数字化运维助力风能加速发展

为了应对气候变化并减少碳排放，当前许多国家都依赖于风能等可再生能源。然而，风电产业也是一个资本密集型行业，这意味着风机和其他资产一样，需要定期进行“后运维“（O&M）以防止发生意外故障。风机的平均寿命为20到25年，因此欧洲和美国在2000年之前安装的许多陆上风机，已经达到了当初设计寿命的终点（EOD）。风电场运营商需要找到一种利润率更高的经营方式，否则他们将面临资产报销和投资失败的风险。

最新数字技术的普及为运营商提供了延长风机使用寿命和优化发电量的机会。根据WindEurope的数据，在即将达到设计寿命的22GW风电中，有18GW可以应用风机寿命延长项目（LTE）。

图片来源：网络

不作为的代价高昂——后运维需要更加积极主动

传统的后运维活动主要集中在日常操作和定期维护上，这种方法依赖于被动决策，寄希望于一切都能正常运行。在最坏的情况下，未被及时发现的问题则会带来价格高昂的补救措施。事实上，后运维相关的支出已经占据了风电项目总成本的10%到20%。此外，许多风电场的所有者还签订了昂贵的后运维合同，以弥补运维技能的不足。尽管像SCADA（监督控制和数据采集）这样的监控系统已经在风电场运行了一段时间，但我们仍然缺乏在问题出现的早期阶段做出精准决策的能力。从数据中提取信息并解释结果以提早发现故障至关重要，而数字化的后运维则可以实现这一点，赋予了风电运营商对风机的更多控制权。

利用数字孪生（digital twins）和AI的力量

数字化能力通过数据来应对后运维业务的相关挑战，而不是用数据来定义这些挑战。传感和捕获准确的原始数据是关键的第一步。许多在过去二十年中建成的风电场并没有配备现代风机中的传感器。因此，我们可以通过雷达的声学特征、无人机图像识别等技术实现非侵入性的计算传感，促进多传感器的融合，从而丰富不同组件的数据采集。接下来，则是采用数据管理和转换技术来清洗和合并数据，使其适合分析引擎进行下一步的使用。

数据工程只是整个蓝图的一部分。基于数据驱动的组件和相关过程的数字孪生（digital twins）系统将结合预测分析，并将以此生成风机性能的解析以支持主动决策和纠正维护。当结合人工智能和机器学习时，这项分析还可以从历史数据中挖掘出新的参数和先行指标。通过先进算法跟踪和分析这些数据，我们可以精准了解到关键风机组件的当前和未来状态。然而，如果数据和分析没有以正确的格式及时提供给操作员、工程师或业务相关者，那么这些分析将毫无意义。如今，许多致力于后运维的企业试图将分析结果与ERP（企业资源计划）系统连接，来实现“服务化”（servitization），这确保了信息的可操作性，也能使分析结果及时得到反馈。

向可靠的低成本风能迈进

风能发电量目前占全球总发电量的4.4%，并预计到2030年将增加到20%。随着政府补贴的减少，风电场必须找到新的方法来降低成本并保持竞争力。数字技术一定是未来的发展方向，它将减少停机时间、降低后运维成本、提高风机的运营效率，从而最终实现低成本的清洁能源产出。

扩博智能，风电业务在全球：

扩博智能已与丹麦、巴西、美国、加拿大、越南、缅甸、泰国、希腊、罗马尼亚、葡萄牙、意大利等不同地区、不同规模的风电厂达成合作，共覆盖29个国家及地区，全球累计巡检80,000+台次，并创下最短巡检时间15分钟、单日陆上巡检记录31台、单日海上巡检记录18台等记录。目前，扩博智能向包括运营商、主机商、叶片制造商、第三方服务提供商等提供全方位的解决方案和服务。在全球各大洲与各主要区域，我们均有专业的智能巡检团队可为您提供一站式服务。

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.