Composite load spectra for select space propulsion structural components

The work performed to develop composite load spectra (CLS) for the Space Shuttle Main Engine (SSME) using probabilistic methods. The three methods were implemented to be the engine system influence model. RASCAL was chosen to be the principal method as most component load models were implemented with the method. Validation of RASCAL was performed. High accuracy comparable to the Monte Carlo method can be obtained if a large enough bin size is used. Generic probabilistic models were developed and implemented for load calculations using the probabilistic methods discussed above. Each engine mission, either a real fighter or a test, has three mission phases: the engine start transient phase, the steady state phase, and the engine cut off transient phase. Power level and engine operating inlet conditions change during a mission. The load calculation module provides the steady-state and quasi-steady state calculation procedures with duty-cycle-data option. The quasi-steady state procedure is for engine transient phase calculations. In addition, a few generic probabilistic load models were also developed for specific conditions. These include the fixed transient spike model, the poison arrival transient spike model, and the rare event model. These generic probabilistic load models provide sufficient latitude for simulating loads with specific conditions. For SSME components, turbine blades, transfer ducts, LOX post, and the high pressure oxidizer turbopump (HPOTP) discharge duct were selected for application of the CLS program. They include static pressure loads and dynamic pressure loads for all four components, centrifugal force for the turbine blade, temperatures of thermal loads for all four components, and structural vibration loads for the ducts and LOX posts

Direct probabilistic load flow in radial distribution systems including wind farms: An approach based on data clustering

© 2018 by the authors. The ongoing study aims to establish a direct probabilistic load flow (PLF) for the analysis of wind integrated radial distribution systems. Because of the stochastic output power of wind farms, it is very important to find a method which can reduce the calculation burden significantly, without having compromising the accuracy of results. In the proposed approach, a K-means based data clustering algorithm is employed, in which all data points are bunched into desired clusters. In this regard, probable agents are selected to run the PLF algorithm. The clustered data are used to employ the Monte Carlo simulation (MCS) method. In this paper, the analysis is performed in terms of simulation run-time. Also, this research follows a two-fold aim. In the first stage, the superiority of data clustering-based MCS over the unsorted data MCS is demonstrated properly. Moreover, the impact of data clustering-based MCS and unsorted data-based MCS is investigated using an indirect probabilistic forward/backward sweep (PFBS) method. Thus, in the second stage, the simulation run-time comparison is carried out rigorously between the proposed direct PLF and the indirect PFBS method to examine the computational burden effects. Simulation results are exhibited on the IEEE 33-bus and 69-bus radial distribution systems

Composite load spectra for select space propulsion structural components

The objective of this program is to develop generic load models with multiple levels of progressive sophistication to simulate the composite load spectra that are induced in space propulsion system components, representative of Space Shuttle Main Engines (SSME), such as transfer ducts, turbine blades, and liquid oxygen (LOX) posts and system ducting. These models will be developed using two independent approaches. The first approach consists of using state-of-the-art probabilistic methods to describe the individual loading conditions and combinations of these loading conditions to synthesize the composite load spectra simulation. The methodology required to combine the various individual load simulation models (hot-gas dynamic, vibrations, instantaneous position, centrifugal field, etc.) into composite load spectra simulation models will be developed under this program. A computer code incorporating the various individual and composite load spectra models will be developed to construct the specific load model desired. The second approach, which is covered under the options portion of the contract, will consist of developing coupled models for composite load spectra simulation which combine the (deterministic) models for composite load dynamic, acoustic, high-pressure and high rotational speed, etc., load simulation using statistically varying coefficients. These coefficients will then be determined using advanced probabilistic simulation methods with and without strategically selected experimental data. This report covers the efforts of the third year of the contract. The overall program status is that the turbine blade loads have been completed and implemented. The transfer duct loads are defined and are being implemented. The thermal loads for all components are defined and coding is being developed. A dynamic pressure load model is under development. The parallel work on the probabilistic methodology is essentially completed. The overall effort is being integrated in an expert system code specifically developed for this project

Feasibility of nominations in stationary gas networks with random load

The paper considers the computation of the probability of feasible load constellations in a stationary gas network with uncertain demand. More precisely, a network with a single entry and several exits with uncertain loads is studied. Feasibility of a load constellation is understood in the sense of an existing flow meeting these loads along with given pressure bounds in the pipes. In a first step, feasibility of deterministic exit loads is characterized algebraically and these general conditions are specified to networks involving at most one cycle. This prerequisite is essential for determining probabilities in a stochastic setting when exit loads are assumed to follow some (joint) Gaussian distribution when modeling uncertain customer demand. The key of our approach is the application of the spheric-radial decomposition of Gaussian random vectors coupled with Quasi Monte-Carlo sampling. This approach requires an efficient algorithmic treatment of the mentioned algebraic relations moreover depending on a scalar parameter. Numerical results are illustrated for different network examples and demonstrate a clear superiority in terms of precision over simple generic Monte-Carlo sampling. They lead to fairly accurate probability values even for moderate sample size

Probabilistic Load Flow Solution Considering Optimal Allocation of SVC in Radial Distribution System

This paper proposes a solution procedure for probabilistic load flow problem considering the optimal allocation of Static Var Compensator (SVC) in radial distribution systems. Pareto Envelope-based Selection Algorithm II (PESA-II) with fuzzy logic decision maker is developed to determine the optimal location and size of SVC based on the minimum total power losses and Voltage Deviation (VD). Combined cumulants and gram-chalier expansion are used for solving the probabilistic load flow problem. The proposed algorithm is tested on 33-bus and 69-bus distribution systems. The developed algorithm gives an acceptable solution with low number of iterations and less computation cost compared with the Monte Carlo method

A review of tools, models and techniques for long-term assessment of distribution systems using OpenDSS and parallel computing

Many distribution system studies require long-term evaluations (e.g. for one year or more): Energy loss minimization, reliability assessment, or optimal rating of distributed energy resources should be based on long-term simulations of the distribution system. This paper summarizes the work carried out by the authors to perform long-term studies of large distribution systems using an OpenDSS-MATLAB environment and parallel computing. The paper details the tools, models, and procedures used by the authors in optimal allocation of distributed resources, reliability assessment of distribution systems with and without distributed generation, optimal rating of energy storage systems, or impact analysis of the solid state transformer. Since in most cases, the developed procedures were implemented for application in a multicore installation, a summary of capabilities required for parallel computing applications is also included. The approaches chosen for carrying out those studies used the traditional Monte Carlo method, clustering techniques or genetic algorithms. Custom-made models for application with OpenDSS were required in some studies: A summary of the characteristics of those models and their implementation are also included.Peer ReviewedPostprint (published version

Impact assessment of high penetration of rooftop PV in municipal electrical networks

Thesis (MEng (Electrical Engineering))--Cape Peninsula University of Technology, 2019There is an increasing global trend of grid connected distributed generation, mainly based on renewable energy sources such as wind and photovoltaic (PV) systems. The proliferation of these intermittent energy sources into the existing networks may subject the network into technical challenges such as voltage rise, equipment over-load, power quality and protection scheme violations. With increased PVDG (mainly rooftop PV) uptake occurring mostly on Low Voltage (LV) feeders, characterised by lack of network visibility and controllability, these technical challenges may be exac-erbated. In the absence of government incentive, current uptake of rooftop PVDG is reliant on customer preference and financial means. Thus make PVDG integration on the network be randomly placed and sized, of which the network distribution operator (NDO) will have no control over. The lack of regulations and interconnection studies conducted on South African networks has resulted in a growing concern amongst util-ities on how the increasing customer-owned rooftop PV systems uptake will impact the existing networks. This study aims to investigate technical impact high penetration of rooftop PV sys-tem will have on the existing LV networks. The load flow (LF) computation is pivotal in determining power system state when subjected to high penetration of rooftop PV. Monte-Carlo based Probabilistic Load Flow (PLF) was proposed and input variables were modelled using Beta probabilistic distribution function (PDF). The proposed im-pact assessment framework was applied on real LV urban residential network situated in Cape Town, South Africa. Simulations were conducted on DIgSILENT PowerFac-tory and the PDF for input variables (Load demand and PV generation) were derived from historic data. Four scenarios were simulated and system performance parameters were recorded such as; voltage magnitude, voltage unbalance factor and equipment thermal loading. Simulation results in the test network indicated thermal loading violation as the main limiting factor in urban residential network. PV system topology (either three-phase or single phase) proved to have significant effect on network hosting capacity, were higher PV penetration can be achieved for a three-phase system. Penetration level as low as 12% were recorded, which is significantly lower than the prescribed guidelines in simplified criteria in NRS097-2-3 standard and therefore raises a concern on the relevance of this standard on all types of networks (in urban network in particu-lar). However, penetration level above NRS097-2-3 limits may be achieved depending on feeder characteristics

Investigation into Photovoltaic Distributed Generation Penetration in the Low Voltage Distribution Network

Significant integration of photovoltaic distributed generation (PVDG) in the low voltage distribution network (LVDN) could potentially pose threats and challenges to the core activity of distribution system operators (DSO), which is to transport electrical energy in a reliable and cost-effective way. The main aim of this research is to investigate the active planning and operation of LVDNs with increased PVDG integration through steady state power system analysis. To address the impacts of voltage profile fluctuation due to power flow modification, this research proposes a probabilistic risk assessment of power quality (PQ) variations and events that may arise due to significant PVDG integration. A Monte Carlo based simulation is applied for the probabilistic risk assessment. This probabilistic approach is used as a tool to assess the likely impacts due to PVDG integration against the extreme-case scenarios. With increased PVDG integration, site overvoltage is a likely impact, whereas voltage unbalance reduces when compared with no or low PVDG penetration cases. This is primarily due to the phase cancellation between the phases. The other aspect of the work highlights the fact that the implementation of existing volumetric charges in conjunction with net-metering can have negative impacts on network operator’s revenue. However, consideration of capacity charges in designing the existing network tariff structure shows incentivising the network operator to perform their core duties under increased integration of PVDG. The site overvoltage issue was also studied and resolved in a novel way, where the active and reactive power of the PVDG inverters at all the PV installed premises were optimally coordinated to increase the PV penetration from 35.7% to 66.7% of the distribution transformer rating. This work further explores how deficiencies in both reactive power control (RPC) and active power control (APC) as separate approaches can be mitigated by suitably combining RPC and APC algorithms. A novel “Q” or “PF” limiter was proposed to restrict frequent switching between the two droop characteristics while ensuring a stabilizing (smoothened) voltage profile in each of the PV installed nodes. This novel approach not only alleviates the voltage fluctuation but also reduces the overall network losses