The key value of Wireless Sensor Networks is the monitoring of physical and industrial environments. This data needs to be transformed into meaningful information that can be used by business decision makers using the Vertoda Framework. Furthermore, much of this data can also play a role in SCADA (Supervisory Control And Data Acquisition)  systems. SCADA systems are fundamental to operations in the pharmaceutical, oil & gas, food processing and other process driven industries and the data provided by WSNs could certainly enrich the information provided by such systems.

One option for connecting WSNs to SCADA systems is to use Vertoda Middleware. The Vertoda Framework will capture the data from the WSNs and distribute pre-sorted and translated information to the SCADA system. This would be an ideal mechanism for fusing the data capturing power of WSNs with the well established SCADA systems used in certain industries. To date, WSNs have not met market expectations for certain sectors. The answer to that issue may be to make their data more easily available to the existing systems used by an industry sector.

The other option for connecting WSNs to SCADA is to connect the SCADA system directly to the WSNs. However, this is more difficult than initally appears as custom software will need to be created for the system. This could be achieved using a bespoke Vertoda Data Capture module.

Within a SCADA model, data is captured by sensors and communicated to a central controller which analyses the data and takes appropriate actions. It is a well established standard and has the flexibility to accomodate wireless networks. However, as noted, up to now WSNs have not been deployed extensively within industries that typically use SCADA systems. It can be argued that the key reason for this is that industries such as pharmaceuticals are regulatory driven and WSNs were too much of risk for plant and process management. The key requirement for the use of WSNs is that they be reliable. WSNs in industries such as oil & gas and pharmaceuticals need to withstand harsh, often outdoor environments. Up to recently, WSNs were still technology that was primarily suited to benign indoor environments but the motes have become much more robust.

WSNs must also easily interoperate with other devices and systems. The data produced by WSNs does not exist in isolation - it is required by other devices and systems to make decisions and take actions. Vertoda solves this problem by making WSN information available to any other system within the organisation. WSNs must facilitate route configuration i.e. if one mote is damaged it must not be a bottleneck or point of failure for the rest of the network. The other area of concern for  the process industry is the perceived insecurity of WSNs - a topic we will return to in a future blog.

The use of wind power to generate energy is growing very quickly. However, as we have noted previously, there are a number of challenges facing wind farms. Wind Turbines are complex devices that require frequent maintenance. One possible aid for wind farms may be wireless sensor networks (WSNs) which can be used for predictive and monitoring purposes.

The key value of wireless sensor networks (WSNs) is their ability to collect data in real-time from physical environments that are often hard to monitor. This data can then be correlated to ascertain trends and product information for analysis and decision making. One way in which WSNs could assist wind farms is in the problem of wind power prediction. The power generated by wind turbines is contingent upon wind speed. Sensor motes can be easily integrated with a wind speed meter (sometimes referred to as an anemometer) to provide real-time data on the wind speed in a wind farm location. This data can then be transformed into information and correlated with historical data on the power generated by a particular wind turbine given that wind speed. Such cumulative information for all the turbines in a wind farm can then be used to predict the power generated by that farm for a particular period. Since the power generated by a farm is ultimately sold to an electricity utility, this information can be used to predict revenue for the organisation for a particular period. Such a solution would be low cost and, given the nature of WSNs, easy to deploy.

In addition to its role in predicting power and revenue generation, wind speed can also be used for operational purposes, for example, to determine the correct blade rotation for the turbine. WSNs can also be used to measure vibrations within the turbine equipment to determine the prospect of failure and prevent unnecessary downtime. Given the requirement for 2 weeks scheduled maintenance mandated by many turbine vendors this is a key issue. WSNs can be used for condition monitoring generally. Condition monitoring offers significant value to a wind farm operator as the cost of downtime is significant not only in terms of equipment repair but also in terms of lost revenue. This issue is further exaceberated by the fact that wind farms are often in locations such as mountains and hills that are hard to access. Indeed, offshore wind farms are becoming more prevalent. The diagnosis by sensor motes of impending failures can result in a number of actions. Sensors embedded within a turbine could interact with the equipment to take a number of actions such as the scheduling of maintenance, the reconfiguration of certain operations or the emergency shutdown of the equipment.

In addition to measuring wind speed, WSNs can be used to measure other characteristics of the physical environment including temperature, humidity, rainfall and light. WSNs can also be used to provide identifications for individual turbines and farms and their data can be fused with Web 2.0 presentation technologies to provide real-time identification of a wind farm, its turbines and the conditions of same. Using 3G, broadband, wireless or satellite communications, data can be transferred from the remote locations in which wind farms typically reside.

The Vertoda Framework can capture data from WSNs and transform this data into meaningful and timely information. Using this information, wind farms can reduce maintenance costs, improve operational efficiencies and more accurately measure their revenues.