Devices In Distribution Networks Research Articles

Techno-economic comparison between commercial energy recovery devices in complex Water Distribution Networks

Water Distribution Networks (WDN) proved to be viable for the exploitation of throttling hydraulic energy. Researchers often focused their attention on the study of Pumps as Turbines (PaTs) for WDNs without considering other solutions. Actually, PaTs are not the only machines that can be employed. Indeed, other solutions exists, e.g. Cross-Flow Turbines, and commercially available Energy Harvesting Control Valves. The novelty of this study regards the selection, for each node, of the best technology among these machines rather than choosing only among PaTs, in order to help the water utilities in the techno-economic decision processes. Regarding PaTs, the authors have updated and integrated PaT-ID, their proprietary decision making tool. A complex real WDN has been used as a case study, characterized by 8 reservoirs and 16 Pressure Reducing Valves (PRVs) installed to balance the network and to reduce water leakages. Although all the three considered type of devices show at least 40% of recovered available energy within the WDN, the best solution form an energy point of view not always could be feasible from an economic point of view.

English

English

Research on Multi-Layer Defense against DDoS Attacks in Intelligent Distribution Networks

With the continuous development of new power systems, the intelligence of distribution networks has been increasingly enhanced. However, network security issues, especially distributed denial-of-service (DDoS) attacks, pose a significant threat to the safe operation of distribution networks. This paper proposes a novel DDoS attack defense mechanism based on software-defined network (SDN) architecture, combining Rényi entropy and multi-level convolutional neural networks, and performs fine-grained analysis and screening of traffic data according to the amount of calculation to improve the accuracy of attack detection and response speed. Experimental verification shows that the proposed method excels in various metrics such as accuracy, precision, recall, and F1-score. It demonstrates significant advantages in dealing with different intensities of DDoS attacks, effectively enhancing the network security of user-side devices in power distribution networks.

Electric shock fault identification method based on DWT-AE-BPNN for residual current devices in power distribution systems

The protection dead-zone and threshold setting difficulties of the residual current devices (RCDs) in low-voltage distribution networks may lead to the misidentification of electric shock fault, resulting in severe life-threatening accidents. This paper proposes an electric shock fault identification method based on artificial intelligence for RCDs. Firstly, Mallat discrete wavelet transform (DWT) is applied to efficiently extract non-stationary electric shock feature signals from the total residual current with various noises, preventing weak non-stationary electric shock feature signals from being filtered out. Based on the average and maximum components of the signal mutation, an adaptive threshold can be determined to detect electric shock accurately, avoiding the false activation of RCDs caused by load fluctuations. Subsequently, an autoencoder (AE) is built to mine the non-linear features in which the signal of electric shock on living gradually rises and the signal of electric shock on non-living remains stable. Finally, a back propagation neural network (BPNN) is trained to classify the electric shock types from the non-linear features. The simulation and experiment have been conducted to obtain total residual current data under different conditions, and the electric shock fault real-time identification hardware platforms are developed. The accuracy of electric shock fault detection and classification can reach 100 %, which has advanced its practical applicability.

Stable Operation Domain Analysis of Back-to-Back Converter Based Flexible Multi-State Switches Considering PLL Dynamics

Flexible multi-state switches (FMSSs), also called soft open point (SOP), are regarded as the core devices in flexible interconnected distribution network. However, due to their more complex control structure and nonlinear characteristics, there is a tight and complex coupling relationship between the transmission power limit and stability of two (or more) converter sides, which brings difficulties to the planning and power flow dispatching control of flexible interconnected distribution networks. In this paper, taking the back-to-back converter based FMSS (BTBC-FMSS) as the research object, the dynamic equivalent circuit connected to the distribution network is constructed, and the transmission active and reactive power expression of the converter on both sides considering PLL dynamics is deduced. The variation rules of transmission power limits on both sides of the device with various structures and control parameters are summarized. Furthermore, the stable operation domain of the BTBC-FMSS is constructed, and the effective measures to improve the transmission power stability of the device are given according to the analysis of the stable operation domain. Finally, a simulation model is built in Matlab/Simulink to verify the effectiveness of the analysis results.

A Real-Time Resource Dispatch Approach for Edge Computing Devices in Digital Distribution Networks Considering Burst Tasks

Edge computing technology can effectively solve huge challenges posed by the large number of terminal devices accessing and massive data processing in digital distribution networks. Burst tasks, such as faults and data requests from the cloud, can occur at any time for edge computing devices in distribution networks. These tasks are unpredictable and usually hold the highest priority and must be completed as soon as possible. Although resources can be reserved partially at each period in the pre-scheduled operation plan, they may still be insufficient to handle burst tasks adequately. A real-time resource dispatch approach for burst tasks is developed in this study to address the above problems. The concept of flexibility for edge computing devices is presented, determining the real-time dispatch duration. Real-time resource dispatch and task handling processing are analyzed in detail, considered as task real-time dispatch models, computation process real-time dispatch constraints, and resource limitation constraints. The proposed real-time resource dispatch approach takes full advantage of the transferable characteristics for partial original plan tasks to adjust the pre-scheduled operation plan and release flexible resources for immediate processing of the burst task, completing burst tasks quickly and minimizing the impact for previous planned tasks on the edge computing device. The capability of the proposed method to efficiently deal with the burst tasks is also verified by the case study.

Real-time coordination of integrated transmission and distribution systems: Flexibility modeling and distributed NMPC scheduling

This paper proposes a real-time distributed operational architecture to coordinate integrated transmission and distribution systems (ITD). At the distribution system level, the distribution system operator (DSO) calculates the aggregated flexibility of all controllable devices by power-energy envelopes and provides them to the transmission system operator (TSO). At the transmission system level, a distributed nonlinear model predictive control (nmpc) approach is proposed to coordinate the economic dispatch of multiple TSOs, considering the aggregated flexibility of all distribution systems. The subproblems of the proposed approach are associated with different TSOs and individual time periods. In addition, the aggregated flexibility of controllable devices in distribution networks is encapsulated, re-calculated, and communicated through the power-energy envelopes, facilitating a reduction in computational complexity and eliminating redundant information exchanges between TSOs and DSOs, thereby enhancing privacy and security. The framework’s effectiveness and applicability in real-world scenarios are validated through simulated operational scenarios on a summer day in Germany, highlighting its robustness in the face of significant prediction mismatches due to severe weather conditions.

Finding influential nodes in complex networks based on Kullback–Leibler model within the neighborhood

As a research hot topic in the field of network security, the implementation of machine learning, such as federated learning, involves information interactions among a large number of distributed network devices. If we regard these distributed network devices and connection relationships as a complex network, we can identify the influential nodes to find the crucial points for optimizing the imbalance of the reliability of devices in federated learning system. This paper will analyze the advantages and disadvantages of existing algorithms for identifying influential nodes in complex networks, and propose a method from the perspective of information dissemination for finding influential nodes based on Kullback–Leibler divergence model within the neighborhood (KLN). Firstly, the KLN algorithm removes a node to simulate the scenario of node failure in the information dissemination process. Secondly, KLN evaluates the loss of information entropy within the neighborhood after node removal by establishing the KL divergence model. Finally, it assesses the damage influence of the removed node by integrating the network attributes and KL divergence model, thus achieving the evaluation of node importance. To validate the performance of KLN, this paper conducts an analysis and comparison of its results with those of 11 other algorithms on 10 networks, using SIR model as a reference. Additionally, a case study was undertaken on a real epidemic propagation network, leading to the proposal of management and control strategies for daily protection based on the influential nodes. The experimental results indicate that KLN effectively evaluates the importance of the removed node using KL model within the neighborhood, and demonstrate better accuracy and applicability across networks of different scales.

Pseudo-metric modelling of distribution network state estimation based on CNN-BiLSTM network and customized HGGA algorithm

To address the insufficient configuration of real-time measurement devices in distribution networks, a state estimation method for distribution networks is proposed. First, both historical data and real-time data are used as the input of the convolutional neural networks-bi-directional long short-term memory (CNN-BiLSTM) network to obtain the pseudo measurements of the branch power and load node injected power with high accuracy at the current moment. Second, the pseudo measurement set of the input state estimation is determined using the customized hybrid greedy genetic algorithm (HGGA) algorithm. Finally, the real-time measurement, pseudo measurement, virtual measurement, and weight set are input into the state estimation program to complete the real-time state estimation of the distribution network based on the weighted least squares method. Additionally, the proposed method is applied to the data fusion of different measurement systems. Theoretical analysis and example verification confirm that the proposed method can effectively improve the accuracy of pseudo measurement modelling and state estimation results.

Fault Section Localization Method for Distribution Network Based on Synchronous Phasor Measurement

Background: The accurate locating of fault sections in a distribution network can lay an effective foundation for the rapid processing of faults. However, the waveform of highresistance grounding faults is relatively weak, which increases the difficulty of fault feature extraction and localization. In addition, the complex operating conditions and interference factors of the actual distribution network can affect the fault section localization method, leading to incorrect location problems. background: The accurate locating of fault sections in distribution network can lay an effective foundation for rapid processing of faults. However, the waveform of high resistance grounding faults is relatively weak, which increases the difficulty of fault feature extraction and localization. In addition, the complex operating conditions and interference factors of the actual distribution network can affect the fault section localization method, leading to incorrect location problems. Objective: In order to overcome the limitations of existing fault section localization methods on fault resistance values and application scenarios, a fault section localization method for distribution networks based on synchronous phasor measurement is proposed in this paper. objective: In order to overcome the limitations of existing fault section localization methods on fault resistance values and application scenarios, a fault section localization method for distribution network based on synchronous phasor measurement is proposed in this paper. Methods: Firstly, the transient zero sequence equivalent network of single-phase to ground faults in the distribution network is analyzed, revealing the differences in zero-sequence current within different sections of the faulty line. At the same time, based on the zero-sequence current waveform recorded by the waveform measurement device in actual distribution network, the characteristics of the waveform in different sections in the time and frequency domains are analyzed. Furthermore, a fault feature extraction method based on wavelet packet transform is proposed to construct fault differential features for different sections. Then, the grey correlation analysis method is adopted to calculate the correlation coefficients between different sections to construct locating criteria, thereby achieving accurate locating of fault sections in distribution network. method: Firstly, the transient zero sequence equivalent network of single-phase to ground faults in the distribution network is analyzed, revealing the differences in zero-sequence current within different sections of the faulty line. At the same time, based on the zero-sequence current waveform recorded by the waveform measurement device in actual distribution network, the characteristics of the waveform in different sections in the time and frequency domains are analyzed. Furthermore, a fault feature extraction method based on wavelet packet transform is proposed to construct fault differential features for different sections. Then, the grey correlation analysis method is adopted to calculate the correlation coefficients between different sections to construct locating criteria, thereby achieving accurate locating of fault sections in distribution network. Results: The experimental results using field data indicate that the localization accuracy can reach 98.90%, and the calculation time is about 102.65ms, which has high localization accuracy and localization efficiency. result: The experimental results using field data indicate that the proposed method has high localization accuracy and efficiency. Conclusion: Through analysis and relevant experiments, it is concluded that the proposed method can accurately locate faults in actual distribution networks, and still has correct locating results for high resistance grounding faults. The effectiveness of the method has been verified.

ОГЛЯД ПРИНЦИПІВ СЕЛЕКТИВНОСТІ ЗАХИСТІВ В РОЗПОДІЛЬЧИХ МЕРЕЖАХ СПОЖИВАЧІВ НАПРУГОЮ 0,4 кВ

The analysis of existing principles of selectivity indicates the complexity and incompleteness of ensuring the coordination of the operation of protection devices in distribution networks of 0.4 kV consumers. Current selectivity is ensured only in the area of action of short-circuit currents. To ensure it, electromagnetic or electronic decouplers with a constant deceleration setting are required. This type of disconnectors is used to perform time selectivity and limit the thermal impact of short-circuit currents on the electrical installation and device. The use of devices with "pseudo-time" selectivity ensures the selectivity of the operation of automatic switches in the area of short-circuit currents. Selectivity "SELLIM" is provided with a speed of 0.01 s, but it is possible only in automatic circuit breakers "Schneider Electric". Logical selectivity requires a communication channel between switching devices and the presence of circuit breakers with appropriate communication ports. It is easiest to ensure selectivity by using switches from the same manufacturer using selectivity tables. But such a solution is not always found in practice. The method of energy selectivity allows, taking into account the ratio between ratings, to implement either partial selectivity or full selectivity up to the limit of the breaking capacity of automatic switches. To ensure energy selectivity between two or more current-limiting devices, it must be taken into account that depending on the location of the short circuit, a different number of circuit breakers will be involved in limiting the emergency current, and therefore, the value of the limited current will also be different. By using circuit breakers with a high level of current limitation and a tripping speed that is inversely related to the short-circuit current, full selectivity can be provided at several levels of the network. This makes it possible to simplify the analysis of selectivity, minimize electrodynamic and thermal effects and reduce the level of voltage drop due to the action of a short circuit. Improvement of electrical installation protection systems makes it possible to design power supply systems without excessive reserves of electrical equipment overload, which reduces their cost, dimensions and weight. On the other hand, in the event of malfunctions in the operation of electrical equipment, this limits emergency currents and prevents failure of the undamaged part of the electrical installation, which significantly reduces downtime. Key words: selectivity, relay protection devices, circuit breakers, electrical devices, 0.4 kV network.

Simultaneous allocation of renewable energy sources and custom power quality devices in electrical distribution networks using artificial rabbits optimization

Abstract This study suggests an optimal renewable energy source (RES) allocation and distribution-static synchronous compensator (D-STATCOM) and passive power filters (PPFs) for an electrical distribution network (EDN) to improve its performance and power quality (PQ). First, the latest metaheuristic artificial rabbits optimization (ARO) is used to locate and size solar photovoltaic (PV), wind turbine (WT) and D-STATCOM units. In the second stage, ratings of single-tuned PPFs and D-STATCOMs at the RESs are determined, considering non-linear loads in the network. The multi-objective function reduces power loss, improves the voltage stability index (VSI) and limits total harmonic distortion. Simulations using the IEEE 33-bus EDN compared the ARO results with those of previous studies. In the first scenario, ideally integrated D-STATCOMs, PVs and WTs reduced losses by 34.79%, 64.74% and 94.15%, respectively. VSI increases from 0.6965 to 0.7749, 0.8804 and 0.967. The optimal WT integration of the first scenario outperformed the PVs and D-STATCOMs. The second step optimizes the WTs and PQ devices for non-linear loads. WTs and D-STATCOMs reduce the maximum total harmonic distortion of the voltage waveform by 5.21% with non-linear loads to 3.23%, while WTs and PPFs reduce it to 4.39%. These scenarios demonstrate how WTs and D-STATCOMs can improve network performance and PQ. The computational efficiency of ARO is compared to that of the pathfinder algorithm, future search algorithm, butterfly optimization algorithm and coyote optimization algorithm. ARO speeds up convergence and improves solution quality and comprehension.

Hierarchical and partitioned planning strategy for closed-loop devices in low-voltage distribution network based on improved KMeans partition method

To improve the reliability of power supply, this paper proposes a hierarchical and partitioned planning strategy for closed-loop devices in low-voltage distribution network. Based on the geographic location and load situation of the distribution network area, an improved KMeans partition method is used to partition the area in the upper layer. In the lower layer, an intelligent algorithm is adopted to decide the numbers and placement locations of mobile low-voltage contact boxes and mobile seamless closed-loop load transfer devices in each partition with the goal of the highest closed-loop safety, the greatest improvement in annual power outage amount and the lowest cost. Finally, the feasibility and effectiveness of the proposed strategy are proved by an example.

A novel optimization strategy for line loss reduction in distribution networks with large penetration of distributed generation

With the large penetration of power electronic devices in distribution networks, power quality problems such as line loss and harmonics have become the main factors affecting the access of distributed generation (DG). At present, the research on DG integration, such as the optimization of the DG capacity using particle swarm optimization algorithm and ant colony algorithm, is mainly focused on the traditional distribution network. In contrast, the research on the power electronic distribution networks, which include a large number of harmonic sources, is not sufficient. This paper presents a novel optimization method for connecting DG to power electronic distribution network to reduce line loss. Firstly, based on the frequency domain model of the distribution network, this paper establishes the harmonic model of distribution network components, including generators, transformers, lines, harmonic sources and non-harmonic sources. Then the location and capacity of DG are optimized using genetic algorithm with network loss reduction as a target and voltage and harmonic as constraints. Finally, the power electronic distribution network is modelled based on the IEEE 34 - node standard model. The obtained results confirmed that the optimization model with harmonic constraints can effectively reduce the line loss by 108.26 kW and the line loss rate by 4.67 % using single DG. Also, when using the double DG, the line loss and the line loss rate are decreased by 118.53 kW and 5.14 %, respectively. In addition, the optimization effect of dual DG is better than that of single DG without considering the cost.

Deep learning-based application for fault location identification and type classification in active distribution grids

The high penetration of distributed energy resources, especially weather-dependent sources, even at the edge of the distribution grids, has increased the power system uncertainties and drastically shifted the operational status quo for the system operators. For the operators to ensure the uninterrupted electricity supply of the end-consumers, the fast and accurate response to fault events is of critical importance. This paper proposes a data-driven fault location identification and types classification application based on the continuous wavelet transformation and convolutional neural networks optimally configured through Bayesian optimization. This application leverages the proliferation of high-resolution measurement devices in distribution networks. It can locate the exact position of the short-circuit faults and classify them into eleven different types. Its intrinsic models grasp the spatial characteristics and the converted in frequency domain temporal ones of the three-phase voltage and current timeseries measurements stemming from the field devices, thus increasing the operators’ visibility of their networks in real-time. We conduct simulations through synthetic data, which we provide in an open-source repository, that replicate a wide range of fault occurrence scenarios with eleven different types, with the resistance ranging from 50Ω to 2kΩ and with duration from 20ms to approximately 2s, under noise conditions injected by devices and load variability. The results showcase the efficacy of the proposed method reaching an accuracy of 91.4% for fault detection, 93.77% for correct branch identification, 94.93% for fault type classification, and RMSE value of 2.45% for location calculation.

Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network

Distribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network: A cloud-edge collaboration architecture

With the construction of the power Internet of Things (IoT), communication between smart devices in urban distribution networks has been gradually moving towards high speed, high compatibility, and low latency, which provides reliable support for reconfiguration optimization in urban distribution networks. Thus, this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution. First, the multi-level dynamic reconfiguration method was discussed, which included feeder-, transformer-, and substation-levels. Subsequently, the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network. The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct “centralized training and decentralized execution” operation modes and improve the learning efficiency of the model. Thereafter, for a multi-agent system, this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy. In the offline learning phase, a Q-learning-based multi-agent conservative Q-learning (MACQL) algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase. In the online learning phase, a multi- agent deep deterministic policy gradient (MADDPG) algorithm based on policy gradients was proposed to explore the action space and update the experience pool. Finally, the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

A comparative investigation of flexible arc suppression devices in distribution network

A comparative investigation of flexible arc suppression devices in distribution network

Multi-objective allocation of switching devices in distribution networks using the Modified Barnacles Mating Optimization algorithm

The present study proposes a new optimization method for the placement of various switches and distributed generation (DG) in distribution networks to enhance network reliability and reduce costs. Since the application of the islanding process in distribution networks is prohibited in most countries, the main idea here is to provide a multi-objective optimization to maximize network reliability and minimize the cost of DG equipment unavailability by considering no islanding process. The study considers an optimal solution to determine the number, location, and type of switching devices by considering distributed generators unavailability index (DGUI), the cost of the equipment, and the reliability of the network (SAIDI and SAIFI). The optimization process of the problem is based on a modified version of the Barnacles Mating Optimization (MBMO) algorithm to improve system efficiency including accuracy and global results. The proposed algorithm is first validated to verify its efficiency and then analyzed for optimization by considering network reliability (including SAIDI and SAIFI), the cost of the equipment, and DGUI. The results are compared with two other methods, i.e., the NSGAII-based method and the SPEA2-based method, to show the effectiveness of the proposed system in different terms. Then, a method based on remote-controlled switches (RCSs) is presented to improve network reliability and expenses.

Fault indicator placement optimization using the cross-entropy method and traffic simulation data

This paper presents an approach to optimize the placement of fault indicator devices in distribution systems using the cross-entropy method and results from traffic simulations. The problem formulation takes into account the impact of the devices on restoration times and costs due to fines related to service interruption reliability indices. Candidate solutions to the problem are evaluated using sequential Monte Carlo simulations, where travel times of maintenance crews are sampled according to data acquired from mobility traffic simulations. Results show the applicability of the approach in different simulation scenarios and the benefits of installing the devices in distribution networks.