A Secure-Coordinated Expansion Planning of Generation and Transmission Using Game Theory and Minimum Singular Value

In this paper a novel method have been proposed for expansion planning of generation and transmission that considered static security of the system such as voltage security margin And loadability limit. In the same study of expansion planning Security constraints of the system are neglected. In this study at the first step minimum singular value technique is used to evaluate voltage security margin and loadabolity limit, in order to select best bus for load incrimination. After it, in order to Supply the load, coordinated expansion planning of generation and transmission is needed, therefor the strategic interaction between transmission company (TransCo) and generation company (GenCo) for Transmission expansion planning (TEP) and generation expansion planning (GEP) in a competitive electricity market is proposed using Game Theory (GT).DOI:http://dx.doi.org/10.11591/ijece.v4i6.668

A Novel Method for Frequency Estimation Considering Instrument Transient Effect

Large disturbances in power systems cause deviation in the frequency from the nominal value. Since the frequency is an important factor in the electrical network parameter measurements, it can cause malfunction of the protection system. In addition, Because of decaying DC and oscillatory components that introduced by CCVT in response of voltage variation during the fault occurrence, cause changes in the value of received voltage of primary side of CCVT. An improved least square method for estimating frequency is presented in this paper. In order to reduce the effect of this transient component, phasor estimation method has been improved by using the least square technique and utilizing knowledge of CCVT design. The capability of the proposed method was verified by several case studies generating signals in PSCAD/EMTDC. The results show the accuracy, speed and capability of the proposed metho

A Novel method to estimate Economic Replacing Time of Transformer Using Monte Carlo Algorithm and ANN

A hybrid method for developing a more principled approach is presented to determine the life expectancy of transformers. The approach is constructed on an economic analysis of the transformers operational characteristics in combination  with the technical issues incorporated in the decision process. In this method, firstly life time of transformer is estimated using a hybrid method based on Monte Carlo algorithm and artificial neural network. Also Pareto distribution function is applied to consider health history of transformer and uncertainty in DP behavior of transformer. In the next step, a method is proposed in order to estimate economic replacement time of transformer. This method is based on the well-known bathtub failure model, containing repairs and scheduled maintenance, in order to achieve at a more economically aim  replacing decision. This aim is obtained in part by considering the uncertainty intrinsic in transformer failures and the corresponding discontinuations in power. In essence, this method organizes a decision support system for determination the life expectancy of a transformer. Simulation results show the high accuracy and functionality of the proposed approach in estimating economic replacing time of the Transformer

Extended Kalman filter-based approach for Nodal pricing in active distribution networks

This article presents an analytical approach based on Extended Kalman Filter (EKF) for nodal pricing in distribution networks containing private distributed generation (DG). An appropriate nodal pricing policy can direct active distribution network (ADN) to optimal operation mode with minimum loss. However, there are several crucial challenges in nodal pricing model such as: equitable loss allocation between DGs, obtain minimum merchandising surplus (MS), and equitable distribution of remuneration between DGs, which is difficult to achieve these goals simultaneously. However, in the proposed method, the issue was embedded in the form of the EKF updates. The measurement update reduces the MS, and in the time update, DG's nodal prices as state variables are modified based on their contribution to the loss reduction. Therefore, all aspects of the problem are considered and modeled simultaneously, which will prepare a realistic state estimation tool for distribution companies in the next step of operation. The proposed method also has the ability to determine the nodal prices for distribution network buses in a wide range of power supply point prices (PSP), which other methods have been failed, especially at very low or high PSP prices. Eventually, using the new method will move system towards to the minimum possible losses with the equitable condition. The application of the proposed nodal pricing method is illustrated on 17-bus radial distribution test systems, and the results are compared with other methods.©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Divergence Distance Based Index for Discriminating Inrush and Internal Fault Currents in Power Transformers

This paper puts forward a new algorithm based on Kullback Leibler divergence (KLD) for discriminating inrush and internal fault currents in power transformers. Specifically, the main idea of this algorithm is to utilize the current signal discrepancy of the distribution with ideal fault current waveforms. To such an aim, the proposed index reproduces the differential current signal. Utilizing fast modified least squares technique (MLSE), the differential current signal is generated. Applying the reconstructed differential current and ideal sinusoidal waveforms to the KLD indicator, the distribution discrepancy of the current signal from ideal sinusoidal waveform discriminates inrush and internal fault currents. Also the internal/external identification (IEI) index is introduced that utilizes the phase content of the signals from CTs on both sides to distinguish internal and external faults, especially the faults accompanied with CT saturation. As a result, the proposed method can distinguish inrush currents, internal and external fault currents with/without current transformer (CT) saturation and internal fault currents during transformer energization. The performance of the proposed index is evaluated using practically recorded data and is further compared with the state-of-the-art

Empirical mode decomposition based algorithm for islanding detection in micro-grids

Owning to extensively enhancement of renewable energy resources in the distribution grids, the employment of such sources is also associated with various issues such as the islanding problem. In this paper, an effective method has been proposed for detection of the islanding in the micro-grids comprised of inverter or direct fed types of distributed generations. The proposed method is designed based on the intrinsic modes of the voltage signal measured at the PCC point. More specifically, through the calculation of the positive sequence of the voltage signal variation (PSVSV) and extracting the signal energy of PSVSV's intrinsic modes, the islanding can be detected. The superiority of the proposed islanding detection method is manifested in the condition where the generation of distributed resources is in balance with the loading consumption. The performance of the proposed method has been evaluated considering the conditions where islanding is difficult to detect or might be mistaken with other phenomena given by loads within the NDZ region, different fault types, and loads with different power factors. The performance evaluation has been carried out through simulations, and furthermore has been compared with the state-of-the-art algorithms

Statistical Discrimination Index Founded on Rate of Change of Phase Angle for Immunization of Transformer Differential Protection Against Inrush Current

Magnetizing inrush currents are known as the most critical potential threat in maloperation of the transformer differential protection. Therefore, there is a need to detect the inrush current from the internal fault current to avoid the differential protection maloperation. Due to the possibility of a current transformer (CT) saturation at a high level of inrush and fault current, the discrimination algorithm should function appropriately in CT saturation conditions. Here, a method is developed based on the rate of phase angle change (RoCoPA) to perform the discrimination process. The proposed discrimination index employs the RoCoPA of the three-phase differential currents and calculates the standard deviations of the RoCoPA of the three-phase differential currents. The discrimination procedure is designed based on the fact that during fault scenarios, the signal remains almost sinusoidal, and as a result, the RoCoPA has the minimum variations. On the contrary, owing to the non-sinusoidal wave shape of the inrush current, the RoCoPA has notable variations. Based on the various simulated and practical scenarios, the proposed index's performance is evaluated considering challenging scenarios. The results reveal that the proposed discrimination index has promising accuracy with average response delay with about half a cycle

Differential Protection Algorithm Founded on Kalman Filter Based Phase Tracking

Owing to the large magnitude of the inrush currents, the functionality of differential protection of power transformer may be threatened in correct discrimination between inrush and internal faults. This paper enhances the functionality of differential protection through a computationally efficient method that utilizes the phase angle current signals of the current transformers (CTs). The proposed discrimination criterion (PDC) tracks the fundamental phase angle of the current signals of the CTs. While during an internal fault, the phase angles of both CTs are almost constant and in phase, during the external fault and inrush currents, the phase angles of both CTs have notable distance. On this ground, this paper employs a developed Kalman filter based phase angle estimator to measure the fundamental phase angles of the current signals of the CTs. Afterward, PDC is introduced that measures the distance between the estimated phase angles of the CT’s currents. Evaluating the effectiveness of the PDC with several simulated and experimental recorded current signals reveals the PDC is able to detect internal faults even in presence of CT saturation and also to deal with inrush and external fault currents

Robust Islanding Detection in Microgrids Employing Rate of Change of Kinetic Energy Over Reactive Power

This study tries to outline a comprehensive approach established on the kinetic energy of the distributed generations (DGs) for islanding mode detection in micro-grids. More specifically, the proposed index calculates the rate of change of kinetic energy over reactive power (ROCOKORP) for each DG at the point of common coupling (PCC). Despite the islanding detection being the most challenging issue during complete match between the load and generation of DGs, the proposed method can swiftly deal with such an issue, therefore confirming its superiority. Other challenging conditions where islanding detection can either be difficult or confused with other events such as loads inside the non-detection zone (NDZ), various fault types, and loads at various power factors have also been considered to investigate the effectivity of the proposed approach. The proposed method’s performance has been verified and evaluated in comparison to the state-of-the-art via a simulation testbed

A Sub-Cycle phase angle distance measure algorithm for power transformer differential protection

Power transformer differential protection may confront mal-functioning in authentic discrimination between inrush and internal faults. To tackle the latter mal-functioning, a new two-stages algorithm based on phase content of the current signal of the current transformers (CTs) is put forward. The proposed algorithm is designed based on the fact that the fundamental phase angle of a fault signal ideally remains constant during the fault. However, during inrush cases, the phase angle varies. Also, during the internal fault, the phase angles of the current signals of CTs are in phase while during the external fault, the phase angles of the current signals of CTs are 180° out of phase. In the first stage, the proposed algorithm calculates the fundamental phase angles of the current signals of the CTs using sub-cycle modified recursive least squares (MRLS). Afterward, normalized mean residue (NMR) is employed to measure distance between the estimated phase angles of the CT’s currents. MRLS and NMR algorithms require limited samples (i.e. 10 and 5 samples respectively) for executing their calculations. Performance evaluation with simulated and experimental recorded current signals shows the ability of the proposed method in discrimination of the internal faults from inrush and external fault currents