1. What is the average salary of a Power Plant Operator II?
The average annual salary of Power Plant Operator II is $80,915.
In case you are finding an easy salary calculator,
the average hourly pay of Power Plant Operator II is $39;
the average weekly pay of Power Plant Operator II is $1,556;
the average monthly pay of Power Plant Operator II is $6,743.
2. Where can a Power Plant Operator II earn the most?
A Power Plant Operator II's earning potential can vary widely depending on several factors, including location, industry, experience, education, and the specific employer.
According to the latest salary data by Salary.com, a Power Plant Operator II earns the most in San Jose, CA, where the annual salary of a Power Plant Operator II is $101,548.
3. What is the highest pay for Power Plant Operator II?
The highest pay for Power Plant Operator II is $98,720.
4. What is the lowest pay for Power Plant Operator II?
The lowest pay for Power Plant Operator II is $64,212.
5. What are the responsibilities of Power Plant Operator II?
Monitors and maintains plant equipment to ensure that power plant power delivery operations function within specification. Controls and repairs power generating facilities and systems to produce bioenergy, hydro, nuclear, solar, thermal or wind power, etc. May require an associate degree. Typically reports to a supervisor. Works under moderate supervision. Gaining or has attained full proficiency in a specific area of discipline. Typically requires 1-3 years of related experience.
6. What are the skills of Power Plant Operator II
Specify the abilities and skills that a person needs in order to carry out the specified job duties. Each competency has five to ten behavioral assertions that can be observed, each with a corresponding performance level (from one to five) that is required for a particular job.
1.)
Troubleshooting: Troubleshooting is a form of problem solving, often applied to repair failed products or processes on a machine or a system. It is a logical, systematic search for the source of a problem in order to solve it, and make the product or process operational again. Troubleshooting is needed to identify the symptoms. Determining the most likely cause is a process of elimination—eliminating potential causes of a problem. Finally, troubleshooting requires confirmation that the solution restores the product or process to its working state. In general, troubleshooting is the identification or diagnosis of "trouble" in the management flow of a system caused by a failure of some kind. The problem is initially described as symptoms of malfunction, and troubleshooting is the process of determining and remedying the causes of these symptoms. A system can be described in terms of its expected, desired or intended behavior (usually, for artificial systems, its purpose). Events or inputs to the system are expected to generate specific results or outputs. (For example, selecting the "print" option from various computer applications is intended to result in a hardcopy emerging from some specific device). Any unexpected or undesirable behavior is a symptom. Troubleshooting is the process of isolating the specific cause or causes of the symptom. Frequently the symptom is a failure of the product or process to produce any results. (Nothing was printed, for example). Corrective action can then be taken to prevent further failures of a similar kind.
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Data Analytics: Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.
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Power Generation: The process of generating electric power from sources of primary energy. For utilities in the electric power industry, it is the stage prior to its delivery (transmission, distribution, etc.) to end users or its storage (the pumped-storage method).