The modification of the proportional–integral (PI) controller with the variable proportional constant for tracking of the grid-connected photovoltaic microinverter output current has been proposed. The obtained results show that in the case when the proportional constant of the PI controller varies in time according to the appropriate law, the microinverter output current sinus shape distortions decrease as compared to the case when the ordinary PI controller is used. The operation of the microinverter with the proposed controller was investigated for the cases when the electrical grid voltage sinus shape is not distorted and when it is distorted by the higher harmonics.

https://www.mdpi.com/2073-8994/12/1/112/pdf

Controller for the Grid-Connected Microinverter Output Current Tracking

Unlike most other digital simulators that generate test waveforms for specific system configurations, playback previously recorded fault waveforms, or randomly generate artificial waveforms, the digital simulator presented in this paper generates the worst case waveforms for specified bounds on noise components present in the relay input signals. As a result, the performance limits of a computer relay can be determined, for specified bounds on its noise components, by performing a single test only. The parameters that define the worst case waveforms; are obtained by utilizing a modified univariate search algorithm. The PC-based simulator was implemented using a general-purpose multifunction card and a graphical programming package called Visual Designer. It did not require any text-based programming for tasks such as interfacing, digital-to-analog conversion, and outputting the discrete analog values of the generated waveform under hardware control. For demonstrating the operation of the digital simulator, a computer relay was also developed in the laboratory and its performance limits were determined.

A Digital Simulator for Determining the Performance Limits of Computer Relays

Support vector machine is a learning technique based on the structural risk minimization principle, and it is also a class of regression method with good generalization ability. The paper firstly introduces the mathematical model of regression least squares support vector machine (LSSVM), and designs incremental learning algorithms by the calculation formula of block matrix, then uses LSSVM to model nonlinear system, based on which to control nonlinear systems by model predictive method. Simulation experiments indicate that the proposed method provides satisfactory performance, and it achieves superior modeling performance to the conventional method based on neural networks, moreover it achieves well control performance.

Using least square svm for nonlinear systems modeling and control

/pdf/energetiskai-efektyvaus-fotovoltinio-mikroinverterio-kurimas-4bu9z09se9.pdf

Energetiškai efektyvaus fotovoltinio mikroinverterio kūrimas

This paper describes a modified univariate search algorithm that overcomes two major limitations of conventional univariate search method. It minimizes the probability of premature convergence to poor local minima by utilizing a non-deterministic search procedure based on an analogy with the analytical univariate search, and improves' the quality of solutions by dealing with populations of solutions rather than with single solutions for solving unconstrained as well as constrained optimization problems involving continuous or discrete variables. Unlike Genetic Algorithms (GA's), which also are based on non-deterministic search and exhibit intrinsic parallelism, the solutions do not interact or mix together to produce new solutions (offspring); instead, new solutions are generated by unilaterally updating a single variable at a time in individual solutions. Results of two test problems are presented and compared with those obtained by standard GA, modified GA, and an optimization program based on the method of feasible directions.

M. Mir

Papers

A modified univariate search algorithm