Maintenance Management Process in the Electricity Distribution Business: Analytic Models. June 2014

Transcription

1 Maintenance Management Process in the Electricity Distribution Business: Analytic Models June 2014

2 Producción Production Asset management Available information Malfunction. Breaking. Loss of service. Replacement. Reference value for parameters with respect to fault conditions. Manufacturer's instructions. Units Total cost Unit cost (thousand of (thousand of ) ) Aerial MVL , ,00 2,52 CT , ,00 1,48 Aerial RBT , ,00 1,61 Substation - Position with conventional switch 1.915, ,00 16,27 Substation - Position with bunkered switch 4.123, ,00 4,83 Aerial HTL 7.348, ,00 2,39 Transformers 1.322, ,00 11,40 Underground RBT , ,00 1,10 Underground MTL , ,00 1,29 Underground HTL 667, ,00 6,10 Mobile equipment - Positon with bunkered switch 343,00 500,00 1,46 Substation - Position with conventional switch 252,00 372,00 1,48 Substation - Position with bunkered switch 169,00 57,00 0,34 Capacitors 53,00 57,00 1,08 Mobile equipment - Positon without conventional switch 29,00 51,00 1,76 Mobile equipment - Positon with conventional switch 12,00 27,00 2,25 Mobile equipment - Positon without bunkered switch 10,00 4,00 0,40 Costs Types of maintenance Schedules: Tasks and frequency. Resource consumption. Interventions Fault record Locations and equipment Consumption. Performance. Activity levels/ performance Service level Equipment activity parameters Time Tiempo Impact on the service level: Availability. Load. Circulated energy. Voltage level supported. Actions, protections. Temperature. Oil composition. Shoots. Producción- Production Predictivo Predictive Producción- Production Non-Predictive predictivo Service interruption. Age of the equipment. Number of interruptions. 2 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

3 Asset management The business process Data integration Data integration and cleaning Analysis Maintenance strategy Data acquisition Data management Analyze and predict Warnings Performance assessment Understand, react and adapt Optimize processes Resource allocation Measure and manage Management model Logistic process and ranges optimization Optimization 3 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

4 Asset management Analytic model How will we organise maintenance schedules to reduce costs? Information Predictive Management algorithms. Modelling and simulation. Quantitative analysis. Forecast. Description Performance metrics. Warnings. Management and reports. Processable intelligence Information for Predictive Intelligence Assessment. Information for decision- making. Information. Processed data. How much income do we waste on account of unnecesary maintenance actions? Which equipment breaks down more often than normal? Which are the patterns of failure? Control and strategy Data. Business information. 4 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

5 Asset management Analytic model Advantages of Analytics Take advantage of the big amount of stored data, not monitored nowadays. Include an estimation of the failure probability in the management process and evaluate the economic impact of these failures: reduce costs by means of better decisions based on analytical data. Optimize management: minimize maintenance costs, minimize downtime, choose the best between repair or replace equipment, Get relevant information to make decisions about maintenance policies and to negotiate contracts. Anticipate new business challenges and improve the response to them. Automate responses to make processes more efficient. 5 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

6 Budgeting and management control processes Integration of the information Anticipation of Maintenance and Logistics processes Asset management Analytic model Data: On-going monitoring Equipment performance (variables) Information on incidents /breakdowns Maintenance actions Asset management database Control panels Failure probability according to value of variables Failure forecast for 6 months, 1 year, 18 months, Component criticality Decision process Condition-based maintenance Delay or catch up on maintenance actions Investment or replacement decisions Standards on replacement parts Direct cost of maintenance Analytic models: Failure probability according to value of variables Failure forecast Simulation of the economic impact of different failure modes: potential damages and loss of profit Maintenance schedules updating Decisions on maintenance policies Lost profits Information from manufacturer Economic assessment: Costs and lost profits Control panels What-if analysis: economic assessment according to maintenance actions Conditions to contract services Evaluate stopping procedures 6 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

7 Asset management Information model Activity data measured by sensors Vibrations Temperature Maintenance management system Actions Maintenance costs: contractors, materials, replacements, Control panels Monitoring of technic and economic variables Technic variable values: monitoring Failure pattern Cost evolution Over excitation Availability evolution Oil levels Oil density Oil viscosity Voltage in the generator Water inlet pressure to the converter refrigerator. SCADA Equipment downtime Asset management database Number of downtimes Incident management system Calculation platform Failure analysis Economic analysis Risk of failure simulator Management of the information obtained from the calculation platform Failure probability Failure forecast Component criticality Simulation of the economic impact of failure models: potential damage and lost profits Simulation of maintenance strategies 7 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

8 ene-10 feb-10 mar-10 abr-10 may-10 jun-10 jul-10 ago-10 sep-10 oct-10 nov-10 dic-10 ene-11 feb-11 mar-11 abr-11 may-11 jun-11 jul-11 ago-11 sep-11 oct-11 nov-11 dic-11 ene-12 feb-12 mar-12 abr-12 may-12 jun-12 jul-12 ago-12 sep-12 oct-12 nov-12 dic-12 Temperatura Temperature Asset management Information model Equipment Clase de equipo: type: Location: Localización: Brand: Marca: ID: Matricula: Transformador 132 kv Subestación Sadurní MWA Transformer 132 kv T Variables Equipment Clase de equipo: type: Location: Localización: Brand: Marca: ID: Matricula: Transformador 132 kv Subestación Sadurní MWA Transformer 132 kv T Maintenance costs Day Día Temperature Temperatura (Cº) correctivo Corrective preventivo Preventive Failure profile Fuego Fire or o explosion explosión Contaminación Oil contamination del from aceite gas por gas Rayos Lighting (thunderstorm) Fallo Isolation aislamiento fault Fallo Design, de material, diseño, material, or human humano fault Sobrecarga Overload Mantenimiento Inadequate maintenance inadecuado Inundación Flood 8% 8% 8% 15% 8% Fuego Fire or o explosión explosion 15% Contaminación Oil contamination del aceite from por gas gas Rayos Lighting (thunderstorm) 7% Fallo Isolation aislamiento fault Fallo Design, de diseño, material, material, or human humano fault 8% Sobrecarga Overload Mantenimiento Inadequate maintenance inadecuado 8% Inundación Flood Conexiones Loose connections sueltas 15% Desconocido Unknown 8 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

9 Failure probability Disponibilidad (%) Availability (%) Probability (%) Probabilidad (%) Asset management Information model Clase Equipment de equipo: type: Marca: Brand: 100% 80% 60% 40% 20% 0% Transformador 132 kv MWA Transformer 132 kv Availability Failure forecast Estimación Estimation tasa de of fallo the a failure partir de probability información de from information vibraciones regarding vibrations Probabilidad Failure probability de fallo Non-Failure Probabilidad de probability no fallo 100% 03% 90% 06% 12% 80% 70% 60% 50% 97% 40% 94% 88% 30% 20% 10% 00% 6 months meses 12 months meses 24 months meses Tasa Transformer de fallo generador failure rate (información (information para for detección): detection) seis 6 months meses Total Transformer B9389AU failure rate: 6 months Total 93.50% 07% Humidity Lighting (thunderstorm) Temperature Temperatura Oil temperature del aceite 97% 03% Loose connections Oil Densidad density del aceite Calentamiento Transformer del generador warming 98% 97% 02% 03% Flood Inadequate maintenance Over-voltage in line Overload Temperature (Cº) Failure probability Tensión Transformer en el generador voltage Nivel de vibraciones Transformer vibrations del generador Velocidad de Transformer rotación rotation speed del generador 98% 97% 99.70% 02% 03% 00% Design, material, or human fault Isolation fault Unknown Oil contamination from gas Fire or explosion 75.00% 80.00% 85.00% 90.00% 95.00% % Probabilidad Non-Failure de probability no fallo Probabilidad Failure probability de fallo Non-Failure probability Failure probability 9 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

10 Coste de diez transformadores de tensión de kv durante tres años ( ) Maintenance cost ( ) Asset management Information model Maintenance strategies 600, ,000 Manteniento Predictive maintenance derivado modelo analítico Mantenimiento Preventive maintenance preventivo Mantenimiento Corrective maintenance correctivo 400, , , , , , , , , , ,117 33,018 33,018 33,018 Tradicional Traditional Preventive Preventivo + Analítico predictive Preventivo Modified modificado preventive (*) + + Modelo Predictive analítico predictive analítico Equipment Clase de equipo: type: Location: Localización: Brand: Marca: ID: Matricula: Transformador 132 kv Subestación Sadurní MWA Transformer 132 kv T What if assessment Current Situación situation actual Sustitución Tap changers de cambiador replacement de tomas Sustitución Bollard replacement de bornas Probabilidad Failure probability Fallo Probabilidad Non-Failure probability no fallo Probabilidad Failure probability Fallo Probabilidad Non-Failure probability no fallo Probabilidad Failure probability Fallo Probabilidad Non-Failure probability no fallo 17% 20% 23% 27% 9% 14% 16% 17% 10% 15% 18% 19% 83% 80% 77% 73% 91% 86% 84% 83% 90% 85% 82% 81% 6 months meses 12 months meses 18 months meses 24 months meses 6 meses months 12 months meses 18 months meses 24 months meses 6 months meses 12 months meses 18 months meses 24 months meses 10 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

11 An example of the use of Analytic models Transformers Assessment A comparative analysis between carrying out the maintenance according to traditional models and the result of applying new predictive models of equipment behavior as a basis for the process Analyzed case: Distribution transformer (132 kv) Daily information about 10 similar transformers has been gathered for the last three years: temperature, gas pressure and gas content in oil Annual maintenance cost for one of the transformer: 25,083 per year. This figure includes corrective and preventive maintenance (8,974 from preventive maintenance) 11 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

12 An example of the use of Analytic models Transformers Predictive model: Weibull distribution. It is a probability distribution which allow us to calculate the failure probability by some relevant variables of the transformer. The cumulative distribution function for the Weibull distribution is: Where: x: a relevant variable γ: location parameter: It indicates the «location» of the function or the first value of x which makes the function different to 0: the origin of the function β: scale parameter or characteristic life (value for the variable (x-γ) for which the failure probability is 63%) α: shape parameter: It determines the function shape 12 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

13 An example of the use of Analytic models Transformers Predictive model: Weibull distribution. Day Día Temperature Temperatura (Cº) Failure Fallo 0 70 No 1 73 No 2 61 No No Yes Si No Temperature Temperatura Failure Fallo Ordinal (Cº) 70 Yes Sí 1 89 Yes Sí 3 91 Yes Sí 4 99 Yes Sí Yes Sí Yes Sí Yes Sí Yes Sí Yes Sí Yes Sí Yes Sí 15 Total Maintenance Management Process in the Electricity Distribution Business: Analytic Models

14 An example of the use of Analytic models Transformers Weibull Distribution for Temperature Weibull Distribution for Gas Pressure Weibull Distribution for Gas Content in Oil 14 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

15 Temperature (ºC) An example of the use of Analytic models Transformers 140 Important damage to equipment Maintenance action Exhaustive monitoring Time 15 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

16 An example of the use of Analytic models Transformers Number of actions per year (hours) Annual cost ( ) Predictive model Traditional Unavailabity time Impact on availabilty in extreme situations 16 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

17 Anticipation of Maintenance and Logistics Processes An example of the use of Analytic models Transformers Weibull function for failures due to temperature of the transformer Time series ARIMA and SARIMA models will be used to predict the evolution of the variables according to historical data on a time horizon. Failure probabilities associated to the values given by the Weibull model will be obtained. ARIMA model (6,0,3) 17 Maintenance Management Process in the Electricity Distribution Business: Analytic Models

18 An example of the use of Analytic models Transformers Ten transformers: Equipment historic data: Preventive maintenance. Corrective maintenance. Maintenance costs. Downtimes Predictive model simulation Management models including preventive and predictive maintenance are simulated. Costs of a maintenance action Stress case Traditional In case the Analytic model, by means of variables on-going monitoring, could detect a likely critical components failure, the avoided breakdown: Its repair cost would vary from 250,000 to 400,000. A downtime of 30 days. Comparation ( ) Preventive + Predictive Variation 382, ,956 21,903 Costs of a maintenance action Traditional Comparation ( ) Preventive + Predictive Variation 382, , , Maintenance Management Process in the Electricity Distribution Business: Analytic Models

19