Youakim Kalaani

Anti-islanding techniques for Inverter-based Distributed Generation systems - A survey

The increased penetration of Inverter-based Distributed Generation (DG) in Smart Grid systems requires an adequate level of monitoring and detection especially under islanding conditions. Islanding occurs when a DG system is disconnected from the rest of the power grid. These DG systems are usually independently owned and controlled, thus when islanding occurs, the electric utility loses control and supervision over that section of the power grid. As a consequence, islanding can present serious safety hazard since a presumed disconnected power line can still unexpectedly be fed by nearby DG sources. Furthermore, prolonged islanding can also prevent reconnection to the power grid and may cause damage due to voltage and frequency excursions. Therefore, islanding detection, which is also called “Anti-Islanding”, is one of the most critical aspects of the integration of DG sources into the power grid. There has been considerable research on developing detection techniques, however, recent breakthroughs in this field have resulted in significant modifications to the Anti-Islanding taxonomy which is the subject of this investigation. In this paper, a comprehensive survey was conducted with the objective of highlighting the latest Anti-Islanding techniques presented in the literature. Extensive comparisons of improvements and limitations of these new techniques was provided. Finally, open research areas in this field were identified.

A passive islanding detection approach for inverter-based distributed generation using rate of change of frequency analysis

Islanding occurs when a Distributed Generation (DG) source continues to energize an isolated section of a power system even after it was disconnected from the main power grid. Since islanding can cause hazardous conditions to people and equipment, current utility standards require that islanding be quickly detected by protective relays and inverters that are parts of the DG system. Passive islanding detection techniques, unlike their active counterparts, monitor system parameters without injecting any disturbance into the grid. Although widely used, passive detection techniques are not very effective in detecting islanding especially in cases where there is small power mismatch and they also may trigger false detection in some non-islanding cases. To address these drawbacks, a novel and effective passive islanding detection technique that conforms to standard regulations has been presented in this paper. The proposed detection technique is based on monitoring the oscillations in the Rate of Change of Frequency (ROCOF) measured at the Point of Common Coupling (PCC) in the system. The proposed detection technique was developed and tested on a grid connected photovoltaic DG system using simulation. Results indicated that this technique was not only capable of detecting islanding when it occurs but also able to accurately distinguish between islanding and non-islanding under a wide range of operating conditions.

An artificial Neural Network model for wind energy estimation

Wind energy resources are ideally suited for distributed generation systems to provide electricity for residential use. This paper proposes a novel method for wind energy estimation in the state of Georgia. This method is based on Artificial Neural Network (ANN) using real data obtained from several weather station sites around the state. The proposed ANN model was trained and then tested using a local station located in Savannah. The ANN inputs are elevation, latitude, longitude, day, temperatures (min/max), and the output is the daily wind speed. The model was efficiently implemented in Simulink environment using closed-form algebraic equations which eliminated the need for repeated training. The ANN model was formulated with suitable numbers of layers/neurons which was trained and tested with excellent regression constant. Furthermore, the ANN model has the ability to interpolate between learning curves to generate wind speed estimates for different locations. It is anticipated that this model will be able to successfully select sites for wind turbine installations for residential applications in the state of Georgia.

A novel passive islanding detection technique for converter-based distributed generation systems

With the increased popularity of distributed generation (DG) in power systems, issues such as unintentional islanding should be efficiently resolved. Islanding condition occurs when part of the electrical power system is disconnected from the rest of the grid and is still energized by a DG unit. Conventional passive islanding detection approaches utilize the power system parameters such as the change of voltage magnitude, frequency deviation, and voltage phase displacement. The main challenge in these approaches is the dependency of these parameters on the islanding conditions which can render some of these approaches ineffective. This paper proposes a novel and a computationally inexpensive passive islanding detection technique for converter-based distributed generation systems. The proposed technique utilizes the converter-induced ripples in the instantaneous voltage amplitude at the point of common coupling to detect islanding. The proposed technique was modeled in a converter-based DG network with photo-voltaic arrays. The proposed technique model was tested using a wide range of islanding and non-islanding conditions and was able to accurately detect islanding under all DG loading conditions.

A technical and economical study of implementing a micro-grid system at an educational institution

Micro-grids are among the basic components in the future distributed generation smart grid systems. The transmission efficiency improvements and the utilization of renewable energy sources are some of the key advantages of using micro-grids in power systems. In this paper, the technical and economical study of implementing a micro-grid system at an educational institution is discussed and presented. Multiple types of distributed generators were considered including gas-fired generator units, solar photovoltaic, fuel cell, and bio-power systems. Several simulations were conducted using HOMER and a detailed economic analysis of implementing a micro-grid system is presented including recommended actions.

Voltage Ripple-Based Passive Islanding Detection Technique for Grid-Connected Photovoltaic Inverters

One of the main challenges of integrating distributed generation into the power grid is islanding, which occurs when a disconnected power line is adversely energized by a local distributed generation source. If islanding is not quickly detected, it can present serious safety and hazardous conditions. Conventional passive detection techniques used today are entirely dependent on the parameters of the power system, which under certain operating conditions may fail to detect islanding. In this paper, a novel and efficient passive islanding detection technique for grid-connected photovoltaic-based inverters is presented. In this technique, the ripple content of the inverter output voltage at the point of common coupling is monitored for deviations using time-domain spectral analysis. Islanding is then detected whenever the ripple spectral content exceeds a preset threshold level for a certain period of time. The performance of this technique was extensively tested and quantified under a wide range of operating conditions. It was determined that the proposed technique did not exhibit any non-detection zone and was able to detect all types of islanding cases within 300 ms of the allowed delay time. Furthermore, the proposed technique was found to be robust and inherently immune to other degrading factors, since it is relatively independent of system parameters, power system scaling, or the number of distributed generation sources present within the islanding zone.

Sizing residential photovoltaic systems in the State of Georgia

This paper proposes a novel photovoltaic (PV) distributed generation design process to optimally size and select the PV system characteristics including energy storage capacities using Artificial Neural Network (ANN). This process is designed to be functional for a wide range of electrical loads and solar irradiance values for residential houses in the State of Georgia. Under this system, two neural network models have been implemented. The first ANN model is used to size the PV house system using inputs such as load requirement (kWh/day) and solar radiation (kWh/m2/day). The outputs of this model are the area needed for PV installation, the peak PV power capacity, number of modules, battery storage capacity, and the battery Ampere Hour. The second ANN model uses the rated power from the first model to select the PV system parameters using a large database of commercially available PV modules. The evaluated PV parameters are summarized as follows: the open-circuit voltage, short-circuit current, maximum voltage and current, cell efficiency, module efficiency, and the number of cells needed for the system. Simulink models were created using a set of algebraic equations that were derived to generate the sizing parameters without the need for retraining the network every time. ANN models were implemented with optimal number of layers and neurons, which were trained, simulated, and verified with 99.99% regression accuracy.

Optimum ANN Empirical Model of Capacitive Deionization Desalination Unit

Capacitive deionisation (CDI) has emerged as a robust energy efficient for water desalination. In this paper, a novel CDI electrosorption process is proposed to increase the efficiency based on real experimental data. It is achieved by artificial neural network (ANN) to develop four models. For problem formulation, closed forms mathematical equations were derived, thus, resulting in a very efficient programming algorithm. Optimum patterns ANN models were validated by implementing two ANN units to drive the CDI electrosorption process. This proposed method was tested and verified using actual and predicted ANN values which yielded excellent results with regression factors between 0.99983 to 1. Optimum patterns are validated in the form characteristics comparisons between genetic and original one. The ANN models their algebraic equations are adopted for various characteristics estimation process. They created with suitable numbers of layers and neurons that provided fast and accurate network training.

Performance impact analysis of solar cell retrofitted electric golf cart

The utilization of golf carts is gaining wide popularity especially in places like universities and large corporations. The conventional gasoline powered golf carts are inefficient and produce very harmful emissions to the environment. Electric golf carts, on the other hand, are quite efficient and environmentally friendly. However, they do not have a long driving range and the batteries need to be frequently recharged. This paper presents a thorough performance analysis of a typical electric golf cart retrofitted with a 100W solar panel. The electrical power requirements, energy savings, and the environmental pollution prevented were also investigated. In this analysis, the performance improvement in the driving range of the golf cart was tested using a series of experiments with and without the solar panel. These experimental results indicated that using a fleet of solar-powered electric golf carts will result in significant energy savings and reduce pollution taking the institution a step forward towards a greener environment.