Y. del Valle

Bio: Y. del Valle is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Particle swarm optimization & Electric power system. The author has an hindex of 7, co-authored 10 publications receiving 2183 citations. Previous affiliations of Y. del Valle include Pontifical Catholic University of Chile.

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

Abstract: 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.

A Proportional-Integrator Type Adaptive Critic Design-Based Neurocontroller for a Static Compensator in a Multimachine Power System

Abstract: A novel nonlinear optimal controller for a static compensator (STATCOM) connected to a power system, using artificial neural networks, is presented in this paper. The action dependent heuristic dynamic programming, a member of the adaptive critic designs family is used for the design of the STATCOM neurocontroller. This neurocontroller provides optimal control based on reinforcement learning and approximate dynamic programming. Using a proportional-integrator approach, the proposed neurocontroller is capable of dealing with actual rather than deviation signals. Simulation results are provided to show that the proposed controller outperforms a conventional PI controller for a STATCOM in a small and large multimachine power system during large-scale faults, as well as small disturbances

A full compensating system for general loads, based on a combination of thyristor binary compensator, and a PWM-IGBT active power filter

Abstract: A full compensating system for distribution networks, able to eliminate harmonics, correct unbalanced loads, and generate or absorb reactive power, is presented. The system is based on a combination of a thyristor binary compensator (TBC), and a pulsewidth-modulation insulated gate bipolar transistor active power filter (APF) connected in cascade. The TBC compensates the fundamental reactive power and balances the load connected to the system. The APF eliminates the harmonics and compensates the small amounts of load unbalances or power factor that the TBC cannot eliminate due to its binary condition. The TBC is based on a chain of binary-scaled capacitors and one inductor per phase. This topology allows, with an adequate number of capacitors, a soft variation of reactive power compensation and a negligible generation of harmonics. The capacitors are switched on when the line voltage reaches its peak value, avoiding inrush currents generation. The inductor helps to balance the load, and absorbs reactive power when required. The APF works measuring the source currents, forcing them to be sinusoidal. The two converters (TBC and APF) work independently, making the control of the system simpler and more reliable. Simulations show that the system is able to respond to many kinds of transient perturbations in no more than a couple of cycles. The paper analyzes the circuit proposed, the way it works and shows some experimental results obtained under operation.

A comparison of PSO and backpropagation for training RBF neural networks for identification of a power system with STATCOM

Abstract: Backpropagation algorithm is the most commonly used algorithm for training artificial neural networks. While being a straightforward procedure, it suffers from extensive computations, relatively slow convergence speed and possible divergence for certain conditions. The efficiency of this method as the training algorithm of a radial basis function neural network (RBFN) is compared with that of particle swarm optimization, for neural network based identification of a small power system with a static compensator. The comparison of the two methods is based on the convergence speed and robustness of each method.

Interpretation of dielectric loss data on service aged polyethylene based power cable systems using VLF test methods

Abstract: In the past, cable system management has proceeded on the basis of cable system age, assuming that the oldest has the lowest reliability. It is now recognized that this constitutes a very coarse assessment and that a more targeted approach would bring a more efficient use of resources. The more targeted approach requires an assessment of the health of cable systems. It is increasingly common for the assessment of aged cable systems to be made through the application of diagnostic measurements. A recent study has shown that Very Low Frequency (VLF) Tan δ is the most commonly deployed cable system diagnostic. The practical use of this technique has been supported by the international standards IEEE Std. 400-2001 and IEEE Std. 400.2-2004. A key part of these standards is the guidance provided to a user that is detailed in the "Figures of Merit". These enable users to make practical improvements to the cable system as they help to identify cable systems that are more likely to fail in service in the near future. To aid these decisions a series of criteria have been developed. The benefit of the criteria described here is that the process for their determination is rational, reproducible, and transparent. The outcomes are supported by a probabilistic assessment of service performance.

Differential Evolution: A Survey of the State-of-the-Art

Abstract: Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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

Abstract: 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.

Particle swarm optimization algorithm: an overview

Abstract: Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.