the load. Consequently, this paper introduces a loss-reduction method for low-voltage distribution networks that leverages load-timing characteristics and adjustment capabilities. This method combines dynamic and static methods to reduce energy consumption from different time scales. To commence, this paper introduced a hierarchical fuzzy C-means algorithm (H-FCM), taking into account the distance and similarity of load curves. Subsequently, a phase sequence adjustment method, grounded in load-timing characteristics, was developed. The typical user load curve, derived from the classification of user loads, serves as the foundation for constructing a long-term commutation model, therefore mitigating the impact of load fluctuations on artificial commutation. Following this, this paper addressed the interruptible and transferable characteristics of various smart homes. This paper proposed a multi-objective transferable load (TL) optimal timing task adjustment model and a peak-shaving control strategy specifically designed for maximum sustainable power reduction of temperature-controlled loads (TCL). These strategies aim to achieve real-time load adjustment, correct static commutation errors, and reduce peak-to-valley differences. Finally, a simulation verification model was established in MATLAB (R2022a). The results show that the proposed method mainly solves the problems of three-phase imbalance and large load peak–valley difference in low-voltage distribution networks and reduces the line loss of low-voltage distribution networks through manual commutation and load adjustment.