An novel multiplexing technique applied on a neural harmonics extraction method is presented in this paper. This structure can be used in nonlinear loads compensation with Active Power Filters. The approach is composed of a neural Phase Lock-Loop and a neural reference current generator based on an efficient formulation of the instantaneous reactive power theory. For the purpose of harmonics suppression and reactive power compensation, the whole architecture is composed of three Adaline Neural Networks whose structure leads to an important consumption of Field Programmable Gate Array resources during implementation. The presented technique uses only one Adaline and keeps the immunity of the proposed approach under non-sinusoidal and unbalanced conditions of voltage. Simulation results of the neural harmonics detection system connected to a reference current control shows balanced and sinusoidal source currents under various conditions. Results with experimental measurement made on an Active Power Filters test bench demonstrate its good performances on harmonics filtering. Moreover, the simple structure from the new approach called mp-q method shows a significant resource reduction.Index Terms-FPGA, active power filters, instantaneous reactive power theory, power quality, resource reduction.

Having access to electricity is one of the big challenges nowadays. It is a basic requirement for any community as it can improve the living standards characterized via the improvement of healthcare, education, and the local economy at large. Henceforth, electricity plays a major role in the economic and social development in many countries. The United Nations are working to ensure an easy access to safe energy technologies as from 2030. To cope with this point of view, this paper aims to use the relation between electricity and the Human Development Index (HDI) in order to predict the value of HDI in a short term. In this relation, two parameters x and y are used to study the dependency between the HDI and electricity consumption and production conjointly. To fulfill our goal, the values of electricity production, electricity consumption and HDI for a period are used into our optimization technique namely Particle Swarm Optimization (PSO). The best values of our parameters x and y are obtained under different scenarios based on three criteria: number of iterations, population, and limit of convergence. The results obtained after simulations leads to the value of HDI of about 0.68 in 2030, with 100 iterations, 100 populations and 0.05 as limit of convergence. The predicted value obtained is closed of the theoretical value presented into the Electricity Sector Development Plan. Thus, this helps to appreciate the impact of energy on the social and economic development of Cameroon.

One of the main challenges in distributed generation is to keep supplying some priority loads when islanding occurs. Unfortunately, most anti-islanding protection (AIP) methods fail in islanding detection if the demand in the islanded loads matches the production in the island. Many active AIP schemes are too slow and cause power quality problems. In this paper, an islanding detection method for inverter-based photovoltaic system (PVS) is presented, operating with a simple adaptive loads shedding algorithm. This method is based on modulating the inverter output voltage at the point of common coupling. This frequency modulation consists in introducing interharmonics as side bands of the inverter voltage. After islanding detection, the adaptive loads shedding algorithm operates so as to keep powered priority loads as possible. AIP scheme and PVSs are implemented in MATLAB/Simulink environment. The effectiveness of the proposed scheme was tested and evaluated under a wide range of operating conditions. These tests determined that the proposed technique does not exhibit any non-detection zone and detects islanding within five electric cycles. No significant effect on power quality is recorded even during islanding detection time. The proposed technique was also robust and more independent of system parameters than some others.

Electricity is becoming an important commodity in Cameroon. Within the years, its consumption and production have led to many studies. Hence, having an idea on its progression is one of research’ concerns. Thus, this paper aims to develop a model for forecasting electricity production and consumption in Cameroon based on Long Short-Term Memory (LSTM). Indeed, the LSTM approach, showing a good ability to grab the long-term dependencies between time steps of electricity production and consumption, allows a good prediction in 2030 of 7178GWh for consumption with 0.067 RMSE and 0.2965% MAPE and 8686GWh for production with 0.1631 RMSE and 0.4291%MAPE. Hence, the proposed model is more reliable, what makes possible to monitor the growth in electricity supply and demand, falling to the study of balance in Cameroon.