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The drive by the government of Tanzania to electrify every village has resulted into expansion of the electrical secondary distribution networks (ESDNs). Therefore, maintenance management is of the highest priority for the smooth operation of the ESDNs to reduce unscheduled downtime and unexpected mechanical failures. Studies show that condition-based predictive maintenance (CBPdM) method allows the utility company to monitor, analyze and process the information obtained from ESDNs transformers. Thus, this study adopts the CBPdM method to develop a maintenance scheduling algorithm that can predict the transformer state, forecast maintenance time based on transformer load profile and schedule its maintenance using a knowledge-based system (KBS). Applying the challenge driven education approach, the requirements for developing an algorithm were established through an extensive literature survey and engagement of the key stakeholders from the Tanzania utility company. Our study uses the Dissolved Gas Analysis tool to collect the transformer parameters used in algorithm design. The parameter analysis was performed using Statistical Package for Social Sciences software. Results show that the designed KBS algorithm minimizes human-related maintenance errors and lowers labour costs as the system makes all the maintenance decisions. Specifically, the proposed maintenance scheduling algorithm reduces downtime maintenance costs by 1.45 times relative to the classical inspection-based maintenance model while significantly saving the maintenance costs. [ABSTRACT FROM AUTHOR]

For over a decade, distributed generations (DGs) have sufficiently convinced the researchers that they are the economic and environment-friendly solution that can be integrated with the centralized generations. The optimal planning of distributed generations requires the appropriate location and sizing and their corresponding control with various power network types to obtain the best of the technical, economical, commercial, and regulatory objectives. Most of these objectives are conflicting in nature and require multi-objective solutions. Therefore, this paper brings a comprehensive literature review and a critical analysis of the state of the art of the optimal multi-objective planning of DG installation in the power network with different objective functions and their constraints. The paper considers the adoption of optimization techniques for distributed generation planning in radial distribution systems from different power system performance viewpoints; it considers the use of different DG types, distribution models, DG variables, and mathematical formulations; and it considers the participation of different countries in the stated DG placement and sizing problem. Moreover, the summary of the literature review and critical analysis of this article helps the researchers and engineers to explore the research gap and to find the future recommendations for the robust optimal planning of the DGs working with various objectives and algorithms. The paper considers the adoption of uncertainties on the load and generation side, the introduction of DGs with energy storage backups, and the testing of DG placement and sizing on large and complex distribution networks. [ABSTRACT FROM AUTHOR]

Domination in graphs has been extensively studied and adopted in many real life applications. The monitoring electrical power system is a variant of a domination problem called power domination problem. Another variant is the zero forcing problem. Determining minimum cardinality of a power dominating set and zero forcing set in a graph are the power domination problem and zero forcing problem, respectively. Both problems are N P -complete. In this paper, we compute the power domination number and the zero forcing number for fully connected cubic networks. [ABSTRACT FROM AUTHOR]

A vertex set S is called a power dominating set of a graph G if every vertex within the system is monitored by the set S following a collection of rules for power grid monitoring. The power domination number of G is the order of a minimal power dominating set of G. In this paper, we solve the power domination number for splitting and degree splitting graph. [ABSTRACT FROM AUTHOR]

This paper presents the idea of a combined analysis of long-term power quality data using cluster analysis (CA) and global power quality indices (GPQIs). The aim of the proposed method is to obtain a solution for the automatic identification and assessment of different power quality condition levels that may be caused by different working conditions of an observed electrical power network (EPN). CA is used for identifying the period when the power quality data represents a different level. GPQIs are proposed to calculate a simplified assessment of the power quality condition of the data collected using CA. Two proposed global power quality indices have been introduced for this purpose, one for 10-min aggregated data and the other for events—the aggregated data index (ADI) and the flagged data index (FDI), respectively. In order to investigate the advantages and disadvantages of the proposed method, several investigations were performed, using real measurements in an electrical power network with distributed generation (DG) supplying the copper mining industry. The investigations assessed the proposed method, examining whether it could identify the impact of DG and other network working conditions on power quality level conditions. The obtained results indicate that the proposed method is a suitable tool for quick comparison between data collected in the identified clusters. Additionally, the proposed method is implemented for the data collected from many measurement points belonging to the observed area of an EPN in a simultaneous and synchronous way. Thus, the proposed method can also be considered for power quality assessment and is an alternative approach to the classic multiparameter analysis of power quality data addressed to particular measurement points.

High-voltage equipment involves both electrical and electronic components. In electrical power network, which consists of rotating machine, power transformers and transmission lines, field enhancement at critical regions can lead to local breakdown [partial discharges (PD)]. The continuous occurrence of PDs can lead to complete breakdown. While in large power equipment sharp edges can be avoided, this is not the case in power converter due to the miniature nature of the semiconductor device. Sharp edges can also be present in any power equipment in the shape of conducting particles, either stuck at a barrier or freely moving in the bulk oil. This creates high-field regions, prone to PD activities. Different power equipment operates at different voltages such as AC, DC, square voltage, pulse voltage, fast-rise transient voltage etc. This study presents the influence of sinusoidal voltage, slow- and fast-rise square voltage on PDs in two different geometries using optical PD measurement technique. Fast-rise square voltage has the lowest PD inception voltage while the sinusoidal voltage has the highest. This is may be due to the influence of homo- and hetero-charges. Fast-rise square voltage displayed higher PD magnitude at inception which may be connected to the rise time of the voltage.

Abstract The lack of a comprehensive decision-making approach at the community level is an important problem that warrants immediate attention. Network-level decision-making algorithms need to solve large-scale optimization problems that pose computational challenges. The complexity of the optimization problems increases when various sources of uncertainty are considered. This research introduces a sequential discrete optimization approach, as a decision-making framework at the community level for recovery management. The proposed mathematical approach leverages approximate dynamic programming along with heuristics for the determination of recovery actions. Our methodology overcomes the curse of dimensionality and manages multi-state, large-scale infrastructure systems following disasters. We also provide computational results showing that our methodology not only incorporates recovery policies of responsible public and private entities within the community but also substantially enhances the performance of their underlying strategies with limited resources. The methodology can be implemented efficiently to identify near-optimal recovery decisions following a severe earthquake based on multiple objectives for an electrical power network of a testbed community coarsely modeled after Gilroy, California, United States. The proposed optimization method supports risk-informed community decision makers within chaotic post-hazard circumstances. [ABSTRACT FROM AUTHOR]

The emulation of low‐loss or lossless one‐dimensional (1D) or 2D transmission mediums using analogue sampled‐data signal processing is presented. Based on discrete‐time wave propagation simulation, transmission lines are emulated with many elementary identical delay elements, implemented by simple equivalent switched‐capacitor (SC) circuits. The accuracy and limitations of this discrete time model are studied in the frame of power network fault location using electromagnetic time‐reversal principle. The sensitivities to non‐ideal effects usually plaguing analogue CMOS SC circuits, such as amplifier finite open‐loop gain, offset, and parasitic charge injection due to clock feedthrough, are evaluated in the same context. It is shown that the SC line emulation is well suited to the presented fault location technique and considerably reduces the fault location time (by a factor up to 100) in comparison to standard digital solutions, allowing fault location resolutions of typically 1% within a few hundred milliseconds. These expectations are confirmed by measurements realised on the presented line model integrated‐circuit, implemented in an AMS 0.35 μm CMOS process. The speed improvement obtained through the presented method is essential, potentially allowing real‐time fault management in power grids. [ABSTRACT FROM AUTHOR]

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This paper presents the idea of a combined analysis of long-term power quality data using cluster analysis (CA) and global power quality indices (GPQIs). The aim of the proposed method is to obtain a solution for the automatic identification and assessment of different power quality condition levels that may be caused by different working conditions of an observed electrical power network (EPN). CA is used for identifying the period when the power quality data represents a different level. GPQIs are proposed to calculate a simplified assessment of the power quality condition of the data collected using CA. Two proposed global power quality indices have been introduced for this purpose, one for 10-min aggregated data and the other for events—the aggregated data index (ADI) and the flagged data index (FDI), respectively. In order to investigate the advantages and disadvantages of the proposed method, several investigations were performed, using real measurements in an electrical power network with distributed generation (DG) supplying the copper mining industry. The investigations assessed the proposed method, examining whether it could identify the impact of DG and other network working conditions on power quality level conditions. The obtained results indicate that the proposed method is a suitable tool for quick comparison between data collected in the identified clusters. Additionally, the proposed method is implemented for the data collected from many measurement points belonging to the observed area of an EPN in a simultaneous and synchronous way. Thus, the proposed method can also be considered for power quality assessment and is an alternative approach to the classic multiparameter analysis of power quality data addressed to particular measurement points. [ABSTRACT FROM AUTHOR]