Systems Integration and Operationalization: Supporting Unmanned Aircraft Systems Integration into the National Airspace System
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During design of the SDO Science and Inertial mode PID controllers, the decision was made to disable the integral torque whenever system stability was in question. Three different schemes were developed to determine when to disable or enable the integral torque, and a trade study was performed to determine which scheme to implement. The trade study compared complexity of the control logic, risk of not reenabling the integral gain in time to reject steady-state error, and the amount of integral torque space used. The first scheme calculated a simplified Routh criterion to determine when to disable the integral torque. The second scheme calculates the PD part of the torque and looked to see if that torque would cause actuator saturation. If so, only the PD torque is used. If not, the integral torque is added. Finally, the third scheme compares the attitude and rate errors to limits and disables the integral torque if either of the errors is greater than the limit. Based on the trade study results, the third scheme was selected. Once it was decided when to disable the integral torque, analysis was performed to determine how to disable the integral torque and whether or not to reset the integrator once the integral torque was reenabled. Three ways to disable the integral torque were investigated: zero the input into the integrator, which causes the integral part of the PID control torque to be held constant; zero the integral torque directly but allow the integrator to continue integrating; or zero the integral torque directly and reset the integrator on integral torque reactivation. The analysis looked at complexity of the control logic, slew time plus settling time between each calibration maneuver step, and ability to reject steady-state error. Based on the results of the analysis, the decision was made to zero the input into the integrator without resetting it. Throughout the analysis, a high fidelity simulation was used to test the various implementation methods.
Integrating 20% or more wind energy into the system and transmitting large sums of wind energy over long distances will require a decision making capability that can handle very large scale power systems with tens of thousands of buses and lines. There is a need to explore innovative analytical and implementation solutions for continuing reliable operations with the most economical integration of additional wind energy in power systems. A number of wind integration solution paths involve the adoption of new operating policies, dynamic scheduling of wind power across interties, pooling integration services, and adopting new transmission scheduling practices. Such practices can be examined by the decision tool developed by this project. This project developed a very efficient decision tool called Wind INtegration Simulator (WINS) and applied WINS to facilitate wind energy integration studies. WINS focused on augmenting the existing power utility capabilities to support collaborative planning, analysis, and wind integration project implementations. WINS also had the capability of simulating energy storage facilities so that feasibility studies of integrated wind energy system applications can be performed for systems with high wind energy penetrations. The development of WINS represents a major expansion of a very efficient decision tool called POwer Market Simulator (POMS), which was developed by IIT and has been used extensively for power system studies for decades. Specifically, WINS provides the following superiorities; (1) An integrated framework is included in WINS for the comprehensive modeling of DC transmission configurations, including mono-pole, bi-pole, tri-pole, back-to-back, and multi-terminal connection, as well as AC/DC converter models including current source converters (CSC) and voltage source converters (VSC); (2) An existing shortcoming of traditional decision tools for wind integration is the limited availability of user interface, i.e., decision results are often text-based demonstrations. WINS includes a powerful visualization tool and user interface capability for transmission analyses, planning, and assessment, which will be of great interest to power market participants, power system planners and operators, and state and federal regulatory entities; and (3) WINS can handle extended transmission models for wind integration studies. WINS models include limitations on transmission flow as well as bus voltage for analyzing power system states. The existing decision tools often consider transmission flow constraints (dc power flow) alone which could result in the over-utilization of existing resources when analyzing wind integration. WINS can be used to assist power market participants including transmission companies, independent system operators, power system operators in vertically integrated utilities, wind energy developers, and regulatory agencies to analyze economics, security, and reliability of various options for wind integration including transmission upgrades and the planning of new transmission facilities. WINS can also be used by industry for the offline training of reliability and operation personnel when analyzing wind integration uncertainties, identifying critical spots in power system operation, analyzing power system vulnerabilities, and providing credible decisions for examining operation and planning options for wind integration. Researches in this project on wind integration included (1) Development of WINS; (2) Transmission Congestion Analysis in the Eastern Interconnection; (3) Analysis of 2030 Large-Scale Wind Energy Integration in the Eastern Interconnection; (4) Large-scale Analysis of 2018 Wind Energy Integration in the Eastern U.S. Interconnection. The research resulted in 33 papers, 9 presentations, 9 PhD degrees, 4 MS degrees, and 7 awards. The education activities in this project on wind energy included (1) Wind Energy Training Facility Development; (2) Wind Energy Course Development.