The high-voltage circuit breaker (HV CB) constitutes an important repairable asset in power systems. Due to its great number in the power network and strategic role in the successful operation of power systems, it is important to ensure its reliability. This paper deals with the estimation of the failure process of CBs considering the impact of age, operation intensity, corrective maintenance, and examination. A general model that integrates the above factors' effects is proposed, which can be reduced to many specific models like virtual age models, scale models, the two combinations, and their regression analysis. The model estimation under the condition of random censoring population data is illustrated, including parameters, reliability indicators, the cumulative intensity function, and goodness-of-fit tests. Moreover, a general procedure to stepwise select valuable effect functions into the general model is presented, thereby obtaining a specific formulation fitting the given data. The above works are applied to a real case to discuss the most probable failure process of the given HV CBs.

Data play an essential role in asset management decisions. The amount of data is increasing through accumu-lating historical data records, new measuring devices, and communication technology, notably with the evolution toward smart grids. Consequently, the management of data quantity and quality is becoming even more relevant for asset managers to meet efficiency and reliability requirements for power grids. In this work, we propose an innovative data quality management framework enabling asset managers (i) to quantify the impact of poor data quality, and (ii) to determine the conditions under which an investment in data quality improvement is required. To this end, an algorithm is used to determine the optimal year for component replacement based on three scenarios, a Reference scenario, an Imperfect information scenario, and an Investment in higher data quality scenario. Our results indicate that (i) the impact on the optimal year of replacement is the highest for middleaged components; (ii) the profitability of investments in data quality improvement depends on various factors, including data quality, and the cost of investment in data quality improvement. Finally, we discuss the implementation of the proposed models to control data quality in practice, while taking into account real-world technological and economic limitations.

In March 2019 the joint working group A3.43 "Tools for lifecycle management of T&D switchgear based on data from condition monitoring systems" started its activities with the goal to provide inside three years a guide on how to use the data from monitoring activities of switchgear.

The working group counts presently 34 members, from 17 different countries met already five times and work is ongoing on the main Technical Brochure (TB) structure as follows: 

1. Executive summary 

2. Introduction

3. Literature Review

4. Condition indicators of T&D switchgear

5. Tools and Criteria for lifecycle management and Switchgear Health Index

6. Compliance with digital substation for data management

7. User’s experiences of end-of-life of T&D switchgear: survey, case studies and interviews

8. Future needs and trends

A literature review offers a glimpse into state of the art tools and techniques for lifecycle management and the equipment used. A very steep increase of publications on this matter is observed in the last years with implications in other aspects beyond lifecycle management.  Second, the condition indicators as determined in the TB737 are taken over and used in this work as proxies for the switchgear variables evaluation.

The main part of the work concentrates on the tools and techniques their framework established. The works from other WG’s on substation level on B3 study committees are introduced as an organic part of the activities. A fundamental decision has been made to follow Health Index concepts as defined in B3.48 concentrating on providing valuable inputs specifically for switchgear. The main focus is on Tools and Criteria for lifecycle management for which the target is to propose models which can condense the specific and deep knowledge of the members to arrive to an advanced and reliable assessment. Some concrete examples are planned to act as guidelines on how to apply the theory to real life scenarios. 

The digitization of substation equipment is an ongoing process, which is radically changing how assets will be controlled in the substation.  IEC 61850 is today the followed standard on which this automation revolution is based for operation and protection.  Four members from study committee B5 are part of the group specifically to identify the potential benefit of a technology which has not been driven by monitoring.  User experiences are under evaluation. Preliminary results, not presented here, have shown mixed opinions between use and necessity.

Finally, the future trends identified so far are discussed.  The role of condition monitoring systems as enablers for future developments in the networks is discussed. 

In the electric system, the consumption varies throughout the year, during the week and during the day. The consumption should be balanced by the production, which is not easy with solar power and wind power, as they lack storage like the dams for hydropower. Then these sources should be modelled as time series. The time series analyzed is the solar and wind power production in Sweden during 5 months. The method is called Peaks over Threshold or POT and it calculates the frequency of peaks above a certain threshold. It also determines the distribution of the size of the peaks, which is the generalized Pareto distribution in this case. Two different clustering methods are tried; one by Lindgren and the other one a modification of Leadbetter's method. The latter one gives the best fit. However, some ten years of data should be analyzed in order to include the seasonal effects.

As smart grids develop, power systems become more complex, and the role of data gain considerable importance for the reliability of power supply. Thus, data processing techniques have to be investigated and compared to increase the efficiency of asset management decisions. In this paper, we explore several black-box models in order to predict power exchanges through a high-voltage direct-current line between Sweden and Denmark, using publicly available data on loads and power prices. An auto-regressive moving average with external input model based on load data provides the most accurate forecasts according to mean square error and other selected criteria. This is the first step to build a more comprehensive model that will also include other technical data such as maintenance and unplanned outages, but also macroeconomic factors. The final goal is to provide network operators with a parsimonious sequential model composed of several modules giving accurate predictions that support efficient asset management decisions. 

Reliable power supply is one of the crucial factors for the development of any industry. Different technologies as well as configurations are available when it comes to investing in new electrical assets. According to [1], the circuit configurations of high voltage substations are strongly influenced by many factors, such as operational requirements, security standards, availability, maintainability, etc. There is always a trade-off between the technical requirements and the cost of investment. This paper aims to quantify the availability of a switchyard with two lines and two transformer bays for various switching configurations. Once the overall availability is determined, the decision for the selection of a switching configuration is made based on the cost-benefit analysis.

The voltage rating of a primary substation is decided based on the scale of an industry. Switching configuration is one of the major factors affecting the overall availability of the substation. Each switching configuration has its own pros and cons in terms of its operation, reliability and investment cost. It is impossible to achieve 100% availability of a substation with any kind of switching scheme. Availability and investment cost of a substation vary with the choice of the configuration and substation technology. There are mainly three available technologies - Air Insulated Switchgear (AIS), Gas Insulated Switchgear (GIS) and Mixed Technology Switchgear (MTS).

This paper evaluates possible choices for an example of supply which is very often seen in practice, providing guidelines for decision-making process in order to optimize profit of an industry, from the supply-reliability point of view. In this paper, a 220/66kV AIS substation for an upcoming process plant is under consideration. The scope of this paper is limited to assess the availability of primary side of the substation. There are two-line bays and two transformer bays in the 220kV switchyard. Down-stream to the 66kV side is not taken into consideration for the analysis purpose.

JWG A3.32 with CIRED completed the Technical Brochure, which provides comprehensive information on Non-Intrusive Condition assessment of the T&D switchgear that can be applied on HV and MV circuit breakers, HV and MV switching equipment, MV reclosers and fault interrupters. Many emerging non-intrusive techniques were reported in the literature, most of them allowing in service evaluation. The JWG reviewed the current and future trends of non-intrusive, especially in service, diagnostic methods completing the work on Condition Assessment of Switchgears dealt in TB 167 and on Online Monitoring HV Substations available in TB 462. The brochure will provide useful information for everybody who is going to apply or improve Condition Assessment Methods to Switchgears.

Thermography or infra-red imaging is a method that measures the temperature of a surface by receiving the infra-red radiation that the surface emits. Thermography is used in, for example, condition measuring of electrical equipment. It shows which parts are heated more than normally due to a higher resistance. Those parts will need maintenance.

In order to get accurate values, thermography needs a high current in the equipment. Thus it is necessary to predict when the current will be high throughout the year. Here a neural network with two layers is used for the prediction. The data set consists of the hourly currents at a point in a Swedish substation from a period of ten years.

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The purpose is to plan when to go to a substation to do thermography. As the prediction is done several months ahead, the outdoor temperature cannot be used. Hence only the time expressed as week, day and hour with different resolutions in the discretization, is used as an explanatory variable. With increasing resolutions in the discretization, the prediction error decreases. Adding inputs based on interaction does not improve the prediction. The results are however not satisfactory as the prediction error is large in comparison with the predicted values of the current and the prediction is biased. One reason is that the prediction should be several months ahead, so the actual temperature cannot be used.

This paper presents a method that can be used to rank disconnectors in the electric power system, according to the condition of one of the most critical parts, the contacts. The temperature of the contacts can be continuously measured by a new type of infrared sensor. For the case study, 54 sensors were placed on six disconnectors together with reference sensors measuring the ambient temperature. The measured temperature at each contact is then regressed against the ambient temperature and the current through the disconnector. Both a linear and a quadratic dependency of the current are tested and the quadratic one explains the data best. The coefficient of the current serves as a measure of the condition of the contact. Then, it becomes possible to identify in which contacts the resistance has increased the most. As a consequence, it is possible to make better maintenance decisions for the disconnectors.