Exploration in stochastic algorithms: An application on MAX-MIN Ant System

In this paper a definition of the exploration performed by stochastic algorithms is proposed. It is based on the observation through cluster analysis of the solutions generated during a run. The probabilities associated by an algorithm to solution components are considered. Moreover, a consequent method for quantifying the exploration is provided. Such a measurement is applied to MAX-MIN Ant System. The results of the experimental analysis allow to observe the impact of the parameters of the algorithm on the exploration.exploration, cluster analysis, MAX-MIN Ant System

Pressurized Lunar Rover

The pressurized lunar rover (PLR) consists of a 7 m long, 3 m diameter cylindrical main vehicle and a trailer which houses the power and heat rejection systems. The main vehicle carries the astronauts, life support systems, navigation and communication systems, directional lighting, cameras, and equipment for exploratory experiments. The PLR shell is constructed of a layered carbon-fiber/foam composite. The rover has six 1.5 m diameter wheels on the main body and two 1.5 m diameter wheels on the trailer. The wheels are constructed of composites and flex to increase traction and shock absorption. The wheels are each attached to a double A-arm aluminum suspension, which allows each wheel 1 m of vertical motion. In conjunction with a 0.75 m ground clearance, the suspension aids the rover in negotiating the uneven lunar terrain. The 15 N-m torque brushless electric motors are mounted with harmonic drive units inside each of the wheels. The rover is steered by electrically varying the speeds of the wheels on either side of the rover. The PLR trailer contains a radiosotope thermoelectric generator providing 6.7 kW. A secondary back-up energy storage system for short-term high-power needs is provided by a bank of batteries. The trailer can be detached to facilitate docking of the main body with the lunar base via an airlock located in the rear of the PLR. The airlock is also used for EVA operation during missions. Life support is a partly regenerative system with air and hygiene water being recycled. A layer of water inside the composite shell surrounds the command center. The water absorbs any damaging radiation, allowing the command center to be used as a safe haven during solar flares. Guidance, navigation, and control are supplied by a strapdown inertial measurement unit that works with the on-board computer. Star mappers provide periodic error correction. The PLR is capable of voice, video, and data transmission. It is equipped with two 5 W X-band transponder, allowing simultaneous transmission and reception. An S-band transponder is used to communicate with the crew during EVA. The PLR has a total mass of 6197 kg. It has a nominal speed of 10 km/hr and a top speed of 18 km/hr. The rover is capable of towing 3 metric tons (in addition to the RTG trailer)

OPTIMAL DESIGN PSS-PID CONTROL ON SINGLE MACHINE INFINITE BUS USING ANT COLONY OPTIMIZATION

Optimization of the controller in a generator can improve system performance. The right parameter optimization is needed to get the optimal performance from the controller. The application of the artificial intelligence method as a parameter optimization method is proposed in this study. By using the smart method based on Ant Colony, the optimal PSS-PID parameters are obtained. With optimal tuning, the system gets optimal Single Machine Infinite Bus (SMIB) system frequency and rotor angle response, indicated by the minimum overshot system response. The SMIB system's stability will be tested. A case study of adding and reducing loads is used, with the proposed control method PSS-PID being optimized using Ant Colony. Based on the analysis using the proposed PSS-PID control, we get the minimum overshoot for the frequency response and rotor angle of the SMIB system. When the load changes at 20 seconds, using the PSS-PID control scheme, the minimum overshoot is -4.316e-06 to 7.598e-05 pu with a settling time of 22.01s. For the rotor angle overshoot response, using the PSS-PID control scheme, the minimum overshoot is -0.01113 to -0.009669 pu with a settling time of 22.36s

Using Particle Swarm Optimization for Power System Stabilizer and energy storage in the SMIB system under load shedding conditions

Generator instability, which manifests as oscillations in frequency and rotor angle, is brought on by sudden disruptions in the power supply. Power System Stabilizer (PSS) and Energy Storage are additional controllers that enhance generator stability. Energy storage types include superconducting magnetic (SMES) and capacitive (CES) storage. If the correct settings are employed, PSS, SMES, and CES coordination can boost system performance. It is necessary to use accurate and effective PSS, SMES, and CES tuning techniques. Artificial intelligence techniques can replace traditional trial-and-error tuning techniques and assist in adjusting controller parameters. According to this study, the PSS, SMES, and CES parameters can be optimized using a method based on particle swarm optimization (PSO). Based on the investigation's findings, PSO executes quick and accurate calculations in the fifth iteration with a fitness function value of 0.007813. The PSO aims to reduce the integral time absolute error (ITAE). With the addition of a load-shedding instance, the case study utilized the Single Machine Infinite Bus (SMIB) technology. The frequency response and rotor angle of the SMIB system are shown via time domain simulation. The analysis's findings demonstrate that the controller combination can offer stability, reducing overshoot oscillations and enabling quick settling times.

Performance Comparison of No-preference and Weighted Sum Objective Methods in Multi-Objective Optimization of AVR-PSS Tuning in Multi-machine Power System

Simultaneous optimization of controllers in power systems is a challenging research due to the inherent nonlinearity of such a system. Multi-objective optimization is a useful tool for tuning excitation controllers and minimizing oscillations that are described through definition of transient and small-signal stability in power systems. In this paper, a Two-Area-Four-Machine (TAFM) power system model is tested on multiple short circuit and load disturbances. A multi-objective performance analysis is investigated by observing the system\u27s behaviour in different cases involving the no-preference method and a priori method called weighted sum objective. The analysis is done through observation of two different objective functions. First objective function includes the sum of the integral of time-weighted absolute errors of rotor speed differences, generator voltage, and tie-line power transfer. Second objective function observes time domain elements: overshoot, undershoot, and settling time of machines\u27 rotor speeds. Results are compared for two methods combined with four different algorithms to provide better insight into the computational performance of each algorithm and objective search method. Algorithms used for controllers\u27 parametrization include two novel algorithms: multi-objective ant lion optimizer (MOALO) and salp swarm algorithm (MOSSA), and two classic algorithms: multi-objective particle swarm optimization with velocity relaxation (MOVRPSO) and simulated annealing (MOSA)

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California

Tracking system study

A digital computer program was generated which mathematically describes an optimal estimator-controller technique as applied to the control of antenna tracking systems used by NASA. Simulation studies utilizing this program were conducted using the IBM 360/91 computer. The basic ideas of applying optimal estimator-controller techniques to antenna tracking systems are discussed. A survey of existing tracking methods is given along with shortcomings and inherent errors. It is explained how these errors can be considerably reduced if optimal estimation and control are used. The modified programs generated in this project are described and the simulation results are summarized. The new algorithms for direct synthesis and stabilization of the systems including nonlinearities, are presented