Smart metering architecture to enable and simulate novel services in - - PowerPoint PPT Presentation

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017 1

Edoardo Patti

• Dept. Of Control and Computer

Engineering, Politecnico di Torino, Torino, Italy edoardo.patti@polito.it

Smart metering architecture to enable and simulate novel services in smart grids

Francesco Arrigo

• Dept. of Energy

Politecnico di Torino, Torino, Italy francesco.arrigo@polito.it

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Outline

2

• Introduction on Smart Metering Infrastructure
• Flexmeter

platform: a real-world case study infrastructure

• IoT-based real-time co-simulation architecture
• Presentation of services and applications

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Outline

3

• Introduction on Smart Metering Infrastructure
• Flexmeter

platform: a real-world case study infrastructure

• IoT-based real-time co-simulation architecture
• Presentation of services and applications

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Introduction

4

The electricity market was introduced in the European countries following the directive concerning “common rules for the internal market in electricity”. Up to now the market is working properly for big producers, retailers and DSO, while the small consumers and prosumers cannot access directly the market and cannot be influenced by price signals. Distributed generation from renewable and non-programmable energy sources is becoming widespread. This requires a more flexible management of distribution grids.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Introduction

5

A Smart Metering Infrastructure (SMI) is an enabling technology that needs to be coupled with innovative services to reach energy management by means of rewards, automation and information. A SMI is needed to:

• Integrate the already available components with novel devices and

technologies;

• Combine and correlate information from meters of different

utilities;

• Provide and promote innovative services to different stakeholders

(e.g. prosumers, DSOs, retailers, new actors);

• Enhance the retail market

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Innovation of SMI

6

An SMI aims at facilitating the access of multiple actors to relevant data to foster the spreading of various innovative services. Major innovations of SMI consist on:

• enabling interoperability between heterogeneous devices in the

grid

• providing a common data exchange platform;
• ffering on-line tools to process and analyse energy data;
• providing API for developing cross-domain distributed services

(e.g. building/district/city management).

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Software Requirements

7

What are the main requirements to be addressed by an SMI?

• Interoperability among heterogeneous systems, technologies and

devices (e.g. PLC, Wi-Fi, ZigBee, etc.)

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Software Requirements

8

What are the main requirements to be addressed by an SMI?

• Interoperability
• Scalability to handle:
• a large number of sensors and devices
• a large number of users
• a large volume of data stored (Big Data domain)
• a large volume of information exchanged and processed

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Software Requirements

9

What are the main requirements to be addressed by an SMI?

• Interoperability
• Scalability
• Reliability to avoid or prevent possible failures, inconsistencies,
• verloads, data missing, etc.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Software Requirements

10

What are the main requirements to be addressed by an SMI?

• Interoperability
• Scalability
• Reliability
• Evolve over the time by supporting rapid modification and

enhancement with low cost and small architectural impacts.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Software Requirements

11

What are the main requirements to be addressed by an SMI?

• Interoperability
• Scalability
• Reliability
• Evolve over the time
• Modularity

by designing the system as a collection

• f

interoperable components that communicate through lightweight mechanisms.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Software Requirements

12

What are the main requirements to be addressed by an SMI?

• Interoperability
• Scalability
• Reliability
• Evolve over the time
• Modularity
• Extendibility to be capable of adding new functionality and

supporting software updating, bugs correction, security policies and permissions updating.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Software Requirements

13

What are the main requirements to be addressed by an SMI?

• Interoperability
• Scalability
• Reliability
• Evolve over the time
• Modularity
• Extendibility
• Decentralization to ensure that each service may implement its

functionalities using the most appropriate technology. Software components perform autonomously.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Software Requirements

14

What are the main requirements to be addressed by an SMI?

• Interoperability
• Scalability
• Reliability
• Evolve over the time
• Modularity
• Extendibility
• Decentralization
• Flexibility on supporting heterogeneous services with different

characteristics and requirements.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Software Requirements

15

What are the main requirements to be addressed by an SMI?

• Interoperability
• Scalability
• Reliability
• Evolve over the time
• Modularity
• Extendibility
• Decentralization
• Flexibility
• Synchronous Communication to access historical data or devices’

functionalities by exploiting request/response approach.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Software Requirements

16

What are the main requirements to be addressed by an SMI?

• Interoperability
• Scalability
• Reliability
• Evolve over the time
• Modularity
• Extendibility
• Decentralization
• Flexibility
• Synchronous Communication
• Asynchronous Communication to allow (Near-) Real-

time data transmission by exploiting publish/subscribe approach and event-based communication to support low latency and scalability.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Software Requirements

17

What are the main requirements to be addressed by an SMI?

• Interoperability
• Scalability
• Reliability
• Evolve over the time
• Modularity
• Extendibility
• Decentralization
• Flexibility
• Synchronous Communication
• Asynchronous Communication
• Standardization to foster data exchange by exploiting

common interfaces (Web services and API) and open data-formats.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Software Requirements

18

What are the main requirements to be addressed by an SMI?

• Interoperability
• Scalability
• Reliability
• Evolve over the time
• Modularity
• Extendibility
• Decentralization
• Flexibility
• Synchronous Communication
• Asynchronous Communication
• Standardization
• Security to guarantee authentication, data access,

confidentially and privacy.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Communication Paradigms

19

Request/Response is a synchronous communication paradigm. The client requests for some data and the server responds to the request.

Client Server

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Communication Paradigms

20

Request/Response is a synchronous communication paradigm. The client requests for some data and the server responds to the request. Publish/subscribe is an asynchronous communication paradigm. It allows the development of loosely-coupled event-based systems. It removes the dependencies between producer and consumer of information.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Enabling technologies

21

Internet-of-Things (IoT) is a global infrastructure for the Information Society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving, interoperable information and communication technologies. Geographical Information System (GIS) is an advanced cartography that maps the geographical location of buildings, energy distribution networks and IoT devices. Cloud computing is an IT paradigm to enable ubiquitous access to shared pools of configurable resources (e.g. servers, storages and applications), which can be rapidly provisioned

• ver the Internet.

Middleware is distributed software to provide functionalities to aggregate and filter the received data from the hardware devices, perform information discovery and accessing devices.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Outline

22

• Introduction on Smart Metering Infrastructure
• Flexmeter

platform: a real-world case study infrastructure

• IoT-based real-time co-simulation architecture
• Presentation of services and applications

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

What is Flexmeter?

23

Flexmeter is a flexible Smart Metering Infrastructure for multiple energy vectors with active prosumers. Flexmeter is a distributed software to manage heterogeneous data- sources and perform (near-)real-time data processing by exploiting:

• multi-service to provide general purpose services;
• substation meters to improve fault tolerance and demand

response capabilities considering local electric generation and storage;

• advanced Non-Intrusive Load Monitoring (NILM) techniques to

profile user behaviours;

• demand response algorithms that exploit information about

energy flows from the meters and the NILM profiles.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Flexmeter’s approach

24

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Flexmeter’s approach

25

Flexmeter integrates meters at substation and the user premises.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Flexmeter’s approach

26

Flexmeter integrates meters at substation and the user premises. The central cloud:

• collects data from different meters;
• post-processes incoming information;
• provides a set of API and tools to foster novel services.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Flexmeter’s approach

27

Middleware Layer Message Broker Communication Engine

Event Sources

Device Manager

Inbound Pipeline

Assets Manager REST API Interface Manager Data storage

Command Destinations Outbound Pipeline

Application Layer Outage Detection Fault Location Network Reconfiguration User Awareness NILM Demand Response Device Integration Layer Hardware Data Sources

Real-Time Simulator Data Sources OPAL RT RTDS

The Flexmeter has been developed following the microservices

• approach. It is organized in three layers:
• Device Integration Layer

It enables the interoperability across heterogeneous devices and simulators

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Flexmeter’s approach

28

Middleware Layer Message Broker Communication Engine

Event Sources

Device Manager

Inbound Pipeline

Assets Manager REST API Interface Manager Data storage

Command Destinations Outbound Pipeline

Application Layer Outage Detection Fault Location Network Reconfiguration User Awareness NILM Demand Response Device Integration Layer Hardware Data Sources

Real-Time Simulator Data Sources OPAL RT RTDS

The Flexmeter has been developed following the microservices

• approach. It is organized in three layers:
• Device Integration Layer
• Middleware Layer

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Flexmeter’s approach

29

Middleware Layer Message Broker Communication Engine

Event Sources

Device Manager

Inbound Pipeline

Assets Manager REST API Interface Manager Data storage

Command Destinations Outbound Pipeline

Application Layer Outage Detection Fault Location Network Reconfiguration User Awareness NILM Demand Response Device Integration Layer Hardware Data Sources

Real-Time Simulator Data Sources OPAL RT RTDS

The Flexmeter has been developed following the microservices

• approach. It is organized in three layers:
• Device Integration Layer
• Middleware Layer

It provides a Publish/Subscribe communication approach trough MQTT

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Flexmeter’s approach

30

Middleware Layer Message Broker Communication Engine

Event Sources

Device Manager

Inbound Pipeline

Assets Manager REST API Interface Manager Data storage

Command Destinations Outbound Pipeline

Application Layer Outage Detection Fault Location Network Reconfiguration User Awareness NILM Demand Response Device Integration Layer Hardware Data Sources

Real-Time Simulator Data Sources OPAL RT RTDS

The Flexmeter has been developed following the microservices

• approach. It is organized in three layers:
• Device Integration Layer
• Middleware Layer

It integrate different non- relational databases for Big Data management.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Flexmeter’s approach

31

Middleware Layer Message Broker Communication Engine

Event Sources

Device Manager

Inbound Pipeline

Assets Manager REST API Interface Manager Data storage

Command Destinations Outbound Pipeline

Application Layer Outage Detection Fault Location Network Reconfiguration User Awareness NILM Demand Response Device Integration Layer Hardware Data Sources

Real-Time Simulator Data Sources OPAL RT RTDS

The Flexmeter has been developed following the microservices

• approach. It is organized in three layers:
• Device Integration Layer
• Middleware Layer

It manages interactions with devices allowing bi-directional communication

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Flexmeter’s approach

32

Middleware Layer Message Broker Communication Engine

Event Sources

Device Manager

Inbound Pipeline

Assets Manager REST API Interface Manager Data storage

Command Destinations Outbound Pipeline

Application Layer Outage Detection Fault Location Network Reconfiguration User Awareness NILM Demand Response Device Integration Layer Hardware Data Sources

Real-Time Simulator Data Sources OPAL RT RTDS

The Flexmeter has been developed following the microservices

• approach. It is organized in three layers:
• Device Integration Layer
• Middleware Layer

It manages different information regarding people, places and things

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Flexmeter’s approach

33

Middleware Layer Message Broker Communication Engine

Event Sources

Device Manager

Inbound Pipeline

Assets Manager REST API Interface Manager Data storage

Command Destinations Outbound Pipeline

Application Layer Outage Detection Fault Location Network Reconfiguration User Awareness NILM Demand Response Device Integration Layer Hardware Data Sources

Real-Time Simulator Data Sources OPAL RT RTDS

The Flexmeter has been developed following the microservices

• approach. It is organized in three layers:
• Device Integration Layer
• Middleware Layer

It handles the interactions between devices and application

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Flexmeter’s approach

34

Middleware Layer Message Broker Communication Engine

Event Sources

Device Manager

Inbound Pipeline

Assets Manager REST API Interface Manager Data storage

Command Destinations Outbound Pipeline

Application Layer Outage Detection Fault Location Network Reconfiguration User Awareness NILM Demand Response Device Integration Layer Hardware Data Sources

Real-Time Simulator Data Sources OPAL RT RTDS

The Flexmeter has been developed following the microservices

• approach. It is organized in three layers:
• Device Integration Layer
• Middleware Layer

It provides REST web services to access information and manage entities in the platform

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Flexmeter’s approach

35

Middleware Layer Message Broker Communication Engine

Event Sources

Device Manager

Inbound Pipeline

Assets Manager REST API Interface Manager Data storage

Command Destinations Outbound Pipeline

Application Layer Outage Detection Fault Location Network Reconfiguration User Awareness NILM Demand Response Device Integration Layer Hardware Data Sources

Real-Time Simulator Data Sources OPAL RT RTDS

The Flexmeter has been developed following the microservices

• approach. It is organized in three layers:
• Device Integration Layer
• Middleware Layer
• Application Layer

It provides a set of tools and API to develop Services

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Impacts on end-users

36

The end-user benefits are:

• knowing

the disaggregated energy

• f

appliances;

• being

aware

• f

consumption for each appliance in terms of energy, money and CO2 footprints;

• discovering the most inefficient appliance;
• comparing

the disaggregated appliance consumption among different time periods;

• observing the energy consumption in real-time and receiving alarms

when the energy situation is not as expected.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Impacts on utility providers and energy operators

37

The benefits

• f

utility providers and energy operators are:

• profiling consumer energy behaviours for

predicting energy demand in the short term;

• offering

personalized pricing policies to consumers after profiling;

• providing more efficient demand response

strategies to

• ptimize

the energy management during peak periods balancing the consumers’ energy loads;

• simulate new control policies with (near-) real-time data;
• observing the energy consumption in real-time and receiving alarms

when the energy situation is not as expected (e.g. energy thefts)

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Performance

38

Latency for MQTT data transmission Latency for sending commands

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Performance

39

CPU occupation over the time CPU occupation over the time

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Outline

40

• Introduction on Smart Metering Infrastructure
• Flexmeter

platform: a real-world case study infrastructure

• IoT-based real-time co-simulation architecture
• Presentation of services and applications

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Real-time co-simulation architecture: Overview

41

To simulate new services a flexible distributed infrastructure for real- time co-simulations in smart grids is needed

Load Simulator Generation Simulator Physical devices/ systems Control and management algorithms

TCP/UDP module MQTT publisher MQTT subscriber

Communication Adapter Real-time Simulator

REST adapter MQTT message broker

Advantages:

• simulate new

power systems

• study

interoperability among different services

• exploiting (near-) real-

time data from smart meters via SMI The platform is flexible and open to include, replace, or enhance the modules for any new use cases/scenarios.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Real-time co-simulation architecture: Real-Time Simulator

42

The Real-Time Simulator (RTS) reproduces the behaviour of a real electric distribution system used to validate:

Load Simulator Generation Simulator Physical devices/ systems Control and management algorithms

TCP/UDP module MQTT publisher MQTT subscriber

Communication Adapter Real-time Simulator

REST adapter MQTT message broker

• new technologies
• management algorithms
• control strategies

RTS performs software in-the- loop or hardware in-the-loop simulations Example of RTS are RTDS or OPAL-RT

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Real-time co-simulation architecture: Communication Adapter

43

The Communication adapter enables data exchange among RTS and

• ther modules exploiting:

Load Simulator Generation Simulator Physical devices/ systems Control and management algorithms

TCP/UDP module MQTT publisher MQTT subscriber

Communication Adapter Real-time Simulator

REST adapter MQTT message broker

• publish/subscribe
• request/response

It translates information from JOSN to RTS data-format

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Real-time co-simulation architecture: Communication Adapter

44

The Communication adapter allows the integration of RTS with SMI. Each simulated grid component is seen by other modules as an IoT device able to send and receive data

Real-Time Simulator Data Sources OPAL RT RTDS

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Real-time co-simulation architecture: Load and Generation Simulator

45

Load and Generation Simulator push time-variant inputs into the running model.

Load Simulator Generation Simulator Physical devices/ systems Control and management algorithms

TCP/UDP module MQTT publisher MQTT subscriber

Communication Adapter Real-time Simulator

REST adapter MQTT message broker

They work as publisher and can be replaced by IoT devices without affecting the rest of the platform

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Real-time co-simulation architecture: Physical devices/systems

46

The Physical devices/system module foresees the integration with real devices through:

Load Simulator Generation Simulator Physical devices/ systems Control and management algorithms

TCP/UDP module MQTT publisher MQTT subscriber

Communication Adapter Real-time Simulator

REST adapter MQTT message broker

• Hardware in the Loop
• Smart

Metering Infrastructure in-the- Loop

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Real-time co-simulation architecture: Control and Management Algorithms

47

The Control and Management Algorithms is a box where services run and can be tested within the platform

Load Simulator Generation Simulator Physical devices/ systems Control and management algorithms

TCP/UDP module MQTT publisher MQTT subscriber

Communication Adapter Real-time Simulator

REST adapter MQTT message broker

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Use-case example

48

Implementation example of Photovoltaic penetration in cities with distributed storage management

Load profiles Photovoltaic simulator MQTT message broker TCP/UDP module MQTT publisher MQTT subscriber

Communication Adapter Real-time Simulator

REST adapter Battery management

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Use-case example

49

MV distribution grid (RTS)

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Use-case example

50

MV distribution grid (RTS)

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Use-case example

51

Input for substation (Load and PV simulators)

MV distribution grid (RTS)

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Use-case example

52

MV distribution grid (RTS) Net consumption power with and without storage (Battery Management)

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Use-case example

53

MV distribution grid (RTS) State Of Charge profile of storage (Battery Management)

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Outline

54

• Introduction on Smart Metering Infrastructure
• Flexmeter

platform: a real-world case study infrastructure

• IoT-based real-time co-simulation architecture
• Presentation of services and applications

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

What is a Service?

55

The International Telecommunication Union (ITU)1 defines Smart Sustainable City as “an innovative city that uses ICT to improve quality

• f life, efficiency of urban operation and services…”

The British Standards Institution describes this innovative smart city as “an effective integration of physical, digital and human systems in the built environment to deliver a sustainable, prosperous and inclusive future for its citizens”. The integration of physical and digital/cyber systems is widely known as “Internet of Things” or “Cyber Physical Systems”

1http://www.itu.int/en/Pages/default.aspx

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

What is a Service?

56

A smart city platform is an ecosystem composing of people, process, tools and technologies. It is a system of systems, where individual, heterogeneous, functional systems are linked together to realize and deliver novel services (features/functionalities) to end-users. Such services are bound to a specific context and stakeholders’ requirements.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

What is a Service?

57

Services for Smart City can be applied in many application domains:

• Health
• Energy
• Transportation
• Environment
• Disaster recovery
• Agriculture
• Education
• Infrastructure utilities
• and many more…

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

What is a Service?

58

ITU categorizes smart city stakeholders into:

• Citizens and citizen organizations (e.g. prosumers)
• Utility providers (e.g. retailers, DSO, etc.)
• ICT

Companies (Telecom Operators, Start-ups, Software Companies)

• Municipalities, City Council and city administration
• National and regional governments
• City services companies
• NGOs
• International, Regional and Multilateral Organizations
• Industry associations
• Academia, research organizations and specialized bodies
• Urban Planners
• Standardization bodies

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Services lifecycle: Overview

59

Services can vary in properties and level of complexity, based on the applicable use case. The lifecycle aspect of these services has to be modelled as:

• Service Definition
• Service Design
• Service Implementation
• Service Delivery
• Service Decommission

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Services lifecycle: Definition

60

In the Service Definition phase, the service is described highlighting the main features and functionalities.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Services lifecycle: Design

61

In the Service Design phase, service’s requirements are analysed and functions, features, interoperability with other entities are identified.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Services lifecycle: Implementation

62

In the Service Implementation phase, information exchange and interactions among system entities are ensured

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Services lifecycle: Delivery

63

In the Service Delivery phase, service is continuously monitored to ensure meeting pre-set KPIs (Key indicators of performance). Potential service improvements are identified that can enhance the service itself or become new service(s).

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Services lifecycle: Decommission

64

The Service Decommission phase includes activities related to disposal or replacement of service or service components

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Services lifecycle: (re-) Definition

65

The Lifecycle loop starts again for updating the service. A re-definition of the service could be needed based on potential improvements resulting from previous phases.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Flexmeter’s services

66

Application Layer Outage Detection Fault Location Network Reconfiguration User Awareness NILM Demand Response

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Non-Intrusive Load Monitoring (NILM)

67

NILM is a methodology to disaggregate individual energy consumption

• f

appliances collected by a single smart meter. Non-Intrusive: customers to not measure individual appliance loads

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Non-Intrusive Load Monitoring (NILM)

68

Benefits:

• Deliver

feedback and visualization

• Cheaper than sub metering

end use appliances (Intrusive load monitoring) Application:

• Real-time

energy/cost feedback

• Energy management
• load research
• equipment diagnostics

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

NILM Approaches

69

Event-based methods is based

• n

detecting appliances On/Off transitions Non event-based methods try to detect whether an appliance is On during a sampled duration

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

On-line NILM: Overview

70

• Machine

learning technique: Event based appliance detection in the loop approach

• Starts by performing full day load profiling: Results are used

to construct and update appliance models in HMM

• On-line: Short time window-based appliance disaggregation

(15-60) minutes

• Cloud based: Integration modules with Flexmeter using

MQTT and REST APIs

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

On-line NILM: Approach

71

Event based appliance Detection:

• Load profiling of full day length data
• Events Detection (On/OFF)
• Feature Extraction
• Events Clustering and Matching

Smart meter Event Based Appliance Detection Appliance Models On-line Disaggreg ation Algorithm Appliances Consumptions

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

On-line NILM: Approach

72

Appliance Signature Modeling HMM:

• From the results of Event based appliance detection
• Based on States of Appliances and probabilistic

transition between states

• Unique per appliance and household

Smart meter Event Based Appliance Detection Appliance Models On-line Disaggreg ation Algorithm Appliances Consumptions

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

On-line NILM: Approach

73

Near real time disaggregation:

• Discontinue Event based
• Smart meter input (15-60) min
• Sliding time window

Smart meter Event Based Appliance Detection Appliance Models On-line Disaggreg ation Algorithm Appliances Consumptions

• Aggregate

consumption model:

• From exported models
• Analysis
• f

aggregate consumption

• Using Factorial HMM
• Energy Estimation

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Demand Response: Overview

74

Accurate measuring can be used to unlock Demand-Response (DR) and Demand Side Management. DR permits achieving a temporary virtual power plant by changing the energy consumption pattern of consumers to fulfil grid operation requirements or economical incentives.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Demand Response: Overview

75

DR-framework integrates Flexmeter with RTS:

• to provide a (near-) real-time co-simulation platform for validation
• f DR-algorithms;
• to integrate real internet-connected smart devices at customer

premises to retrieve energy information. It can be used in a real-world by replacing RTS with the real grid

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Demand Response: Energy Aggregation Platform

76

The Energy Aggregation Platform is a “virtual box” to deploy or replace easily a DR-policy without affecting the rest of the framework.

Energy API Manager Data Storage REST Client Algorithm Manager

Demand Response Policy Demand Side Management Policy

Multi-Tenant Proxy

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Demand Response: Energy Aggregation Platform

77

The Energy Aggregation Platform is a “virtual box” to deploy or replace easily a DR-policy without affecting the rest of the framework. It consists of five modules.

Energy API Manager Data Storage REST Client Algorithm Manager

Demand Response Policy Demand Side Management Policy

Multi-Tenant Proxy

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Demand Response: Energy Aggregation Platform

78

The Energy Aggregation Platform is a “virtual box” to deploy or replace easily a DR-policy without affecting the rest of the framework. It consists of five modules.

Energy API Manager Data Storage REST Client Algorithm Manager

Demand Response Policy Demand Side Management Policy

Multi-Tenant Proxy

It retrieves energy data from Flexmeter

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Demand Response: Energy Aggregation Platform

79

The Energy Aggregation Platform is a “virtual box” to deploy or replace easily a DR-policy without affecting the rest of the framework. It consists of five modules.

Energy API Manager Data Storage REST Client Algorithm Manager

Demand Response Policy Demand Side Management Policy

Multi-Tenant Proxy

It manages actions to be performed and completed requests

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Demand Response: Energy Aggregation Platform

80

The Energy Aggregation Platform is a “virtual box” to deploy or replace easily a DR-policy without affecting the rest of the framework. It consists of five modules.

Energy API Manager Data Storage REST Client Algorithm Manager

Demand Response Policy Demand Side Management Policy

Multi-Tenant Proxy

It is the “virtual box” that contains and executes DR-policies

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Demand Response: Energy Aggregation Platform

81

The Energy Aggregation Platform is a “virtual box” to deploy or replace easily a DR-policy without affecting the rest of the framework. It consists of five modules.

Energy API Manager Data Storage REST Client Algorithm Manager

Demand Response Policy Demand Side Management Policy

Multi-Tenant Proxy

It offers REST APIs to request a new action

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Demand Response: Energy Aggregation Platform

82

The Energy Aggregation Platform is a “virtual box” to deploy or replace easily a DR-policy without affecting the rest of the framework. It consists of five modules.

Energy API Manager Data Storage REST Client Algorithm Manager

Demand Response Policy Demand Side Management Policy

Multi-Tenant Proxy

It manages authentications and creates new EAP instances

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

User Awareness

83

Our behaviours affect the environment. User-awareness on energy consumption can positively affect the energy savings at home through a proper user-awareness and notification system.

7-17%

ENERGY SAVE

630 PEOPLE 2 SURVEYS A FOCUS GROUP

A participatory-design is needed to identify functional requirements, strengths and improvements requested We defined guidelines to ergonomic energy-aware Android application promoting user-awareness and green behaviors.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

User Awareness

84

82%

24 – 29 YEARS OLD

79%

BACHELOR’s/MASTER’s DEGREE 79% €500 and €699 BILL

DISAGGREGATED INFO - MAJOR INTEREST PROFILES

96%

24 – 29 YEARS OLD

95%

BACHELOR’s/MASTER’s DEGREE 95% €500 and €699 BILL

RELEVANCE PERCEPTION OF ENERGY AWARENESS

22%

FAULTY APPLIANCE

16%

INEFFICIENT APPLIANCE 16% UNUSUAL ACTIVITY

IMPORTANCE PERCEPTION OF PROPOSED SERVECES

Do you have any additional comment?

MONITORING ENERGY PRODUCTION SYSTEM NOTIFICATIONS AND ALERTS POSITIVE REINFORCEMENT

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

User Awareness

85

Distribution of level of education of surveyed people Distribution of age of surveyed people

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

User Awareness

86

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Impacts on end-users

87

The end-user benefits are:

• knowing

the disaggregated energy

• f

appliances;

• being

aware

• f

consumption for each appliance in terms of energy, money and CO2 footprints;

• discovering the most inefficient appliance;
• comparing

the disaggregated appliance consumption among different time periods;

• observing the energy consumption in real-time and receiving alarms

when the energy situation is not as expected.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

User Awareness

88

Main Activity Consumption Activity Detailed Consumption Activity

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Outage Detection and Fault Location

89

• Output: an easy to understand overview of the MV and LV network

status, and, in case of faults, an alarm indicating the portion of network and users de-energized.

• Approach: The algorithm will get as input the measurements from

the network metering system and from the users’ meters. It will integrate these data together and with the already available data from the HV/MV stations. The analysis of these integrated data will allow for providing the required output.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Basic Taxonomy (I)

90

Fault detection: the process

• f

detection of the occurrence of a fault (permanent or transient); Outage detection: the process of detection of the occurrence of an

• utage during a permanent fault;

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Basic Taxonomy (II)

91

Outage location: a combination of techniques which are applied to find the outage area and the protective devices involved in fault clearing; Fault location: Finding the location of the faults that caused the resulting outage situation.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Current implemented technology

92

In conventional distribution systems, operators mostly depend on trouble calls, made by the customers whose electricity service is interrupted, to localize outages. 1) Entering the trouble call information on forms; 2) Matching the information obtained from a sufficient number of calls with feeder configuration diagrams and maps; 3) Determine the upstream transformer from which the customers are served, the protective devices involved in fault clearing and the outage area; 4) Sectioning and reenergizing the lines, using signal injection devices, patrolling and finding outage evidences …

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Advanced system requirements

93

Process

• f

a conventional

• utage

management system: Process

• f

an advanced

• utage

management system:

Restoration of customers on healthy sections of feeder (40-80 min.) Patrolling (15-30 min.) Outage mapping and decision making (10-15 min.) Repair or replacement (1-4 hrs.) Repair crew dispatching and travelling (15-30 min.) Fault isolation (1-5 min.) Normal operation Protection system

• peration

and fault notification

Restoration of customers on healthy sections of feeder (less than 1 min.) Patrolling (1-5 min.) Fault location and decision making (some seconds.) Repair or replacement (1-4 hrs.) Repair crew dispatching and travelling (15-30 min.) Fault isolation (some seconds.) Protection system operation and fault notification Normal operation

Future systems should be able to exploit fast algorithms and new metering technologies to solve outages in less than a minute!

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Algorithms and new techniques

94

For Outage detection and Outage location is enough that meters are able to send «last gasp» messages when sensing Voltage collapsing and deviced no more energized (we need UPS to make comunications possible). Fault detection is already intercepted by the activation of protection relays. Fault location needs new «fast» algorithms. In particular we have 4 families:

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Impedance based methods

95

The method is based on knowledge of the line impedences of the whole grid and on the Voltage and current Knowledge at the HV/MV substation. d is found by using iterative methods. In branched distribution networks; equivalent networks for every path are computed and multiple locations can be found!

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Voltage sparse measurement method

96

Votage sags are even simpler, but they need more measurements point in the distribution grid to work in the best way. In particular we need to record the voltage sags/fault currents happening after the fault. Then for each node a fault is simulated and voltage sags/fault currents are computed and compared with the measured ones using a simple index

The node found with the biggest index is the culprit! For this system to perform well, we need more measurements points and real time communication since uncertainties in measurements affects results

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Proposed methods and Tests

97

Two tests were used combining different grids and methods to validate these new algortihms functionalities and complementarities with the Fleximeter architecture. 1. A voltage sag method in top of an impedance based method is used Software in the Loop with a portion of the Turin Distribution System to validate the FLEXIMETER architecture. 2. A fast fault method using voltage sags is constructed to improve the velocity of the fault location procedure.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017 98

Test 1: Turin (I)

The methods join the the good points of the previous methods overcoming their respective drawbacks. Moreover it needs just two measurements points (at the HV/MV substation and another point, e.x. MV/LV station). The method provide only one result (like the sparse voltage measurement method) and moreover it is not affected too much by uncertainty measurements (like impedence based methods)

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Test 1: Turin distribution system (II)

99

Control strategy

Fault Location

A SLG fault between substations 204813 and 203975, 177 meters far from substation 204813.

Retrieved voltage and current waveforms for a single-line to ground fault

Voltage and current phasors are computed by using the Discrete Fourier Transform

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Test 2: Fast Fault Location

100

To reduce the time needed a voltage sparse measurement method is applied but without selecting all nodes but only the one which goes toward a bigger index I.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Network Reconfiguration: The Problem

101

Medium voltage Transmission Networks have usually a weakly meshed network structure, but is radially operated. This means that a certain number

• f lines, called tie-lines, are kept open.

This type of operation is usually chosen by DSOs because it makes easier and cheaper to design and operate the network protections. Network reconfiguration is therefore usually performed at the MV level and consist in modifying the DN topology by operating remotely controlled sectionalizing switches, but keeping the radial operation of the network. The idea is to minimize grid power losses by operation on the reconfiguration

• f the grid (other targets could also be obtained like voltage reduction).

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Application requirements (I)

102

To operate a good reconfiguration requirements we need a real time comunication platform and different grid services

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Application requirements (II)

103

In particular the Network Topology Reconfiguration application needs:

• Updated information on open or close lines
• network characteristics
• Updated information on the State of the grid

(voltage, currents and power flows) The State Estimation Service providesthe State of the grid. To do that it makes uses

• f a vast grid of measurements comunicating real time.

It takes into account redudancy information and uncertainties on measurements by internal algorithms like Weighted Least Squares. Starting from the Concentrator levels, measurements are sent to Low Voltage level SE and finally to the Medium Voltage level State Estimator.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

!

" #$%&

= |!

"|

*+,( !

" )

/

0" = 1 − !

" #$%&

Network reconfiguration Algortihm

104

The optimizzation problem is the following:

Where x is the id of the of the network reconfiguration (represented by a boolean vector to define if switches are closed or open). To solve minimization problem we stick to a GREEDY algorithm, which does not guarantee to find the optimum, but guaranties high velocity.

load flow for the meshed configurat ion Apply Kruskal’s algorithm to find the proposed configuration Compute a new load flow and comprare with the current topology New Configuration identificantion Process

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Test Case: System Topology

105

A simple MV grid with

• ne degree of freedom

for reconfiguration is choosen. Uncertainties

• f

smart meters are taken into account. Proper loads and generation profiles are chosen for a day of simulations with a resolution

• f
• ne

minute is performed. The various services are runned in different machines to quantify the communication latency

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Test Case: Generation profiles

106

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Test case: Results (I)

107

1. The State Estimator works well. 2. The reconfiguration algorithm decrease

• f

40% the losses in the grid

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Test case: Results (II)

108

• 3. Voltage profiles are

improved

• 4. Even in the absence of

some measurements the State Estimator provide good estimations of the grid load

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Contacts

109

Edoardo Patti, Dept. Of Control and

Computer Engineering, Politecnico di Torino Italy

Email: edoardo.patti@polito.it Publications: http://eda.polito.it/edoardo-patti/ Google scholar: https://goo.gl/gARx7B YouTube: https://goo.gl/Jyqqwx Francesco Arrigo, Power System Group, Department of Energy, Politecnico di Torino, Italy. e-mail: francesco.arrigo@polito.it

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

References

110

• Krylovskiy A., Jahn M., Patti E., Designing a Smart City Internet of Things Platform

with Microservice Architecture. In: 3rd International Conference on Future Internet of Things and Cloud (FiCloud 2015), IEEE, Rome, 24-26 August 2015. DOI: 10.1109/FiCloud.2015.55

• Patti E., Pons E., Martellacci D., Boni Castagnetti F., Acquaviva A., Macii E.,

multiFLEX: Flexible Multi-Utility, Multi-Service Smart Metering Architecture for Energy Vectors with Active Prosumers. In: 4th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2015), Lisbon, Portugal, 20th - 22th May 2015. DOI: 10.5220/0005483202880293.

• Patti E., Acquaviva A., IoT platform for Smart Cities: requirements and

implementation case studies. In: 2nd International Forum on Research and Technologies for Society and Industry (RTSI 2016), Bologna, Italy, 7-9 Sep. 2016. DOI: 10.1109/RTSI.2016.7740618

• Pau M., Patti E., Barbierato L., Estebsari A., Pons E., Ponci F., Monti A., A Cloud-

based Smart Metering Infrastructure for Distribution Grid Services and Automation. In: Sustainable Energy, Grids and Networks, Elsevier. DOI: 10.1016/j.segan.2017.08.001.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

References

111

• Patti E., Acquaviva A., Macii E., Enable Sensor Networks Interoperability in Smart

Public Spaces through a Service Oriented Approach. In: 5th IEEE International Workshop on Advances in Sensors and Interfaces, IWASI, Bari, Italy, 13-14 June 2013, ISBN: 978-1-4799-0039-8. DOI: 10.1109/IWASI.2013.6576081.

• Estebsari A., Pons E., Bahmanyar A., Jamali S., Patti E., Acquaviva A., Emerging

Smart Meters in Electrical Distribution Systems: Opportunities and Challenges. In: 24th Iranian Conference on Electrical Engineering (ICEE 2016), Shiraz, Iran, 10 - 12 May 2016. DOI: 10.1109/IranianCEE.2016.7585682

• Bottaccioli L., Estebsari A., Pons E., Bompard E., Macii E., Patti E., Acquaviva A., A

Flexible Distributed Infrastructure for Real-Time Co-Simulations in Smart Grids. In: IEEE Transactions on Industrial Informatics. DOI: 10.1109/TII.2017.2702206.

• Estebsari A., Pons E., Patti E., Mengistu M., Bompard E., Bahmanyar A., Jamali S.,

An IoT Realization in an Interdepartmental Real Time Simulation Lab for Distribution System Control and Management Studies. In: 16th IEEE International conference on Environment and Electrical Engineering (EEEIC 2016), Florence, Italy, 7-10 June 2016. DOI: 10.1109/EEEIC.2016.7555699

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

References

112

• Bottaccioli L., Estebsari A., Patti E., Pons E., Acquaviva A., A novel integrated real-

time simulation platform for assessing photovoltaic penetration impacts in smart

• grids. In: 8th International Conference on Sustainability in Energy and Buildings

(SEB-16), Torino, Italy, 11-13 September 2016. DOI: 10.1016/j.egypro.2017.03.240

• Bottaccioli L., Macii E., Patti E., Estebsari A., Pons E., Acquaviva A., PVInGrid: A

Distributed Infrastructure for evaluating the integration of Photovoltaic systems in Smart Grid. In: 8th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS 2017), Caprica (Lisbon), Portugal, 03-05 May 2017. DOI: 10.1007/978-3-319-56077-9_31

• Bahmanyar A., Estebsari A., Pons E., Patti E., Jamali S., Bompard E., Acquaviva A.,

Fast Fault Location for Fast Restoration of Smart Electrical Distribution Grids. In: IEEE International Smart Cities Conference (ISC2), Trento, Italy, 12-15 September

• 2016. DOI: 10.1109/ISC2.2016.7580741
• Pau M., Patti E., Barbierato L., Estebsari A., Pons E., Ponci F., Monti A., Low Voltage

System State Estimation based on Smart Metering Infrastructure. In: IEEE international workshop on applied measurements for power systems (AMPS 2016), Aachen, Germany, 28-30 September 2016. DOI: 10.1109/AMPS.2016.7602804

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

References

113

• Aliberti A., Camarda C., Ferro V., Acquaviva A., Patti E., A Participatory Design

Approach for Energy-Aware Mobile App for Smart Home Monitoring. In: 6th Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2017), Porto, Portugal, 22-24 April, 2017. DOI: 10.5220/0006299001580165

• Bottaccioli L., Patti E., Acquaviva A., Macii E., Jarre M., Noussan M., A tool-chain to

foster a new business model for photovoltaic systems integration exploiting an Energy Community approach. In: 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015), Luxembourg, 8-11September

• 2015. DOI: 10.1109/ETFA.2015.7301559
• Lee, S. J., Choi, M. S., Kang, S. H., Jin, B. G., Lee, D. S., Ahn, B. S., ... & Wee, S. B.

(2004). An intelligent and efficient fault location and diagnosis scheme for radial distribution systems. IEEE transactions on power delivery, 19(2), 524-532.

• Pereira, R. A. F., da Silva, L. G. W., Kezunovic, M., & Mantovani, J. R. S. (2009).

Improved fault location on distribution feeders based on matching during-fault voltage sags. IEEE Transactions on Power Delivery, 24(2), 852-862.

IEEE International Conference on Innovative Smart Grid Technologies (ISGT 2017) September 26th, 2017

Thank You