ENERGY TRANSITION
This article discusses four grid technological innovations that are changing the way power substations work and giving grid operators the ability to better manage those substations and the network as a whole–paving the way to meet changing energy demands.
Key Challenges and Opportunities with Grid Modernization
The energy transition is at hand, but its success relies on our ability to adapt our aging power infrastructure to support the integration of more renewable energy resources. Innovation and the digital transformation of the grid substation are crucial to evolving our technology rapidly enough to meet the world’s rising demands of green energy. In this article, we discuss four grid technological innovations that are changing the way power substations work and giving grid operators the ability to better manage those substations and the network as a whole–paving the way to meet changing energy demands.
First, we examine the use of data in the move from time-based to condition-based maintenance and performance of primary substation equipment, such as power transformers. We also explore grid sensing with analytics to improve network utilization. We then consider the new flexible architectures of the digital substation, and how they create a more scalable, grid-reliable control and automation platform that is ideal for tomorrow’s power system evolutions. Next, we look at grid system integrity and protection schemes for high-speed grid stability controls. Finally, we discover how secured wireless technologies, such as licensed/unlicensed radios or private LTE (long-term evolution) networks, are bringing industrial-grade reliability and security with the integration of distributed energy resources (DERs).
Grid Infrastructure - Major Challenges and Opportunities
The electrical grid has been largely evolving for over 100+ years. So, why now? In speaking with many global utilities and heavy power users, three clear factors have emerged as being driving forces: the need and push for decarbonization, the decentralization caused by the growth of renewables and DERs, and digitization–our ability to connect more assets and visualize more data than ever before. Digital transformation enabled by grid modernization technology in the transmission and distribution system will play a central role in the energy transition.
Power grid infrastructure has been built and maintained over the past century to deliver reliable, safer, and affordable electricity across communities and industries. With the integration of large renewable energy farms on the transmission grid, and distributed energy resources on distribution networks, several challenges and opportunities have presented themselves during the modernizing of decades-old grid infrastructures.
Digital transformation with grid modernization technology for the transmission and distribution system will play the central role between renewable generation and prosumers of energy. The four key aspects shown in Figure 1 illustrate grid modernization through innovation and digital transformation, including:
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Improving grid performance and utilization of the existing infrastructure with innovations in grid asset (e.g., transformers, feeders) monitoring;
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Enhancing grid reliability and flexibility with digital substations and software-defined automation and controls;
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Maintaining grid stability and resiliency on large integrations of inverter-based renewable energy resources using system integrity protection schemes; and
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Enabling cyber security with end-to-end communication for geographically dispersed DERs.
Grid innovations and digital transformation of power substations are accelerating the energy transition for global utilities.
Figure 1. Grid challenges and opportunities
Evolving Grid Analytics Innovations
Power grid networks and equipment infrastructure are monitored and controlled by edge devices with sensors and actuators with some level of intelligent analytics. As illustrated in Figure 2, the evolution of grid edge analytics innovations moves from a reactive management strategy that responds to failures after they occur to a more autonomous future state with self-healing.
The majority of existing grid edge analytics are reactive to failures to limit damage, with limited responsiveness to restore automatically. The predictive stage may utilize system or asset models with a combination of physics and machine learning methods, including anomaly detection, classifications, and/or learned patterns. Further grid innovations can be applied to suggest potential implications and recommended actions. Finally, the autonomous grid with self-healing would use edge-to-edge communications with very limited or no human interventions.
Figure 2. Evolution of grid analytics steps applying to monitoring and controls
Grid Innovations for Online Asset Condition Monitoring
The aging of the existing installed base and the lack of sufficient upgrades of the grid infrastructure are challenging for grid reliability, resiliency, and affordability.
There are several methods of condition monitoring, generally categorized as the following:
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Automatic and online: Continuous monitoring applied while the asset is running; analysis performed without human intervention
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Automatic and offline: Scheduled monitoring applied when the asset is stopped; analysis performed without human intervention.
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Manual and online: Continuous monitoring applied while the asset is running; analysis performed manually on collected data.
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Manual and offline: Scheduled monitoring applied when the asset is stopped; analysis performed manually on collected data.
Table 1. Grid asset management challenges and solution strategies
Normally, the online methods require additional devices connected to the sensors applied to the equipment whereas the offline methods require scheduled shutdown to perform the tests. Autonomous monitoring and diagnostics need enough processing capabilities and memory within an edge device whereas manual monitoring and diagnostics require time from an expert team to perform analysis and prepare asset health reports.
Power Transformer Example
An example of power transformer equipment monitoring and diagnostics is presented below which can provide risk index and score with actionable condition-based maintenance through a combination of electrical, chemical, and thermal data. Power transformers are among the most critical power substation assets required to maintain a reliable and efficient power supply. Yet more than half the transformers in the developed world are several decades old or older. With almost half of transformer failures caused by insulation degradation and electrical abnormalities due to aging, extending the life of these devices has become a top priority for major power utilities.
Figure 3 presents integrated condition monitoring of the transformer using protection relay and dissolved gas analyzer data. Traditionally, a transformer protection relay is connected to current and voltage transformers to obtain the currents and voltages needed to detect electrical faults. It is interfaced with circuit breakers for control actions. In addition, dissolved gas analyzer devices connected to a transformer can draw oil samples regularly and perform continuous chemical analysis.
In the past, these two distinct systems (electrical protection and chemical monitoring) have not been interconnected. Since both systems provide much-needed asset and performance management data, bringing them together provides a more comprehensive view of the transformer’s overall health as shown in the figure. This integrated approach of combining two systems (a protection relay with electrical functions and a dissolved gas analyzer with chemical analysis) allows tracking of trending incipient fault events with the same timestamps, a correlation between the electrical and chemical models, and integrated asset health analysis and reporting.
Figure 3. Holistic electrical, chemical and thermal models for transformer monitoring and diagnostics
With deep equipment design expertise, proactive functions are designed to continuously perform online equipment condition monitoring in combination with protection and control measurements of the equipment to be protected. Proactive functions take a holistic approach to the equipment to be protected, capturing the entire evolution of a potential failure, including:
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detecting an anomaly or degradation of a subcomponent of the asset,
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alarming the condition, and
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recording and logging the signals and evolving changes.
Proactive functions are essential to detect critical asset degradation or anomaly conditions before they evolve into catastrophic failures. This avoidance of costs, damage, and reliability costs justifies the investment in this digital transformation.
The energy transition is at hand, but its success relies on our ability to adapt our aging power infrastructure to support the integration of more renewable energy resources.
Figure 4. Fleet-view of grid asset risk indices
Data correlation models are time-aligned and trended among electrical, chemical, and thermal characteristics through a range of transformer conditions. Reports from the system (including the energization record, integrated transformer fault report, and transformer health report) allow capturing key operational dynamics that provide insight about both internal and through-fault conditions. Critical transformer data is compiled into reports and models that are easy to interpret, supporting informed decision-making. This ultimately delivers conditions for asset management optimization and potential for a transformer life extension, enabling operators to reduce capital and operational expenditures while maintaining the power system reliability demanded by today’s market.
With advancements in data analytics, interoperable data communication, and high-speed networking, the availability of data for advanced analytics is ushering in a new era of predictive analytics. New predictive analytics can be applied to build proactive functions which do not just limit but can prevent catastrophic events. Digital protection relays are checking for anomalies around every 2ms. Instead of witnessing the evolution of equipment failure until the set threshold of traditional protection function is reached, proactive functions can provide early warning upon detection of abnormal operating conditions. These can be key inputs to managing assets using digital data.
Figure 5 presents an example of a transformer model from its health report. When a protective relay detects an internal fault, it trips the transformer. External or through faults, however, can result in high current flow through the transformer before the device is tripped offline, causing significant stress on the transformer. A typical distribution utility experiences about 15 to 20 through faults each year even in advanced distribution systems. Research demonstrates that many transformers that fail catastrophically start showing indications of problems immediately following a through fault event. Developing situations such as these are more easily detected with visibility into pre- and post-fault electrical, chemical, and thermal data. In the transformer relay, this data is delivered via the user-friendly integrated transformer fault report. This graphical fault report can provide a clear visual indication of the fault condition as well as any changes to the transformer’s health.
Figure 5. An example of holistic transformer health models
This integrated solution provides a heightened level of visibility, allowing the data to be applied in a meaningful way to maintenance or troubleshooting needs. An asset health report gives a snapshot of the transformer’s condition, and helps utilities analyze the following:
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Risk assessment of the transformer population in service, especially critical transformers and important connections;
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Condition assessment of individual transformers, identifying those which may require follow-up such as visual inspection, diagnostic measurements, and maintenance; and
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Proactive risk mitigation actions for transformer asset management.
An Example of Grid Utilization and Fault Location
The integration of renewable generation into existing grids is increasing. One of the main challenges is that power flows are uneven and decentralized, and line loading at specific points in the network becomes uncertain with the change in the feed. This challenge is reduced with real-time or near-time grid visibility. Using dynamic line rating data monitored by sensors, the power handling capacity of the line at specific points can be determined. This allows the utility to provide improved power quality and a better-managed system with increased visibility into the network. With a regulatory requirement to minimize outage times, it is important to detect and locate faults and disturbances across the distribution grid using overhead line monitoring devices. The integration of monitoring data received from line monitoring devices with distribution management system helps improve the reliability and safety of the system, and thus optimizes the use of the network. Typically, line monitoring devices are mounted at strategic points in the overhead network. The system is shown in Figure 6.
Figure 6. Line monitoring interface system
These line sensors measure current and calculate both amplitude and phase of the root-mean-square value, and have built-in communications. The sensors can pick up fault currents and report this current data back via radio to the sensor network gateway (SNG). The SNG links a network of sensors together, sending commands to the sensors and receiving data. This data consists of current values, fault event current data, or other related information, such as temperature, etc. The SNG links back to the data acquisition communicator unit. The unit is generally mounted in the substation, and its function is to record the activity of the sensor network. It acts as a database for line status including events like line loading, tripping faults, or sub-tripping events. The data acquisition communicator also links the sensor network to the system software controls.
The system software controls the entire network at one central location and can include a virtual line sensing gateway that provides access to faults and line loading data centrally. It also provides a platform for line monitoring analytical applications such as:
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fault location and fault signature,
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dynamic line rating,
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local sag/clearance estimation, and
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remote system diagnostics and maintenance applications including remote SNG and line sensor firmware updates.