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Power transformer monitoring is an essential asset management routine for reliable operation of transmission and distribution networks. Over the last two decades, several mathematical and soft computing-based health assessing approaches have been developed based on various power transformer diagnostic tests. However, the tests data correlation analysis and attributes prioritization are not yet adequately established in the current transformer health estimation methods. Therefore, this article presents a hybrid analytic hierarchy process (AHP)-based grey relational analysis as a potential multicriteria decision-making tool for transformer health assessment. The proposed approach facilitates a comparative correlation between transformer samples and attributes prioritization with authenticated hierarchal analysis. The closely relatable attributes are grouped into three distinct grades to ensure transparency. These grades are further correlated using an artificial intelligence (AI) fuzzy logic (FL) technique. Through this integration, reliable numerical health indices can be obtained to help classify the transformer health condition. Moreover, the general complexity issues observed in forming expert rules with high number of FL inputs are completely nullified with reduced three input grades and maximum input–output feasible fuzzy combination. The proposed hybrid model is validated using preknown health conditions of 730 transformers assessed by Saudi electric company in Gulf region and compared with some transformer health prediction methods published in the literatures. Comparison reveals the superiority of the proposed model that achieved a prediction accuracy more than 96%. Contrary to other techniques published in the literature, the proposed model does not call for extensive training data and can facilitate real-time condition monitoring of power transformers.

Substation Automation System (SAS) is currently in a matured state of technology that shall facilitate transformational changes from conventional protection scheme. IEC 61850 protocol is considered as the crux of digital SAS due to its multifunction features that include seamless communication, ability to integrate various intelligent electronic devices, potential for improved real-time condition monitoring, reliable protection, and control of critical electrical assets. Because the application of IEC 61850 in SAS is relatively new and has not fully implemented in many substations yet, further feasibility studies using multivendor equipment to assess its performance under different operating conditions is imperative. In this article, a practical reliable and efficient reverse blocking over current bus bar protection scheme based on IEC 61850 is implemented and tested. Also, a comparison of digital SAS and conventional protection scheme is presented to highlight the superiority of the former one. Experimental results attest the reliability and effectiveness of the proposed digital protection scheme along with the accuracy and security of transmitting data packets using sampled values and generic objective-oriented substation event communication protocols adopted by IEC 61850.

Optimum allocation of flexible AC transmission system (FACTS) can improve the power grids performances such as available transmission capacity (ATC) and voltage stability. Optimal allocation of FACTS with multiple optimization objectives for power systems comprise multiple random variables is still a challenging task to be solved. This paper derives a scenario generation method for systems containing multiple random variables first. Then, a thyristor-controlled series capacitor (TCSC) multi-objective optimal allocation model with ATC and voltage stability L index as an objective function is established. By adding the chaos initialization and the variable inertia weight setting, an improved multi-objective particle swarm optimization (MOPSO) algorithm is also proposed to easily solve the established model. Finally, based on the improved IEEE-30 bus system, the non-inferior solutions of multiple system scenarios are compared and analyzed. Simulation results show that the proposed scenario processing method, the TCSC multi-objective optimal allocation model and the improved MOPSO algorithm are effective in solving related problems.

Background: Winding deformation is one of the most common faults an operating power transformer experiences over its operational life. Thus, it is essential to detect and rectify such faults at early stages to avoid potential catastrophic consequences to the transformer. At present, methods published in the literature for transformer winding fault diagnosis are mainly focused on identifying fault type and quantifying its extent without giving much attention to the identification of fault location. Methods: This paper presents a method based on a genetic algorithm and support vector machine (GA-SVM) to improve the faults’ classification of power transformers in terms of type and location. In this regard, a sinusoidal sweep signal in the frequency range of 600 kHz to 1MHz is applied to one terminal of the transformer winding. A mathematical index of the induced current at the head and end of the transformer winding under various fault conditions is used to extract unique features that are fed to a Support Vector Machine (SVM) model for training. Parameters of the SVM model are optimized using a Genetic Algorithm (GA). Results: The effectiveness of mathematical indicators to extract fault type characteristics and the proposed fault classification model for fault diagnosis is demonstrated through extensive simulation analysis for various transformer winding faults at different locations. Conclusion: The proposed model can effectively identify different fault types and determine their location within the transformer winding, and the diagnostic rates of the fault type and fault location are 100% and 90%, respectively.

Short-term irradiance variability because of the passing clouds of unknown size, direction, and speed is a key issue for power grid planners because of the unexpected fluctuation in the generated power of photovoltaic (PV) systems. In order to handle this issue, several models have been presented in the literature to estimate the variability of the PV systems output power during cloudy and partial shading events. However, the estimation error of all presented models in the literature is relatively high. To comply with the utility guidelines of limiting the PV systems generated power variability to a level less than 10% of the PV systems capacity per minute, a more accurate power estimation model is essential to precisely calculate the required energy storage backup. In this article, a new model to estimate the output power of a group of rooftop PV systems during cloudy events using only one sensor is proposed. In this regard, two new strategies are adopted. First, day time is divided into three-time segments in which each segment exhibits almost the same weather conditions. Second, the data of each time series are analyzed using the wavelet transform to divide them into high and low-frequency modes. The frequency modes are then used to train a gene expression programming model to estimate the entire generated power of the PV systems. The proposed model features higher accuracy than other models presented in the literature. A sensitivity analysis is performed to quantify the effect of various parameters on the accuracy of the proposed model whose robustness is validated through practical data collected from a group of distributed rooftop PV systems in Brisbane, QLD, Australia. Results reveal that the maximum average mean absolute error of the proposed model is 7.49%, whereas it is 12.12% for the existing models in the literature.

The issue of power quality is one of the prominent factors in determining the reliability of power systems. It is inevitable to consider power quality issues and include appropriate mitigation techniques in the design of any future power system project construction. Voltage sag and flicker are the two most common short disturbance types of power quality issues that frequently occur in power systems. A widely accepted mitigation strategy to enhance the power quality of disturbed networks is by connecting a flexible ac transmission system. In this paper, a superconducting magnetic energy storage (SMES) unit along with hysteresis-fuzzy controller to suppress the severe impacts of voltage sag and flicker events on the power quality of a power system is proposed, implemented, and tested. The proposed SMES configuration with hysteresis current control and fuzzy logic controller is effective, simple, and easy to implement. Simulation results show the ability of the proposed SMES controller in compensating sag and/or flicker distortions.

The very fast transient overvoltage/overcurrent (VFTO/VFTC) signals generated during disconnectors switching operation may result in a significant threat to the reliability of power grids. While there are several commercial types of disconnectors, there is still a lack of understanding of the electromagnetic interference characteristics that is generated during the very fast transient process of the disconnectors. Based on the transient voltage and current data of disconnector capacitive small current test, this paper extracts the characteristics of the corresponding macropulse and monopulse waveforms. Also, the paper presents a comprehensive statistical analysis of the transient waveform characteristic data through probability density function fitting and Kolmogorov–Smirnov (K-S) test of the sample cumulative empirical distribution. The statistical results show that the probability density distribution of the switching operation macropulse waveform follows generalized extreme value and inverse gaussian distribution. The maximum peak values of VFTO and VFTC monopulse are found to be 50 kV and 370 A with a respective rise time at the range of nano- and micro- seconds. The frequency bands of VFTO and VFTC macro waveforms are basically distributed within 16 MHz. Results of this paper can pave the way into a more scientific basis for the design of disconnectors considering the strong electromagnetic disturbance and the formulation of system level electromagnetic compatibility standard tests, ensure the safe and stable operation of the power grid.

Towards addressing the concerns of conventional power systems including reliability and security, establishing modern Smart Grids (SGs) has been given much attention by the global electric utility applications during the last few years. One of the key advantageous of SGs is its ability for two-way communication and bi-directional power flow that facilitates the inclusion of distributed energy resources, real time monitoring and self-healing systems. As such, the SG employs a large number of digital devices that are installed at various locations to enrich the observability and controllability of the system. This calls for the necessity of employing Internet of Things (IoT) to achieve reliable integration of all digital devices and proper tracing of various apparatuses in the grid. In this paper, the communication technology, architectural design, cutting-edge applications, and protocols of IoT-assisted SG systems are comprehensively reviewed. The main concerns, future challenges, and research gaps of IoT-assisted smart grid systems are highlighted in detail. Based on this review, key findings are concluded to pave the path for further research directions on IoT-assisted SG systems.

In this paper, optimized hybrid off-grid renewable energy systems (HRES) have been designed for two divisional locations in Dhaka and Khulna in Bangladesh. An analysis is conducted using five different load dispatch strategies to find the best dispatch strategy for a cost-effective and technically feasible Islanded hybrid microgrid that will support the growing power demand. A comparative analysis among the various dispatch strategies is also presented to find out the best and worst dispatch strategies for the proposed HRESs. The two HRESs consist of solar PV, a backup diesel generator, wind turbine, load demand of 23.31 kW, and a battery storage system. HOMER predictive strategy, Generator Order, Load Following, Combined Dispatch, and Cycle Charging dispatch strategy has been adopted for evaluation. The two HRESs are optimized for CO2emission, Levelized Cost of Energy, and Net Present Cost minimization along with a feasible and stable voltage-frequency response on basis of the five dispatch techniques. The techno-economic analysis of the two HRESs is conducted using the HOMER Pro software platform. For the power system response analysis, MATLAB/Simulink is used. This study provides a complete guideline for determining the optimum component sizing ensuring optimum operation and possible costing estimation for the optimized performance of the two HRESs for different dispatch techniques. Also, a comparative study among the designed HRESs, other hybrid systems, and conventional power plants is conducted to prove the significance of this research work.

AbstractThis paper investigates the performance of a recently proposed online transformer internal fault detection technique through detailed non-linear three-dimensional finite element modelling of the windings, magnetic core and transformer tank. The online technique considers correlation of transformer instantaneous input and output voltage difference and input current at the power frequency and uses the ellipse shape ΔV-I locus as the finger print of the transformer that could be measured every cycle to identify any incipient faults. The technique is simple, fast and suitable for online monitoring of in-service transformers. A detailed three-dimensional finite element model of single-phase transformer is developed and various physical winding deformations with different fault levels are simulated to assess their impacts on the online ΔV-I locus. Astransformer field testing under various internal fault conditions cannot be easily conducted, the main contributions of this paper are accurate finite element based implementation, testing and performance evaluation of the online fault detection approach. Furthermore, the impact of winding short circuit fault on the ΔV-I locus has been also measured and validated.