為了應(yīng)對(duì)氣候變化并減少碳排放，當(dāng)前許多國(guó)家都依賴于風(fēng)能等可再生能源。然而，風(fēng)電產(chǎn)業(yè)也是一個(gè)資本密集型行業(yè)，這意味著風(fēng)機(jī)和其他資產(chǎn)一樣，需要定期進(jìn)行“后運(yùn)維“（O&M）以防止發(fā)生意外故障。風(fēng)機(jī)的平均壽命為20到25年，因此歐洲和美國(guó)在2000年之前安裝的許多陸上風(fēng)機(jī)，已經(jīng)達(dá)到了當(dāng)初設(shè)計(jì)壽命的終點(diǎn)（EOD）。風(fēng)電場(chǎng)運(yùn)營(yíng)商需要找到一種利潤(rùn)率更高的經(jīng)營(yíng)方式，否則他們將面臨資產(chǎn)報(bào)銷和投資失敗的風(fēng)險(xiǎn)。

最新數(shù)字技術(shù)的普及為運(yùn)營(yíng)商提供了延長(zhǎng)風(fēng)機(jī)使用壽命和優(yōu)化發(fā)電量的機(jī)會(huì)。根據(jù)WindEurope的數(shù)據(jù)，在即將達(dá)到設(shè)計(jì)壽命的22GW風(fēng)電中，有18GW可以應(yīng)用風(fēng)機(jī)壽命延長(zhǎng)項(xiàng)目（LTE）。

圖片來(lái)源：網(wǎng)絡(luò)

不作為的代價(jià)高昂——后運(yùn)維需要更加積極主動(dòng)

傳統(tǒng)的后運(yùn)維活動(dòng)主要集中在日常操作和定期維護(hù)上，這種方法依賴于被動(dòng)決策，寄希望于一切都能正常運(yùn)行。在最壞的情況下，未被及時(shí)發(fā)現(xiàn)的問題則會(huì)帶來(lái)價(jià)格高昂的補(bǔ)救措施。事實(shí)上，后運(yùn)維相關(guān)的支出已經(jīng)占據(jù)了風(fēng)電項(xiàng)目總成本的10%到20%。此外，許多風(fēng)電場(chǎng)的所有者還簽訂了昂貴的后運(yùn)維合同，以彌補(bǔ)運(yùn)維技能的不足。盡管像SCADA（監(jiān)督控制和數(shù)據(jù)采集）這樣的監(jiān)控系統(tǒng)已經(jīng)在風(fēng)電場(chǎng)運(yùn)行了一段時(shí)間，但我們?nèi)匀蝗狈υ趩栴}出現(xiàn)的早期階段做出精準(zhǔn)決策的能力。從數(shù)據(jù)中提取信息并解釋結(jié)果以提早發(fā)現(xiàn)故障至關(guān)重要，而數(shù)字化的后運(yùn)維則可以實(shí)現(xiàn)這一點(diǎn)，賦予了風(fēng)電運(yùn)營(yíng)商對(duì)風(fēng)機(jī)的更多控制權(quán)。

利用數(shù)字孿生（digital twins）和AI的力量

數(shù)字化能力通過(guò)數(shù)據(jù)來(lái)應(yīng)對(duì)后運(yùn)維業(yè)務(wù)的相關(guān)挑戰(zhàn)，而不是用數(shù)據(jù)來(lái)定義這些挑戰(zhàn)。傳感和捕獲準(zhǔn)確的原始數(shù)據(jù)是關(guān)鍵的第一步。許多在過(guò)去二十年中建成的風(fēng)電場(chǎng)并沒有配備現(xiàn)代風(fēng)機(jī)中的傳感器。因此，我們可以通過(guò)雷達(dá)的聲學(xué)特征、無(wú)人機(jī)圖像識(shí)別等技術(shù)實(shí)現(xiàn)非侵入性的計(jì)算傳感，促進(jìn)多傳感器的融合，從而豐富不同組件的數(shù)據(jù)采集。接下來(lái)，則是采用數(shù)據(jù)管理和轉(zhuǎn)換技術(shù)來(lái)清洗和合并數(shù)據(jù)，使其適合分析引擎進(jìn)行下一步的使用。

數(shù)據(jù)工程只是整個(gè)藍(lán)圖的一部分。基于數(shù)據(jù)驅(qū)動(dòng)的組件和相關(guān)過(guò)程的數(shù)字孿生（digital twins）系統(tǒng)將結(jié)合預(yù)測(cè)分析，并將以此生成風(fēng)機(jī)性能的解析以支持主動(dòng)決策和糾正維護(hù)。當(dāng)結(jié)合人工智能和機(jī)器學(xué)習(xí)時(shí)，這項(xiàng)分析還可以從歷史數(shù)據(jù)中挖掘出新的參數(shù)和先行指標(biāo)。通過(guò)先進(jìn)算法跟蹤和分析這些數(shù)據(jù)，我們可以精準(zhǔn)了解到關(guān)鍵風(fēng)機(jī)組件的當(dāng)前和未來(lái)狀態(tài)。然而，如果數(shù)據(jù)和分析沒有以正確的格式及時(shí)提供給操作員、工程師或業(yè)務(wù)相關(guān)者，那么這些分析將毫無(wú)意義。如今，許多致力于后運(yùn)維的企業(yè)試圖將分析結(jié)果與ERP（企業(yè)資源計(jì)劃）系統(tǒng)連接，來(lái)實(shí)現(xiàn)“服務(wù)化”（servitization），這確保了信息的可操作性，也能使分析結(jié)果及時(shí)得到反饋。

向可靠的低成本風(fēng)能邁進(jìn)

風(fēng)能發(fā)電量目前占全球總發(fā)電量的4.4%，并預(yù)計(jì)到2030年將增加到20%。隨著政府補(bǔ)貼的減少，風(fēng)電場(chǎng)必須找到新的方法來(lái)降低成本并保持競(jìng)爭(zhēng)力。數(shù)字技術(shù)一定是未來(lái)的發(fā)展方向，它將減少停機(jī)時(shí)間、降低后運(yùn)維成本、提高風(fēng)機(jī)的運(yùn)營(yíng)效率，從而最終實(shí)現(xiàn)低成本的清潔能源產(chǎn)出。

擴(kuò)博智能，風(fēng)電業(yè)務(wù)在全球：

擴(kuò)博智能已與丹麥、巴西、美國(guó)、加拿大、越南、緬甸、泰國(guó)、希臘、羅馬尼亞、葡萄牙、意大利等不同地區(qū)、不同規(guī)模的風(fēng)電廠達(dá)成合作，共覆蓋29個(gè)國(guó)家及地區(qū)，全球累計(jì)巡檢80,000+臺(tái)次，并創(chuàng)下最短巡檢時(shí)間15分鐘、單日陸上巡檢記錄31臺(tái)、單日海上巡檢記錄18臺(tái)等記錄。目前，擴(kuò)博智能向包括運(yùn)營(yíng)商、主機(jī)商、葉片制造商、第三方服務(wù)提供商等提供全方位的解決方案和服務(wù)。在全球各大洲與各主要區(qū)域，我們均有專業(yè)的智能巡檢團(tuán)隊(duì)可為您提供一站式服務(wù)。

英文原文

Wind Energy Gets a Much-needed Boost with Digital O&M

ORONO, Maine (AP) — As waves grew and gusts increased, a wind turbine bobbed gently, its blades spinning with a gentle woosh. The tempest reached a crescendo with little drama other than splashing water.

Many countries are relying on wind energy among other renewable resources to tackle climate change and reduce carbon emissions. However, it’s a capital-intensive sector and wind turbines like any other asset require regular operations and maintenance (O&M) to prevent unplanned breakdowns and repairs. The average shelf life of a wind turbine is 20 to 25 years. This means that many onshore wind farms in Europe and the U.S. installed before the year 2000 have already arrived at the end of design (EOD) life. Wind farm operators will need to find more profitable ways to run their business, or risk decommissioning wind assets and writing off the investment.

The proliferation of new digital technologies has given an opportunity for operators to increase the useful life of wind turbines and optimize the power yield. According to WindEurope, out of 22GW of wind power that is coming to its EOD life, 18GW will be eligible for lifetime extension (LTE) projects.

The cost of inaction is high—O&M needs to become more proactive

Traditional O&M activities centered around routine operations and scheduled maintenance, an approach that relied on reactive decision making with the hope that everything was working fine. In worst case scenarios, undetected problems would result in expensive, corrective actions. In fact, O&M accounts for approximately 10 to 20% of the total cost of energy for a wind project. In addition, many wind farm owners signed expensive maintenance contracts to fill the O&M skill gap. While monitoring systems such as SCADA (supervisory control and data acquisition) have been used for a while on wind farms, what’s missing is the sophistication needed to arrive at insightful decisions during the early stages of a problem. The ability to extract information from data and interpret outcomes for early detection of failures is critical. Digital O&M has now made this possible, giving wind operators more control over turbine performance.

Utilizing the power of data with digital twins and AI

Digital technologies use data as a vehicle to tackle O&M business challenges, as opposed to using it to define those challenges. Sensing and capturing accurate raw data is a critical first step. Many windfarms that were commissioned in the last two decades are not equipped with sensors found in modern wind turbines. Unobtrusive computational sensing through radar-based acoustic signature, drone-based imagery, and other related technologies enables multi-sensor fusion for enriching data capture across different components. The next step is employing data management and transformation techniques, including cleansing and merging the data to make it fit for consumption by analytical engines. 

Data engineering is just one piece of the puzzle. Data-driven digital twins of the components and its adjoining processes combined with predictive analytics will generate insights on performance to support proactive decision-making and corrective maintenance. When combined with artificial intelligence and machine learning, analytics can mine new parameters and lead indicators from historical data. This data can be tracked and analyzed using advanced algorithms to understand the current and future state of critical wind turbine components.

But analytics will be irrelevant, if the data and insights are not available to the operator, engineer or business stakeholder in the right format, in a timely manner. Many enterprises with a broader O&M vision are using ‘servitization’ by linking analytics with ERP (enterprise resource planning) systems. This ensures a process-driven approach in which information is actionable, and it triggers the right response.

Moving towards reliable, low-cost wind energy

Wind power represents 4.4% of the total generated power and is likely to increase up to 20% by 2030. With governments reducing subsidies, wind farms have to find new ways to cut costs and stay competitive. Digital technologies are the way forward. It will reduce downtime, cut O&M costs and improve the operational efficiency of wind turbines. The result is increased clean energy production at low costs.