Next Gen Power Quality improvement with Industry 4.0 Solutions

Published On: Oct 08, 2019


Industry 4.0 is expected to revolutionise the industrial production capabilities and processes. Power Quality and general electrical network readiness is critical to implementing Industry 4.0. At the same time, Industry 4.0 technologies are poised to redefine the way we monitor and improve Power Quality. For the last few years, there is a surge in demand for IoT enabled energy and PQ analysers to monitor the power distortions. However, IoT is a very narrow way of assessing Industry 4.0. The scope of leveraging Industry 4.0 in PQ monitoring is far more extensive and promising. In this blog, we look at the potential of leveraging Industry 4.0 for improving PQ in ways that have never been possible in the past.

Often, in the movies, the flicker or a sudden power cut is used as one of the indicators of presence of ghosts. In the Indian context where beliefs play a key role in personal and professional life, it is not uncommon to blame the performance of equipment on luck, or relate it to auspicious time etc. The acceptance of ‘not so normal’ behavior of the equipment must be also understood from the perspective of the individual belief and adequate awareness.

This blog focuses on highlighting and providing a rational view behind some of the irrational ways of treating the electrical networks.

INTRODUCTION

Industry 4.0 must be understood in context of its encompassing technologies. The technologies include a vast range starting from cloud technology, IoT, Analytics, Artificial Intelligence, Augmented and Virtual Reality, additive manufacturing and cybersecurity. The sum of resultant benefits from these technologies is greater than the whole. Today, IoT has become the face of Industry 4.0 technologies for Power Quality, mostly due to greater number of products and research in the space. However, other Industry 4.0 technologies too are making waves when it comes to improving the Power Quality.

In the Indian context – the ‘Vaastu Shastra’ plays a highly influential role. While the debate around scientific validation of principles in ‘Vaastu Shastra’ are a separate discussion, its popularity is beyond any debate.

In this blog, we focus on developing an understanding the full range of technologies that are under the industry 4.0 web, their interconnections and importance in collectively improving the Power Quality. We will also look at some specific examples of research and proven solutions in each of the Industry 4.0 technologies in consideration.

THE CLOUD, INDUSTRIAL INTERNET OF THINGS AND ANALYTICS FOR PQ MONITORING

What is it?
Industry 4.0 brings together machines, advanced analytics and people at work. As a network of connected devices through communications technologies Power Quality can be monitored and valuable new insights can be built through extensive set of data. At the heart of the IoT ecosystem is data, remotely collected from sensors and fed to cloud based applications.

Several IoT software platforms are emerging which can be utilised to store, analyse information and visualise in a way that enables quick decision making.

What is the potential?

Continuous monitoring of PQ

  • Sensors coupled with Smart Meters and PQ Analysers continuously stream PQ data to central control units or cloud-based platforms for further analysis and actions
  • Specialised PQ sensors are under development to monitor current and voltage. These can be connected to PQ Anlaysers or PLC control panels to predict power disruptions in advance.
  • Advanced Metering Infrastructure (AMI) has a key role with the potential to seamlessly integrate IoT with PQ sensors in the existing traditional or Smart-Grid based architecture of connected Intelligent Electronic Devices (IEDs)
  • Critical Asset or Equipment monitoring for specific PQ parameters using IoT for maximum uptime and predictive failures

Predictive models for improving PQ

  • Data can be analyzed through big data techniques, to enable specific insights at a highly granular level to understand the deviations in localized supply and use of electricity
  • Predictive analytics models can be built to monitor the assets such as transformers, motors etc. and electrical network conditions to predict deterioration in power quality before it leads to power disruption or other adverse impacts.

Industry Examples

Distribution Transformer Monitoring using IIoT and Data Analytics
Power Quality has a key role in shaping the life and performance of the distribution transformer unit. IoT and data analytics can be leveraged to gather and analyse information in real time on various parameters that affect the performance of the transformers.

Some of the popular use cases in improving the life and performance of transformer using good PQ includes:

  • Recognize when transformers are overloaded; Check and identify patterns using co-relation
  • Acquire real time information about voltage, power factor, current, harmonics and unbalance from LV network and help deliver high-quality electricity
  • Reduce downtime by quick identification of transformer breakdowns due to overlapping
  • Identify transformer loading, changes in capacity over a period and present the data in an actionable format.
  • Access useful transformer monitoring information for assets management and preventive maintenance.

ARTIFICIAL INTELLIGENCE

What is it?
The Artificial Intelligence (AI) is branch of science and computational engineering focused on enabling machines or systems that can use insights for decision making. Use of intelligence-based systems in electrical networks or equipment is not new. However, AI systems are backed by strong computational processing and leverage on a very long-term data range as they take decisions. Currently, there are mainly three artificial intelligence -based techniques that are widely used to improve the reliability of electrical network in general and power quality in specific. These include expert systems, artificial neural network and fuzzy logic system. These systems can be leveraged to solve power quality issues manifested in voltage, current of power distribution or key asset or equipment.

What is the potential?
Emergence of devices that respond and automatically resolve PQ issues in the electrical systems provides a huge potential given the unpredictable nature of PQ events. Various AI techniques can be used as per their strength and limitations to build such devices and apply rapid intelligence in real-time. Given below are some of the most popular AI techniques used in case of power system reliability.

An Expert System obtains the knowledge of a human expert in a specific domain and converts the same into a form that can be used or implemented by the machine. Typically, these are programs that have built-in proficiency in specialised fields and operate on knowledge or rules defined by humans.

Artificial Neural Networks are biologically inspired systems which convert a set of inputs into a set of outputs by a network of neurons. Each neuron produces one output based on the function of input.

Fuzzy logic derives its logic from human brains. The fuzzy logic based systems come with expressive power and ability to model highly complex problems.

The potential for use of neural networks are available in several forms:
Simulations

  • Planning of power system reliability and good power quality in generation expansion planning, power system reliability, transmission expansion planning, reactive power planning etc.

Fault Diagnosis and automated resolution

  • Control of power system like voltage control, stability control, power flow control, load frequency control.
  • Automation of power system like restoration, management, fault diagnosis, network security

Example from Industry
Power Quality disturbances include a vast range of events. One of the challenges in improving PQ is identification and classification of this vast range of disturbances. Artificial Neural Networks (ANNs) are useful in categorising power quality disturbances as transients, long and short duration voltage interruption, voltage fluctuations and power frequency variations. These disturbances can be detected precisely by using a neural network and further classified into transmission line faults and voltage disturbances.

For instance, ANN can be used to numerically calculate the values of inductance, capacitance and resistance in a transmission line by considering various factors such as environmental factors, unbalancing conditions, and other possible issues as inputs.

Another example is detecting the type of disturbances and their magnitude in the electrical network in real time. The rapidly developing power electronics technology has made it possible to mitigate specific power quality problems. The dynamic voltage restorer provides a solution to compensate voltage sag/swell by injecting voltage in to the sensitive system. Dynamic voltage restorer automatically detects and injects the voltage components through an injecting transformer. With fuzzy logic systems, modelled and simulated on popular software such as MATLAB, voltage restorers can correct repeated occurrences of the power quality problems.

AUGMENTED AND VIRTUAL REALITY

What is it?
Modern, smart grid-based power systems are one of the most complex and critical infrastructures in the digitised society. The power systems serve as the backbone of almost every economic activity and therefore in the interest of every country to secure their operation against cyber risks and threats. Distribution utilities must examine combination of cyberattacks and operating conditions to identify risks to the electrical networks in order to ensure stability. Cybersecurity of an electrical network is affected by the structure of power systems, as well as by communication protocols and standards used within the network.

The range of threats to the electrical network from cybersecurity are vast. In the context of PQ, the threats to reliability can emerge from attacks on communication systems that have become crucial in management of electrical networks. For instance, false alarms, opening of unauthorised human access, erroneous signals and responses from equipment critical for PQ such as PQ Analysers, Active Harmonic Filters, UPS in a smart grid can lead to highly complex power disturbances.

What is the potential (damage)?

  • The most publicized cyberattack on electric infrastructure took place in Ukraine in 2015. Hackers were able to successfully compromise information systems of three energy distribution companies in Ukraine and temporarily disrupt electricity supply to the end consumers.
  • The Utah renewables energy was hit by a rather rare cybersecurity attack in March this year. The attack led to temporarily disrupting communications with several solar and wind installations of the company. The root cause of the attack was traced back to the known vulnerability in the Cisco communication system, which was due for a firmware upgrade.
  • In India, the Kudankulam nuclear power plant reported cyber attacks on its admin systems. The digital transformation of the power plant and distribution system has been aiding PQ. But Cyber-attacks is the ugly side of this transformation that the managers in utilities will have to deal in future.

A recent study by Siemens-Poneman Study: Cyber attacks on power utilities observes that these attacks are growing in numbers as well as complexity. A total of 1,700+ utility professionals responsible for cybersecurity within their companies participated in this study worldwide study:

  • 56% of respondents reported their companies suffered one or more shutdowns or loss of operational data per year.
  • Over 25% of the respondents reported being impacted by mega attacks
  • Nearly two-thirds of respondents say that sophisticated attacks are a top challenge, and more than half expect an attack in the next 12 months
  • Only 42 percent rated their cyber readiness as high, and only 31 percent rated readiness to respond to or contain a breach as high

The findings above show that the cyberattacks on power systems shall soon be as common as the virus attacks on PCs. While the Cyber-attacks may not directly be targeted to PQ, the results of breach of cyber-security can definitely impact the PQ in many ways.

CONCLUSION

Industry 4.0 technologies promise an automated monitoring and response to issues that adversely impact the reliability and safety of electrical network. With the new upsides there are new risks that will have to be addressed. The mutual dependency and interconnectedness of critical infrastructure, enabled by digitisation, is making it easy to monitor and diagnose PQ issues, but at the same time raise new concerns in managing the complexity, communications and security of the network.

To realise the true potential of Industry 4.0 technologies requires an imagination of the ‘transformed’ electrical network environment. For instance, extensive use of AR (Augmented Reality) or VR (Virtual Reality) in solving electrical network issues can really become mainstream way of working as remote and collaborative teams of technicians….just like remote surgeries by specialist doctors. More of intelligent devices that automatically respond to the PQ anomalies in the electrical network in real-time can be the game changers. The set of technologies under Industry 4.0 promise a ‘transformative’ change for improving the PQ and the electrical networks themselves when backed by an equivalent vision. The question really is how quickly the industry and its people would accept and adapt to new ways of working made possible through Industry 4.0.

REFERENCES

  1. Novel Internet of Things Platform for In-Building Power Quality Submetering – https://www.mdpi.com/2076-3417/8/8/1320/pdf-vor
  2. Artificial Intelligence in Power Systems by R.Pasupathi Nath, V.Nishanth Balaji http://iosrjournals.org/iosr-jce/papers/necon/volume-1/B.pdf
  3. Adaptive Method for Power Quality Improvement through Minimization of Harmonics Using Artificial Intelligence – https://www.researchgate.net/publication/315109673_Adaptive_Method_for_Power_Quality_Improvement_through_Minimization_of_Harmonics_Using_Artificial_Intelligence
  4. Comparison of Different AI Techniques for Power Quality Improvement using Statcom https://pdfs.semanticscholar.org/b532/1c380cffda1d020316464f1e28e7ab550a4e.pdf
  5. Assessing the Impact of Cybersecurity Attacks on Power Systems by Athanasios Dagoumas – https://www.mdpi.com/1996-1073/12/4/725/pdf
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