An Augmented Jacobian Method for Power Flow Analysis of Weakly Looped Distribution Systems with PV Buses

A power flow analysis method for weakly looped distribution systems with PV buses is proposed in this paper. The proposed method is computationally more efficient and more robust compared with the conventional compensation methods. The robustness is achieved by embedding the boundary conditions of loops and PV buses into the Jacobian matrix. The computational efficiency is achieved by the carefully designed factorization of Jacobian matrix. Test results on a 33 bus system are presented.

Iron stability in drinking water distribution systems in a city of China

A field study on the estimation and analysis of iron stability in drinking water distribution system was carried out in a city of China. The stability of iron ion was estimated by pC-pH figure. It was found that iron ion was unstable, with a high Fe （OH）3 precipitation tendency and obvious increase in turbidity. The outer layer of the corrosion scale was compact, while the inner core was porous. The main composition of the scale was iron, and the possible compound constitutes of the outer scale were α-FeOH, γ-FeOOH, α-Fe2O3, γ-F2O3, FeCl3, while the inner were Fe3O4, FeCl2, FeCO3. According to the characteristics of the corrosion scale, it was thought that the main reason for iron instability was iron release from corrosion scale. Many factors such as pipe materials, dissolved oxygen and chlorine residual affect iron release. Generally, higher iron release occurred with lower dissolved oxygen or chlorine residual concentration, while lower iron release occurred with higher dissolved oxygen or chlorine residual concentration. The reason was considered that the passivated out layer of scale of ferric oxide was broken down by reductive reaction in a condition of low oxidants concentration, which would result more rapid corrosion of the nine and red water phenomenon.

Leak Detection in Water Distribution Systems Using Bayesian Theory and Fisher’s Law

A leak detection method based on Bayesian theory and Fisher’s law was developed for water distribution systems. A hydraulic model was associated with the parameters of leaks (location, extent). The randomness of parameter values was quantified by probability density function and updated by Bayesian theory. Values of the parameters were estimated based on Fisher’s law. The amount of leaks was estimated by back propagation neural network. Based on flow characteristics in water distribution systems, the location of leaks can be estimated. The effectiveness of the proposed method was illustrated by simulated leak data of node pressure head and flow rate of pipelines in a test pipe network, and the leaks were spotted accurately and renovated on time.

Partial Least Squares Regression Model to Predict Water Quality in Urban Water Distribution Systems

The water distribution system of one residential district in Tianjin is taken as an example to analyze the changes of water quality.Partial least squares(PLS) regression model,in which the turbidity and Fe are regarded as control objectives,is used to establish the statistical model.The experimental results indicate that the PLS regression model has good predicted results of water quality compared with the monitored data.The percentages of absolute relative error(below 15%,20%,30%) are 44.4%,66.7%,100%(turbidity) and 33.3%,44.4%,77.8%(Fe) on the 4th sampling point;77.8%,88.9%,88.9%(turbidity) and 44.4%,55.6%,66.7%(Fe) on the 5th sampling point.

Assessment on reliability of water quality in water distribution systems

Water leaving the treatment works is usually of a high quality but its properties change during the transportation stage. Increasing awareness of the quality of the service provided within the water industry today and assessing the reliability of the water quality in a distribution system has become a major significance for decision on system operation based on water quality in distribution networks. Using together a water age model, a chlorine decay model and a model of acceptable maximum water age can assess the reliability of the water quality in a distribution system. First, the nodal water age values in a certain complex distribution system can be calculated by the water age model. Then, the acceptable maximum water age value in the distribution system is obtained based on the chlorine decay model. The nodes at which the water age values are below the maximum value are regarded as reliable nodes. Finally, the reliability index on the percentile weighted by the nodal demands reflects the reliability of the water quality in the distribution system. The approach has been applied in a real water distribution network. The contour plot based on the water age values determines a surface of the reliability of the water quality. At any time, this surface is used to locate high water age but poor reliability areas, which identify parts of the network that may be of poor water quality. As a result, the contour water age provides a valuable aid for a straight insight into the water quality in the distribution system.

Kinetics of Chlorine Decay in Water Distribution Systems

A combined first and second-order model, which includes bulk decay and wall decay, was developed to describe chlorine decay in water distribution systems. In the model the bulk decay has complex relationships with total organic carbon (TOC), the initial chlorine concentration and the temperature. Except for the initial stages they can be simplified into a linear increase with TOC, a linear decrease with initial chlorine concentration and an exponential relationship with the temperature. The model also explains why chlorine decays rapidly in the initial stages. The parameters of model are determined by deriving the best fitness with experimental data. And the accuracy of model has been verified by using the experimental data and the monitoring data in a distribution system.

Repulsive firefly algorithm-based optimal switching device placement in power distribution systems

To achieve optimal configuration of switching devices in a power distribution system,this paper proposes a repulsive firefly algorithm-based optimal switching device placement method.In this method,the influence of territorial repulsion during firefly courtship is considered.The algorithm is practically applied to optimize the position and quantity of switching devices,while avoiding its convergence to the local optimal solution.The experimental simulation results have showed that the proposed repulsive firefly algorithm is feasible and effective,with satisfying global search capability and convergence speed,holding potential applications in setting value calculation of relay protection and distribution network automation control.

Least Squares Fitting Based Fault Classification in Distribution Systems

Power systems are the largest and most complex human made systems, consisting of thousands of electrical sources, loads, transmission and distribution lines, power transformers, circuit breakers, etc. where faults always occurred. Faults can cause personnel and equipment safety problems, and can result in significant disruption to power supply and thus financial losses. In this paper we will present comprehensive mathematical suite to detect and classify fault dependent models of various types of power systems. This work will extract fault unique signatures by using polarization ellipse during the healthy condition and the polarization will be circular shape with radius equal the rated voltage of the system, but during the fault condition the polarization will be ellipse shape and the fault signature will be defined according the ellipse parameters major axis, minor axis, ellipticity and orientation angle, by using least squares criterion will define the ellipse parameters this system will identify and classify. This paper will be a milestone for extended paper based on the proposed mathematical modelling and applying it to identify, classify and localize with simulation model.

A Hybrid Decoupled Power Flow Method for Balanced Power Distribution Systems

This paper proposes a hybrid decoupled power flow method for balanced power distribution systems with distributed generation sources. The method formulates the power flow equations in active power and reactive power decoupled form with polar coordinates. Second-order terms are included in the active power mismatch iteration, and constant Jacobian and Hessian matrices are used. A hybrid direct and indirect solution technique is used to achieve efficiency and robustness of the algorithm. Active power correction is solved by means of a sparse lower triangular and upper triangular （LU） decomposition algorithm with partial pivoting, and the reactive power correction is solved by means of restarted generalized minimal residual algorithm with an incomplete LU pre-conditioner. Typical distribution generation models and distribution load models are included. The impact of zero-impedance branches is explicitly modeled through reconfiguring of the adjacent branches with impedances. Numerical examples on a sample distribution system with widespread photovoltaic installations are given to demonstrate the effectiveness of the proposed method.

Preliminary Data on Legionella Detection in Water Distribution Systems in Cameroon

After the declaration of the first case of Legionnaire＇s disease in Cameroon in 2007, the Centre Pasteur of Cameroon implemented the detection method for Legionella. The introduction of this new method was put in places in order to investigate Legionella spp. colonization of water distribution systems （WDS） of large buildings including hospitals, hotels and Off Shore Exploitations Sites （OSES） in an attempt to identify risk factors for Legionella spp. Water systems of 6 hotels, 6 hospitals and 6 ships were investigated for the presence of Legionella spp.. A total of 130 samples were collected, 77 from hotels, 27 from hospitals and 26 from ships. 51 Legionella spp. were isolated from 41 （31.54%） water samples. Of a total of 51 positive isolates, 40/51 （78.4%） were L. pneumophila with 21 （52.5%） Legionellapneumophila serogroup （sg） 1, 16 （40%） L. pneumophila sg 5, 2 （5%） L. pneumophila sg 6, 1 （2.5%） L. pneumophila sg 7 and 11/51 Legionella spp. with 10 （90%） L. anisa, 1 （10%） Legionella dumoffii. 5 L. pneumophila sg 1 were associated with 5 L. pneumophila sg 5 and 4 L. pneumophila sg 1 were associated with 4 L. anisa. These results showed that WDS of hospitals, hotels and ships can be heavily colonized by Legionella spp. and may present a risk of Legionnaires＇ disease. Based on these preliminary results, we have just put in place a Legionella survey protocol in Cameroon.

Alternative Solutions to Mitigate Problems due to Neutral Conductors Theft in MV （Medium Voltage） Power Distribution Systems

This paper aims at analyzing the impact of the neutral conductor absence at specific sections over the performance of the power distribution lines, and proposing alternative solutions to mitigate the problems caused by the neutral conductor theft. Simulations are made by the software lnterplan and show that the absence of neutral conductor at specific sections of power distribution lines may increase the neutral-to-ground voltages, which compromises the system＇s safety. The solution developed keeps the technical performance of the power distribution system at satisfactory levels, regarding the voltage profile, or, at least, close to the level before the neutral conductor＇s theft.

HPDS133-bus:A Benchmark Test System for Distribution Systems with High Penetration of Distributed Generation

The benchmark test system for distribution systems is necessary and important for system analysis and result data checking.In order to meet the development demand of the distribution systems which integrate with a high penetration of distributed generation(DG),a benchmark test system for the distribution systems which adapts to current and future development trends is proposed.Based on the concentric zone theory and the typical regional settlement model in China,three typical distribution system scenarios corresponding to different system circle structures and regional development characteristics are designed in this paper.In order to adapt to the trend that the distribution systems will present the mixed state of AC and DC,a typical improvement scheme of distribution systems in urban scenario is presented.The benchmark test system of low-voltage distribution systems in a rural scenario is also designed because of the high penetration of DG.Moreover,the system structure,load parameters,integrated capacity and output characteristics of varies types of DG in different scenarios are described in detail.Finally,the simulation results,such as the time series power flow,reliability evaluation and hosting capacity of DGs are given.The benchmark test system proposed in this paper will lay a foundation for future research on distribution systems with a high proportion of DG and effectively support the distribution systems planning,design,operations and dispatches.

Intermittent Arc Fault Detection Based on Machine Learning in Resonant Grounding Distribution Systems

In resonant grounding systems,most single-phaseto-ground faults evolve from IAFs(Intermittent Arc Faults).Earlier detection of IAFs can facilitate fault avoidance.This work proposes a novel method based on machine learning for detecting IAFs in three steps.First,the feature of zero-sequence current is automatically extracted and selected by a newlydesigned FINET(“For IAFs,Neuron Elaboration Net”),instead of traditional feature selection based on time-frequency decomposition.Moreover,data of the zero-sequence current divided by different time windows are successively input into the trained FINET.A proposed PSF(principal-subordinate factor)analyses the results obtained from FINET to improve anti-interference in the mentioned IAF detection algorithm.Experiments using PSCAD/EMTDC software simulation data show the proposed method is feasible and highly adaptable.In addition,the detection result of on-site recorded data demonstrates the effectiveness of the proposed method in practical resonant grounding systems.

Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingthe significance of using cutting-edge technology to meet rising privacy issues in digital identity.

A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the system.This technology has been widely used and has developed rapidly in big data systems across various fields.An increasing number of users are participating in application systems that use blockchain as their underlying architecture.As the number of transactions and the capital involved in blockchain grow,ensuring information security becomes imperative.Addressing the verification of transactional information security and privacy has emerged as a critical challenge.Blockchain-based verification methods can effectively eliminate the need for centralized third-party organizations.However,the efficiency of nodes in storing and verifying blockchain data faces unprecedented challenges.To address this issue,this paper introduces an efficient verification scheme for transaction security.Initially,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all transactions.Subsequently,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous trees.Theoretical analyses and simulation experiments conclusively demonstrate the superior performance of this scheme.When verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional methods.The findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of Things.This scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

Observer-based dynamic event-triggered control for distributed parameter systems over mobile sensor-plus-actuator networks

We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.

Effects of O3/Cl2 disinfection on corrosion and opportunistic pathogens growth in drinking water distribution systems

The effects of O3/Cl2 disinfection on corrosion and the growth of opportunistic pathogens in drinking water distribution systems were studied using annular reactors （ARs）. The corrosion process and most probable number （MPN） analysis indicated that the higher content of iron-oxidizing bacteria and iron-reducing bacteria in biofilms of the AR treated with O3/Cl2 induced higher Fe304 formation in corrosion scales. These corrosion scales became more stable than the ones that formed in the AR treated with Cl2 alone. O3/Cl2 disinfection inhibited corrosion and iron release efficiently by changing the content of corrosion-related bacteria. Moreover, ozone disinfection inactivated or damaged the opportunistic pathogens due to its strong oxidizing properties. The damaged bacteria resulting from initial ozone treatment were inactivated by the subsequent chlorine disinfection. Compared with the AR treated with Cl2 alone, the opportunistic pathogens M. auium and L. pneumophila were not detectable in effluents of the AR treated with O3/Cl2, and decreased to （4.60 ± 0.14） and （3.09 ± 0.12） loglo （gene copies/g corrosion scales） in biofilms, respectively. The amoeba counts were also lower in the AR treated with O3/Cl2. Therefore, O3/Cl2 disinfection can effectively control opportunistic pathogens in effluents and biofilms of an AR used as a model for a drinking water distribution system.

Dynamic Load Restoration Considering the Interdependencies Between Power Distribution Systems and Urban Transportation Systems

After a major outage,mobile emergency resources(MERs)can be dispatched via the transportation system(TS)for service restoration to critical loads in the power distribution system(PDS).In this case study,the efficiency of service restoration in the PDS is associated with the traffic efficiency of the TS,and vice versa,because the PDS and TS are mutually coupled through traffic lights and MERs.This paper proposes a service restoration method considering interdependency between the PDS and TS,which is formulated as a mixed-integer linear program(MILP).The objective includes maximizing the efficiency of both PDS restoration and TS.By solving the MILP,the dynamic load restoration and MER dispatch strategies can be obtained.For the PDS,the availability of MERs,including mobile sources and repair crews,relates to their dispatch in the TS,and their relationship is formulated as mathematical models.For the TS,the dynamic traffic flow is modeled using the cell transmission model and the effect of traffic lights is considered.Case studies validate the effectiveness of the proposed method.

Characterization of bacterial community and iron corrosion in drinking water distribution systems with O3-biological activated carbon treatment

Bacterial community structure and iron corrosion were investigated for simulated drinking water distribution systems（DWDSs） composed of annular reactors incorporating three different treatments： ozone, biologically activated carbon and chlorination（O3-BAC-Cl2）;ozone and chlorination（O3-Cl2）; or chlorination alone（Cl2）. The lowest corrosion rate and iron release, along with more Fe3O4 formation, occurred in DWDSs with O3-BAC-Cl2 compared to those without a BAC filter. It was verified that O3-BAC influenced the bacterial community greatly to promote the relative advantage of nitrate-reducing bacteria（NRB）in DWDSs. Moreover, the advantaged NRB induced active Fe（III） reduction coupled to Fe（II） oxidation, enhancing Fe3O4 formation and inhibiting corrosion. In addition, O3-BAC pretreatment could reduce high-molecular-weight fractions of dissolved organic carbon effectively to promote iron particle aggregation and inhibit further iron release. Our findings indicated that the O3-BAC treatment, besides removing organic pollutants in water, was also a good approach for controlling cast iron corrosion and iron release in DWDSs.

Fault Line Detection Using Waveform Fusion and One-dimensional Convolutional Neural Network in Resonant Grounding Distribution Systems

Effective features are essential for fault diagnosis.Due to the faint characteristics of a single line-to-ground(SLG)fault,fault line detection has become a challenge in resonant grounding distribution systems.This paper proposes a novel fault line detection method using waveform fusion and one-dimensional convolutional neural networks(1-D CNN).After an SLG fault occurs,the first-half waves of zero-sequence currents are collected and superimposed with each other to achieve waveform fusion.The compelling feature of fused waveforms is extracted by 1-D CNN to determine whether the fused waveform source contains the fault line.Then,the 1-D CNN output is used to update the value of the counter in order to identify the fault line.Given the lack of fault data in existing distribution systems,the proposed method only needs a small quantity of data for model training and fault line detection.In addition,the proposed method owns fault-tolerant performance.Even if a few samples are misjudged,the fault line can still be detected correctly based on the full output results of 1-D CNN.Experimental results verified that the proposed method can work effectively under various fault conditions.