Optimal Network Configuration Research Articles

A Nondominated Sorting Stochastic Fractal Search Algorithm for Multiobjective Distribution Network Reconfiguration with Distributed Generations

This paper aims to propose a new multiobjective algorithm for multiobjective distributed network reconfiguration (DNR) with the placements of distributed generation (DG) in radial distribution networks (RDNs). The new proposed algorithm, called the nondominated sorting stochastic fractal search (NSSFS), is a new multiobjective version of the original SFS algorithm. NSSFS incorporated fast nondominated sorting strategies, crowding distance computation, and selection mechanism into SFS to find and maintain the best nondominated solutions. The proposed NSSFS algorithm was tested with eight multiobjective benchmark test functions to validate its performance. The NSSFS was then implemented to define the optimal network configuration, positions, and sizes of DG units in the RDNs, where real power loss, voltage profile, and voltage stability index were optimized simultaneously. The implementation of multiobjective DNR-DG (MODNR-DG) significantly enhanced the performance of the system. Based on the comparison outcomes, the NSSFS algorithm obtained better solution quality than other multiobjective techniques, proving the effectiveness of NSSFS in dealing with the MODNR-DG problem.

Voltage Profile Enhancement and Loss Minimization Using Optimal Placement and Sizing of Distributed Generation in Reconfigured Network

Power loss and voltage instability are major problems in distribution systems. However, these problems are typically mitigated by efficient network reconfiguration, including the integration of distributed generation (DG) units in the distribution network. In this regard, the optimal placement and sizing of DGs are crucial. Otherwise, the network performance will be degraded. This study is conducted to optimally locate and sizing of DGs into a radial distribution network before and after reconfiguration. A multi-objective particle swarm optimization algorithm is utilized to determine the optimal placement and sizing of the DGs before and after reconfiguration of the radial network. An optimal network configuration with DG coordination in an active distribution network overcomes power losses, uplifts voltage profiles, and improves the system stability, reliability, and efficiency. For considering the actual power system scenarios, a penalty factor is also considered, this penalty factor plays a crucial role in the minimization of total power loss and voltage profile enhancement. The simulation results showed a significant improvement in the percentage power loss reduction (32% and 68.05% before and after reconfiguration, respectively) with the inclusion of DG units in the test system. Similarly, the minimum bus voltage of the system is improved by 4.9% and 6.53% before and after reconfiguration, respectively. The comparative study is performed, and the results showed the effectiveness of the proposed method in reducing the voltage deviation and power loss of the distribution system. The proposed algorithm is evaluated on the IEEE-33 bus radial distribution system, using MATLAB software.

RF Canceller Tuning Acceleration Using Neural Network Machine Learning for In-Band Full-Duplex Systems

The fifth-generation wireless system framework provides the option to evaluate the performance of in-band full-duplex (IBFD) operation through flexible duplexing. The resulting self-interference, however, must be mitigated within a fraction of a symbol duration for successful communication. This paper introduces the use of neural network machine learning to accelerate the tuning of multi-tap adaptive RF cancellers. Additionally, the optimal network configurations, input data structures and training dataset densities that optimize the performance of this technique are presented. The tuning results of a prototype system using a two-tap canceller were measured over 20 and 100 MHz bandwidths centered at 2.5 GHz, and demonstrated averages of 40 dB cancellation and 6 tuning iterations. These results are compared to a survey of previously-reported adaptive cancellers, and illustrate that this novel application of machine learning to RF canceller tuning provides the fastest convergence speed to date, which can enable IBFD operation in dynamic interference environments.

Distribution Network Reconfiguration Based on NoisyNet Deep Q-Learning Network

The distribution network reconfiguration (DNR) aims at minimizing the power losses and improving the voltage profile. Traditional model-based methods exactly need the network parameters to derive the optimal configuration of the distribution network. This paper proposes a DNR method based on model-free reinforcement learning (RL) approach. The proposed method adopts NoisyNet deep Q-learning network (DQN), by which the exploration can be automatically realized without need of tuning the exploration parameters, in order to accelerate the training process and improve the optimization performance. The proposed method is validated by the simulation results.

Integrated energy system planning of distribution network considering load timing coupling characteristics

The integrated energy system can effectively improve energy utilization and reduce environmental pollution. Existing research mainly focuses on the equipment type, capacity and optimal configuration of the pipeline network of the integrated energy system in the microgrid. However, there are few studies on the planning of integrated energy systems at the distribution network level. Therefore, under the condition of fully considering the time-series coupling characteristics of the cooling and heating load, a mathematical model of the integrated energy system planning of the distribution network is established in this article. A method of planning and solving the integrated energy system of the distribution network based on the K-means clustering algorithm is proposed to realize the coordinated planning of multiple integrated energy stations in the distribution network. An example of distribution network in Xinjiang region verifies the rationality and effectiveness of the proposed method.

A Novel Enhanced Quantum PSO for Optimal Network Configuration in Heterogeneous Industrial IoT

A novel enhanced quantum particle swarm optimization algorithm for IIoT deployments is proposed. It provides enhanced connectivity, reduced energy consumption, and optimized delay. We consider heterogeneous scenarios of network topologies for optimal path configuration by exploring and exploiting the hunts. It uses multiple inputs from heterogeneous IIoT into quantum and bio-inspired optimization techniques. The differential evolution operator and crossover operations are used for information interchange among the nodes to avoid trapping into local minima. The different topology scenarios are simulated to study the impact of $p$ -degrees of connectivity concerning objective functions’ evaluation and compared with existing techniques. The results demonstrate that our algorithm consumes a minimum of 30.3% lesser energy. Furthermore, it offers improved searching precision and convergence swiftness in the possible search space for $p$ -disjoint paths and reduces the delay by a minimum of 26.7%. Our algorithm also improves the throughput by a minimum of 29.87% since the quantum swarm inclines to generate additional diverse paths from multiple source nodes to the gateway.

Ultra-durable superhydrophobic surfaces from 3D self-similar network via co-spraying of polymer microspheres and nanoparticles

Establishing mechanically durable superhydrophobic (SH) surface using adhesive polymer and nanoparticles (NPs) is an effective, scalable and simple approach, however, the optimizing 3D coupling of two components is highly challenging. Herein, a novel approach involving co-spraying of polystyrene (PS) microspheres (MPs) and perfluorinated chained nanoparticles (F-CNPs) and subsequent hot-press is reported. An optimal configuration of self-similar 3D network is established, in which adhesive polymer and decorated nanoparticles are fully utilized. Hence, our coating remains superhydrophobicity even after 1200 cm unidirectional abrasion under 10 kPa. More impressively, the superhydrophobicity could be maintained even after 14 h of water jetting impact (~25 kPa) or 230 cycles of freeze-thaw cycles. We believe our network design of full utilizing adhesive polymer and superhydrophobic particles enhances the potential applications of durable superhydrophobic surfaces in various harsh environments.

Optimization of the grid configuration by the method of contour optimization

In the article the question of a choice of an optimum network by a method of contour optimization and calculation of operating modes of a network is considered. A 110/35/10 kV substation project has been developed. The basic and reserve protection of the power transformer is chosen. A startup project of the optimal scheme of power supply of consumers with 110 kV network has been developed. Measures and means have been selected to ensure safe working conditions during the operation of the 110 kV network. The function of discounted costs for an overhead line with a nominal voltage of 110 kV is formed. Actuality of theme. When designing one of the main tasks is to optimize the configuration of power grids to achieve the maximum possible economic-nominal effect, without violating these technical requirements. The purpose of work is to optimize the network by the method of contour optimization and calculation of network modes. Object of research: 110 kV electrical distribution network. Subject of research: network configuration optimization. Research methods. Methods of theoretical bases of electrical engineering and electric networks, mathematical modeling, numerical methods of solving nonlinear systems of equations.

Optimal Configuration of LoRa Networks in Smart Cities

Long range (LoRa) is a wireless communication standard specifically targeted for resource-constrained Internet of Things (IoT) devices. LoRa is a promising solution for smart city applications as it can provide long-range connectivity with a low energy consumption. The number of LoRa-based networks is growing due to its operation in the unlicensed radio bands and the ease of network deployments. However, the scalability of such networks suffers as the number of deployed devices increases. In particular, the network performance drops due to increased contention and interference in the unlicensed LoRa radio bands. This results in an increased number of dropped messages and, therefore, unreliable network communications. Nevertheless, network performance can be improved by appropriately configuring the radio parameters of each node. To this end, in this article we formulate integer linear programming models to configure LoRa nodes with the optimal parameters that allow all devices to reliably send data with a low energy consumption. We evaluate the performance of our solutions through extensive network simulations considering different types of realistic deployments. We find that our solution consistently achieves a higher delivery ratio (up to 8% higher) than the state of the art with minimal energy consumption. Moreover, the higher delivery ratio is achieved by a large percentage of nodes in each network, thereby resulting in a fair allocation of radio resources. Finally, the optimal network configurations are obtained within a short time, usually much faster than the state of the art. Thus, our solution can be readily used by network operators to determine optimal configurations for their IoT deployments, resulting in improved network reliability.

Distributed Optimization of Visual Sensor Networks for Coverage of a Large-Scale 3-D Scene

Visual coverage is an important task for environment perception. In this article, the coverage of a large-scale 3-D scene represented by a polygon mesh model is considered, and a visual sensor network deployment algorithm is proposed through the combination of space partition, greedy and local search procedures. Comparing with existing approaches, the proposed algorithm can handle large-scale 3-D polygon meshes much faster in a scalable and distributed way, with superior coverage performance. First, we propose a new data structure called “chunk-triangle” in order to accelerate the computing process to identify visible triangles for a given camera. Furthermore, a GPU-based parallel algorithm is presented to shorten the time consumed for occlusion detection. Second, a new fast, scalable and distributed deployment approach is proposed for a camera sensor network to cover large-scale 3-D polygon meshes. The deployment algorithm generates a solution space of individual candidate cameras followed by camera selection. In camera selection, we partition the target scene space into some regions and conduct greedy search, respectively, in each region in order to choose a preliminary set of cameras with high initial coverage quality. Then, a local search strategy is further conducted to improve the coverage performance by compensating for the lost in rough space partition, and thus, results in an optimal deployment configuration of the camera network. Comparative evaluation results demonstrate the advantages of the proposed approach versus existing methods in terms of time cost, scalability, and coverage performance.

Long term transmission expansion planning to improve power system resilience against cascading outages

A Transmission Expansion Planning (TEP) problem is proposed to find the optimal configuration of the transmission network with considering security and resilience constraints. Due to the importance of blackouts and cascading failure, the proposed Resilient TEP (RTEP) minimizes the effects of cascading outages in term of load curtailment. In order to estimate the size of cascading outages, based on the total lost load, an iterative algorithm is proposed to analyze and simulate the steady state mechanism of cascading outages. A set of initiating events are defined as the triggering points of the cascading failure and the consequent chain of related outages are identified to determine the blackout size. In addition to the resilience constraints, the security constraints based on N-1 security criterion are also considered. Based on the Benders Decomposition (BD) algorithm, a multi-stage solution procedure is developed to handle the investment decisions, security constraints, and resilience requirements efficiently. In order to achieve the optimal resilient TEP configuration, all the master and sub-problems are formulated as Mixed Integer Programming optimization models. The proposed RTEP is implemented over the IEEE 24-bus test system.

FEATURES OF USING REMOTE CONTROL SWITCHING DEVICES IN THE PROCESS OF FORMING ACTIVE DISTRIBUTION NETWORKS

Widespread introduction of sources of distributed generation in 6-10 kV networks leads to the need to use new technical means and develop an appropriate methodology for controlling regimes. At the same time, the formation of active distribution networks, as a stage of implementation of the Smart Grid concept, envisages, along with their automation, the provision of adequate information monitoring of electricity transmission and distribution processes. Only under these conditions there is a possibility of the decision of many problems of management of operating modes of distribution networks with necessary level of efficiency. Based on this, in this paper the problem of choosing the optimal configuration of the distribution network is considered as a task of operational management. To make economically justified decisions to change the topology of the distribution network, the article developed a new model for predicting the electrical load and output power of distributed generation sources. The peculiarity of this model is its adaptability and the ability to simultaneously predict the values of the relevant parameters of the mode during predetermined time intervals.

A Secured and Intelligent Communication Scheme for IIoT-enabled Pervasive Edge Computing.

Industrial Internet of Things (IIoT) ensures reliable and efficient data exchanges among the industrial processes using Artificial Intelligence (AI) within the cyber-physical systems. In the IIoT ecosystem, devices of industrial applications communicate with each other with little human intervention. They need to act intelligently to safeguard the data confidentiality and devices' authenticity. The ability to gather, process, and store real-time data depends on the quality of data, network connectivity, and processing capabilities of these devices. Pervasive Edge Computing (PEC) is gaining popularity nowadays due to the resource limitations imposed on the sensor-embedded IIoT devices. PEC processes the gathered data at the network edge to reduce the response time for these devices. However, PEC faces numerous research challenges in terms of secured communication, network connectivity, and resource utilization of the edge servers. To address these challenges, we propose a secured and intelligent communication scheme for PEC in an IIoT-enabled infrastructure. In the proposed scheme, forged identities of adversaries, i.e., Sybil devices, are detected by IIoT devices and shared with edge servers to prevent upstream transmission of their malicious data. Upon Sybil attack detection, each edge server executes a parallel Artificial Bee Colony (pABC) algorithm to perform optimal network configuration of IIoT devices. Each edge server performs the job migration to their neighboring servers for load balancing and better network performance, based on their processing and storage capabilities. The experimental results justify the efficiency of our proposed scheme in terms of Sybil attack detection, the convergence curves of our pABC algorithm, delay, throughput, and control overhead of data communication using PEC for IIoT.

Optimization of electric distribution network configuration for power loss reduction based on enhanced binary cuckoo search algorithm

This paper presents a new network reconfiguration (NR) method using enhanced binary cuckoo search algorithm (EBCSA) to reduce power loss in the distribution system. Enhanced binary cuckoo search algorithm (EBCSA) is developed by transferring the continuous cuckoo search algorithm (CSA) to the binary domain and adding a new local search mechanism. Calculation results on different test systems demonstrate that EBCSA has capability for searching the optimal network configuration with greater success rate, better optimal solution quality and smaller number of average convergence iterations than those of binary CSA, binary coyote optimization algorithm, binary genetic algorithm and binary particle swarm optimization. Furthermore, the effect of the transfer function transferring from continuous domain to binary domain and the value of the parameters of EBCSA for the NR problem are also evaluated to choose the suitable values. The results show that EBCSA is a tool worth considering for the NR problem.

Diseño óptimo de cadenas de suministros reversas

El creciente uso de agroquímicos sumado al daño que los envases contaminados causan al medio ambiente y a la salud de la población, hacen necesario establecer una cadena de suministros reversa para la recuperación y el correcto tratamiento de los envases. Se propone un modelo MILP multi-periodo para definir la configuración óptima de la cadena de suministros reversa de envases vacíos de agroquímicos, persiguiendo el objetivo de minimizar el costo total actualizado de toda la red.

Performance analysis of software defined network using intent monitor and reroute method on ONOS controller

Software Defined Network (SDN) provides high service flexibility to optimize network configuration based on network traffic. Traffic management able to solve traffic density in SDN. However, it will misuse the network bandwidth and links. One variant of the SDN controller, namely Open Network Operating System (ONOS), provides an Intent Monitor and Reroute (IMR) method that can optimize traffic management based on the description of an object in the ONOS application. This method can optimize the network bandwidth and links of SDN. The IMR can monitor the network and reconfigure the network to restore network connectivity by maximizing the use of each link when transmitting data. This study examines the impact of using IMR with a custom topology on ONOS to find the best scenario by performing traffic management on a data plane consisting of switches totaling 8-12 switches. The parameters measured in this study are bandwidth usage and quality of service (QoS). The results obtained in this study are IMR able to optimize the use of each link and maximize bandwidth usage in a network when distributing data and following TIPHON standards

Design and optimisation of an innovative two-hub-and-spoke network for the Mediterranean short-sea-shipping market

Short Sea Shipping (SSS) is generally considered as a privileged transport mode in terms of reducing road congestion and related external costs. In the last two decades, SSS has attracted a lot of attention in the European Union, where it is also regarded as a key factor for economic and social cohesion between countries. In this regard, the present study proposes a new network design that aims at improving and increasing freight traffic between the north-western and the south-eastern shores of the Mediterranean Basin. The proposed model, designed for Ro-Ro (Roll-on Roll-off) freight transport, tries to overcome the limits of the existing multi-port-calling services through an innovative two-hub-based scheme of better scheduled and reliable frequencies and more competitive delivery times. The operating parameters of the new network are determined using a specially designed optimisation approach based on two separate but related mathematical models: a Mixed Integer Linear Programming Model for Service Frequency Selection and a Mixed Integer Linear Programming Model for Service Timetabling. Numerical tests, performed on real demand and supply data covering 23 ports in 8 Mediterranean countries, show the validity of the proposed approach to determine an optimal network configuration for the organisation of transport services in the area.

Asymmetric Entrainment Structure of Pierce Oscillator Circuit

Experimental study is presented to uncover entrainment property of Pierce oscillator circuit (i.e., one of the most standard circuits for crystal oscillators) subject to sinusoidal external forcing. In contrast to the well-known Arnold tongues observed in a wide class of forced nonlinear systems, asymmetric shape was observed as an entrainment region of the circuit in two-dimensional parameter space spanned by the external forcing strength and frequency. Computation of the impedance curve revealed that the asymmetric shape is due to positive (inductive) or negative (capacitive) reactance of the Pierce circuit, which facilitates or inhibits the circuit oscillations. We extend this insight into two coupled Pierce circuits and show that, to efficiently achieve their synchronization, it is advantageous to drive a slow oscillator circuit by a fast circuit rather than to drive a fast circuit by a slow circuit. Our study provides a guideline on an optimal network configuration for coupled crystal oscillators, which may find applications in distributed digital clocks and wireless communication systems.

Modeling the effect of non‐linear process parameters on the prediction of hydrogen production by steam reforming of bio‐oil and glycerol using artificial neural network

Biomass-derived substrates such as bio-oil and glycerol are gaining wide acceptability as feedstocks to produce hydrogen using a steam reforming process. The wide acceptability can be attributed to a huge amount of glycerol and bio-oil obtained as by-products of biodiesel production and pyrolysis processes. Several parameters have been reported to affect the production of hydrogen by biomass steam reforming. This study investigates the effect of non-linear process parameters on the prediction of hydrogen production by biomass (bio-oil and glycerol) steam reforming using artificial neural network (ANN) modeling technique. Twenty different multilayer ANN model architectures were tested using datasets obtained from the bio-oil and glycerol steam reforming. Two algorithms namely Levenberg-Marquardt and Bayesian regularization were employed for the training of the ANNs. An optimized network configuration consisting of 3 input layer 14 hidden neurons, 1 output layer, and 3 input layer, 5 hidden neurons, and 1 output layer were obtained for the Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by bio-oil steam reforming. While an optimized network configuration consisting of 5 input nodes, 9 hidden neurons, 1 output node, and 5 input nodes, 8 hidden neurons, and 1 output node were obtained for Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by glycerol steam reforming. Based on the optimized network, the predicted hydrogen production from the bio-oil and glycerol steam agreed with the actual values with the coefficient of determination (R2) > 0.9. A low mean square error of 3.024 × 10−24 and 6.22 × 10−15 for the optimized for Levenberg-Marquardt and Bayesian regularization-trained ANN, respectively. The neural network analyses of the two processes showed that reaction temperature and glycerol-to-water molar ratio were the most relevant factors that influenced the production of hydrogen by bio-oil and glycerol steam reforming, respectively. This study has demonstrated the robustness of the ANN as a technique for investigating the effect of non-linear process parameters on hydrogen production by bio-oil and glycerol steam reforming.

Network reconfiguration and DG output including real time optimal switching sequence for system improvement

ABSTRACT This research proposes an optimal switching sequence path method in order to minimise power losses during the network switching operation. Apart from this contribution, simultaneous optimal network reconfiguration and optimal DG output generation were also presented. The proposed methodology involves (1) optimal radial network reconfiguration and DG output simultaneously and (2) real time optimal sequence of switching operations required to convert the network from the original configuration to the optimal configuration obtained from (1). The proposed method is applied to reduce power losses and improve the overall voltage profile of the system. The chosen optimisation techniques include (EP), (PSO), (GSA), and (FA). To assess the capabilities of the proposed method, simulations using MATLAB were carried out on IEEE 33-bus and IEEE 69-bus radial distribution networks. The obtained results demonstrate the effectiveness of the proposed strategy to determine the sequence path of switching operations, as well as the optimal network configuration and optimal generation output of DG units. For more validation, real-time analysis test using PSCAD/EMTDC software is used to check the voltage stability of the system during switching process. The test proved that the optimal switching sequence does not cause over voltage or under voltage when implemented in real-time conditions.