A Novel Defender-Attacker-Defender Model for Resilient Distributed Generator Planning with Network Reconfiguration and Demand Response

To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.

Distributed generator-based distribution system service restoration strategy and model-free control methods

The rapid growth of distributed generator(DG)capacities has introduced additional controllable assets to improve the performance of distribution systems in terms of service restoration.Renewable DGs are of particular interest to utility companies,but the stochastic nature of intermittent renewable DGs could have a negative impact on the electric grid if they are not properly handled.In this study,we investigate distribution system service restoration using DGs as the primary power source,and we develop an effective approach to handle the uncertainty of renewable DGs under extreme conditions.The distribution system service restoration problem can be described as a mixed-integer second-order cone programming model by modifying the radial topology constraints and power flow equations.The uncertainty of renewable DGs will be modeled using a chance-constrained approach.Furthermore,the forecast errors and noises in real-time operation are solved using a novel model-free control algorithm that can automatically track the trajectory of real-time DG output.The proposed service restoration strategy and model-free control algorithm are validated using an IEEE 123-bus test system.

Application of honey-bee mating optimization on state estimation of a power distribution system including distributed generators

We present a new approach based on honey-bee mating optimization to estimate the state variables in distribution networks including distributed generators. The proposed method considers practical models of electrical equipments such as static var compensators, voltage regulators, and under-load tap changer transformers, which have usually nonlinear and discrete characteristics. The feasibility of the proposed approach is demonstrated by comparison with the methods based on neural networks, ant colony optimization, and genetic algorithms for two test systems, a network with 34-bus radial test feeders and a realistic 80-bus 20 kV network.

Fault location method for petal-shaped distribution network with inverter-interfaced distributed generators

In this paper,a fault location method for the petal-shaped distribution network(PSDN)with inverter-interfaced distributed generators(IIDGs)is proposed to shorten the time of manual inspection.In order to calculate the fault position,the closed-loop structure of the PSDN is skillfully exploited,and the common control strategies of IIDGs are considered.For asymmetrical faults,a fault line identification formula based on the negative-sequence current phase differences is presented,and a fault location formula only utilizing the negative-sequence current amplitudes is derived to calculated the fault position.For symmetrical faults,the positive-sequence current at both ends of lines and the current output from IIDGs are used to identify the fault line,and the positive-sequence current on multiple lines are used to pinpoint the fault position.In this method,corresponding current phasors are separated into amplitudes and phases to satisfy the limitation of communication level.The simulation results show that the error is generally less than 1%,and the accuracy of the proposed method is not affected by the fault type,fault position,fault resistance,load current,and the IIDG penetration.

Analysis on Harmonics of Distribution Network with Distributed Generators

In this paper, the authors analyze the influence that connection of many distributed generators （DGs） has on the harmonics of the distribution network from the viewpoints of harmonic current, harmonic voltage, and voltage total harmonic distortion （THD）, when it is assumed that the harmonic voltages or the harmonic currents in a distribution network increases by connection of many DGs through the inverters. The analysis on the influence of DGs is carried out by using a standard analytical model of distribution network with DGs based on the practical data. The authors evaluate quantitatively the influence which harmonics generated from DGs has on the harmonics of the distribution network, and analyze visually about propagation of harmonics within the distribution network. In addition, the authors also verify about the restraint effect of the harmonics in the network scale by the active filters installation.

Distributed Generators Location and Capacity Effect on Voltage Profile Improvement and Power Losses Reduction Using Genetic Algorithm

This paper presents a powerful approach to find the optimal size and location of distributed generation units in a distribution system using GA （Genetic Optimization algorithm）. It is proved that GA method is fast and easy tool to enable the planners to select accurate and the optimum size of generators to improve the system voltage profile in addition to reduce the active and reactive power loss. GA fitness function is introduced including the active power losses, reactive power losses and the cumulative voltage deviation variables with selecting weight of each variable. GA fitness function is subjected to voltage constraints, active and reactive power losses constraints and DG size constraint.

An Introduction Effect Evaluation Tool for Distributed Generators

This paper presents practical distribution system equipment models such as various distributed generators, voltage regulators, and loads for fast three phase unbalanced load flow calculation in distribution systems. The method can calculate voltage and current of distribution systems, in which distributed generators are introduced. The calculation time of the proposed method is about 40 times faster than that of the Newton-Raphson method. Moreover, an introduction effect evaluation tool for distributed generators using the proposed three phase unbalanced load flow calculation is presented. It provides various functions such as a power system network diagram creation function and a voltage profile chart display function. Therefore, the introduction effect evaluation of distributed generators in distribution systems can be evaluated quite easily.

Performance Enhancement of Distribution Systems via Distribution Network Reconfiguration and Distributed Generator Allocation Considering Uncertain Environment

The emergence of dispersed generation,smart grids,and deregulated electricity markets has increased the focus on enhancing the performance of distribution systems.This paper proposes a method to reduce the energy loss and improve the reliability of distribution systems by performing distribution network reconfiguration(DNR)and distributed generator(DG)allocation.In this study,the intermittent nature of renewable-based DGs and the load profile are considered using a probabilistic method.The study investigates different annual plans based on the seasonal power profiles of DGs and the load to minimize the combined cost function of annual energy loss and annual energy not served.The proposed method is implemented using the firefly algorithm(FA),which is one of the meta-heuristic optimization algorithms.Several case studies are investigated using the IEEE 33-bus distribution system to highlight the effectiveness of the method.

Equivalent model of multi-type distributed generators under faults with fast-iterative calculation method based on improved PSO algorithm

There are various types of distributed generators (DGs) with different grid integration strategies. The transient characteristics of the fault currents provided by the DGs are different to those of conventional synchronous generators. In this paper, a distribution network with multi-type DGs is investigated, including consideration of DG low-voltage ride through (LVRT). The fault current characteristics of two typical DGs, i.e. an inverter-interfaced distributed generator (IIDG) and a doubly-fed induction generator (DFIG), are analyzed, considering the specific operation modes. Based on analysis of the fault characteristics, an equivalent model of the multi-type DGs under symmetrical/asymmetrical fault conditions is established. A fast-iterative fault calculation method for enhancing the calculation efficiency while avoiding local convergence is then proposed using an improved particle swarm optimization (PSO) algorithm. A simulation system of the distribution network with multi-type DGs is established in PSCAD/EMTDC. The simulation results validate the high accuracy and calculation efficiency of the proposed calculation method of the fault components. This can assist in the settings of the protection threshold.

Pressure control of PEMFC distributed power generator

Control design is important for PEMFC （proton exchange membrane fuel cell） distributed power generator to satisfy user requirement for safe and stable operation. For a complex multi-variable dynamic system, a dynamic simulation model is first established. In view of close coupling and non-linear relationships between variables, the intelligent auto-adapted PI decoupling control method is used. From the simulation results it is found that, by bringing quadratic performance index in the single neuron, constructing adaptive PI controller, and adjusting gas flow rates through the second pressure relief valve and air compressor coordinately, both anode and cathode pressures can be maintained at ideal levels.

Close-formed bound on generalized distributed antenna system capacity

The composite channel models of the generalized distributed antenna system （GDAS） such as Rayleigh-lognormal fading are studied. Then comparisons are performed between the GDAS and the traditional multiple-input multiple-output （MIMO） system to analyze the ergodic capacity of the GDAS and make conclusions that it is impossible to achieve an analytical expression for the ergodic capacity of the GDAS. Moreover, in order to evaluate the performance of the ergodic capacity of the GDAS conveniently, the analytical lower bound and upper bound of the ergodic capacity of the GDAS are derived by using the results from multivariate statistics and matrix inequalities, under the scenarios of Rayleigh-lognormal fading and equal power allocation scheme at transmitter. Finally, the analytical bounds are verified by comparisons with the numerical results.

SPECTRUM DISTRIBUTION OF THE SEDOND ORDER GENERALIZED DISTRIBUTED PARAMETER SYSTEMS

Spectrum distribution of the second order generalized distributed parameter system was discussed via the functional analysis and operator theory in Hilbert space. The solutions of the problem and the constructive expression of the solutions are given by the generalized inverse one of bounded linear operator. This is theoretically important for studying the stabilization and asymptotic stability of the second order generalized distributed parameter system.

Distributed spatio-temporal generative adversarial networks

Owing to the wide range of applications in various fields,generative models have become increasingly popular.However,they do not handle spatio-temporal features well.Inspired by the recent advances in these models,this paper designs a distributed spatio-temporal generative adversarial network(STGAN-D)that,given some initial data and random noise,generates a consecutive sequence of spatio-temporal samples which have a logical relationship.This paper builds a spatio-temporal discriminator to distinguish whether the samples generated by the generator meet the requirements for time and space coherence,and builds a controller for distributed training of the network gradient updated to separate the model training and parameter updating,to improve the network training rate.The model is trained on the skeletal dataset and the traffic dataset.In contrast to traditional generative adversarial networks(GANs),the proposed STGAN-D can generate logically coherent samples with the corresponding spatial and temporal features while avoiding mode collapse.In addition,this paper shows that the proposed model can generate different styles of spatio-temporal samples given different random noise inputs,and the controller can improve the network training rate.This model will extend the potential range of applications of GANs to areas such as traffic information simulation and multiagent adversarial simulation.

Generalized Distributed Multicell Architecture:Group Cell

In order to make full use of advanced technologies for future mobile communications systems such as Space Time Code (STC), Joint Transmission (JT) and Multiple Input Multiple Output (MIMO), and to meet the requirements of high-bit-rate multimedia services, new network topologies should be studied. Generalized distributed multicell architecture can take full advantage of multi-antenna technologies and solve the problem of frequent handover caused by higher carrier frequencies. Group handover, the handover policy based on the architecture, can eliminate the cell edge effect. Furthermore, by applying the concept of group handover to 3G mobile communication systems, the Fast Cell Group Selection (FCGS) scheme can effectively improve the data rate for cell edge users.

Designing a Protection Scheme in Micro-Grid Systems with DG Using Central Protection Unite and Multiple Setting Group Protection Relays

Distributed generators now is widely used in electrical power networks, in some cases it works seasonally, and some types works at special weather conditions like photo voltaic systems and wind energy, and due to this continuous changes in generation condition, the fault current level in network will be affected, this changes in fault current level will affect in the coordination between protection relays and to keep the coordination at right way, an adaptive protection system is required that can adaptive its setting according to generation changes, the fault current level in each case is evaluated using ETAP software, and the required relay setting in each case is also evaluated using Grey Wolf Optimizer (GWO) algorithm, and to select suitable setting which required in each condition, to select the active setting group of protection relay according to generation capacity, central protection unite can be used, and to improve protection stability and minimizing relays tripping time, a proposed method for selecting suitable backup relay is used, which leads to decrease relays tripping time and increase system stability, output settings for relays in all cases achieved our constrains.

Low complexity joint source-channel decoding for transmission of wavelet compressed images

To utilize residual redundancy to reduce the error induced by fading channels and decrease the complexity of the field model to describe the probability structure for residual redundancy, a simplified statistical model for residual redundancy and a low complexity joint source-channel decoding（JSCD） algorithm are proposed. The complicated residual redundancy in wavelet compressed images is decomposed into several independent 1-D probability check equations composed of Markov chains and it is regarded as a natural channel code with a structure similar to the low density parity check （LDPC） code. A parallel sum-product （SP） and iterative JSCD algorithm is proposed. Simulation results show that the proposed JSCD algorithm can make full use of residual redundancy in different directions to correct errors and improve the peak signal noise ratio （PSNR） of the reconstructed image and reduce the complexity and delay of JSCD. The performance of JSCD is more robust than the traditional separated encoding system with arithmetic coding in the same data rate.

Data-driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks

Integration of distributed energy storage(DES)is beneficial for mitigating voltage fluctuations in highly distributed generator(DG)-penetrated active distribution networks(ADNs).Based on an accurate physical model of ADN,conventional model-based methods can realize optimal control of DES.However,absence of network parameters and complex operational states of ADN poses challenges to model-based methods.This paper proposes a data-driven predictive voltage control method for DES.First,considering time-series constraints,a data-driven predictive control model is formulated for DES by using measurement data.Then,a data-driven coordination method is proposed for DES and DGs in each area.Through boundary information interaction,voltage mitigation effects can be improved by interarea coordination control.Finally,control performance is tested on a modified IEEE 33-node test case.Case studies demonstrate that by fully utilizing multi-source data,the proposed predictive control method can effectively regulate DES and DGs to mitigate voltage violations.

Examination for Experimental Verification of Improvement of Power Quality in Distribution System by PCS

In the future, the power quality will decrease by the introduction of a lot of renewable energy sources. The topic of this research is a new method of operation of PCS （power conditioning systems） in the future distribution system. The purpose of this research is development of PCS with a function of improvement of the distribution system. Therefore, the authors propose a method of the power quality improvement of the distribution system by PCS. In addition, the authors construct the control logic to use in PCS The control logic suggests adding harmonic restraint function to conventional control. These were verified by simulation and an experiment. As the results, we confirmed that basic operation of PCS being carried out, harmonics were restrained, and power quality had improved.

Integrated Power Conversion for Multiple DC Voltage Sources and Its Power Quality

In a smart grid, electric loads are supplied by various DC （direct current） power sources, such as solar cells or batteries. On the other hand, traditional AC （alternating current） loads like a directly connected induction motors will also be used. In the circumstances, a smart power conversion unit is one of key components, which can integrate these DC or AC apparatus and trade power among them. Authors have developed an integrated power converter based on a well-known circuit topology of flying capacitor multilevel converter. This paper describes the detail of the circuit topology and its characteristics depending on designed parameters. The achieved power quality is also verified by simulation study.

A Newly-Discovered GPD-GEV Relationship Together with Comparing Their Models of Extreme Precipitation in Summer

It has been theoretically proven that at a high threshold an approximate expression for a quantile of GEV （Generalized Extreme Values） distribution can be derived from GPD （Generalized Pareto Distribution）. Afterwards, a quantile of extreme rainfall events in a certain return period is found using L-moment estimation and extreme rainfall events simulated by GPD and GEV, with all aspects of their results compared. Numerical simulations show that POT （Peaks Over Threshold）-based GPD is advantageous in its simple operation and subjected to practically no effect of the sample size of the primitive series, producing steady high-precision fittings in the whole field of values （including the high-end heavy tailed）. In comparison, BM （Block Maximum）-based GEV is limited, to some extent, to the probability and quantile simulation, thereby showing that GPD is an extension of GEV, the former being of greater utility and higher significance to climate research compared to the latter.