This paper presents the use of a novel approach in assessing the generation reliability of a hybrid mini-grid system (HMS) based on the optimal design result obtained from the HOMER software. A typical Nigerian rural community – Lade II in Kwara State was used as a case study where the energy demand for the residential and commercial loads was 2.5MWh/day and 171kWh/day respectively. The optimized HMS results from HOMER comprising of a solar photovoltaic (PV) array (1.5MW), diesel generators (350kW) and battery storage (1200 units) has a combined least net present cost of $4,909,206 and a levelized electricity tariff of $0.396 per kWh. Contrasting the HMS with a diesel-only system for the community, an approximate 97% reduction in all pollutant emissions was observed. Furthermore, fluctuations in diesel fuel prices, variations in average solar insolation, and variations in the solar PV's capital/replacement costs were utilized in conducting a sensitivity analysis for the HMS. The capacity outage probability table (COPT) was utilized in validating the reliability of the simulation results obtained from HOMER. The HMS was observed to experience a load loss of 0.769MW, 0.594MW & 0.419MW when zero, one and two diesel generator(s) respectively were operational for all of the Solar PV's and Batteries being off-line. The loss of load probability (LOLP), loss of load expectation (LOLE), and total expected load loss (ELL) obtained from the COPT were 5.76 × 10 −8 , 5.0457 × 10 −4 hr/yr and 0.025344Watt respectively. The results show the reliability of the HMS and also depicts a highly economical and feasible hybrid energy system.
Cognitive radio has received considerable amount of attention as a promising technique to provide dynamic spectrum allocation. Spectrum sensing is one of the basic functions in the cognitive radio and is crucial to all other functions. Software- defined radios (SDRs) are considered due to its very high flexibility and have become a common platform for CR implementation replacing expensive spectrum analysers. The most popular among various SDR platforms is the universal software-defined radio peripheral (USRP). This paper presents a real-time swept spectrum sensing solution based on USRP B210. It also presents a detailed explanation of the concept of energy detection and the methodology for wide-band sensing. Finally, the performance of the proposed sensing solution is analysed through FFT graphs and spectrogram plot taken for 8 hours. The results showed that the proposed sensing solution was capable of achieving high resolution in the frequency domain of the wide band measured which implies that wide bands with heterogenous signals like the ISM band can be accurately resolved and analysed.
The aim of this paper is to present and evaluate artificial neural network model used for path loss prediction of signal propagation in the VHF/UHF spectrum in Edo state.Measurement data obtained from three television broadcasting stations in Edo state, operating at 189.25MHz, 479.25MHz, and 743.25MHz, is used to train and evaluate the artificial neural network. A two layer neural network with one hidden and one output layer is evaluated regarding prediction accuracy and generalization properties. The path loss prediction results obtained by using the artificial neural network model are evaluated against the Hata and Walfisch-Ikegami empirical path loss models .Result analysis shows that the artificial neural network performs well as regards to prediction accuracy and generalization ability. The ANN performed better across all performance measures in comparison to the Hata and Walfisch-Ikegami and Line of Sight models in estimating path loss in vhf/uhf spectrum in Edo state.
Noise estimation has been used majorly in imaging processing and voice speech recognition applications. Therefore, researchers have found optimal solutions to non-stationary noise estimation. Particularly, there is a proposed method that estimates spectral noise in a noisy speech signal which is based on two observations; speech pauses and approximation of power spectral densities of the noisy signal to the true noise during speech pauses. Though from recent studies, the observations obtained cannot be inferred for other types of signals especially RF signals and have not been tested on signals in the frequency domain, this paper bridges that gap of research and presents the results, analysis, and conclusion on the findings concerning the noise estimation with RF signals using an extension of the proposed method in the frequency domain. It presents a detailed methodology of implementation of the minimum statistics method for noise estimation in python 3 code which was tested with RF signals and thus met the requirement of dynamic thresholding with spectrum occupancy measurement.
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