Engin Karatepe

Global MPPT Scheme for Photovoltaic String Inverters Based on Restricted Voltage Window Search Algorithm

String inverter photovoltaic (PV) systems with bypass diodes require improved global maximum power point tracking (GMPPT) algorithms to effectively reach the absolute maximum power operating point. Several GMPPT algorithms have been proposed to deal with this problem, but most of them require scanning wide voltage ranges of the PV array from nearly zero voltage to open-circuit voltage that increases the scanning time and, in turn, causes energy loss. This paper presents a novel GMPPT method which significantly restricts the voltage window search range and tracks the global power peak rapidly in all shading conditions. Simulation tests and experimental comparisons with another GMPPT algorithm are presented to highlight the features of the presented approach.

Controlling of artificial neural network for fault diagnosis of photovoltaic array

High penetration of photovoltaic (PV) systems is expected to play important roles as power generation source in the near future. One of the typical deployments of PV systems is without supervisory mechanisms to monitor the physical conditions of cells or modules. In the longer term operation, the cells or modules may undergo fault conditions since they are exposure to the environment. Manually module checking is not recommended in this case because of time-consuming, less accuracy and potentially danger to the operator. Therefore, provision of early automatic diagnosis technique with quick and efficient responses is highly necessary. Since high accuracy is the important issue in the diagnosis problems, the paper present fault diagnosis method using three-layered artificial neural network. A single artificial neural network (ANN) is not suitable to provide precise solution for this fault identification. Therefore, several ANNs are developed, then automatic control based module voltage terminal is established. The proposed method is simple and accurate to detect the exact location of short-circuit condition of PV modules in array.

A new approach to fuzzy wavelet system modeling

In this paper, we propose simple but effective two different fuzzy wavelet networks (FWNs) for system identification. The FWNs combine the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and discrete wavelet transforms (DWT). The proposed FWNs consist of a set of if-then rules and, then parts are series expansion in terms of wavelets functions. In the first system, while the only one scale parameter is changing with it corresponding rule number, translation parameter sets are fixed in each rule. As for the second system, DWT is used completely by using wavelet frames. The performance of proposed fuzzy models is illustrated by examples and compared with previously published examples. Simulation results indicate the remarkable capabilities of the proposed methods. It is worth noting that the second FWN achieves high function approximation accuracy and fast convergence.

Long-term Performance Comparison of Multiple Distributed Generation Allocations Using a Clustering-based Method

Abstract Distributed generation is becoming a part of the strategic plans of electricity providers for effective system management. The proper planning of multiple distributed generation units plays an important role in modern power systems to offer a highly reliable system. Computation of power flows is one of the major tasks in system planning studies. Conventional load flow analysis methods are impractical to evaluate every possible or probable combination of loads and different allocations of distributed generation units because of the extremely large computational effort required. This article proposes an expansion method to perform load flow analysis with consideration of multiple distributed generation integration and load uncertainties. The proposed approach offers a method to handle the impacts of all possible allocations of distributed generation units without increasing computational efforts. The impacts of placement and penetration level of multiple distributed generation on power losses, voltage deviation, and line capacity are investigated under load uncertainty over a long-term period on the IEEE 57-, IEEE 30-, IEEE 14-, and 9-bus networks for future planning study purposes. The study results indicate that the proposed method has significantly reduced the computational efforts while maintaining a high degree of accuracy in evaluating various possible scenarios in which multiple distributed generation units have to be integrated into the grid.

Transmission Expansion Planning for Wind Turbine Integrated Power Systems Considering Contingency

Integration of wind turbines introduces new challenges in terms of planning criteria. In this study, a new transmission expansion planning methodology considering N-1 contingency conditions is proposed to minimize investment cost and curtailed wind energy over planning period. To deal with the uncertainty of load and output power of wind turbines, probabilistic method based on clustering is used for determination of load and wind model. To incorporate the wind power curtailment into the proposed methodology, optimal power flow which uses DC-power flow equations is utilized by including cost functions of generators and overall optimization is carried out by using an integer genetic algorithm. Finally, the proposed methodology is applied to the modified IEEE RTS 24-bus test system by considering different case studies in order to show the effects of including cost functions.

Fuzzy wavelet network identification of optimum operating point of non-crystalline silicon solar cells

The emerging non-crystalline silicon (c-Si) solar cell technologies are starting to make significant inroads into solar cell markets. Most of the researchers have focused on c-Si solar cell in maximum power points tracking applications of photovoltaic (PV) systems. However, the characteristics of non-c-Si solar cell technologies at maximum power point (MPP) have different trends in current-voltage characteristics. For this reason, determining the optimum operating point is very important for different solar cell technologies to increase the efficiency of PV systems. In this paper, it has been shown that the use of fuzzy system coupled with a discrete wavelet network in Takagi-Sugeno type model structure is capable of identifying the MPP voltage of different non-c-Si solar cells with very high accuracy. The performance of the fuzzy-wavelet network (FWN) method has been compared with other ANN structures, such as radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS) and three layered feed-forward neural network (TFFN). The simulation results show that the single FWN architecture has superior approximation accuracy over the other methods and a very good generalization capability for different operating conditions and different technologies.

Investigation of ANN performance for tracking the optimum points of PV module under partially shaded conditions

Solving partially shaded condition still remains an important task in the PV system practice. Under such condition, the global maximum point shifts continuously in wide voltage range from the local maxima, which make it difficult for conventional controllers. Intelligent techniques based on artificial neural network are well-known as the promising methods to identify the global maxima. However, there are many variants of neural networks and they have strong and weak points during the implementation. This paper investigates the performance of radial basis function (RBF) neural network and three layered feed-forward neural network (TFFN) under partial shadow operation of PV module. These two ANN structures are well-recognized for optimization, forecasting and control in PV system application due to the simplicity of structure and high performance accuracy. The investigation is focused on the network structure, training and validation process of these methods. To determine to which method is preferable to handle this task, the adaptive neuro-fuzzy inference system (ANFIS) is used as the comparator. The proposed method is verified and tested using developed real-time simulator.

MILP Approach for Bilevel Transmission and Reactive Power Planning Considering Wind Curtailment

In this study, two important planning problems in power systems that are transmission expansion and reactive power are formulated as a mixed-integer linear programming taking into account the bilevel structure due to the consideration of market clearing under several load-wind scenarios. The objective of the proposed method is to minimize the installation cost of transmission lines, reactive power sources, and the annual operation costs of conventional generators corresponding to the curtailed wind energy while maintaining the reliable system operation. Lower level problems of the bilevel structure are designated for the market clearing which is formulated by using the linearized optimal power flow equations. In order to obtain mixed-integer linear programming formulation, the so-called lower level problems are represented by using primal-dual formulation. By using the proposed method, power system planners will be able to find economical investment plans by considering the balance between wind power curtailment and the installation of transmission lines and reactive power sources.

Feasibility of Artificial Neural Network for Maximum Power Point Estimation of Non Crystalline-Si Photovoltaic Modules

#DUVTCEV—Solar cell markets are growing favorably. The emerging non crystalline silicon (c-Si) technologies are starting to make significant in-roads into solar cell markets. The most of the artificial neural network (ANN) have been used in maximum power points tracking applications for c-Si solar cell technology. However, the characteristics of different solar cell technologies at maximum power point (MPP) have different trends in currentvoltage characteristic. In this reason, the investigation of feasibility using neural networks is very important for different solar cell technologies to increase the efficiency of photovoltaic (PV) systems. The paper investigates three different ANN structures, such as radial basis function (RBF), adaptive neurofuzzy inference system (ANFIS) and three layered feed-forward neural network (TFFN) for identification the optimum operating voltage of non c-Si PV modules. These ANN models have been trained and verified for double junction amorphous Si (2j a-Si), triple junction amorphous Si (3j a-Si), Cadmium Indium Diselenide (CIS) and thin film Cadmium Telluride (CdTe) solar cell technologies. The results show that the flexibility of training process, the simplicity of network structure and the accuracy of validation error are important factors to select a suitable ANN model.

Neural network based distributed generation allocation for minimizing voltage fluctuation due to uncertainty of the output power

The problem of distributed generation (DG) allocation and sizing is of great importance, since improper integration of DG units cause to take a bad turn in terms of power quality and system efficiency at high penetration levels. In this reason, allocation of DG is not a trivial optimization problem. The reactive and active power fluctuation of DG will lead to voltage fluctuation, especially for wind or photovoltaic power generators. Their output powers are more unpredictable due to the intermittent wind speed and irradiation. In this paper, the effects of reactive and active powers of DG on voltage profile are analyzed by including their output power fluctuation and an artificial neural network (ANN) based decision support system are developed to be used in management and planning of DG integration. The proposed system can be used to determine suitable bus to reduce the voltage fluctuation of critical buses. The simulation results presented shows the effectiveness of the method.