Distribution State Estimation Patents: Leaders & Trends 2026
- Filings nearly tripled in three years — from 6 in 2021 to 16 in 2024, a +167% rise, before the most recent two years understate activity due to publication lag.
- Ownership is concentrated but not locked — the top 5 assignees hold 35.0% of all 80 records, and the top 10 hold 58.8%, leaving a long tail of single- and few-filing entrants.
- Filing is overwhelmingly China-centred — 72 of the receiving-office records sit in China, against single-digit counts across WIPO, the US, Europe, India and Japan combined.
Filing growth compares 2021 (6 records) with 2024 (16) — a three-year span. 2024 is the most recent year we treat as complete: publication lags filing by roughly 18 months, so 2025 onwards are still filling in and any growth rate that ends there would understate the field. Top-5 share is the combined record count of the five largest assignees divided by all 80 records in scope (CR5), not by the ranked leaders only.
What this landscape covers
Distribution network state estimation with limited sensing addresses a structural problem: distribution feeders carry far fewer meters and sensors than transmission networks, so operators must infer voltages, currents and loading from sparse, noisy or pseudo-measurements. The patent activity captured here spans pseudo-measurement modelling, low-observability estimation algorithms, and the software and AI layers built around them, drawn from 80 published records filed between 2015 and 2026.
The dataset is dominated by grid operators and university-affiliated research institutes rather than component vendors, which shapes the claim style: many filings protect estimation methods and data-processing pipelines rather than physical sensing hardware. That matters for freedom-to-operate work, because method claims tied to specific algorithmic steps are often easier to design around than hardware claims, but they can still block a particular computational approach outright.
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Filing trend and technology composition
Two views of the same 80 records: how filing activity has moved year over year, and which IPC subclasses the claims actually sit in.
Filing trend, 2017-2026
Annual filings rose from 2 in 2017 to a peak of 16 in 2024, with the 2021-2024 span alone showing a +167% increase. 2025 and 2026 figures are still incomplete because publication typically lags filing by around 18 months, so the apparent slowdown in the most recent two years should not be read as a real drop in activity.
IPC subclass distribution
H02J (power supply and grid systems) appears in 98.8% of the 80 records, confirming this is fundamentally a grid-systems field rather than a pure measurement or software field. G06F (electric digital data processing) reaches 50.0%, and G06Q and G06N each clear roughly a quarter of records, showing that estimation methods are increasingly wrapped in data-processing and AI-model claims rather than filed as standalone grid hardware. G01R (electric and magnetic measurement) sits lower at 15.0%, and B60L, H03H and H04L each appear only once, marking them as fringe rather than core to this corpus.
Shares are the percentage of the 80 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Distribution Network State Estimation with Limited Sensing with Eureka
This page is one run against one query. Ask Eureka your own question about distribution network state estimation with limited sensing and every answer comes back with the patent numbers behind it.
Try EurekaMost-cited records and a representative filing
WO2015083472A1 — Power distribution system state estimation device, state estimation method, and state estimation program
Filed by Hitachi in 2015, this filing estimates individual sensor errors within a distribution system across multiple time cross-sections, using stored measurement values, system configuration data and per-node maximum error bounds to compute weighting factors and iteratively refine state estimates.One of the earliest filings in this dataset and a useful reference point for how sensor-error weighting has been claimed before later AI-driven approaches.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | CN107425520A | 一种含节点注入功率不确定性的主动配电网三相区间状态估计方法 | 34 |
| 2 | CN105633956A | 一种基于Spiking神经网络伪量测建模的配电网三相状态估计方法 | 24 |
| 3 | WO2016078477A1 | 一种变电站三相线性广义状态估计方法 | 23 |
| 4 | CN105391059A | 一种基于电流量测变换的分布式发电系统状态估计方法 | 17 |
| 5 | CN114389312A | 一种配电网分布式状态估计方法 | 15 |
| 6 | CN105701568A | 一种启发式的配电网状态估计量测位置快速优化方法 | 13 |
| 7 | CN105375484A | 一种基于PMU的电力系统分布式动态状态估计方法 | 13 |
| 8 | WO2015083472A1 | Power distribution system state estimation device, state estimation method, and state estimation program | 12 |
| 9 | CN108649574A | 一种基于三种量测数据的配电网快速状态估计方法 | 11 |
| 10 | CN105071388A | 一种基于极大似然估计的配电网状态估计方法 | 11 |
Citation counts reward earlier filings simply because they have had longer to accumulate citations within the searched corpus; treat this table as a map of influential prior art, not a ranking of current importance.
Patent titles are shown in the language they were filed in, not translated, so that each record stays verifiable against the original filing — a translated title will not match in Eureka or in any national register. Each row carries its publication number; clicking a row searches Eureka by that number.
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Three patterns stand out once the raw counts are set against each other: where filings sit technically, how concentrated ownership is, and where geography narrows the field.
Estimation is increasingly a software claim
Half of the 80 records carry a G06F (digital data processing) class alongside H02J, and over a quarter carry G06N (AI models). That means a growing share of new filings protect the computation around state estimation rather than sensing hardware itself, which raises the bar for anyone hoping to design around an existing method purely by changing sensor placement.
A leading cluster, then a long tail
The top 5 assignees account for 35.0% of all 80 records, and the top 10 account for 58.8%. With 74 companies ranked overall, the remaining share is spread thin, so most organisations in this field hold only one or two filings — useful context when judging whether a given applicant is a strategic filer or an opportunistic one.
Filing is concentrated in one jurisdiction
China accounts for the large majority of receiving-office filings, with WIPO, the United States, Europe, India and Japan each recording only a handful. Anyone assessing global freedom-to-operate risk in this field should weight Chinese prior art heavily, since the density of domestic filing there is not matched elsewhere.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to distribution network state estimation with limited sensing, with the prior art for and against each one.
Assignee landscape and where activity is moving
Grid operators and university research institutes dominate the ranked list, with co-filing patterns showing operator-institute pairs working together rather than pure corporate competition.
A single leader, no runaway share
The top-ranked assignee holds 7 of the 80 records, with fifth place at 5 and tenth place at 3. The gap from first to fifth is narrow enough that no single organisation controls the field outright, even though the top 10 together hold 58.8% of all records.
Recent-year activity has cooled among top filers
Several of the most active historical assignees show zero filings in the latest tracked year, including a -100% year-on-year drop for the overall leader. Given the roughly 18-month publication lag, this likely reflects incomplete recent data rather than a genuine pullback, but it is worth monitoring rather than assuming continuity.
Operator-institute pairings recur
Co-assignee pairs in this dataset repeatedly link a grid operator with an affiliated research institute or provincial power company, each pairing appearing twice. That points to internal R&D-to-operations pipelines rather than cross-company joint ventures as the dominant collaboration model here.
| Assignee | Recent year | YoY |
|---|---|---|
| Hohai University | 1 | — |
| State Grid Corporation of China | 0 | -100% |
| North China Electric Power University | 0 | — |
| State Grid Jiangsu Electric Power Co., Ltd. | 0 | — |
| Southeast University | 0 | — |
| Tsinghua University | 0 | — |
| State Grid Jiangsu Electric Power Co., Ltd. Research Institute | 0 | — |
| Electric Power Research Institute, China Southern Power Grid Co., Ltd. | 0 | -100% |
Where to take this analysis
The dataset points to a field where software and AI-layer claims are growing faster than hardware claims, and where geographic concentration leaves clear gaps for review.
Map claim scope against the top 10 assignees
Concentration at 58.8% among 10 assignees makes a targeted claim-chart review of that group a practical starting point before broader freedom-to-operate work.
Explore assignee claims in EurekaTrack the AI-model class as it grows
G06N already touches 26.3% of records; watching new filings in this class closely could reveal where estimation methods are shifting fastest.
Set up a monitoring search in EurekaCommon questions about this landscape
It is the set of techniques used to infer the operating state of a power distribution feeder — voltages, currents, power flows — when only a small fraction of nodes carry real-time meters or sensors. Because distribution networks are far less instrumented than transmission networks, operators rely on pseudo-measurements, historical load profiles and statistical or AI-based estimation algorithms to fill the gaps. The patent activity in this space covers both the estimation algorithms themselves and the data-processing pipelines that generate the pseudo-measurements they consume.
The ranked list covers 74 assignees, dominated by Chinese grid operators and affiliated university research institutes rather than independent hardware vendors. The top-ranked assignee holds 7 of the 80 records in scope, with the top 5 combined holding 35.0% and the top 10 combined holding 58.8%. Below that concentration sits a long tail of organisations with only one or two filings each, so the field is led but not locked up by any single player.
Of the receiving offices recorded across this dataset, 72 filings went through China compared with single-digit counts through WIPO, the United States, Europe, India and Japan. This likely reflects the scale of China's distribution grid modernisation programmes and the concentration of state grid operators and university research institutes among the top filers. Anyone assessing global risk or opportunity in this space should treat Chinese prior art as the primary body to search, while still checking the smaller international filings for freedom-to-operate purposes outside China.
Filings grew sharply from 6 in 2021 to a peak of 16 in 2024, a rise of 167% over that span. The two most recent years show lower counts, but that is expected: patent publication typically lags actual filing by around 18 months, so 2025 and 2026 records are still incomplete rather than reflecting a genuine slowdown. Based on the completed trend through 2024, the field was still accelerating, not cooling.
Nearly all records — 98.8% of the 80 in scope — carry an H02J classification, confirming this is fundamentally a power-grid-systems field. But 50.0% also carry G06F for digital data processing, and roughly a quarter each carry G06Q and G06N, showing that a large share of recent claims are written around computation and AI models rather than physical sensing equipment. A filer entering this space should expect to search software-style method claims as closely as any hardware prior art, since that is where much of the recent claim density sits.
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company’s registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.