Improving the Performance of Particle Swarm Optimization Using Adaptive Critics Designs

Swarm intelligence algorithms are based on natural behaviors. Particle swarm optimization (PSO) is a stochastic search and optimization tool. Changes in the PSO parameters, namely the inertia weight and the cognitive and social acceleration constants, affect the performance of the search process. This paper presents a novel method to dynamically change the values of these parameters during the search. Adaptive critic design (ACD) has been applied for dynamically changing the values of the PSO parameters

Adaptive Critics for Dynamic Particle Swarm Optimization

This work introduces a novel technique for dynamic particle swarm optimization (DPSO) using adaptive critic designs. The adaptation between global and local search in an optimization algorithm is critical for optimization problems especially in a dynamically changing environment or process over time. The inertia weight in particle swarm optimization (PSO) is dynamically adjusted in this paper in order to provide a nonlinear search capability for the PSO algorithm. Results on benchmark functions in the literature are provided

NSF CAREER: Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems

The NSF CAREER program is a premier program that emphasizes the importance the foundation places on the early development of academic careers solely dedicated to stimulating the discovery process in which the excitement of research enriched by inspired teaching and enthusiastic learning. This paper describes the research and education experiences gained by the principal investigator and his research collaborators and students as a result of a NSF CAREER proposal been awarded by the power, control and adaptive networks (PCAN) program of the electrical, communications and cyber systems division, effective June 1, 2004. In addition, suggestions on writing a winning NSF CAREER proposal are presented

Bio-Inspired Algorithms for the Design of Multiple Optimal Power System Stabilizers: SPPSO and BFA

Damping intra-area and interarea oscillations are critical to optimal power flow and stability in a power system. Power system stabilizers (PSSs) are effective damping devices, as they provide auxiliary control signals to the excitation systems of generators. The proper selection of PSS parameters to accommodate variations in the power system dynamics is important and is a challenging task particularly when several PSSs are involved. Two classical bio-inspired algorithms, which are smallpopulation- based particle swarm optimization (SPPSO) and bacterial foraging algorithm (BFA), are presented in this paper for the simultaneous design of multiple optimal PSSs in two power systems. A classical PSO with a small population of particles is called SPPSO in this paper. The SPPSO uses the regeneration concept, introduced in this paper, to attain the same performance as a PSO algorithm with a large population. Both algorithms use time domain information to obtain the objective function for the determination of the optimal parameters of the PSSs. The effectiveness of the two algorithms is evaluated and compared for damping the system oscillations during small and large disturbances, and their robustness is illustrated using the transient energy analysis. In addition, the computational complexities of the two algorithms are also presented

Design of Non-Uniform Linear Array via Linear Programming and Particle Swarm Optimization and Studies on Phased Array Calibration

For a linear array, the excitation coefficients of each element and its geometry play an important role, because they will determine the radiation pattern of the given array. Side Lobe Level (SLL) is one of the key parameters to evaluate the radiation pattern of the array. Generally speaking, we desire SLL to be as low as possible. For the linear array with uniform spacing, there are some classic methods to calculate the excitation coefficients to make the radiation pattern satisfy the given requirements. For the linear array with non-uniform spacing, linear programming and particle swarm optimization are proposed to calculate the excitation coefficients to make the array get minimum SLL in this thesis. They are demonstrated for symmetric and asymmetric array in the first part of this thesis. In the second part of this thesis, a simple method is proposed for correcting excitation coefficients of a linear phased array. This proposed method corrects the coefficients through using the Normalized Least Means Squares(NLMS) algorithm, dither signal and a near-field sensor being used for sensing the field emitted by the array. The advantage of this proposed method is that it avoids the problem of estimating the largest eigenvalue of the coefficient matrix to get optimal step size. Its robustness in different environments is demonstrated as well as the effect of noise with various Signal-to-Noise Ratio (SNR), and mutual coupling. In addition, the effect of using discrete dither signal to the array is considered, because the continuous dither signal cannot be generated in practice

Bio inspired techniques for simultaneous design of multiple optimal power system stabilizers

Bio-inspired techniques are fields of study that are inspired from topics of connectionism, social behavior and emergence. Researchers have ventured into the intricacies involved with the techniques and devised algorithms based on their study. Such techniques are the focus of this thesis. The two bio-inspired techniques used for simultaneous design of power system stabilizers (PSSs) in this study are - Particle Swam Optimization (PSO) and Bacteria Foraging Algorithm (BFA). The work in this thesis is presented in three papers as follows: Paper 1 -This paper introduces an improved PSO called Small Population based PSO (SPPSO) with less number of particles and unique regeneration concept. The efficacy of the algorithm is evaluated for the simultaneous design of power system stabilizers (PSSs) on the two-area and 16 machine power systems. Paper 2 - The second paper presents a new algorithm - Bacterial Foraging Algorithm (BFA) for simultaneous tuning of multiple PSSs on a 16 machine power system. The variants of the BFA like the run length and the swarming are explored for better performance for two different design techniques and the results are compared. Paper 3 - The third paper compares SPPSO and BFA towards simultaneous tuning of multiple PSSs on two-area and Nigerian power system. This paper presents both algorithms as a first step towards online optimization and proposes to implement these algorithms in real power systems in near future --Abstract, page iv

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed

Characterization of bees algorithm into the Mahalanobis-Taguchi system for classification

Mahalanobis-Taguchi System (MTS) is a pattern recognition tool employing Mahalanobis Distance (MD) and Taguchi Robust Engineering philosophy to explore and exploit data in multidimensional systems. In order to improve recognition accuracy of the MTS, features that do not provide useful and beneficial information to the recognition function is removed. A matrix called Orthogonal Array (OA) to search for the useful features is utilized by MTS to accomplished the search. However, the deployment of OA as the feature selection search method is seen as ineffective. The fixed-scheme structure of the OA provides a non-heuristic search nature which leads to suboptimal solution. Therefore, it is the objective of this research to develop an algorithm utilizing Bees Algorithm (BA) to replace the OA. It will act as the alternative feature selection search strategy in order to enhance the search mechanism in a more heuristic manner. To understand the mechanism of the Bees Algorithm, the characteristics of the algorithmic nature of the algorithm is determined. Unlike other research reported in the literature, the proposed characterization framework is similar to Taguchi-sound approach because Larger the Better (LTB) type of signal-to-noise formulation is used as the algorithm’s objective function. The Smallest Position Value (SPV) discretization method is adopted by which the combinations of features are indexed in an enumeration list consisting of all possible feature combinations. The list formed a search landscape for the bee agents in exploring the potential solution. The proposed characterization framework is validated by comparing it against three different case studies, all focused on performance in terms of Signal-to-Noise Ratio gain (SNR gain), classification accuracy and computational speed against the OA. The results from the case studies showed that the characterization of the BA into the MTS framework improved the performance of the MTS particularly on the SNR gain. It recorded more than 50% improvement (on average) and nearly 4% improvement on the classification accuracy (on average) in comparison to the OA. However, the OA on average was found to be 30 times faster than the BA in terms of computational speed. Future research on improving the computational speed aspect of the BA is suggested. This study concludes that the characterization of BA into the MTS optimization methodology effectively improved the performances of the MTS, particularly with respect of the SNR gain performance and the classification accuracy when compared to the OA

Computational approaches for voltage stability monitoring and control in power systems

The electric power grid is a complex, non-linear, non-stationary system comprising of thousands of components such as generators, transformers, transmission lines and advanced power electronics based control devices, and customer loads. The complexity of the grid has been further increased by the introduction of smart grid technologies. Smart grid technology adds to the traditional power grids advanced methods of communication, computation and control as well as increased use of renewable energy sources such as wind and solar farms and a higher penetration of plug-in electric vehicles among others. The smart grid has resulted in much more distributed generation, bi-directional powerflows between customers and the grid, and the semi-autonomous control of subsystems. Due to this added complexity of the grid and the need to maintain reliable, quality, efficient, economical, and environmentally friendly power supply, advanced monitoring and control technologies are needed for real-time operation of various systems that integrate into the transmission and distribution network. In this dissertation, the development of computational intelligence methods for on-line monitoring of voltage stability in a power system is presented. In order to carry out on-line assessment of voltage stability, data from Phasor Measurement Units (PMUs) is utilized. An intelligent algorithm for optimal location of PMUs for voltage stability monitoring is developed. PMU information is used for estimation of voltage stability load index in a power system with plug-in electric vehicle and wind farm included. The estimated voltage stability index is applied in the development of an adaptive dynamic programming based optimal secondary voltage controller to coordinate the reactive power capability of two FACTS devices --Abstract, page iii

Reinforcement Learning Applied to Trading Systems: A Survey

Financial domain tasks, such as trading in market exchanges, are challenging and have long attracted researchers. The recent achievements and the consequent notoriety of Reinforcement Learning (RL) have also increased its adoption in trading tasks. RL uses a framework with well-established formal concepts, which raises its attractiveness in learning profitable trading strategies. However, RL use without due attention in the financial area can prevent new researchers from following standards or failing to adopt relevant conceptual guidelines. In this work, we embrace the seminal RL technical fundamentals, concepts, and recommendations to perform a unified, theoretically-grounded examination and comparison of previous research that could serve as a structuring guide for the field of study. A selection of twenty-nine articles was reviewed under our classification that considers RL's most common formulations and design patterns from a large volume of available studies. This classification allowed for precise inspection of the most relevant aspects regarding data input, preprocessing, state and action composition, adopted RL techniques, evaluation setups, and overall results. Our analysis approach organized around fundamental RL concepts allowed for a clear identification of current system design best practices, gaps that require further investigation, and promising research opportunities. Finally, this review attempts to promote the development of this field of study by facilitating researchers' commitment to standards adherence and helping them to avoid straying away from the RL constructs' firm ground.Comment: 38 page