Radial Power Distribution Research Articles

Fault Location Estimator Design for Power Distribution System Using Artificial Neural Network

Fault location in distribution system is critical issue to increase the availability of power supply by reducing the time of interruption for maintenance in electric utility companies. Fault location estimator for power distribution system using artificial neural network is developed for line to ground, line to line, line to line to ground and three phases to ground faults in distribution system. To develop this estimator is one of rural radial power distribution feeder in Ethiopia, south west reign, Aba substation Tarcha line feeder is used as a test feeder. This feeder is simulated using ETAP software to generate data for different fault condition, with different fault resistance and loading conditions, which is the fault phase voltage and current. The generated data is preprocessed and put as an input for neural network to be trained. MATLAB R2016a neural network toolbox to train ANN and programming toolbox is used to develop graphic user interface for fault estimator. The feed forward multi-layer network topologies of neural network with improved back propagation, Liebenberg Marquardt learning algorithm is used to train the network. After the network is trained the mean square error performance, regression plot and error histogram analysis was made and found to have an excellent performance with regression coefficient 0.99929 , validation performance of 0.000102 and error histogram range 0.015 to 0.019. In this thesis for practical implementation the fault records at the test feeder is handled by intelligent electronic device (IED) installed at the substation feeders. The fault record of IED can be read by PCM600 tool using laptop or manually using IEDs human machine interface, this fault recorded data feed to the graphic user interface to estimate the fault location as well as the fault type. Finally it is found that artificial neural networks are one of the alternate options in fault estimator design for distribution system where sufficient distribution network data are available with narrow fault location distance range from the substation. This has benefits in assisting for maintenance plan, saving efforts in fault location finding and economic benefits by reducing interruption time. Keywords : Power distribution, artificial intelligence, neural network, feed forward network DOI: 10.7176/JETP/12-5-01 Publication date: November 30 th 2022

An improved equilibrium optimiser-based algorithm for dynamic network reconfiguration and renewable DG allocation under time-varying load and generation

This paper presents an improved equilibrium optimiser (IEO)-based method for dynamic hourly network reconfiguration (NR) with optimal allocation of dispatchable and non-dispatchable renewable distributed generator (DG) units in radial power distribution networks. In this work, solar- and wind-based DGs are considered non-dispatchable DG, while biomass DGs are considered dispatchable DG. The output of biomass DGs is adjusted according to the 24-hour load demand curve and that of solar and wind DG follow their hourly power generation. For this purpose, a multi-objective function is developed with consideration of hourly power loss minimisation and profit maximisation without violating the system’s operational constraints. The viability and efficacy of the proposed method are tested on IEEE 33-bus and 69-bus distribution systems for five distinct scenarios of network reconfiguration and renewable DG allocation. The analysis shows that the simultaneous NR and DG have a more substantial impact than considering the cases separately.

CAMPUS-ANNULAR: Preliminary multiphysics modeling of annular fuel performance under normal operating and accident conditions

The internal and external cooled annular fuel was proposed firstly by MIT, which is found to be able to improve fuel performance dramatically. Based on the CAMPUS code, an annular fuel performance code called CAMPUS-ANNULAR is further developed in this work. And the multiphysics models of annular UO2 fuel equipped with Zircaloy cladding are developed under reactor normal operating, Loss Of Coolant Accident (LOCA) and Reactivity Initiated Accident (RIA) conditions, respectively. Then the BISON cladding failure criterion utilized under LOCA condition is introduced, and a loss of coolant and reflood model is implemented under LOCA condition for the first time. Additionally, the radial power distribution of annular fuel is introduced and analyzed in this work. Finally, the models implemented in this work are verified and the annular fuel performance under normal operating, LOCA and RIA conditions are investigated and compared with those of solid fuel. The fuel maximum temperature is found to be reduced dramatically by using annular fuel under normal operating, LOCA and RIA conditions, which may further lead to the cladding failure time to be delayed significantly under LOCA condition. Besides, the risk of fuel melting is found to be reduced by using annular fuel under RIA condition.

Subchannel analysis of critical heat flux in radially Non-uniform heated rod assembly at low flow rate

The effect of radial power distribution and low flow rate on critical heat flux (CHF) was investigated. In this study, based on the CHF experimental results of Columbia University, the research validated the feasibility of the subchannel analysis code CTF to predict CHF. The results show that CTF code using the EPRI empirical CHF correlation can predict the CHF experimental results well. Due to radially non-uniform power distribution, the quality and void fraction of hot subchannels are higher than other subchannels. The influence of the lateral flow will cause the counterflow in low power region under the beginning of two-phase flow at low flow rate. Furthermore, the predicted average CHF of rod assembly increases with the increase of inlet mass flow rate and the decrease of inlet temperature. When CHF happens in the rod assembly, the local CHF is mainly due to the low mass flow rate and high quality.

Sizing and Sitting of Battery Energy Storage Systems in Distribution Networks with Transient Stability Consideration

In this study, the capacity and location of battery energy storage systems (BESSs) in a distribution network were evaluated to increase the stability and reliability of power systems by applying the proposed transient stability indicators. The search capability of particle swarm optimization (PSO) combined with Pareto optimality in MATLAB R2019b was used to solve multi-objective problems in steady-state conditions, including active power loss, voltage deviation, and total operating cost. The BESS capacity and location were then derived using the Manhattan distance method. Subsequently, the BESS control model was constructed using the commercial software DIgSILENT PowerFactory 2021 to evaluate generator power smoothing and short-term voltage stability index during the transient response and determine the final capacity and location of BESS in distribution networks. Finally, the accuracy of the proposed method was verified by considering the daily load and solar power generation curves using the IEEE 33-bus radial power distribution network.

Fuel loading pattern optimization of ALLEGRO fast reactor using genetic algorithms

In this work the genetic algorithmic (GA) optimization technique is used to find the optimal fuel loading pattern in the experimental fast reactor ALLEGRO. The penalty function method is used to solve this multi-objective combinatorial optimization problem. To evaluate each loading pattern, an interface between the genetic algorithm code and the ERANOS 2.3 deterministic neutronic simulation code was developed. For the simulation of the fuel cycle in ERANOS, the diffusion approach and a 7-group energy structure are used to reduce the computational cost. An integer variable encoding scheme was defined to represent the fuel loading patterns. Two crossover operators, the partially mapped crossover (PMX) and the order crossover (OX), combined with the random mutation operator were implemented as an alternative to the classical operators used in GA. To evaluate the performance of the code the results are compared using two objective functions and two crossover operators. In the first case the operating cycle length is maximized by maximizing the k-eff value at the EOC by satisfying the constraints on the power peaking factor over the cycle, the excess reactivity at the BOC, and the linear heat generation rate constraints. In addition, a condition was imposed that there should be no more than two contiguous positions in the pattern to preserve the power distribution uniformity and to avoid high power peaking factors. The code works very well, and a good solution was found in a reasonable computation time. The best solution corresponds to the PMX operator, and the running time is twice as short compared to the OX operator. In the second case study, a power distribution flatness term is included in the objective function. In this case, a more uniform radial power distribution is obtained but associated with a decrease in cycle length due to the penalty of the flatness term.

Performance Enhancement of Radial Power Distribution Networks Using Network Reconfiguration and Optimal Planning of Solar Photovoltaic-Based Distributed Generation and Shunt Capacitors

In this work, an efficient hybrid optimization approach entitled harmony search and particle artificial bee colony algorithm is proposed to deal with the distribution network reconfiguration and solar photovoltaic-based distributed generation and shunt capacitor deployment in power distribution networks to improve the operating performance of power distribution networks. The proposed hybrid algorithm combines the exploration and exploitation capability of both algorithms to achieve optimal results. The optimization problem is formalized which includes distributed generation and shunt capacitor locations, open/close state of switches as discrete decision variables, and the optimum operating point of compensation devices as continuous variables. An efficient spanning tree approach is utilized to track the optimal topology of the network. The validity of the proposed hybrid algorithm in handling the optimal planning problem of the distribution network is assured through eight different operating scenarios at three discrete load levels. The efficiency of the proposed performance enhancement approaches was validated using 69 node and 118 node distribution networks. The obtained results are compared against similar techniques presented in the literature.

Thermal hydraulic modeling of solid-fueled nuclear thermal propulsion reactors part II: Full-core coupled neutronic and thermal hydraulic analysis

This paper focuses on the extension and application of the previously validated thermal–hydraulic engineering code ntpThermo to perform multi-channel analysis for full-core coupled neutronic and thermal–hydraulic simulations. This paper also introduces the Basilisk code, which enables coupled full-core neutronic and thermal–hydraulic simulations by integrating Serpent, the continuous energy Monte-Carlo neutron transport code, and ntpThermo within a flexible object-oriented framework. Today, limited data exists pertaining to coupled multiphysics analysis of low-enriched uranium nuclear thermal propulsion reactors. More specifically, the impact of thermal–hydraulic feedback on the neutronic solution for full core applications is not well quantified. In this paper full-core coupled neutronic and thermal–hydraulic simulations are performed to ascertain the importance of thermal–hydraulic feedback on the predicted reactivity and local power distributions. The reactor design analyzed adheres to the current industry ground rules in an effort to provide useful insights for the current industry design effort. The results in this paper demonstrate that thermal–hydraulic feedback must be considered to accurately predict reactivity; otherwise, errors on the order of hundreds to thousands of pcms can be observed. Additionally, neglecting thermal–hydraulic feedback can trigger inconsistencies between the radial power distribution and the fuel orificing pattern. A design approach that doesn’t account for thermal–hydraulic feedback can lead to a final design that may experience thermal failure via excessive fuel temperatures, and would require a reduction of reactor power. The latter will cause a subsequent decrease in exit propellant temperatures and thus specific impulse of the rocket engine. Sensitivity studies were conducted to determine the importance of specific thermal–hydraulic fields and revealed that the local moderator temperature has a notable impact on the local power distributions.

A feasibility study of SMART reactor power performance optimizations-part 2: Reflector material selection

The neutron reflector is a general component in the nuclear reactor design. The original SMART reactor design used light water as a reflector surrounding the fission zone. However, this design has low uranium utilization rates in the outermost fuel assemblies, so poor fuel economy. In this study, six potential reflector materials, i.e., heavy water, graphite, beryllium metal and its oxide, steel, and tungsten carbide has been investigated as the neutron reflector for SMART. Firstly, the materials’ cross-sections of neutron scattering and (n, 2n) reactions have been cited from ENDF data libraries and analyzed. This analysis found for the neutrons with energy lower than 1 eV, the 9Be atom has six barn elastic scattering cross-sections, and the 9Be(n, 2n)8Be reaction can compensate for neutron leakage. Then, OpenMC is employed to simulate the effect of these reflector materials on power distribution and depletion. Compared with the original design, the beryllium oxide can improve the initial keff from 1.22906 to 1.27446, flat the radial direction power distribution. The analysis on both scales agrees that the beryllium oxide is an efficient neutron reflector choice with good material properties.

Neutronic investigation of a VVER-1200 (Th-233U)O2 fuel assembly with protactinium oxide as a burnable absorber coated on the outer surface of the fuel rods

ABSTRACT Due to the obvious excess reactivity associated with using 233U as a fissile material, fresh fuel must include a burnable absorber (BA) to reduce excess reactivity. As a result, the objective of this study is to look into the use of protactinium oxide (PaO2) as a thin BA layer coating on the outer surface of the fuel rod for the VVER-1200 assembly, which is fueled by (Th-233U)O2. Through neutronic calculation, the thicknesses of PaO2 as a BA were determined using the Monte Carlo code MCNP6. For comparison, UO2 and (Th-233U)O2 assemblies without absorbers were used as references. The study suggests that using PaO2 with a thickness ranging from 0.11 to 0.13 mm can reduce the early excess reactivity of the (Th-233U)O2 assembly while also increasing the discharge burnup by an average of 2.1 GWd/ton when compared to the UO2 assembly. Furthermore, it was found that the selected (Th-233U)O2 + PaO2 assemblies exhibit negative fuel temperature and boron worth coefficients. However, these assemblies exhibit positive moderator temperature coefficients. Neutron spectra, radial power distribution, and fission product poisons were also evaluated to understand the viability of using PaO2 as a BA for reactivity control in the VVER-1200 assembly under consideration.

Optimal Sizing and Allocation of Distributed Generation in the Radial Power Distribution System Using Honey Badger Algorithm

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.

Optimal Routing and Design of Radial Medium Voltage Power Distribution Networks Considering Cables Short Circuit Withstand-Capacity

Cables short circuit withstand-capacity plays a crucial role in medium voltage distribution system design. For a given short circuit level at substation, fault current at any point in a radial distribution system depends on the location with respect to the substation, distributed generation as well as size and length of upstream cable sections. This paper addresses optimal routing, cable sizing, distributed generation allocation and sizing of radial medium voltage power distribution network considering cables short circuit withstand capacity. Both synchronous-based and inverter-based distributed generators are considered. Complete formulation of cables short circuit constraints (SCCs) is introduced in mixed-integer linear programming (MILP) form. A Benders-decomposition-based approach is used to solve the problem by considering short circuit at each system bus in a separate slave feasibility problem. Combinatorial cuts are proposed to exclude infeasible solutions in each iteration by investigating upstream routes, cable sizes and distributed generation. The proposed optimization framework and solution algorithm are applied on 54 bus, 84 bus and 138 bus systems. The results revealed potential saving in investment, maintenance and losses costs compared to upgrading method. Also the proposed solution algorithm showed great saving in the number of added constraints to the original problem.

Modified Minimum Spanning Tree for Optimised DC Microgrid Cabling Design

The construction of low-cost nanogrids could help speed up the electrification rate in remote communities in sub-Saharan Africa. One of the challenges in nanogrid design for these communities is the uncertainty in future load demand profiles impacting the cabling topology. We propose two new wiring design approaches for radial direct current power distribution systems. The first one is a modified minimum spanning tree (MST) algorithm and the second one is an adaptive shuffled frog leaping algorithm (SFLA). Their objective is to identify the optimal cable path and the lowest cost wiring characteristics to electrify rural areas with poor infrastructural development. A comparative study of computation burdens has shown the applicability limits relative to the SFLA based approach and encouraged the implementation of the MST as it is faster and does not imply any limitation regarding the number of dwellings to electrify, while providing low-cost wiring design option. The latter is applied to a village located in Kenya and demonstrated more than 25% savings on the entire system cabling cost compared to a classical wiring design based on shortest-path calculation.

A novel fault location approach for radial power distribution systems

Power distribution systems (PDS) are increasingly complex and spread over long distances and in different locations. Given their radial configuration, the loads could be inserted at the same distances from the substation. This leads to multiple estimation of fault location (FL) and yields time consuming for iterative FL algorithms. In this article, we provide a novel practical approach to fault localization in order to defeat these limitations. The central idea of the proposed approach is to divide the multilateral distribution system into a possible number of mono-lateral sub systems (MLS) using a proposed communicating sensor. Next, we apply two different fault locator algorithms only to the defective MLS. The first variant of the approach is based on the impedance method, while the second variant is non-parametric used only when there is lack in the line data. To test the proposed technique in practice, we used the IEEE 13 Node test feeder, and a real Algerian PDS. The results obtained clearly show the contribution of the dedicated method for locating faults in the PDS.

Comparative analysis between homogeneous and heterogeneous models of gas cooled fast reactor core (GFR-2400)

Abstract The main objective of the presented work is to investigate the effectiveness of homogenization of the GFR-2400 core in simulating its neutronic characteristics. To explore and evaluate the neutronic behavior of the large scale Gas cooled Fast Reactor GFR-2400 core, two computer models (homogeneous and heterogeneous) were designed using the MCNPX code. The designed heterogeneous model has been validated by comparing its results with a previously published paper. The results of both models were compared with each other to study the effect of fuel homogeneity on the radial flux and power distribution through the core. As part of a safety analysis of the reactor core for both designs, the reactivity worth of control rods, Effective delayed neutron fraction (ß eff), Doppler constant and Depressurization effect have been analyzed and compared. The variations of the effective multiplication factor (k eff), the minor actinides concentration and the most important fission products as a function of operation time have been investigated for the presented designs.

Neutronics and Thermohydraulics Coupling Analysis on Novel Organic Cooled Reactor Based on Single-Channel Model

A conceptual design for a 100 MW(th) organic cooled reactor is proposed with the application of organic fluid HB-40 as a coolant. In order to obtain the axial and radial power distribution, a physical model for the proposed core is developed using the Monte Carlo particle transport code MCNP. Moreover, a single-channel model is developed using multi-physics simulation software COMSOL to calculate the temperature distribution of the fuel elements and corresponding coolant channels. A neutronics-thermohydraulics coupling calculation method is established to couple the MCNP code and COMSOL software by means of mesh mapping and data transfer. Through coupling calculation, the high fidelity power and temperature distribution can be obtained. The key thermohydraulics parameters of the hottest channel in the core are investigated, and the changes after coupling calculation are analyzed. The proposed core design and coupling method can be used as a reference for further optimization of organic cooled reactors.

Validating the serpent-ants Calculation Chain Using BEAVRS Fresh Core Hot Zero Power Data

Abstract The serpent Monte Carlo code and the serpent-ants two-step calculation chain are used to model the hot zero power physics tests described in the BEAVRS benchmark. The predicted critical boron concentrations, control rod group worths, and isothermal temperature coefficients are compared between serpent and serpent-ants as well as against the experimental measurements. Furthermore, radial power distributions in the unrodded and rodded core configurations are compared between serpent and serpent-ants. In addition to providing results using a best practices calculation chain, the effects of several simplifications or omissions in the group constant generation process on the results are estimated. Both the direct and two-step neutronics solutions provide results close to the measured values. Comparison between the measured data and the direct serpent Monte Carlo solution yields root-mean-square (RMS) differences of 12.1 mg/kg, 25.1×10−5 and 0.67×10−5 K−1 for boron, control rod worths and temperature coefficients, respectively. The two-step serpent-ants solution reaches a similar level of accuracy with RMS differences of 17.4 mg/kg, 23.6×10−5 and 0.29×10−5 K−1. The match in the radial power distribution between serpent and serpent-ants was very good with the RMS and MAXimum (MAX) for pin power errors being 1.31% and 4.99%, respectively, in the unrodded core and 1.67% (RMS) and 8.39% (MAX) in the rodded core.

Multi-service provision for electric vehicles in power-transportation networks towards a low-carbon transition: A hierarchical and hybrid multi-agent reinforcement learning approach

In order to achieve the target of carbon peak and carbon neutrality, electric vehicles (EVs) have increasingly received a prominent interest to electrify the transportation sector due to their advantages of mobility and flexibility on handling complicated transportation and power networks. However, it is still challenging to realize the significant potential of EVs towards an emerging low-carbon transition. Previous works have focused on vehicle-to-grid (V2G) technology that allows for an increased utilization of EVs to make arbitrage by the temporal differentials of electricity prices. Nevertheless, the economic potential of EVs flexibility may not be fully exploited lacking an appropriate business model. This paper addresses this challenge by developing a coupled power-transportation network for cooperative EVs to optimize the provision of multiple inter-dependent services, including charging service, demand management service, carbon intensity service, and balancing service. In order to unlock this value, the EVs operation problem has already been tackled using model-based optimization approaches, which may raise privacy issues since the requirement for global information and also can be time consuming due to the high variability of transportation and power networks. In this paper, we propose a model-free hierarchical and hybrid multi-agent reinforcement learning method to learn the routing and scheduling decisions of EVs in a coupled power-transportation network with the objective of optimizing multi-service provisions. To this end, EVs do not reply on any knowledge of the simulated environment and are capable of handling system uncertainties via the learning process. Extensive case studies based on a 15-bus radial power distribution network and a 9-node 12-edge transportation network are developed to show that the proposed method outperforms the conventional learning algorithms in terms of policy quality and convergence speed. Finally, the generalizability and scalability are also investigated for different environment circumstances and EV numbers.

Supplementing fuel behaviour analyses via coupled Monte Carlo neutronics and fission product solution

The accuracy of power distribution solution and burnup algorithms in standalone fuel behaviour codes is limited. For improved accuracy, fuel behaviour codes can be coupled with external reactor physics solvers. A coupled calculation system between the Monte Carlo neutronics and burnup calculation solver Serpent and the fuel behaviour solver TRANSURANUS was developed in an earlier work. The data exchanged between the solvers included fuel pin axial and radial power and temperature distributions, the axial fast flux distribution in the cladding and the changes in the pin radii. In this work the coupling was further improved by developing the capability to transfer and utilize Serpent calculated nuclide compositions in TRANSURANUS within the coupled calculations. Additionally, support for corrector type burnup algorithms in the coupled solution was now implemented for increased accuracy of the nuclide solution. The new capabilities were demonstrated with a coupled single rod burnup calculation utilizing power and coolant history data from the Loviisa nuclear power plant. The focus at this stage was on fission product nuclides as their accumulation adversely affects the fuel performance, making their accurate solution important to ensure safe yet economical use of fuel. The demonstration showed small but visible differences when compared to simulations that were run without using fission products calculated by Serpent. The developed capabilities will now facilitate import of further fission product nuclides to be taken into account in TRANSURANUS.

Control Rod Modeling and Worth Calculation for a Typical 1100 MWe Nuclear Power Plant Using WIMS/D4 and CITATION

A typical 1100 MWe pressurized water reactor (PWR) is a second unit installed at the coastal site of Pakistan. In this paper, verification analysis of reactivity control worth by means of rod cluster control assemblies (RCCAs) for startup and operational conditions of this typical nuclear power plant (CNPP) has been performed. Neutronics analysis of fresh core is carried out at beginning of life (BOL) to determine the effect of grey and black control rod clusters on the core reactivity for startup and operating conditions. The combination of WIMS/D4 and CITATION computer codes equipped with JENDL-3.3 data library is used for the first time for core physics calculations of neutronic safety parameters. The differential and integral worth of control banks is derived from the computed results. The effect of control bank clusters on core radial power distribution is studied precisely. Radial power distribution in the core is evaluated for numerous configurations of control banks fully inserted and withdrawn. The accuracy of computed results is validated against the reference values of Nuclear Design Report (NDR) of 1100 MWe typical CNPP. It has been observed that WIMS-D4/CITATION shows its capability to effectively calculate the reactor physics parameters.