Saeed Lotfifard

A novel wavelet-based algorithm for discrimination of internal faults from magnetizing inrush currents in power transformers

In this paper, a new algorithm based on processing differential current is proposed for digital differential protection of power transformers by considering different behaviors of the differential currents under fault and inrush current conditions. In this method, a criterion function is defined in terms of difference of amplitude of wavelet coefficients over a specific frequency band. The criterion function is then used for three phases, and internal faults are precisely discriminated from inrush current less than a quarter a cycle after the disturbance; this is one advantage of the method. Another advantage of the proposed method is that the fault detection algorithm does not depend on the selection of thresholds. The merit of this method is demonstrated by simulation of different faults and switching conditions on a power transformer using PSCAD/EMTDC software. Also the proposed algorithm is tested offline using data collected from a prototype laboratory three-phase power transformer. The test results show that the new algorithm is very quick and accurate

Detection of Symmetrical Faults by Distance Relays During Power Swings

For maintaining security of distance relays, power swing blocking is necessary to prevent unintended operation under power swings. To be dependable, distance relays must operate whenever a fault occurs. Therefore, detecting faults during power swings is an important issue since the relay should be able to differentiate the fault condition and not be blocked during that time. This paper presents a new method for detecting a symmetrical fault during a power swing, based on extracting components of the current waveform using the Prony method. The merit of the method is demonstrated by simulating different faults during power swing conditions using the Alternate Transients Program version of the Electromagnetic Transients Program.

Voltage Sag Data Utilization for Distribution Fault Location

Fault location in distribution systems is an important function for outage management and service restoration directly impacting feeder reliability. In this paper, a fault location method based on matching calculated voltage sag data and data gathered at some nodes in the network is proposed. A method for characterization of voltage sags is utilized to reduce amount of transferred data. The proposed method can pinpoint fault location precisely, and is applicable to any complex distribution systems with load taps, laterals, and sub-laterals, single-phase loads, as well as networks with heterogeneous lines. The performance of the proposed method is demonstrated on the IEEE 123-node distribution test system via computer simulations in Alternate Transients Program software.

Dynamic Model Predictive-Based Energy Management of DG Integrated Distribution Systems

This paper presents a new dynamic control strategy to control active and reactive power in distributed-generation integrated distribution systems. The distribution system comprises several microgrids that have a local energy-management system. The proposed method improves voltage and frequency profile in the distribution system by applying control action ahead of time. Moreover, the proposed method can operate with minimal topology information of the microgrid as it directly balances generation and consumption of power in the microgrid. Another advantage of the proposed method is that it can use fast and expensive sources, such as gas turbine generators, to balance power during transients and let slower and cheaper generators gradually take over after the transients are damped out. The proposed method can be implemented online. Therefore, it can efficiently use the time-variant reactive capabilities of the DGs to compensate reactive power needs of the system.

A Systematic Approach for Ranking Distribution Systems Fault Location Algorithms and Eliminating False Estimates

The need for distribution reliability enhancement in the age of smart grids requires reliable methods for locating faults on distribution systems leading to a faster service restoration and maintenance cost optimization. Given the numerous fault location methods, one faces the challenge of objectively evaluating and selecting the most proper method. In this paper, a two-step approach is proposed and discussed for ranking available fault location methods that takes into account application requirements and modeling limitations and uncertainties. The ranking method formulated as uncertainty analysis utilizes 2 n + 1 point estimation to calculate the statistical moments of the fault location estimation error. These moments plugged into the Chebyshevs inequality provide a basis for ranking the fault location method. The selected method may still suffer from multiple fault location estimations. To address this caveat, voltage sag characteristics reported by few intelligent electronic devices (IEDs) along the feeder are utilized. The number and location of these IEDs are determined through an optimal approach specifically formulated for this problem. The proposed two-step ranking methodology and the IED placement optimization approach were implemented on a simulated distribution system and their effectiveness was demonstrated through a few select scenarios and case studies.

Spatiotemporal Modeling of Wind Generation for Optimal Energy Storage Sizing

Ever increasing penetration of wind power generation along with the integration of energy storage systems (ESSs) makes the successive states of the power system interdependent and more stochastic. Appropriate stochastic modeling of wind power is required to deal with the existence of uncertainty either in observations of the data (spatial) or in the characteristics that drive the evolution of the data (temporal). Particularly, for capturing spatiotemporal interdependencies and determining energy storage requirements, this paper proposes a versatile model using advanced statistical modeling based on the vine-copula theory. To tackle the complexity and computational burden of modeling high-dimensional wind data, a systematic truncation method is utilized that significantly reduces computational burden of the method while preserving the required accuracy. By constructing a graphical dependency model, unlike existing autoregressive and Markov chain models, the proposed method can replicate the exact autocorrelation function (ACF) and cross-correlation function (CCF), while retaining the correct distribution of the original data as well as the effective dependence between different sites under study. The practical importance of the proposed model is demonstrated through an example of ESS sizing for wind power.

Improved Overcurrent Protection Using Symmetrical Components

Transient currents are quite common in power systems. They can be presented for both faults and switching events. In the case of switching events, such transients should not cause an overcurrent relay operation; therefore a dependable and secure relay response becomes a critical matter. Meanwhile, proper techniques must be used to prevent undesirable relay operations due to transient current and harmonics of current in the power system. This paper uses the concept of symmetrical components to discriminate fault from nonfault events. For this purpose, a criterion function is proposed using the above-mentioned components. The advantage of the proposed algorithm is shown by simulation of some typical transient currents due to transformer energizing and induction motor starting using EMTDC/PSCAD software

Prony-Based Optimal Bayes Fault Classification of Overcurrent Protection

The development of deregulation and demand for high-quality electrical energy has lead to a new requirement in different fields of power systems. In the protection field, this means that high sensitivity and fast operation during the fault are required while maltripping of relay protection is not acceptable. One case that may lead to a maltrip of the high-sensitive overcurrent relay is the starting current of the induction motor or inrush current of the transformer. This transient current has the potential to affect the correct operation of protection relays close to the component being switched. In the case of switching events, such transients must not lead to overcurrent relay operation; therefore, a reliable and secure relay response becomes a critical matter. Meanwhile, proper techniques must be used to prevent maltripping of such relays, due to transient currents in the network. In this paper, the optimal Bayes classifier is utilized to develop a method for discriminating the fault from nonfault events. The proposed method has been designed based on extracting the modal parameters of the current waveform using the Prony method. By feeding the fundamental frequency damping and ratio of the 2nd harmonic amplitude over the fundamental harmonic amplitude to the classifier, the fault case is discriminated from the switching case. The suitable performance of this algorithm is demonstrated by simulation of different faults and switching conditions on a power system using PSCAD/EMTDC software.

Pareto Dominance-Based Multiobjective Optimization Method for Distribution Network Reconfiguration

With ever increasing deployment of automation and communication systems in smart grids, distribution network reconfiguration is becoming a viable solution for improving the operation of power grids. A novel hybrid optimization algorithm is proposed in this paper that determines Pareto frontiers, as the candidate solutions, for multiobjective distribution network reconfiguration problem. The proposed hybrid optimization algorithm combines the concept of fuzzy Pareto dominance with shuffled frog leaping algorithm (SFLA) to recognize optimal nondominated solutions identified by SFLA. The local search step of SFLA is also customized for power systems application so that it automatically creates and analyzes only the feasible and radial configurations in its optimization procedure, which significantly increases the convergence speed of the algorithm. Moreover, an adaptive reliability-based frog encoding is introduced that supervises the algorithm to concentrate on more reliable network topologies. The performance of the proposed method is demonstrated on a 136-bus electricity distribution network.

Modeling and Health Monitoring of DC Side of Photovoltaic Array

In this paper, a health monitoring method for photovoltaic (PV) systems based on probabilistic neural network (PNN) is proposed that detects and classifies short- and open-circuit faults in real time. To implement and validate the proposed method in computer programs, a new approach for modeling PV systems is proposed that only requires information from manufacturers datasheet reported under normal-operating cell temperature (NOCT) conditions and standard-operating test conditions (STCs). The proposed model precisely represents characteristics of PV systems at different temperatures, as the temperature dependency of parameters such as ideality factor, series resistance, and thermal voltage is considered in the proposed model. Although this model can be applied to a variety of applications, it is specifically used to test and validate the performance of the proposed fault detection and classification method.