Remote Wind Energy SCADA Control System

Like Old-Fashioned Farming, Tending a Wind Farm Is a 24/7/365 Job. At Iberdrola Renewables, the New SCADA System Is a Key "Farm Hand"

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Our management of multi-station configurations uses PcVue's advanced tools to ensure the coherence of the configuration data and deployment on all of the stations, especially for all of our geographically remote applications. PcVue's centralized configuration provides the capabilities for the management and traceability of the various versions of applications and their changes. It also supports automatic updating of the stations that make up the supervisory system. At each start-up of a station on the network, PcVue automatically runs consistency checks of the versions in use.

In the United States, we are currently producing nearly 5000 megawatts of wind power from more than 40 independent power plants using about 2500 wind turbines. Each wind turbine supplies up to about 300 data points, which equates to approximately 700,000 to 850,000 I/O data points on several servers. To cope with the diverse demands of maintaining our wind farm, the PcVue application alarms are highly configurable. Alarm messages may be printed, viewed in alarm lists and archived. Our operators can configure alarm behavior using groups, filters, sorting, acknowledgement and masking. They also create alarm counters and associate specific actions with an alarm. Alarms can be acknowledged by our operators directly from mimics, and automatically broadcast to all nodes on the network.

Iberdrola Renewables also is using OPC (www.opcfoundation.org) and others as the communications protocol to pull data from the various PLCs. Many wind farm applications often use OPC and the KEPServerEX driver from Kepware (www.kepware.com) to communicate seamlessly with disparate systems. We use PcVue's OPC Data Access Client and OPC DA XML Client for exchange of real-time data with communication servers, and OPC DA Server to facilitate data exchange with third-party applications.

The data acquisition that occurs is routed back to the National Control Center. I manage the teams developing integrated control systems at the National Control Center. We chose PcVue's software, as it had already proven itself to be scaleable, user-friendly and highly functional in the Spanish operations. PcVue provides a single-user view that allows an easy visual display and overall management of the myriad systems in place from the PLCs, HMIs and the control systems equipped on the turbines. As we monitor avian migration and weather in addition to controlling and managing our turbines, we needed a system that would provide a simple, easy-to-read GUI, so that we can react at a moment's notice.

The new PcVue SCADA software integrates and connects with the wind turbines via the PcVue GUI interface acting as a light client to the PcVue application and managing up to 2.5 million data elements. This configuration provides our operators with the necessary information about the turbine signals. We are using this distributed, client-server architecture with redundancy in order to ensure that the design is fault-tolerant. Using PcVue's built-in redundancy features, we are able to ensure continuity of data collection in the event of the failure of a system component. PcVue also supports dual networks both for communication with field equipment and among PcVue stations. Each component and each station in the configuration has a validity status to enable operators to view the condition of the system in real time. These client stations are communicating via OPC with the redundant communication front ends connected to the high-speed network.

Two-Level Architecture

Using the PcVue architecture, our operators can see in-depth details on the data of the remote wind farms in a real-time display status. Given the large volume of information, and in order to facilitate operation and maintenance of the facilities, the supervision appears in two levels.

A first supervision level gives us an overview of the most relevant alarms, values and counters, enough to supervise the turbines in a usual situation and detect failures that need to be corrected. A second, detailed supervision level, triggered on an operator's request, enables supervision of all of the turbine's data selected, so that our operators can immediately and accurately diagnose failures that occurred and determine the necessary operations. Data received can be processed as setpoints, historical storages, alarm management, trending, etc.

Our control system in each installation collects the operational information from the generators, associated substation and other components. The control system is connected to the control center through a remote communication channel, and, therefore, facilitates maintenance tasks. Our National Control Center receives this information and processes it into an organized and simplified structure that enables easy identification and diagnosis of failures. This diagnosis triggers the appropriate actions for its solution: remote reset or activation of local maintenance teams. As a result, average downtime decreases, thus increasing availability. Our operators can see in-depth data from the remote wind farms.

Iberdrola Renewables in the United States continues to expand the systems for both legacy and new generating stations, and, so far, it has met our expectations, and the system is working very well. This will become the standard process for all later facilities, so that it becomes our typical "out-of-the-box" solution.


Harm Toren is managing director of Operations Services/Wind Operations for Iberdrola Renewables in Portland, Ore.

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  • I would be interested to hear more about how Iberdrola monitors efficiency. I have encounted very few wind farm scada systems which perform much meaningful analysis. There are some good papers on the use of wind farm scada data but very few wind farmers seem to employ them. www.scadaminer.com/applications/wind-turbine-scada-analysis/ gives some good examples of things to look out for in scada data which require varying levels of analysis to discover.

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