Distributed optimization in power networks and general multi-agent systems

The dissertation studies the general area of complex networked systems that consist of interconnected and active heterogeneous components and usually operate in uncertain environments and with incomplete information. Problems associated with those systems are typically large-scale and computationally intractable, yet they are also very well-structured and have features that can be exploited by appropriate modeling and computational methods. The goal of this thesis is to develop foundational theories and tools to exploit those structures that can lead to computationally-efficient and distributed solutions, and apply them to improve systems operations and architecture.

Specifically, the thesis focuses on two concrete areas. The first one is to design distributed rules to manage distributed energy resources in the power network. The power network is undergoing a fundamental transformation. The future smart grid, especially on the distribution system, will be a large-scale network of distributed energy resources (DERs), each introducing random and rapid fluctuations in power supply, demand, voltage and frequency. These DERs provide a tremendous opportunity for sustainability, efficiency, and power reliability. However, there are daunting technical challenges in managing these DERs and optimizing their operation. The focus of this dissertation is to develop scalable, distributed, and real-time control and optimization to achieve system-wide efficiency, reliability, and robustness for the future power grid. In particular, we will present how to explore the power network structure to design efficient and distributed market and algorithms for the energy management. We will also show how to connect the algorithms with physical dynamics and existing control mechanisms for real-time control in power networks.

The second focus is to develop distributed optimization rules for general multi-agent engineering systems. A central goal in multiagent systems is to design local control laws for the individual agents to ensure that the emergent global behavior is desirable with respect to the given system level objective. Ideally, a system designer seeks to satisfy this goal while conditioning each agent’s control on the least amount of information possible. Our work focused on achieving this goal using the framework of game theory. In particular, we derived a systematic methodology for designing local agent objective functions that guarantees (i) an equivalence between the resulting game-theoretic equilibria and the system level design objective and (ii) that the resulting game possesses an inherent structure that can be exploited for distributed learning, e.g., potential games. The control design can then be completed by applying any distributed learning algorithm that guarantees convergence to the game-theoretic equilibrium. One main advantage of this game theoretic approach is that it provides a hierarchical decomposition between the decomposition of the systemic objective (game design) and the specific local decision rules (distributed learning algorithms). This decomposition provides the system designer with tremendous flexibility to meet the design objectives and constraints inherent in a broad class of multiagent systems. Furthermore, in many settings the resulting controllers will be inherently robust to a host of uncertainties including asynchronous clock rates, delays in information, and component failures.

Electrical resistance and thermoelectric power of the metastable phases in tellurium-base alloys

By using techniques of rapid quenching from the melt, metastable phases have been obtained in ternary alloys which contain tellurium as a major component and two of the three noble metals (Cu, Ag, Au) as minor components. The metastable phases found in this investigation are either simple cubic or amorphous. The formation of the simple cubic phase is discussed. The electrical resistance and the thermoelectric power of the simple cubic alloy (Au₃₀Te₇₀) have been measured and interpreted in terms of atomic bondings. The semiconducting properties of a metastable amorphous alloy (Au₅Cu₂₅Te₇₀) have been measured. The experimental results are discussed in connection with a theoretical consideration of the validity of band theory in an amorphous solid. The existence of extrinsic conduction in an amorphous semiconductor is suggested by the result of electrical resistance and thermoelectric power measurements.

The oculomotor system: (1) Vertical-horizontal interaction and signal recognition, (2) Time delays and power spectra

In the first section of this thesis, two-dimensional properties of the human eye movement control system were studied. The vertical - horizontal interaction was investigated by using a two-dimensional target motion consisting of a sinusoid in one of the directions vertical or horizontal, and low-pass filtered Gaussian random motion of variable bandwidth (and hence information content) in the orthogonal direction. It was found that the random motion reduced the efficiency of the sinusoidal tracking. However, the sinusoidal tracking was only slightly dependent on the bandwidth of the random motion. Thus the system should be thought of as consisting of two independent channels with a small amount of mutual cross-talk.

These target motions were then rotated to discover whether or not the system is capable of recognizing the two-component nature of the target motion. That is, the sinusoid was presented along an oblique line (neither vertical nor horizontal) with the random motion orthogonal to it. The system did not simply track the vertical and horizontal components of motion, but rotated its frame of reference so that its two tracking channels coincided with the directions of the two target motion components. This recognition occurred even when the two orthogonal motions were both random, but with different bandwidths.

In the second section, time delays, prediction and power spectra were examined. Time delays were calculated in response to various periodic signals, various bandwidths of narrow-band Gaussian random motions and sinusoids. It was demonstrated that prediction occurred only when the target motion was periodic, and only if the harmonic content was such that the signal was sufficiently narrow-band. It appears as if general periodic motions are split into predictive and non-predictive components.

For unpredictable motions, the relationship between the time delay and the average speed of the retinal image was linear. Based on this I proposed a model explaining the time delays for both random and periodic motions. My experiments did not prove that the system is sampled data, or that it is continuous. However, the model can be interpreted as representative of a sample data system whose sample interval is a function of the target motion.

It was shown that increasing the bandwidth of the low-pass filtered Gaussian random motion resulted in an increase of the eye movement bandwidth. Some properties of the eyeball-muscle dynamics and the extraocular muscle "active state tension" were derived.

Welding of Al alloy plates using a novel high-average-power pulsed CO2 laser

A novel high-average-power pulsed CO2 laser with a unique electrode structure is presented. The operation of a 5-kW transverse-flow CO2 laser with the preionized pulse-train switched technique results in pulsation of the laser power, and the average laser power is about 5 kW. The characteristic of this technique is switching the preionized pulses into pulse trains so as to use the small preionized power (hundreds of watts) to control the large main-discharge power (tens of kilowatts). By this means, the cost and the complexity of the power supply are greatly reduced. The welding of LF2, LF21, LD2, and LY12 aluminum alloy plates has been successfully achieved using this laser. (c) 2005 Society of Photo-Optical Instrumentation Engineers.

Welding of Al alloy plates using a novel high-average-power pulsed CO2 laser

A novel high-average-power pulsed CO2 laser with a unique electrode structure is presented. The operation of a 5-kW transverse-flow CO2 laser with the preionized pulse-train switched technique results in pulsation of the laser power, and the average laser power is about 5 kW. The characteristic of this technique is switching the preionized pulses into pulse trains so as to use the small preionized power (hundreds of watts) to control the large main-discharge power (tens of kilowatts). By this means, the cost and the complexity of the power supply are greatly reduced. The welding of LF2, LF21, LD2, and LY12 aluminum alloy plates has been successfully achieved using this laser. (c) 2005 Society of Photo-Optical Instrumentation Engineers.

Modeling Innovative Power Take-Off Based on Double-Acting Hydraulic Cylinders Array for Wave Energy Conversion

One of the key systems of a Wave Energy Converter for extraction of wave energy is the Power Take-Off (PTO) device. This device transforms the mechanical energy of a moving body into electrical energy. This paper describes the model of an innovative PTO based on independently activated double-acting hydraulic cylinders array. The model has been developed using a simulation tool, based on a port-based approach to model hydraulics systems. The components and subsystems used in the model have been parameterized as real components and their values experimentally obtained from an existing prototype. In fact, the model takes into account most of the hydraulic losses of each component. The simulations show the flexibility to apply different restraining torques to the input movement depending on the geometrical configuration and the hydraulic cylinders on duty, easily modified by a control law. The combination of these two actions allows suitable flexibility to adapt the device to different sea states whilst optimizing the energy extraction. The model has been validated using a real test bench showing good correlations between simulation and experimental tests.

Eurobalise-Train communication modelling to assess interferences in railway control signalling systems

The evolution of the railway sector depends, to a great extent, on the deployment of advanced railway signalling systems. These signalling systems are based on communication architectures that must cope with complex electromagnetical environments. This paper is outlined in the context of developing the necessary tools to allow the quick deployment of these signalling systems by contributing to an easier analysis of their behaviour under the effect of electromagnetical interferences. Specifically, this paper presents the modelling of the Eurobalise-train communication flow in a general purpose simulation tool. It is critical to guarantee this communication link since any lack of communication may lead to a stop of the train and availability problems. In order to model precisely this communication link we used real measurements done in a laboratory equipped with elements defined in the suitable subsets. Through the simulation study carried out, we obtained performance indicators of the physical layer such as the received power, SNR and BER. The modelling presented in this paper is a required step to be able to provide quality of service indicators related to perturbed scenarios.

Generalized power method for sparse principal component analysis

In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We propose two single-unit and two block optimization formulations of the sparse PCA problem, aimed at extracting a single sparse dominant principal component of a data matrix, or more components at once, respectively. While the initial formulations involve nonconvex functions, and are therefore computationally intractable, we rewrite them into the form of an optimization program involving maximization of a convex function on a compact set. The dimension of the search space is decreased enormously if the data matrix has many more columns (variables) than rows. We then propose and analyze a simple gradient method suited for the task. It appears that our algorithm has best convergence properties in the case when either the objective function or the feasible set are strongly convex, which is the case with our single-unit formulations and can be enforced in the block case. Finally, we demonstrate numerically on a set of random and gene expression test problems that our approach outperforms existing algorithms both in quality of the obtained solution and in computational speed. © 2010 Michel Journée, Yurii Nesterov, Peter Richtárik and Rodolphe Sepulchre.

High power, low-jitter encoded picosecond pulse generation using an rf-locked self-q-switched multicontact GaAs/GaAlAs diode laser

The RF locking of a self-Q-switching diode laser is shown to reduce the jitter of a 2.48 GHz train of 1 W peak power picosecond pulses to less than 300 fs. By using direct modulation of the loss in the Q-switched laser, direct encoding of data has been achieved at rates in excess of 2 Gbit/s.

Picosecond pulse generation in narrow stripe mode-locked quantum dot master oscillator power amplifier

This paper reports a monolithically integrated mode-locked narrow stripe QD MOPA operating at 1300nm generating a stable 20GHz pulse train with an average power of 46.4mW and a pulse duration of 8.3ps. © Optical Society of America.

Generation of 140 fs pulse train with widely tunable repetition rate through cascaded fibre compression techniques

The authors present numerical simulations of ultrashort pulse generation by a technique of linear spectral broadening in phase modulators and compression in dispersion compensating fibre, followed by a further stage of soliton compression in dispersion shifted fibre. This laser system is predicted to generate pulses of 140 fs duration with a peak power of 1.5 kW over a wide, user selectable repetition rate range while maintaining consistent characteristics of stability and pulse quality. The use of fibre compressors and commercially available modulators is expected to make the system setup compact and cost-effective. © The Institution of Engineering and Technology 2014.

SOC dynamic power management using artificial neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79, 1.45, 1.18-competitive separately for traditional timeout PM, adaptive predictive PM and stochastic PM.

A differential recursive scheme for suppression of Peak to average power ratio for OFDM

A differential recursive scheme for suppression of Peak to average power ratio (PAPR) for Orthogonal frequency division multiplexing (OFDM) signal is proposed in this thesis. The pseudo-randomized modulating vector for the subcarrier series is differentially phase-encoded between successive components in frequency domain first, and recursion manipulates several samples of Inverse fast Fourier transformation (IFFT) output in time domain. Theoretical analysis and experimental result exhibit advantage of differential recursive scheme over direct output scheme in PAPR suppression. And the overall block diagram of the scheme is also given.

A voltage-adjustable three-phase rectifier with constant power flow

An three phase adjustable output voltage rectifier with constant power flow based on waveform gap patching principle is resented. By patching the gapes in the phase currents in parallel way as well as the ripple of the output voltage in series way, it implements the constant power flow from the three-phase line to the DC output without using any line frequency (and its harmonics) energy storage components. Principally, by treating only 22.4% power of the needed power output, this rectifier can supply constant power flow with adjustable output voltages without bring about any harmonic interferences to the power utility and achieve unite power factor.

Dynamic power management approaches based on neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system. by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level policies. We proposed two PAY policies-Back propagation Power Management (BPPM) and Radial Basis Function Power management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79,145,1.18-competitive separately for traditional timeout PM, adaptive predictive PM and stochastic PM.