Control of an Active Filter by Duty Cycle Modulation (DCM) for the Harmonic Decontamination of a Three-Phase Electrical Network

This paper presents the control of the active filter by Duty Cycle Modulation (DCM) for the harmonic pollution control of a three-phase electrical system. Several research works have preceded this work, but most of them deal with the control by pulse-width modulation and the differences lie around the THD reduction performance, the quality of the wave obtained, the simplicity of the scheme and the cost of the control. For this purpose, we propose a very innovative approach to active filter control which aims at reducing the current THD at a lower rate than pulse-width modulation techniques improving at the same time the wave quality with a more simplified control scheme. First we present the schematic diagram of the active filter, then we present the design of the control of this active filter based on the real and imaginary instantaneous power method which uses the algebraic “Clark” transformation. The Duty Cycle Modulation controller is used to obtain the required control commands to be injected to the inverter. We then developed a simulink model of the active filter to validate this study. The simulations were performed in the Matlab/Simulink environment. The results obtained show a significant improvement on the harmonic current control techniques for the THD of 1.02% which was 26.25%, the elimination of noise in the signal and the simplicity of the control with an easier implementation than the pulse width modulation (PWM) make the Duty Cycle Modulation (DCM) control a very promising tool.

Hydropower Production Optimization from Inflow: Case Study of Songloulou Hydroplant

The model of nonlinear power generation function is developed to generate optimal operational policies for Songloulou inflow in Cameroon and test these policies in real time conditions. Our model is used to adjust operational regimes for the Songloulou reservoir under varying flows (turbined and deversed) using a dynamic program. A more interesting approach, proposed in this article, consists of combining both the principle of decomposition by resources (or quantities) and the technique of dynamic programming. Dynamic programming is an appropriating optimization algorithm that is used for complex non-linear inflow operational policies and strategies. In this case study, our optimization model is used and confirmed maximizing large scale of hydropower in a period of time step by the integration of several. The high non linearity of our study object is the first stage of difficulty which brought us to combined least squared and Time Varying Acceleration Coefficients Particle Swarm (TVACPSO) to obtain appropriate production function which generated optimal operational policies for the Songloulou hydropower in sub-Saharan region and after we tested it in the company policies operational at real time conditions. The model could be successfully applied to other hydropower dams in the region.

Optimal Placement and Sizing of Distributed Energy Generation in an Electrical Network Using the Hybrid Algorithm of Bee Colonies and Newton Raphson

Distributed generation (DG) is gaining in importance due to the growing demand for electrical energy and the key role it plays in reducing actual energy losses, lowering operating costs and improving voltage stability. In this paper, we propose to inject distributed power generation into a distribution system while minimizing active energy losses. This injection should be done at a grid node (which is a point where energy can be injected into or recovered from the grid) that will be considered the optimal node when total active losses in the radial distribution system are minimal. The focus is on meeting energy demand using renewable energy sources. The main criterion is the minimization of active energy losses during injection. The method used is the algorithm of bee colony (ABC) associated with Newtonian energy flow transfer equations. The method has been implemented in MATLAB for optimal node search in IEEE 14, 33 and 57 nodes networks. The active energy loss results of this hybrid algorithm were compared with the results of previous searches. This comparison shows that the proposed algorithm allows to have reduced losses with the power injected that we have found.

Variability of Performance Indices of Photovoltaic Solar Panels in Operating Conditions in the Littoral Zone of Cameroon

Photovoltaic cells are generally manufactured under standard test conditions. The operating conditions, very often induce performance losses different from those initially given by the manufacturer. This article presents an experimental acquisition and analysis system that integrates the synthetic efficiency ratio (SER) as a hybrid analysis tool to evaluate the performance of a monocrystalline photovoltaic solar panel, in this case the LW-MS90 panel in the city of Douala. The meteorological data obtained experimentally was used to evaluate these performances according to the manufacturer’s model in MATLAB/Simulink. By comparison with the experimental performances, the results quantify through a certain number of indices, a minimal power drop according to the acquired irradiance estimated at 3.45%. The interest of this approach is to contribute to the prediction of the operating performance of PV panels in the installation phase in non-standard areas.

Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

To evaluate the performance of a photovoltaic panel, several parameters must be extracted from the photovoltaic. These parameters are very important for the evaluation, monitoring and optimization of photovoltaic. Among the methods developed to extract photovoltaic parameters from current-voltage (I-V) characteristic curve, metaheuristic algorithms are the most used nowadays. A new metaheuristic algorithm namely enhanced vibrating particles system algorithm is presented here to extract the best values of parameters of a photovoltaic cell. Five recent algorithms (grey wolf optimization (GWO), moth-flame optimization algorithm (MFOA), multi-verse optimizer (MVO), whale optimization algorithm (WAO), salp swarm-inspired algorithm (SSA)) are also implemented on the same computer. Enhanced vibrating particles system is inspired by the free vibration of the single degree of freedom systems with viscous damping. To extract the photovoltaic parameters using enhanced vibrating particles system algorithm, the problem can be set as an optimization problem with the objective to minimize the difference between measured and estimated current. Four case studies have been implemented here. The results and comparison with other methods exhibit high accuracy and validity of the proposed enhanced vibrating particles system algorithm to extract parameters of a photovoltaic cell and module.