Performance evaluation of 200W solar photovoltaic panel considering Bauchi microclimatic conditions

Measurement and modeling of broadband and spectral terrestrial solar radiation is important for the evaluation and deployment of solar renewable energy systems. This paper focuses on optimizing the performance of 200W solar module taking into consideration the local climatic conditions of Bauchi locality. The uncertainty in life cycle savings for solar thermal and photovoltaic (PV) systems as linearly correlated with uncertainty in solar resource data. These uncertainties paved way for the need to conduct a critical assessment of the resource. Assessment of the solar resource for these technologies rely upon measured data, where available. In this paper, we present the development of mathematical model of photovoltaic solar cells based on their detailed single diode equivalent circuit representation. Pertinent simulation models for PV solar module both for an ideal weather situation and for taking into consideration the effects of microclimatic conditions that prevail in Bauchi as evaluated and compared with benchmarks available. The complete model of the PV system was implemented using MATLAB/Simulink platform. The standard characteristic curves for the 200W solar panel are as presented. The simulation of the ideal PV system made use of standard test conditions (STC) to facilitate comparison with the existing benchmark results in the literature. The analysis of the characteristics performance curves returned an average VOC = 42.9v and ISC = 4.21A. The simulation results further revealed that the power delivered by the 200W monocrystalline solar module of 144.3W @620W/m2, 35ºC as recorded for Bauchi under all climatic conditions as evaluated. The benchmark values obtained in the laboratory are VOC = 45.5V, ISC = 5.92A and 200-W under the Standard test condition (STC) conditions of cell temperature 25ºC, solar irradiance of 1000W/m2 and air mass (AM) of 1.5. The average conversion efficiency and fill factor as evaluated are 0.77 and 16% respectively. This result agrees with the benchmark of module efficiency of >15.66% recorded at STC. The results conclusively reveal that the microclimate of a locality essentially affects the performances of solar PV systems deployed to each location on the globe. Therefore, utilization of these parameters is essential for consideration in the design of solar systems in all localities

Simulation Model for Assessing Transient Performance of Capacitive Voltage Transformers

In determining the correct operation of relays of a protection scheme, proper representation of instrument transformers and their behavior in conditions where there can be transient is very critical. This paper presents a simulation model for assessing the transient performance of capacitive voltage transformers (CVTs). In order to test the validity of the developed model, four CVT operational conditions are considered using field data collected from one of the Nigerian electric utility 330/132/33 kV substations. The model simulation results revealed various configuration performance responses that could affect relay protection schemes to different degrees. As expected, the CVT responses showed that faults initiated at zero voltage crossing, which is the worst transient condition, produced transient voltage magnitude up to 40% of the nominal voltage while faults initiated at the crest produced minimum transient voltage magnitude. It is shown that the model developed for the selected instrument transformer yielded satisfactory results

Adaptive Load Frequency Control of Nigerian Hydrothermal System Using Unsupervised and Supervised Learning Neural Networks

This work presents a novel load frequency control design approach for a two-area power system that relies on unsupervised and supervised learning neural network structure. Central to this approach is the prediction of the load disturbance of each area at every minute interval that is uniquely assigned to a cluster via unsupervised learning process. The controller feedback gains corresponding to each cluster center are determined using modal control technique. Thereafter, supervised learning neural network (SLNN) is employed to learn the mapping between each cluster center and its feedback gains. A real time load disturbance in either or both areas activates the appropriate SLNN to generate the corresponding feedback gains. The effectiveness of the control framework is evaluated on the Nigerian hydrothermal system. Several far-reaching simulation results obtained from the test system are presented and discussed to highlight the advantages of the proposed approach

Reactive Power and Voltage Control of the Nigerian Grid System using Micro-Genetic Algorithm

In this paper, a micro-genetic based approach to the optimization of reactive power and voltage profiles improvement and real power loss minimization is presented. The reactive power control devices such as generators, tap positions of on-load tap changer of transformers, shunt reactors are used to correct voltage limits violations while simultaneously reducing the system real power losses. Genetic algorithms (GAs) are well-known global search techniques anchored on the mechanisms of natural selection and genetics. Because of the time intensive nature of the conventional GA, the micro-GA is proposed as a more time efficient alternative. The feasibility and effectiveness of the developed algorithm is tested and verified on the Nigerian grid power system for three case studies scenarios preset in the power world simulator. The far-reaching simulation results that validate the effectiveness of the developed tool are presented and discussed in depth

Particle Swarm Optimization Tuned Flatness-Based Generator Excitation Controller

An optimal transient controller for a synchronous generator in a multi-machine power system is designed using the concept of flatness-based feedback linearization in this paper. The computation of the flat output and corresponding controller for reduced order model of the synchronous generator is presented. The required feedback gains used to close the linearization loop is optimized using particle swarm optimization for maximum damping. Typical results obtained for transient disturbances on a two-area, four-generator power system equipped with the proposed controller on one generator and conventional power system stabilizers on the remaining generators are presented. The effectiveness of the flatness-based controller for multi-machine power systems is discussed

Comparative Application of Differential Evolution and Particle Swarm Techniques to Reactive Power and Voltage Control

This paper presents the comparative application of two metaheuristic approaches: Differential Evolution (DE) and Particle Swarm Optimization (PSO) to the solution of the reactive power and voltage control problem. Efficient distribution of reactive power in an electric network leads to minimization of the system losses and improvement of the system voltage profile. It can be achieved by varying the excitation of generators or the on-load tap changer positions of transformers as well as by switching of discrete portions of inductors or capacitors etc. This constitutes a typical mixed integer non-linear optimization problem for the solution of which metaheuristic techniques have proven well suited in principle. The feasibility, effectiveness and generic nature of both DE and PSO approaches investigated are exemplarily demonstrated on the Nigerian grid system and the New England power system. Comparisons were made between the two approaches in terms of the solution quality and convergence characteristics. The simulation results revealed that both approaches were able to remove the voltage limit violations, but PSO procured in some instances slightly higher power loss reduction as compared with DE; on the other hand DE required a lower number of function evaluations as compared with PSO. Consideration of computational effort is relevant for potential real time on line application

Computational Enhancement of Genetic Algorithm Via Control Device Pre-Selection Mechanism for Power System Reactive Power/Voltage Control

In this paper, the application of a novel and computationally enhances genetic algorithm (GA) for solving the reactive power dispatch problem is presented. In order to attain a significant reduction in the computational time of GA, a systematic procedure of reactive power control device pre-selection mechanism is herein proposed to choose a-priori subsets of the available control devices, which maximally influence buses experiencing voltage limit violations. The GA reactive power dispatch module then accesses such judiciously pre-selected control device candidates to determine their optimal settings. A pragmatic scheme aimed at further curtailing the number of the final control actions entertained is also set forth. The far-reaching simulation results obtained for two case study scenarios using the proposed algorithmic procedures on a German utility network of Duisburg, replicated on an operator-training simulator, are presented and fully discussed in depth

Differential Evolution Approach for Reactive Power Optimization of Nigerian Grid System

The goal of reactive power dispatch is to minimize the system losses and improve the system voltage profiles at all times. This is achieved by adjusting various generating units\u27 excitation systems continuously, discrete tap positions of on-load tap changers of transformers as well as switching of correct doses of inductors or capacitors. This is a mixed integer non-linear optimization problem. In this paper, the differential evolution (DE), a novel evolutionary computation technique which was originally designed for continuous problems is applied to solve this problem. DE appears to ally qualities of established computational intelligence (CI) techniques with a more striking computational performance, thus suggesting the possibility of having the potential for on line applications in the control center; comparison work with other techniques is presently conducted. The developed tool was demonstrated on the Nigerian power system grid for three case scenarios preset on the power world simulator which was linked with DE for power flow calculation (fitness check of solutions). The results achieved revealed that DE procured a significant reduction of real power losses while simultaneously keeping the voltage profiles within the acceptable limits

Genetic Algorithms Based Economic Dispatch with Application to Coordination of Nigerian Thermal Power Plants

The main focus of this paper is on the application of genetic algorithm (GA) to search for an optimal solution to a realistically formulated economic dispatch (ED) problem. GA is a global search technique based on principles inspired from the genetic and evolution mechanism observed in natural biological systems. A major drawback of the conventional GA (CGA) approach is that it can be time consuming. The micro-GA (µGA) approach has been proposed as a better time efficient alternative for some engineering problems. The effectiveness of CGA and µGA. to solving ED problem is initially verified on an IEEE 3-generating unit, 6-bus test system. Simulation results obtained on this network using CGA and µGA validate their effectiveness when compared with the published results obtained via the classical and the Hopfield neural network approaches. Finally, both GA approaches have been successfully applied to the coordination of the Nigerian 31-bus system fed by four thermal and three hydro generating units. Herein, use has been made of the loss formula developed for the Nigerian system from several power flow studies. For the Nigerian case study, the µGA. is shown to exhibit superior performance than the CGA from both optimal generation allocations and computational time viewpoints

Neural Network Modeling and Simulation of A 265W Photovoltaic Array

ABSTRACT This paper presents the Neural Network modeling and simulation of a 265 Watts photovoltaic array installed at the Faculty of Engineering and Engineering Technology of Abubakar Tafawa Balewa University, Bauchi, Nigeria. Hitherto, Mathematical modeling is the favoured method for characterizing photovoltaic (PV) arrays. This approach would require detailed information on the physical parameters relating to the solar cell material, which may not be readily available. Even in situations where the required information is provided on the manufacturer's datasheet, it tends not to be very accurate as it is not representative of the actual field performance of the array. Thus results obtained from mathematical modeling of photovoltaic arrays are only accurate to the extent of the accuracy of the model parameters. A better PV array characterization approach is to use Neural Network modeling because it does not require any physical definitions of the array and hence has the potential to provide a superior method of characterization than the already established conventional techniques. In this paper, two Radial Basis Function Neural Network (RBFNN) trained models are employed to simulate the performance of a 265 Watts photovoltaic array. The first model predicts the array I-V and P-V curves while the second predicts its maximum power for all operating weather conditions. Results of array performance plots show close correlation with those obtained through conventional mathematical modeling. RBFNN returned absolute errors of 1.794 %, 1.594 % and 1.262 % with respect to PV maximum power predictions for harmattan, cloudy and clear sunny seasons respectively