A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described,

Particle Swarm Optimization.

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts across many areas of modern day living. This offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. The proliferation of these devices in a communicating-actuating network creates the Internet of Things (IoT), wherein, sensors and actuators blend seamlessly with the environment around us, and the information is shared across platforms in order to develop a common operating picture (COP). Fuelled by the recent adaptation of a variety of enabling device technologies such as RFID tags and readers, near field communication (NFC) devices and embedded sensor and actuator nodes, the IoT has stepped out of its infancy and is the the next revolutionary technology in transforming the Internet into a fully integrated Future Internet. As we move from www (static pages web) to web2 (social networking web) to web3 (ubiquitous computing web), the need for data-on-demand using sophisticated intuitive queries increases significantly. This paper presents a cloud centric vision for worldwide implementation of Internet of Things. The key enabling technologies and application domains that are likely to drive IoT research in the near future are discussed. A cloud implementation using Aneka, which is based on interaction of private and public clouds is presented. We conclude our IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.

pdf/internet-of-things-iot-a-vision-architectural-elements-and-3gx3587ieu.pdf

Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions

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.

/pdf/differential-evolution-a-survey-of-the-state-of-the-art-3lz840pz2t.pdf

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

Multilevel converters have been under research and development for more than three decades and have found successful industrial application. However, this is still a technology under development, and many new contributions and new commercial topologies have been reported in the last few years. The aim of this paper is to group and review these recent contributions, in order to establish the current state of the art and trends of the technology, to provide readers with a comprehensive and insightful review of where multilevel converter technology stands and is heading. This paper first presents a brief overview of well-established multilevel converters strongly oriented to their current state in industrial applications to then center the discussion on the new converters that have made their way into the industry. In addition, new promising topologies are discussed. Recent advances made in modulation and control of multilevel converters are also addressed. A great part of this paper is devoted to show nontraditional applications powered by multilevel converters and how multilevel converters are becoming an enabling technology in many industrial sectors. Finally, some future trends and challenges in the further development of this technology are discussed to motivate future contributions that address open problems and explore new possibilities.

/pdf/recent-advances-and-industrial-applications-of-multilevel-26fluutoc9.pdf

Recent Advances and Industrial Applications of Multilevel Converters

In an all-electric ship power system, the power quality problems mainly arise due to the pulsed loads, which cause the degradation of the overall system performance. The paper proposes the application of DSTATCOM to improve these power quality problems of an electric ship. DSTATCOM is a shunt compensation device, which regulates the bus voltage by injecting reactive power during the pulsed load operations. The control strategy of DSTATCOM plays an important role to meet the objectives. The paper proposes a controller design strategy which is based on particle swarm optimization (PSO). PSO, an algorithm that falls into swarm intelligence family, is very effective in solving non-linear optimization problems. Here, the optimal parameters of a controller are found using PSO. To evaluate the performance of the proposed controller, a simplified model of a ship power system is developed in MATLAB/SIMULINK environment, which comprises of a 36 MW generator, 10 MW propulsion motor and pulsed loads of different values of real and reactive power. The effectiveness of the DSTATCOM and the PSO based controller are examined on the test system for pulsed loads of 100, 200 and 500 millisecond durations and also for a pulse train of 100 millisecond interval.

/pdf/a-dstatcom-controller-tuned-by-particle-swarm-optimization-26b6za5u8h.pdf

A DSTATCOM controller tuned by Particle Swarm Optimization for an Electric Ship Power System

Modern interconnected electric power systems are made up of a large number of buses to meet the demand of electricity across large geographical distances. The large number of buses and interconnections across multiple areas result in a complex network and cause an increase in computational requirements on the processor. In order to meet the requirements of this increased complexity for state estimation, distributed estimation is getting attention nowadays. A new approach based on Cellular Computational Network (CCN) for static state estimation is proposed to overcome the computational demand of large power networks in general. The CCN architecture requires a cell at every bus where the states need to be estimated. A cell uses locally available information to estimate voltage magnitude and angle of its bus. The cells exploit output information of other cells in some electrical proximity prior to computing the outputs for next time step. Beside the promise of scalability of the CCN architecture, a fully observable system for state estimation and other applications can be realized. As the traditional estimators take all the measurements at a time and executes the estimation, missing some of the measurements may cause it to loose observability. In this paper, CCN based architecture is implemented with the popular Weighted Least Square (WLS) estimator on nonlinear power flow equations to estimate off-line data. Through simulation, the scalability and observability of the CCN based framework is investigated.

Scalable cellular computational network based WLS state estimator for power systems

Interconnected power systems with large-scale penetration of photovoltaic (PV) power introduce frequency and tie-line power flow fluctuations. This is due to the variability and uncertainty characteristics of PV power. This makes automatic generation control (AGC) to be more challenging. In other words, maintaining system frequencies and tie-line power flows at the desired values, also known as “tie-line bias control” is difficult. In this paper, an enhanced tie-line bias control method is proposed by predicting PV power generation and bus frequencies. A cyber-physical two-area power system with a large PV plant consisting of phasor measurement units (PMUs) is studied. The use of synchrophasor networks consisting of PMUs can enable smooth power system operations overcoming the challenges of PV power variability and uncertainty. However, the use of PMUs in power system control creates vulnerabilities for cyber-attacks that could jeopardize the power system operations. It is shown that the frequency prediction using a virtual synchrophasor network (VSN) can mitigate the impact(s) of denial of service (DoS) attacks on physical PMUs. Enhanced AGC performance is investigated under different weather and load conditions including a weather profile during the “Great American Eclipse” of August $21^{st}, 2017$. Typical results indicate that the enhanced AGC structure provides a resilient and sustainable tie-line bias control in uncertain environments.

/pdf/resilient-and-sustainable-tie-line-bias-control-for-a-power-21ubmf5yg2.pdf

Resilient and Sustainable Tie-Line Bias Control for a Power System in Uncertain Environments

This paper presents an intuitive and simple-to-implement control scheme to improve the performance of three-phase boost-type PWM rectifiers under harmonic and unbalanced input conditions Unlike most other control strategies, the proposed method does not need to extract either the harmonic or the negative-sequence components in the supply voltages and currents A near-synchronous reference frame is used to determine the positive-sequence fundamental-frequency component in the input voltages Utilizing only the extracted component, the dc-link voltage control and power factor control are implemented independently to determine the phase angle and magnitude of the PWM reference voltage The commanded rectifier voltage adjustments are superimposed upon the grid voltages in such a way that the distortions (both harmonic and negative sequence components) are effectively cancelled By employing a near-synchronous reference frame, no line-synchronization algorithm or hardware PLL is needed, so very little computational effort is required for its implementation Simulation results show that the proposed method performs very well under extreme harmonic and unbalanced conditions such as when one or even two phases of the grid voltages are zero In order to further verify its effectiveness, a laboratory hardware platform has been developed

/pdf/cancellation-predictive-control-for-three-phase-pwm-2mupql2q9d.pdf

Cancellation Predictive Control for Three-Phase PWM Rectifiers under Harmonic and Unbalanced Input Conditions

The application of a multi-population based Population-Based Incremental Learning (PBIL) to power system controller design is presented in this paper. PBIL is a combination of evolutionary optimization and competitive learning derived from artificial neural networks. Single population PBIL has recently received increasing attention in various engineering fields due to its effectiveness., easy implementation and robustness. Despite these strengths., PBIL still suffers from issues of loss of diversity in the population. The use of multi-population is seen as one way of increasing the diversity in the population. The approach is applied to power system controller design. Simulations results show that the multi-population PBIL approach performs better than the standard PBIL and is as effective as PBIL where adaptive learning is used.

/pdf/power-system-controller-design-using-multi-population-pbil-3r51nq185y.pdf

Power system controller design using multi-population PBIL

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