Ganesh K. Venayagamoorthy

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.

Plug-in Vehicles and Renewable Energy Sources for Cost and Emission Reductions

The electricity and transportation industries are the main sources of greenhouse gas emissions on Earth. Renewable energy, mainly wind and solar, can reduce emission from the electricity industry (mainly from power plants). Likewise, next-generation plug-in vehicles, which include plug-in hybrid electric vehicles (EVs) and EVs with vehicle-to-grid capability, referred to as “gridable vehicles” (GVs) by the authors, can reduce emission from the transportation industry. GVs can be used as loads, energy sources (small portable power plants), and energy storages in a smart grid integrated with renewable energy sources (RESs). Smart grid operation to reduce both cost and emission simultaneously is a very complex task considering smart charging and discharging of GVs in a distributed energy source and load environment. If a large number of GVs is connected to the electric grid randomly, peak load will be very high. The use of traditional thermal power plants will be economically and environmentally expensive to support the electrified transportation. The intelligent scheduling and control of GVs as loads and/or sources have great potential for evolving a sustainable integrated electricity and transportation infrastructure. Cost and emission reductions in a smart grid by maximum utilization of GVs and RESs are presented in this paper. Possible models for GV applications, including the smart grid model, are given, and results are presented. The smart grid model offers the best potential for maximum utilization of RESs to reduce cost and emission from the electricity industry.

Real-Time Implementation of a STATCOM on a Wind Farm Equipped with Doubly Fed Induction Generators

Voltage stability is a key issue to achieve the uninterrupted operation of wind farms equipped with doubly fed induction generators (DFIGs) during grid faults. This paper investigates the application of a static synchronous compensator (STATCOM) to assist with the uninterrupted operation of a wind turbine driving a DFIG, which is connected to a power network, during grid faults. The control schemes of the DFIG rotor- and grid-side converters and the STATCOM are suitably designed and coordinated. The system is implemented in real-time on a real time digital simulator. Results show that the STATCOM improves the transient voltage stability and therefore helps the wind turbine generator system to remain in service during grid faults.

Resource Scheduling Under Uncertainty in a Smart Grid With Renewables and Plug-in Vehicles

The power system and transportation sector are our planets main sources of greenhouse gas emissions. Renewable energy sources (RESs), mainly wind and solar, can reduce emissions from the electric energy sector; however, they are very intermittent. Likewise, next generation plug-in vehicles, which include plug-in hybrid electric vehicles and electric vehicles with vehicle-to-grid capability, referred to as gridable vehicles (GVs) by the authors, can reduce emissions from the transportation sector. GVs can be used as loads, energy sources (small portable power plants) and energy storage units in a smart grid integrated with renewable energy sources. However, uncertainty surrounds the controllability of GVs. Forecasted load is used in unit commitment (UC); however, the actual load usually differs from the forecasted one. Thus, UC with plug-in vehicles under uncertainty in a smart grid is very complex considering smart charging and discharging to and from various energy sources and loads to reduce both cost and emissions. A set of valid scenarios is considered for the uncertainties of wind and solar energy sources, load and GVs. In this paper, an optimization algorithm is used to minimize the expected cost and emissions of the UC schedule for the set of scenarios. Results are presented indicating that the smart grid has the potential to maximally utilize RESs and GVs to reduce cost and emissions from the power system and transportation sector.

Recognition of facial expressions using Gabor wavelets and learning vector quantization

Facial expression recognition has potential applications in different aspects of day-to-day life not yet realized due to absence of effective expression recognition techniques. This paper discusses the application of Gabor filter based feature extraction in combination with learning vector quantization (LVQ) for recognition of seven different facial expressions from still pictures of the human face. The results presented here are better in several aspects from earlier work in facial expression recognition. Firstly, it is observed that LVQ based feature classification technique proposed in this study performs better in recognizing fear expressions than multilayer perceptron (MLP) based classification technique used in earlier work. Secondly, this study indicates that the Japanese Female Facial Expression (JAFFE) database contains expressers that expressed expressions incorrectly and these incorrect images adversely affect the development of a reliable facial expression recognition system. By excluding the two expressers from the data set, an improvement in recognition rate from 87.51% to 90.22% has been achieved. The present study, therefore, proves the feasibility of computer vision based facial expression recognition for practical applications like surveillance and human computer interaction.

Efficient Utilization of Renewable Energy Sources by Gridable Vehicles in Cyber-Physical Energy Systems

The main sources of emission today are from the electric power and transportation sectors. One of the main goals of a cyber-physical energy system (CPES) is the integration of renewable energy sources and gridable vehicles (GVs) to maximize emission reduction. GVs can be used as loads, sources and energy storages in CPES. A large CPES is very complex considering all conventional and green distributed energy resources, dynamic data from sensors, and smart operations (e.g., charging/discharging, control, etc.) from/to the grid to reduce both cost and emission. If large number of GVs are connected to the electric grid randomly, peak load will be very high. The use of conventional thermal power plants will be economically expensive and environmentally unfriendly to sustain the electrified transportation. Intelligent scheduling and control of elements of energy systems have great potential for evolving a sustainable integrated electricity and transportation infrastructure. The maximum utilization of renewable energy sources using GVs for sustainable CPES (minimum cost and emission) is presented in this paper. Three models are described and results of the smart grid model show the highest potential for sustainability.

Time series prediction with recurrent neural networks using a hybrid PSO-EA algorithm

To predict the 100 missing values from the time series consisting of 5000 data given for the IJCNN 2004 time series prediction competition, we applied an architecture which automates the design of recurrent neural networks using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching abilities of these two global optimization methods, the evolution of individuals is no longer restricted to be in the same generation, and better performed individuals may produce offspring to replace those with poor performance. The novel algorithm is then applied to the recurrent neural network for the time series prediction. The experimental results show that our approach gives good performance in predicting the missing values from the time series.

Optimal PSO for collective robotic search applications

Unmanned vehicles/mobile robots are of particular interest in target tracing applications since there are many areas where a human cannot explore. Different means of control have been investigated for unmanned vehicles with various algorithms like genetic algorithms, evolutionary computations, neural networks etc. This work presents the application of particle swarm optimization (PSO) for collective robotic search. The performance of the PSO algorithm depends on various parameters called quality factors and these parameters are determined using a secondary PSO. Results are presented to show that the performance of PSO algorithm and search is improved for a single and multiple target searches.

Multiple Reference Frame-Based Control of Three-Phase PWM Boost Rectifiers under Unbalanced and Distorted Input Conditions

Many control algorithms and circuits for three-phase pulse width modulation active rectifiers have been proposed in the past decades. In most of the research, it is often assumed that the input voltages are balanced or contain only fundamental frequency components. In this paper, a selective harmonic compensation method is proposed based on an improved multiple reference frame algorithm, which decouples signals of different frequencies before reference frame transformation. This technique eliminates interactions between the fundamental-frequency positive-sequence components and harmonic and/or negative-sequence components in the input currents, so that fast and accurate regulation of harmonic and unbalanced currents can be achieved. A decoupled phase-locked loop algorithm is used for proper synchronization with the utility voltage, which also benefits from the multiple reference frame technique. The proposed control method leads to considerable reduction in low-order harmonic contents in the rectifier input current and achieves almost zero steady-state error through feedback loops. Extensive experimental tests based on a fixed-point digital signal processor controlled 2 kW prototype are used to verify the effectiveness of the proposed ideas.

Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization

In the last decade, recurrent neural networks (RNNs) have attracted more efforts in inferring genetic regulatory networks (GRNs), using time series gene expression data from microarray experiments. This is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. However, RNNs are well known for training difficulty. Traditional gradient descent-based methods are easily stuck in local minima and the computation of the derivatives is also not always possible. Here, the performance of three evolutionary-swarm computation technology-based methods, known as differential evolution (DE), particle swarm optimization (PSO), and the hybrid of DE and PSO (DEPSO), in training RNNs is investigated. Furthermore, the gene networks are reconstructed via the identification of the gene interactions, which are explained through corresponding connection weight matrices. The experimental results on two data sets studied in this paper demonstrate that the DEPSO algorithm performs better in RNN training. Also, the RNN-based model can provide meaningful insight in capturing the nonlinear dynamics of genetic networks and revealing genetic regulatory interactions.