BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Helsinki BEGIN:DAYLIGHT DTSTART:20220327T040000 TZOFFSETFROM:+0200 TZOFFSETTO:+0300 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:EEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20221030T030000 TZOFFSETFROM:+0300 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:EET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20221227T093149Z UID:0FB288F1-EDA2-4DCE-8025-1FDBCE018A27 DTSTART;TZID=Europe/Helsinki:20220815T083000 DTEND;TZID=Europe/Helsinki:20220819T153000 DESCRIPTION:Our chapter together with Aalto University is offering an inten sive 5-day 4-credit course on the Condition Monitoring and Diagnostics of Power Assets taught by Dr. Murtaza Hashmi\, a recognized Condition Monitor ing Expert at Power Systems. The assessment of the course will be based on attendance\, active participation in the discussions\, and performance in Group Work and Home Work activities. It is worth mentioning that the cour se is free for our IEEE Members. Please note that we only have 6 places av ailable for this course.\n\nPlease find the additional information as foll ows.\n\nCourse General Information:\n\nThis course will address state-of-t he-art condition monitoring techniques used for different assets in electr ical power systems. A brief overview of condition monitoring and diagnosti cs\, associated monitoring parameters\, and tools for analysis will be dis cussed. Special focus will be given on understanding advanced diagnostics technique of Partial Discharge (PD)\, its types and characteristic\, detec tion principles and methods\, analysis criteria and standards\, PD signals propagation and attenuation\, and calibration technique. The advanced con dition monitoring and diagnostics techniques will be described for transfo rmers\, switchgears\, power cables\, and other electrical equipment instal led in the substation including the details of pilot projects\, case studi es and real-time measurements. An overview of smart sensing infrastructure \, its application for digital transformation\, and digital substations wi ll be carried out which build the basis of Industry 4.0 for advanced condi tion monitoring\, diagnostics\, and predictive maintenance applications in power systems.\n\nLearning Outcomes:\n\nUpon successful completion of thi s course\, students will be able to:\n\n- Explain the meaning of condition monitoring & diagnostics and its applications\n- Explain PD monitoring te chniques and standards\, limits and advantages and disadvantages of online and offline PD monitoring\, associated sensors\, PD data analysis and int erpretation for the condition assessment of electrical equipment.\n- Expla in different advanced condition monitoring & diagnostics systems for trans formers\, switchgears\, power cables\, and other electrical equipment inst alled in the substation.\n- Develop condition monitoring plan to install a dvanced systems for the diagnostics and predictive maintenance of electric al power assets\n- Explain elements of smart sensing infrastructure and de velop strategy to implement it in power systems under Industry 4.0 initiat ive\n\nLearning Resources:\n\nA set of course presentation slides\, and su pporting materials based on publications\, white papers\, case studies\, a nd technology brochures will be available.\n\nLearning and Teaching Activi ties:\n\nThis course relies on lectures and interactive discussions as the primary delivery mechanism. A Group Work will be assigned to the students in the classroom to reinforce the theoretical concepts encountered in lec tures and deliver brief presentation by each Group. In addition\, a Home W ork will be assigned to the students in the form of writing an essay to su mmaries the details of advanced condition monitoring systems for power ass ets to gauge their progress and understanding.\n\nAssessment:\n\nThe asses sment of the course will be based on the attendance\, active participation in the discussions\, and performance in Group Work and Home Work activiti es.\n\nLanguage:\n\nThe working language of the course is English.\n\nCo-s ponsored by: Aalto University\n\nSpeaker(s): Murtaza Hashmi \, \n\nAgenda: \nCourse Agenda (15 – 19th August 2022\, Timing 08:30 – 15:30):\n\nDa y 1\, August 15\, (PD Monitoring & Diagnostics in Electrical Equipment)\n\ n- Understanding condition monitoring & diagnostics\, its significance\, a ssociated parameters\, and advanced tools for analysis.\n- Partial Dischar ge (PD)\, its types & characteristic\, detection principles & methods\, an alysis criteria and standards.\n- PD signals propagation and attenuation\, different types of noises or interferences and their elimination.\n- Cali bration of online PD monitoring system\n\nDay 2\, August 16\, (Condition M onitoring & Diagnostics of Transformers)\n\n- Online Dissolved Gas Analysi s (DGA) monitoring system\n- Online drying & dehydrating breather systems\ n- Bushing monitoring system for bushing and winding health condition asse ssment\n- Other monitoring systems based on PD measurements using ultrason ic and high frequency sensors\n\nDay 3\, August 17\, (Condition Monitoring & Diagnostics of Switchgears and Power Cables)\n\n- Online PD monitoring system for Gas Insulated Substations (GIS)\n- Online asset monitoring syst ems for Air Insulated Substations (AIS)\n- Condition monitoring systems fo r power cables\n- PD monitoring & diagnostics case studies\n\nDay 4\, Augu st 18\, (Condition Monitoring of Other Electrical Equipment)\n\n- PD monit oring in rotating machines\n- Room-Temperature Vulcanizing (RTV) silicon r ubber coating assessment for insulators\, bushings and surge arresters\n- Fault detection & localization system for overhead distribution lines\n- C ondition monitoring and predictive maintenance using advanced UV\, optical and infrared imaging techniques\n\nDay 5\, August 19\, (Smart Sensing Inf rastructure and Smart Grids)\n\n- Components of Industry 4.0 and its appli cation in digital transformation\n- Smart sensing infrastructure for smart grids\n- Digital substations with advanced sensing technologies\n- Group Work: Important elements of CM system – Sensors\, data communication\, d ata analysis\, decision-making\n- Explanation of Home Work: An essay on ad vanced condition monitoring systems for transformers\, switchgears\, and p ower cables – Technology\, implications\, and challenges for implementat ion!\n\nRoom: 2103\, Bldg: Maarintie 8\, Aalto University\, Otaniemi\, Esp oo\, Sodra Finlands Lan\, Finland LOCATION:Room: 2103\, Bldg: Maarintie 8\, Aalto University\, Otaniemi\, Esp oo\, Sodra Finlands Lan\, Finland ORGANIZER:Mahdi.Pourakbari@ieee.org SEQUENCE:5 SUMMARY:Summer Course at Aalto University on Condition Monitoring and Diagn ostics of Power Assets URL;VALUE=URI:https://events.vtools.ieee.org/m/313763 X-ALT-DESC:Description: <br /><p>Our chapter together with Aalto University is offering an intensive 5-day <strong>4-credit</strong> <span class="il" >course</span>&nbsp\;on the <strong>Condition Monitoring and Diagnostics o f Power Assets</strong><strong>&nbsp\;</strong>taught by&nbsp\;<strong>Dr. Murtaza Hashmi</strong>\, a recognized Condition Monitoring Expert at Pow er Systems. The assessment of the course will be based on attendance\, act ive participation in the discussions\, and performance in Group Work and H ome Work activities. It is worth mentioning that the <span class="il">cour se is&nbsp\;<strong>free</strong></span><strong>&nbsp\;</strong>for our<st rong>&nbsp\;IEEE Members</strong>. Please<strong>&nbsp\;note that</strong> we only have <strong><span style="text-decoration: underline\;">6</span>< u><strong>&nbsp\;</strong>places</u></strong>&nbsp\;available for this cou rse.</p>\n<p>Please find the additional information as follows.&nbsp\;&nbs p\;</p>\n<p><span style="text-decoration: underline\;"><strong>Course Gene ral Information:</strong></span></p>\n<p>This course will address state-of -the-art condition monitoring techniques used for different assets in elec trical power systems. A brief overview of condition monitoring and diagnos tics\, associated monitoring parameters\, and tools for analysis will be d iscussed. Special focus will be given on understanding advanced diagnostic s technique of Partial Discharge (PD)\, its types and characteristic\, det ection principles and methods\, analysis criteria and standards\, PD signa ls propagation and attenuation\, and calibration technique. The advanced c ondition monitoring and diagnostics techniques will be described for trans formers\, switchgears\, power cables\, and other electrical equipment inst alled in the substation including the details of pilot projects\, case stu dies and real-time measurements. An overview of smart sensing infrastructu re\, its application for digital transformation\, and digital substations will be carried out which build the basis of Industry 4.0 for advanced con dition monitoring\, diagnostics\, and predictive maintenance applications in power systems.</p>\n<p><strong>Learning Outcomes:</strong></p>\n<p>Upon successful completion of this course\, students will be able to:</p>\n<ul >\n<li>Explain the meaning of condition monitoring &amp\; diagnostics and its applications</li>\n<li>Explain PD monitoring techniques and standards\ , limits and advantages and disadvantages of online and offline PD monitor ing\, associated sensors\, PD data analysis and interpretation for the con dition assessment of electrical equipment.</li>\n<li>Explain different adv anced condition monitoring &amp\; diagnostics systems for transformers\, s witchgears\, power cables\, and other electrical equipment installed in th e substation.</li>\n<li>Develop condition monitoring plan to install advan ced systems for the diagnostics and predictive maintenance of electrical p ower assets</li>\n<li>Explain elements of smart sensing infrastructure and develop strategy to implement it in power systems under Industry 4.0 init iative</li>\n</ul>\n<p><span style="text-decoration: underline\;"><strong> Learning Resources:</strong></span></p>\n<p>A set of course presentation s lides\, and supporting materials based on publications\, white papers\, ca se studies\, and technology brochures will be available.</p>\n<p><span sty le="text-decoration: underline\;"><strong>Learning and Teaching Activities :</strong></span></p>\n<p>This course relies on lectures and interactive d iscussions as the primary delivery mechanism. A Group Work will be assigne d to the students in the classroom to reinforce the theoretical concepts e ncountered in lectures and deliver brief presentation by each Group. In ad dition\, a Home Work will be assigned to the students in the form of writi ng an essay to summaries the details of advanced condition monitoring syst ems for power assets to gauge their progress and understanding.</p>\n<p><s pan style="text-decoration: underline\;"><strong>Assessment:</strong></spa n></p>\n<p>The assessment of the course will be based on the attendance\, active participation in the discussions\, and performance in Group Work an d Home Work activities.</p>\n<p><span style="text-decoration: underline\;" ><strong>Language:</strong></span></p>\n<p>The working language of the cou rse is&nbsp\;<strong>English</strong>.</p><br /><br />Agenda: <br /><p><st rong>Course Agenda (15 &ndash\; 19<sup>th</sup> August 2022\, Timing 08:30 &ndash\; 15:30):</strong></p>\n<p><strong>Day 1\, August 15\,</strong><em > </em><strong><em>(</em></strong><strong><em>PD Monitoring &amp\; Diagnos tics in Electrical Equipment)</em></strong></p>\n<ul>\n<li>Understanding c ondition monitoring &amp\; diagnostics\, its significance\, associated par ameters\, and advanced tools for analysis.</li>\n<li>Partial Discharge (PD )\, its types &amp\; characteristic\, detection principles &amp\; methods\ , analysis criteria and standards.</li>\n<li>PD signals propagation and at tenuation\, different types of noises or interferences and their eliminati on.</li>\n<li>Calibration of online PD monitoring system</li>\n</ul>\n<p>< strong>Day 2\, August 16\, </strong><strong><em>(Condition Monitoring &amp \; Diagnostics of Transformers)</em></strong></p>\n<ul>\n<li>Online Dissol ved Gas Analysis (DGA) monitoring system</li>\n<li>Online drying &amp\; de hydrating breather systems</li>\n<li>Bushing monitoring system for bushing and winding health condition assessment</li>\n<li>Other monitoring system s based on PD measurements using ultrasonic and high frequency sensors</li >\n</ul>\n<p><strong>Day 3\, August 17\, </strong><strong><em>(Condition M onitoring &amp\; Diagnostics of Switchgears and Power Cables)</em></strong ></p>\n<ul>\n<li>Online PD monitoring system for Gas Insulated Substations (GIS)</li>\n<li>Online asset monitoring systems for Air Insulated Substat ions (AIS)</li>\n<li>Condition monitoring systems for power cables</li>\n< li>PD monitoring &amp\; diagnostics case studies</li>\n</ul>\n<p><strong>D ay 4\, August 18\, </strong><strong><em>(Condition Monitoring of Other Ele ctrical Equipment)</em></strong></p>\n<ul>\n<li>PD monitoring in rotating machines</li>\n<li>Room-Temperature Vulcanizing (RTV) silicon rubber coati ng assessment for insulators\, bushings and surge arresters</li>\n<li>Faul t detection &amp\; localization system for overhead distribution lines</li >\n<li>Condition monitoring and predictive maintenance using advanced UV\, optical and infrared imaging techniques</li>\n</ul>\n<p><strong>Day 5\, A ugust 19\, </strong><strong><em>(Smart Sensing Infrastructure and Smart Gr ids)</em></strong></p>\n<ul>\n<li>Components of Industry 4.0 and its appli cation in digital transformation</li>\n<li>Smart sensing infrastructure fo r smart grids</li>\n<li>Digital substations with advanced sensing technolo gies</li>\n<li><u>Group Work: </u>Important elements of CM system &ndash\; Sensors\, data communication\, data analysis\, decision-making</li>\n<li> <u>Explanation of Home Work</u>: An essay on advanced condition monitoring systems for transformers\, switchgears\, and power cables &ndash\; Techno logy\, implications\, and challenges for implementation!</li>\n</ul> END:VEVENT END:VCALENDAR