Optimization of the photovoltaic systems on the North Cameroon interconnected electrical grid
18 Oct 2021-international journal of energy and environmental engineering (Springer Science and Business Media LLC)-pp 1-13
TL;DR: In this paper, the authors presented an active filter based on a cascaded multicellular inverter for three-phase PV systems connected to the North Cameroon interconnected grid, which can reduce the harmonic distortion from 23.06% to 0.42% when the active power of the PV generators is injected into the electrical grids.
Abstract: Active filters based on multicellular inverters are an efficient, robust, and reliable means of large-scale photovoltaic systems for the next generation of smart grids. This paper presents active filters based on a cascaded multicellular inverter for three-phase PV systems connected to the North Cameroon interconnected grid. The proposed system consists of the boost chopper connected to the grids, via the 7-level inverters located before the multicellular active filters with five switching cells per arm. The contribution of this paper is due to the improved P&O MPPT algorithm for the extraction of the maximum power produced by the PV generators and the appropriate choice of the active filters to reduce the harmonic distortion rate to an acceptable value by the grid regulations for a decentralized generation. After synchronization of the system with the electrical grids, the voltage and current of the grid remain in phase. This means that the power factor is corrected. The results show that the system can reduce the harmonic distortion from 23.06% to 0.42% when the active power of the photovoltaic generators is injected into the electrical grids.
Citations
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TL;DR: In this article , the authors proposed a particle swarm optimization (PSO) algorithm for sizing and reducing power losses of a hybrid photovoltaic and wind system in a radial distribution network (RDN) by developing an objective function, thanks to particles swarm optimization.
13 citations
TL;DR: In this paper , the impact of partial shading on PV module performance in the Sudano-Sahelian climate conditions of Cameroon was evaluated using MATLAB/Simulink for 12 months with data from the town of Yagoua.
Abstract: Partial shading is a factor that influences the performance of a PV module. The study sought to evaluate the impact of partial shading on PV module performance in the Sudano-Sahelian climate conditions of Cameroon. The behavior of the PV module was simulated using MATLAB/Simulink for 12 months with data from the town of Yagoua. The power, current, and voltage losses of the PV module were estimated by varying the partial shading rate from 5.0% to 95.0%, with an increase factor of 5.0%. The results show that, when the shading ranges from 5.0% to 55.0%, the power and current losses are very significant and vary from 3.0% to 52.0% and 3.0%–53.0%, respectively. The voltage in this shading range remains almost invariant. For shading from 60.0% to 95.0%, the power losses increase slightly and reach approximately 60.0%. A very small current loss is observed, varying from 1.0% to 3.0%. Significant voltage losses are noted and vary from 55.0% to 59.0%. From 40.0% shading rate onwards, a mismatch is observed on the power-voltage characteristics curve by the presence of two maximum power points. This method can be used to evaluate the efficiency of different PV array topologies under partial shading. The results show the importance of paying attention to partial shading, however small its occurrence.
3 citations
TL;DR: In this paper , a less conservative approach based on hyperstability analysis is proposed to deal with the tracking error involved in Popov's inequality, and sufficient conditions that ensure the asymptotic stability of the closed-loop system are established and formulated in term of a nonlinear part which is designed with appropriate proportional and derivative gains.
Abstract: This paper is concerned with the fast state observer for a class of continuous-time linear systems with unknown bounded parameters and sufficiently slowly time varying which satisfy the usual assumptions of conventional state observer for time-invariant plants. A less conservative approach based on hyperstability analysis is proposed to deal with the tracking error involved in Popov’s inequality. Sufficient conditions that ensure the asymptotic stability of the closed-loop system are established and formulated in term of a nonlinear part which is designed with appropriate proportional and derivative gains. This observer included the derivative of the estimation error. The results obtained are satisfactory and less conservative than the Lyapunov stability analysis for the estimation error dynamic system. Also, it is showed that with a good choice of Proportional-Derivative (PD) gains, it is possible to reduce in this case to zero, the estimation error on the one hand, and on the other hand to reduce it to small residues in an asymptotic way. Finally, a numerical example of a lateral motion of CESSNA 182 aircraft system is presented to reconstruct the sideslip angle and the roll angle, respectively, and to highlight the efficiency of the approach that has been developed.
TL;DR: In this paper , the authors presented a method for optimal sizing of a micro grid connected to a hybrid source to ensure the continuity and quality of energy in a locality with a stochastically changing population.
Abstract: This paper presents a method for optimal sizing of a Micro grid connected to a hybrid source to ensure the continuity and quality of energy in a locality with a stochastically changing population. The hybrid system is composed of a solar photovoltaic system, a wind turbine, and an energy storage system. The reliability of the system is evaluated based on the voltage level regulation on IEEE 33-bus and IEEE 69-bus standards. Power factor correction is per-formed, despite some reliability and robustness constraints. This work focus-es on energy management in a hybrid system considering climatic distur-bances on the one hand, and on the other hand, this work evaluates the energy quality and the cost of energy. A combination of genetic algorithms of particle swarm optimization (CGAPSO) shows high convergence speed, which illustrates the robustness of the proposed system. The study of this system shows its feasibility and compliance with standards. The results obtained show a significant reduction in the
References
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TL;DR: In this article, the use of ABC (artificial bee colony) algorithm for the maximum power point tracking (MPPT) of a PV system using a DC-DC converter was proposed.
Abstract: Energy structures from non-conventional energy source has become highly demanded nowadays. In this way, the maximum power extraction from photovoltaic (PV) systems has attracted the attention, therefore an optimization technique is necessary to improve the performance of solar systems. This article proposes the use of ABC (artificial bee colony) algorithm for the maximum power point tracking (MPPT) of a PV system using a DC-DC converter. The procedure of the ABC MPPT algorithm is using data values from PV module, the P-V characteristic is identified and the optimal voltage is selected. Then, the MPPT strategy is applied to obtain the voltage reference for the outer PI control loop, which in turn provides the current reference to the predictive digital current programmed control. A real-time and high-speed simulator (PLECS RT Box 1) and a digital signal controller (DSC) are used to implement the hardware-in-the-loop system to obtain the results. The general system does not have a high computational cost and can be implemented in a commercial low-cost DSC (TI 28069M). The proposed MPPT strategy is compared to the conventional perturb and observe method, results show the proposed method archives a much superior performance.
68 citations
TL;DR: An in-depth overview of the EMS optimization problem of IMGs by systematically analyzing the most representative studies is provided, including framework, time-frame, uncertainty handling approach, optimizer, objective function, and constraints.
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57 citations
TL;DR: In this article, state-of-the-art machine learning algorithms, namely Support Vector Machines (SVM), KNN, Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the smart grid.
Abstract: The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. Machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state-of-the-art machine learning algorithms, namely Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy.
41 citations
TL;DR: The optimization efficiency and superiority of the proposed multi-objective firefly algorithm based hosting capacity enhancement approach is validated by comparing the results with those obtained by popular multi-Objective PSO (MOPSO) and non-dominated sorting genetic algorithm (NSGA-II) under similar objectives.
41 citations