IoT in Power Energy & Utilities: Successful Business Transformation

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Today I experienced an unusual drift on my IoT thoughts for Energy, Utilities and Power Distribution Organizations worldwide, while I was on my morning walk welcoming sunshine with birds chirping sweetly around, butterflies adding beauty with fragrance of jasmine around  and then found that electricity outage beyond 4 hours since early morning has impacted almost all standing at Gas Stations ( Petroleum Pumps ) running huge queues, DG sets running high contributing to more air pollution and retail shops, stores without light , however surviving on  Inverters which won’t last a long. This triggered me to convert my thoughts into actions which further motivated me to finalize this one, which could help many others out there looking for benefits of IoT in Power Energy Utilities – Generation and Distribution companies.

So here is my detailed analysis and successful transformation for one of the major Power and Energy Distribution and Supply Company who is responsible for providing reliable and affordable electricity to approximately 7.1 million customers across 104,000 square miles and six states. There are several states which are under this company umbrella. In order to cover this load, they own approximately 50GW of generation capacity provided mostly by hydro, nuclear, coal-fired, combustion-turbine and combined-cycle power.

This case discusses the use of a particular kind of Thinga phasor measurement unit (PMU) — which is a device that measures the electrical waves on an electricity grid using a common time source for synchronization. Time synchronization allows synchronized, real-time measurements of multiple remote measurement points on the grid. The resulting measurement is known as a synchrophasor. Some think PMUs can revolutionize the way power systems are managed and controlled. A PMU can be a dedicated device, or the function can be incorporated into a protective relay or other device. Power Energy Company Energy’s vision is to integrate the PMUs into system protection, system planning and system operations to provide more reliable power. The plan is to install up to 104 PMUs at 52 substations, providing 100% coverage of 500kV buses, 230kV buses and 500kV lines, as well as 60% of 230kV lines.

  • Things PMU technology was introduced more than 30 years ago but did not get much attention until the Northeast blackout of 2003, where it became clear that more detailed monitoring capability for the electricity grid was needed. The American Recovery and Reinvestment Act (ARRA) of 2009 (think back to the Great Recession of 2008) paved the way for wide deployment of the technology in the U.S. transmission grid and inspired other across nations to analyze benefits and apply the same for the benefits towards their nationsPMUs take measurements at the power, frequency, voltage, current and phasor angle (i.e. where you are on the power sine wave). Previous systems only took readings every 3–4 seconds. On the other hand, modern devices now take readings at a speed of 60 times per second or beyond, resulting in over 200x times the amount of data. This more frequent interval provides a much more detailed view of the power grid and allows detection of sub-second changes that were completely missed before.Because these measurements are time-series information and it’s important to know that the power is identical at every point in the grid, accurate time measurements are vital. PMUs have GPS receivers built in, not to determine the location, but so all can get the same, accurate time signal. This is sufficient because GPS systems provide time accuracy in the nano-second range, and this accuracy is most critical in the measurement of phasor angles. By comparing the phasor angles between locations, you can get a measure of the power flow between the locations.Figure:  Phase Measurement Unit (PMU) An example of a PMU is the SEL-421, produced by Schweitzer Engineering Labs. The SEL-421 uses multiple processing devices in a parallel architecture, including an embedded microprocessor, a digital signal processing microprocessor and an FPGA. Its operating system is designed for real-time applications. Software is also packaged to run Ethernet communications and web servers.
  • Connect : Typically, data from each PMU is reported to a Phasor Data Collector (PDC) via TCP/IP and stored for analysis.

Figure shows the structure of a typical PMU network. Once PMUs synchronize with the GPS, each measurement sent across the network includes a time stamp. Electrical-distribution line measurements are sent by PMUs over a network connection to a PDC. The PMU communications standard is the IEEE 5C37.118 protocol, which defines data conventions, measurement accuracies and communication formats.

PMUs and PDCs need to be shielded from the larger network; a security gateway is typically implemented to provide an interface between the critical network components and the Internet. These gateways (firewalls) should have three main properties: all traffic must enter; only trusted traffic may pass; and the firewall is immune to penetration. This includes traffic from the PMU to the PDC and vice versa. If a component tries to connect to the PMU or PDC that is not on the trusted list, then it is not allowed to pass. The other job of the security gateway is to establish a VPN connection between substations, which allows measurement and configuration data to be sent securely between substations. Typically, the data is encrypted as it’s sent across the network and when it reaches the designated security gateway, the gateway checks to see if the packets were delayed or replayed and deciphers the packet. The measurements are then recorded in the PDC’s database. PMUs and PDCs are subject to common vulnerabilities, including denial of service, physical, man-in-the-middle, packet analysis, malicious code injection and data-spoofing attacks.

Former ABC News anchor, Ted Koppel, wrote a book called Lights Out: A Cyberattack, A Nation Unprepared, Surviving the Aftermath. In a recent interview he said, “Back in 2010, ten former senior top officials — two former directors of the CIA, two former secretaries of defense, two former national security advisers — wrote a letter to a congressional committee. It was a secret letter, which spelled out their findings after dealing with the best experts they could find within the government. They came to the conclusion that tens of millions of people, in the wake of a Cyberattack on one of the grids, could be without power for a period up to two years.”

  • Collect : Time-series data from Power Energy Company’s many machines are collected in an OSISoft PI time-series database; however, in this particular case, where data is solely coming from the PMUs, it is first being stored in a proprietary SAS file structure; SAS Enterprise Miner uses this database for learning and analysis. Once the learning is complete, the execution is managed through SAS’s Event Stream Processing (ESP). By definition, ESP software does not collect data and only processes it as it arrives. ESP technologies include event visualization, event databases, event-driven middleware and event-processing languages. ESP products enable many different applications such as algorithmic trading in financial services, RFID event-processing applications, fraud detection, process monitoring, and location-based services in telecommunications.
  • Learn : The amount of data measured and sent to the PDC at 60 samples per second from each PMU is far too much for the operator to make any sense of on a real-time basis. SAS Enterprise Miner was used to build a decision tree surrounding the PMU measurements. The main purpose is to detect and understand events that are affecting the power grid, with the objective of keeping the grid stable. They have learned there are a number of time-series techniques that are necessary for the different aspects of providing the needed answers. The analysis flow breaks down into three areas: Did something happen? What happened? How bad was it? 
  • Event Detection : For event detection, the PMUs generate 60 measurements per second on hundreds of sensors and tags. Fortunately, a majority of the time (>99.99%) they indicate that no extraordinary event is occurring. Because there are time-series patterns present, they can be modeled and used to detect when there is a deviation from the normal pattern. Determining these models allows Power Energy Company to look forward with a very short-term forecast and then instantly detect an event of interest.Event IdentificationOnce you’ve detected an event, you have to identify the type of event. An event of interest doesn’t necessarily mean there is a problem or that one will develop. Some events are random, like a lightning strike or a tree hitting a power line, while others represent some type of equipment failure. Power Energy Company has determined that many of these events produce a similar signature in the data stream because time-series-similarity analysis and time-series clustering have been able to match the incoming events to previously seen events. Knowingwhich previous event signatures are non-consequential allows them to safely ignore them. Figures  show how similarity analysis is used for event identification.
  • Event Quantification : For some events, the question is if the magnitude of the event gives cause for concern. An example is oscillation on the power grid. Small but diminishing oscillations are not necessarily a problem, but larger ones that are increasing may require further attention. Once the event type is identified, each has some specialized techniques to determine their magnitude and consequence.
  • Do : Voltage stability is a growing concern for most large power systems and the challenges of providing a reliable power service are only increasing. Reliability of power varies significantly across the globe. For example, the U.S. has an average of 9 hours of disruptions each year for every consumer; those interruptions are estimated to result in economic losses of least $150B annually. Compared to other industrialized countries, the reliability of the U.S. grid is 5–10 times less than in major European countries. The average electricity consumer in the U.S. has to cope with approximately 30 times more service interruptions each year than in Japan or Singapore.Power Energy Company Energy’s work shows the potential of using data to predict problems in time for operators to respond and in doing so, thereby delivering precision power with the help of IoT and Analytics. I hope such adaptions becomes large scale and avoid so many loses due to power failures, faulty lines, equipment.As you all would have enjoyed the fascinating benefits and practical utilization of #IoT_in_Power, I would continue my passion for Retail, Transport and Auto Manufacturing industries too in upcoming articles sequentially. Appreciate the time spent and do suggest me if there are any more such wonderful successful Business Transformation, Case studies in Power, Energy & Utilities which are revolutionizing the benefits of IoT and Cloud Technology evolution further….. Happy reading! #FaizAShaikh | Twitter: @shkfaizalam | LinkedIn: Faiz A. Shaikh

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Faiz A. Shaikh MBA MLE℠CISA ITSM

Available for exploring new horizons. Proud To Be MLE : "Member of Leaders Excellence @ Harvard Square"Faiz A. Shaikh brings over 21+ years of International , National IMS Pre-Sales, Delivery ( Support and Operations ), PMO, COE, Practice management experience in working with Fortune 100 and Global 500 companies. Experienced in building new geographic regions (such as West Coast and Mid-West regions in US, Middle East, and APAC ), New Service Offerings RIMS, IMS, AMS.Faiz has specialization in existing and emerging markets, technology, trends and service offerings not only focused to the Pre-Sales, Delivery ( Support and Operations ), but also on waste reduction , LEAN, process , efficiency improvements, resource retention, client vendor partnership management, collaborating across verticals to meet and exceed Org Vision.Faiz is proficient at reducing costs and providing thoughtful leadership which has not only enabled the rapid growth of start-up and major client companies especially in unstructured environments, but also being an effective leader and mentor, a ingenious analyst and problem solver who unswerving identifies new business opportunities, whilst formulating and reporting strategic plans.Faiz overall possess 21+ years in IMS across sectors, verticals like - BFSI, BNFS, Insurance, Healthcare and Life Science, Retail, Logistics, Telecom, Tours and Travels, Auto and Manufacturing globally.Specialties : Account Management, Program Portfolio Delivery Management, People management,Pre-Sales, Business Development, Client Partner Vendor Management, Governance and Compliance. Additionally his core expertise is into Practice Development, Center of Excellence ( COE ), Offshore Delivery Center ( ODC ), Systems Integration, Tools and Engineering, Usability, Mobility, Enterprise Implementation, IT Outsourcing and Transition Management, CxO Advisory,Principal Analyst,, Alliance Vendor Development, and Partner Management.

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