On the Remuneration to Electrical Utilities and Budgetary Allocation for Substation Maintenance Management

The liberalization of electricity markets has produced a great change in electrical utilities. One of these changes has affected the methodology for setting their remuneration. Depending on the country, these are different. Despite the wide range of remuneration methodologies for the electricity market of each country, they all feature one common element: the remuneration of operation and maintenance. One of the messages that this remuneration transmits is the need to extend the useful life of the facilities to allow sustainable development. This article focuses on the remuneration schemes of electrical utilities, the classification of substations for the definition of their maintenance programs, and the budget allocation for the execution of maintenance in these critical infrastructures. The particularity of these facilities, in which it is generally necessary to de-energize some of their parts for maintenance, has also been taken into account. To this end, a simple methodology currently used is presented based on the standardization of the bays of the substations and their classification into levels of importance. This classification into levels enables the facilities to be grouped according to similarities in their maintenance plans, although they differ from each other in terms of the periodicity of the application of maintenance procedures. This methodology guarantees a similar distribution of maintenance activities and financial needs over the years. In addition, the methodology allows one to know the importance of each substation (since the greater the equivalent weight, the greater the importance). Finally, the application of the proposed methodology in a real case is presented. It shows the simplicity, effectiveness, and lamination of the budgetary allocation of the proposed methodology, this being the main contribution of the formulation.

Global Methodology for Electrical Utilities Maintenance Assessment Based on Risk-Informed Decision Making

Modern electrical power utilities must deal with the replacement of large portions of their assets as they reach the end of their useful life. Their assets may also become obsolete due to technological changes or due to reaching their capacity limits. Major upgrades are also often necessary due to the need to grow capacity or because of the transition to more efficient and carbon-free power alternatives. Consequently, electrical power utilities are exposed to significant risks and uncertainties that have mostly external origins. In this context, an effective framework should be developed and implemented to maximize value from assets, ensure sustainable operations and deliver adequate customer service. Recent developments show that combining the concepts of asset management and resilience offers strong potential for such a framework—not only for electrical utilities, but for industry, too. Given that the quality and continuity of service are critical factors, the concept of Value of Lost Load (VoLL) is an important indicator for assessing the value of undelivered electrical energy due to planned or unplanned outages. This paper presents a novel approach for integrating the power grid reliability simulator into a holistic framework for asset management and electrical power utility resilience. The proposed approach provides a sound foundation for Risk-Informed Decision Making in asset management. Among other things, it considers asset performance as well as the impact of both current grid topology and customer profiles on grid reliability and VoLL. A case study on a major North American electrical power utility demonstrates the applicability of the proposed methodology in assessing maintenance strategy.

Case Study on Automatic Power Factor Compensation for Industrial Power

In an electrical power grid, for an equal amount of usable power transmitted, a load with a coffee power factor draws more current than a load with a small power factor. The higher currents increase the loss of energy in the distribution network and require larger wires and other equipment. Due to the costs of larger equipment and waste energy, electrical utilities will usually charge a much higher cost to industrial or commercial customers where a low power factor is present. Low-power factor linear loads (such as induction motors) are often corrected with a passive condenser or inductor network. The present drawn from the system is distorted by non-linear loads, like rectifiers. In such cases, the correction of the active or passive power factor may also counteract the distortion and lift the factor of the facility. The facility factor correction devices may also be installed at a central substation, opened over a distribution grid, or built into power-consuming equipment.

Ham Radio Forms a Planet-Sized Space Weather Sensor Network

For researchers who monitor the effects of solar activity on Earth’s atmosphere, telecommunications, and electrical utilities, amateur radio signals a golden age of crowdsourced science.

Traceable measurements of harmonic (2 to 150) kHz emissions in smart grids: uncertainty calculation

The necessity of measuring harmonic emissions between 2 and 150 kHz is outlined by several standard committees and electrical utilities. This paper presents a measurement system and its traceable characterization designed to acquire and analyse voltages up to 230 V and currents up to 100 A with harmonics up to 150 kHz that may occur in smart grids. The uncertainty estimation is carried out and described in detail for both the fundamental and supraharmonics components. From a metrological point of view, ensuring the traceability of current measurements for frequencies higher than 100 kHz and dealing with the complexity of uncertainty determination are bottlenecks related to supraharmonics measurements that this paper proposes an approach to deal with.

Fault Detection of a Medium Voltage Cable Joint using Support Vector Machine Algorithm

Fault detection of the cable joints is one of significant problems in the electrical utilities and industrial companies to increase the network stability as the system interruption can make side effects for both power generation units, renewable energy generation units and other power sources beside of the costumers. In this paper, fault detection of a 20kV XLPE cable joint had been studied using the measured partial discharge (PD) signals and also support vector machine algorithm. In this study, the measured data had been classified based on proposed features as the indices of data classification and they had been used in the classifier algorithm to determine fault based on measured signals and the corresponding obtained features. The results show that the proposed features and applied algorithm could determine the faults in the cable joints with an appropriate range of accuracy. This study could develop the previous studies on a widely used cable joint. This research can be helpful for the electrical utilities to increase network stability

Classification neutral to ground (NG) voltage using levenberg-marquardt neural network (LMNN)

<span>In electrical systems nowadays, power quality issues have become a major concern for customers and electrical utilities. The high Neutral to ground (NG) voltage are one of the power quality issues which could cause adverse effect such as malfunction the devices, neutral overheating and electricity shock. Thus, the high NG voltage should be classified in order to perform the mitigation work accurately. This paper presents the classification of neutral to ground voltage using Levenberg-Marquardt Neural Network (LMNN) technique. The Discrete Wavelet Transform (DWT) is applied in this method to extract the features of NG voltage which needed in classification process. The result shows the LMNN perform accurately in classify the types of NG voltage, where its accuracy result is reach more than 90% accuracy.</span>