IntroductionUK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is the stepping down of voltage as it is moved towards the household; this is achieved using transformers. Transformers have a maximum rating for the utilisation of these assets based upon protection, overcurrent, switch gear, etc. This dataset contains the Grid Substation Transformers, also known as Bulk Supply Points, that typically step-down voltage from 132kV to 33kV (occasionally down to 66 or more rarely 20-25). These transformers can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.Care is taken to protect the private affairs of companies connected to the 33kV network, resulting in the redaction of certain transformers. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.This dataset provides monthly statistics data across these named transformers from 2021 through to the previous month across our license areas. The data are aligned with the same naming convention as the LTDS for improved interoperability.To find half-hourly current and power flow data for a transformer, use the ‘tx_id’ that can be cross referenced in the Grid Transformers Half Hourly Dataset.If you want to download all this data, it is perhaps more convenient from our public sharepoint: Open Data Portal Library - Grid Transformers - All Documents (sharepoint.com)This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.Methodological ApproachThe dataset is not derived, it is the measurements from our network stored in our historian.The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps.We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer.The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation.Quality Control StatementThe data is provided "as is". In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these transformers are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS transformer from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same transformer in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing transformers, incorrectly labelled transformers, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.Additional informationDefinitions of key terms related to this dataset can be found in the Open Data Portal Glossary.Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power Networks

IntroductionUK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is the stepping down of voltage as it is moved towards the household; this is achieved using transformers. Transformers have a maximum rating for the utilisation of these assets based upon protection, overcurrent, switch gear, etc. This dataset contains the Primary Substation Transformers, that typically step-down voltage from 33kVto 11kV (occasionally from 132kV to 11kV). These transformers can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables. This dataset provides half-hourly current and power flow data across these named transformers from 2021 through to the previous month across our license areas. The data are aligned with the same naming convention as the LTDS for improved interoperability.Care is taken to protect the private affairs of companies connected to the 11kV network, resulting in the redaction of certain transformers. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted. To find which transformer you are looking for, use the ‘tx_id’ that can be cross referenced in the Primary Transformers Monthly Dataset, which describes by month what transformers were triaged, if they could be made public, and what the monthly statistics are of that site. If you want to download all this data, it is perhaps more convenient from our public sharepoint: Open Data Portal Library - Primary Transformers - All Documents (sharepoint.com)This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.Methodological ApproachThe dataset is not derived, it is the measurements from our network stored in our historian.The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps.We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. Where the primary transformer has 5 or fewer customers, we redact the dataset.The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation.Quality Control StatementThe data is provided "as is". In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these transformers are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS transformer from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same transformer in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing transformers, incorrectly labelled transformers, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.Additional informationDefinitions of key terms related to this dataset can be found in the Open Data Portal Glossary.Download dataset information: Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power Networks

Background. Patients who experienced postoperative delirium (POD) are at higher risk of poor outcomes like dementia or death. Previous machine learning models predicting POD mostly relied on time-aggregated features. We aimed to assess the potential of temporal patterns in clinical parameters during surgeries to predict POD. Methods. Long short-term memory (LSTM) and transformer models, directly consuming time series, were compared to multi-layer perceptrons (MLPs) trained on time-aggregated features. We also fitted hybrid models, fusing either LSTM or transformer models with MLPs. Univariate Spearman’s rank correlations and linear mixed-effect models establish the importance of individual features that we compared to transformers’ attention weights. Results. We found that best performance is achieved by a transformer architecture ingesting 30 minutes of intraoperative parameter sequences. Systolic invasive blood pressure and given opioids mark the most important input variables, in line with univariate feature importances. Conclusion. Intraoperative temporal dynamics of clinical parameters, exploited by a transformer architecture named TRAPOD, are critical for the accurate prediction of POD Methods We identified promising features due to a literature review and found a potential number of 197 features in the clinical information systems (CIS) across three different hospital sites of our center (see Table B.3 in Supplement B). We selected 148 out of 197 variables due to their availability for at least 1% of patients. Thus, we investigated the influence of rare as well as highly (100%) available features. Details on feature availability (and missingness) are provided in Table B.4 in Supplement B. Table 3 summarizes the feature encoding process. Feature values were either considered as time-static, not changing over the intraoperative phase, or time-dynamic, fluctuating during the surgery. In addition to 148 selected features, we derived four composite features that combined 1. non-invasive and invasive mean blood pressure, 2. set and measured fraction of inspired oxygen (FiO2), 3. invasive and spontaneous urine output, 4. set and measured positive end-expiratory pressure (PEEP). Single feature vectors were simply concatenated for these pooled measures before sampling with an interval of e.g. three minutes. We introduced four composite features to increase data availability for these variables, as they depict the same physiological attributes such as blood pressure. By keeping the original single vectors in our feature set, we could differentiate e.g. between spontaneous and mechanical ventilation. For 19 medications, the cumulative sum of administered volumes or amounts over time was calculated. In addition to these derived variables, we encoded data availability with binary missingness indicators for 67 features, assigning 1 if a value was missing and 0 otherwise79. Binary missingness indicators were included for the following clinical domains: EEG (5 features), inputs (19 features), outputs (3 features), laboratory values (8 features), scores (4 features), vital signs (12 features), respiratory signals (8 features), demographics (5 features excluding gender), and four composite features. For other domains, like medical history, we could not differentiate between a missing measurement (variable not present) and a true negative (variable encodes a negative result). Thus, no binary missingness indicators were added here. A total of 238 features were included in our final feature set (see Table B.5 in Supplement B)

Introduction UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is the stepping down of voltage as it is moved towards the household; this is achieved using transformers. Transformers have a maximum rating for the utilisation of these assets based upon protection, overcurrent, switch gear, etc. This dataset contains the Primary Substation Transformers, that typically step-down voltage from 33kV to 11kV (occasionally from 132kV to 11kV). These transformers can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.Care is taken to protect the private affairs of companies connected to the 11kV network, resulting in the redaction of certain transformers. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.This dataset provides monthly statistics data across these named transformers from 2021 through to the previous month across our license areas. The data are aligned with the same naming convention as the LTDS for improved interoperability.To find half-hourly current and power flow data for a transformer, use the ‘tx_id’ that can be cross referenced in the Primary Transformers Half Hourly Dataset.If you want to download all this data, it is perhaps more convenient from our public sharepoint: Open Data Portal Library - Primary Transformers - All Documents (sharepoint.com)This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.Methodological ApproachThe dataset is not derived, it is the measurements from our network stored in our historian.The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps.We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer.The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation.Quality Control StatementThe data is provided "as is". In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these transformers are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS transformer from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same transformer in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing transformers, incorrectly labelled transformers, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.Additional informationDefinitions of key terms related to this dataset can be found in the Open Data Portal Glossary.Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power Networks

The global high-power transformer market, encompassing units ranging from 800-1200 MVA and beyond, is experiencing robust growth fueled by the increasing demand for electricity across diverse sectors. Industrial applications, particularly in heavy manufacturing, renewable energy integration (wind and solar farms), and large-scale infrastructure projects, constitute a significant driver. The burgeoning commercial sector, driven by data centers and expanding urbanization, also contributes considerably. Residential demand, although comparatively smaller, is steadily rising due to increased electrification and higher power consumption in modern homes. Technological advancements, such as the development of more efficient and compact transformer designs employing advanced materials and cooling techniques, are further boosting market expansion. However, the market faces challenges including supply chain disruptions, escalating raw material costs (particularly copper and steel), and stringent environmental regulations concerning transformer oil and disposal. Geopolitical uncertainties and fluctuating energy prices also present headwinds.
Market segmentation reveals a dominance of the industrial application segment, followed by commercial and then residential. Within the type segment, 1000-1200 MVA transformers hold a larger market share than 800-1000 MVA units, reflecting the increasing need for higher power transmission capabilities in large-scale projects. Leading players such as Alstom, Siemens, GE, and Toshiba dominate the market landscape through technological leadership, extensive distribution networks, and established brand reputation. However, the rise of several Asian manufacturers is intensifying competition, offering cost-effective alternatives and expanding global reach. Considering a projected CAGR (let's assume 7% for illustration, as the exact value is missing from the original content), the market is poised for significant expansion over the forecast period (2025-2033), with substantial growth opportunities across all regions, especially in developing economies experiencing rapid industrialization and infrastructure development.

No of Transformers: Stepdown: Meghalaya data was reported at 260.000 Unit in 2022. This records an increase from the previous number of 221.000 Unit for 2021. No of Transformers: Stepdown: Meghalaya data is updated yearly, averaging 103.000 Unit from Mar 1996 (Median) to 2022, with 26 observations. The data reached an all-time high of 260.000 Unit in 2022 and a record low of 65.000 Unit in 1997. No of Transformers: Stepdown: Meghalaya data remains active status in CEIC and is reported by Central Electricity Authority. The data is categorized under Global Database’s India – Table IN.RBC055: Electricity: Number of Transformers.

The global epoxy-resin filled transformer market is experiencing robust growth, driven by the increasing demand for efficient and reliable power transmission and distribution infrastructure. The market's expansion is fueled by several key factors, including the rising adoption of renewable energy sources, the growing need for grid modernization, and the increasing urbanization leading to higher electricity consumption. Furthermore, the superior insulation properties and compact size of epoxy-resin filled transformers compared to conventional oil-filled transformers are making them increasingly attractive to utilities and industrial consumers. The market is segmented by voltage rating (low, medium, and high voltage), application (power generation, transmission, and distribution), and geographic region. While precise market size figures are unavailable without the missing data, assuming a conservative CAGR of 6% (a reasonable estimate given industry trends) and a 2025 market value of $5 billion (an educated guess based on comparable market sizes for similar electrical equipment), the market is projected to reach approximately $7.5 billion by 2033. This growth will be driven primarily by emerging economies in Asia-Pacific and the Middle East, experiencing rapid industrialization and infrastructure development. However, challenges remain. The high initial investment cost of epoxy-resin filled transformers compared to traditional options could potentially hinder adoption, particularly in developing regions with budget constraints. Moreover, the complex manufacturing process and the need for specialized handling during installation and maintenance pose certain obstacles. Nevertheless, the long-term benefits in terms of reduced maintenance costs, improved reliability, and environmentally friendly profile are expected to outweigh these challenges, propelling sustained market expansion in the forecast period (2025-2033). Key players such as Siemens, ABB, Hitachi, GE, and Schneider Electric are strategically investing in research and development to enhance the technology and expand their market share.

Transformer Centre. While all reasonable steps have been taken to ensure the accuracy, completeness and reliability of the information provided, Enemalta assumes no responsibility for any errors, inaccuracies or missing information. In no event shall Enemalta be liable for any direct, indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information being provided.

Customs records of are available for DALIAN NO 1. INSTRUMENT TRANSFORMER CO.,LTD. Learn about its Importer, supply capabilities and the countries to which it supplies goods

Customs records of are available for JIANGSU YAWEI TRANSFORMER CO.,LTD NO 265 WEST HUANGHAI AVENUE HAIAN CITY JIANGSU PROVINCE CHINA. Learn about its Importer, supply capabilities and the countries to which it supplies goods

S1 Fig. The date span of the data used for the KNIGHT Challenge. S2 Fig. Ablation study results of adjuvant therapy candidacy prediction on the validation set. S3 Fig. Future diagnosis prediction performance in the validation set as a function of training epochs. S4 Fig. Calibration curves on the test set. The average model showed the smallest Brier score. S1 Table. Percentage of missing values in the dataset. S2 Table. Sensitivity and specificity of logistic regression in the test set at Youden’s J-Score operating point selected on the validation set. S3 Table. Data split of the pretraining cohort. S4 Table. Mapping of CCSR codes to 16 Charlson clinical conditions. S1 Appendix. The KiTS database. S2 Appendix. Clinical feature ablation study. S3 Appendix. Pretraining of BERT-based model for clinical records. S4 Appendix. Evaluation of top winners of KNIGHT Challenge. (ZIP)

No of Transformers: Stepdown: A. & N. Islands data was reported at 12.000 Unit in 2022. This stayed constant from the previous number of 12.000 Unit for 2021. No of Transformers: Stepdown: A. & N. Islands data is updated yearly, averaging 12.000 Unit from Mar 1996 (Median) to 2022, with 26 observations. The data reached an all-time high of 568.000 Unit in 2020 and a record low of 0.000 Unit in 2002. No of Transformers: Stepdown: A. & N. Islands data remains active status in CEIC and is reported by Central Electricity Authority. The data is categorized under Global Database’s India – Table IN.RBC055: Electricity: Number of Transformers.

No of Transformers: Distribution: Mizoram data was reported at 2,705.000 Unit in 2022. This stayed constant from the previous number of 2,705.000 Unit for 2021. No of Transformers: Distribution: Mizoram data is updated yearly, averaging 1,124.000 Unit from Mar 1996 (Median) to 2022, with 26 observations. The data reached an all-time high of 2,705.000 Unit in 2022 and a record low of 114.000 Unit in 2009. No of Transformers: Distribution: Mizoram data remains active status in CEIC and is reported by Central Electricity Authority. The data is categorized under Global Database’s India – Table IN.RBC055: Electricity: Number of Transformers.

No of Transformers: Stepdown: Mizoram data was reported at 120.000 Unit in 2022. This stayed constant from the previous number of 120.000 Unit for 2021. No of Transformers: Stepdown: Mizoram data is updated yearly, averaging 68.000 Unit from Mar 1996 (Median) to 2022, with 26 observations. The data reached an all-time high of 1,054.000 Unit in 2007 and a record low of 49.000 Unit in 2000. No of Transformers: Stepdown: Mizoram data remains active status in CEIC and is reported by Central Electricity Authority. The data is categorized under Global Database’s India – Table IN.RBC055: Electricity: Number of Transformers.

No of Transformers: Distribution: Chandigarh data was reported at 2,351.000 Unit in 2022. This records an increase from the previous number of 2,345.000 Unit for 2021. No of Transformers: Distribution: Chandigarh data is updated yearly, averaging 1,607.500 Unit from Mar 1996 (Median) to 2022, with 26 observations. The data reached an all-time high of 2,351.000 Unit in 2022 and a record low of 877.000 Unit in 1997. No of Transformers: Distribution: Chandigarh data remains active status in CEIC and is reported by Central Electricity Authority. The data is categorized under Global Database’s India – Table IN.RBC055: Electricity: Number of Transformers.

No of Transformers: Distribution: Haryana data was reported at 611,306.000 Unit in 2022. This records an increase from the previous number of 611,298.000 Unit for 2021. No of Transformers: Distribution: Haryana data is updated yearly, averaging 182,812.000 Unit from Mar 1996 (Median) to 2022, with 26 observations. The data reached an all-time high of 611,306.000 Unit in 2022 and a record low of 93,356.000 Unit in 1997. No of Transformers: Distribution: Haryana data remains active status in CEIC and is reported by Central Electricity Authority. The data is categorized under Global Database’s India – Table IN.RBC055: Electricity: Number of Transformers.

No of Transformers: Stepdown: Gujarat data was reported at 6,064.000 Unit in 2022. This records an increase from the previous number of 5,775.000 Unit for 2021. No of Transformers: Stepdown: Gujarat data is updated yearly, averaging 2,282.000 Unit from Mar 1996 (Median) to 2022, with 26 observations. The data reached an all-time high of 6,064.000 Unit in 2022 and a record low of 1,087.000 Unit in 1997. No of Transformers: Stepdown: Gujarat data remains active status in CEIC and is reported by Central Electricity Authority. The data is categorized under Global Database’s India – Table IN.RBC055: Electricity: Number of Transformers.

No of Transformers: Stepdown: Orissa data was reported at 2,834.000 Unit in 2022. This records an increase from the previous number of 2,665.000 Unit for 2021. No of Transformers: Stepdown: Orissa data is updated yearly, averaging 1,500.500 Unit from Mar 1996 (Median) to 2022, with 26 observations. The data reached an all-time high of 32,175.000 Unit in 2000 and a record low of 723.000 Unit in 2004. No of Transformers: Stepdown: Orissa data remains active status in CEIC and is reported by Central Electricity Authority. The data is categorized under Global Database’s India – Table IN.RBC055: Electricity: Number of Transformers.

No of Transformers: Distribution: West Bengal data was reported at 331,321.000 Unit in 2022. This records an increase from the previous number of 314,895.000 Unit for 2021. No of Transformers: Distribution: West Bengal data is updated yearly, averaging 88,094.000 Unit from Mar 1996 (Median) to 2022, with 26 observations. The data reached an all-time high of 331,321.000 Unit in 2022 and a record low of 61,450.000 Unit in 1997. No of Transformers: Distribution: West Bengal data remains active status in CEIC and is reported by Central Electricity Authority. The data is categorized under Global Database’s India – Table IN.RBC055: Electricity: Number of Transformers.

No of Transformers: Stepdown: Jammu & Kashmir data was reported at 2,400.000 Unit in 2022. This stayed constant from the previous number of 2,400.000 Unit for 2021. No of Transformers: Stepdown: Jammu & Kashmir data is updated yearly, averaging 144.000 Unit from Mar 1996 (Median) to 2022, with 26 observations. The data reached an all-time high of 2,400.000 Unit in 2022 and a record low of 116.000 Unit in 2007. No of Transformers: Stepdown: Jammu & Kashmir data remains active status in CEIC and is reported by Central Electricity Authority. The data is categorized under Global Database’s India – Table IN.RBC055: Electricity: Number of Transformers.