Ozone is generally produced by means of ozonizer utilized a silent discharge. The authors propose a new type ozonizer with a cylindrical rotating electrode in this paper. The ozonizer consists of two ceramic electrodes and metallic wire electrodes mounted on a rotating cylinder. The structure is capable of enhancement for silent discharge by rotation of the cylinder under the same applied voltage and we used 50Hz A.C. voltage in this experiments. It is observed that discharge current，discharge power and production efficiency of ozone are improved as compare with The no rotation of the cylinder. The maximum ozone concentration is achieved at 800rpm in rotation speed of the cylinder independent to the applied voltage. To the contrary，the maximum discharge power which correspond to ozone yield is found at the 600rpm in the rotating speed of the cylinder independent to the applied voltage too. The difference between the both maximum would be interpreted the existence of micro discharge in the gap. The obtained maximum ozone concentration is about 8.8 g/m³ and this value is two times larger than the value is two times larger than the value at the no rotation of the cylinder. The maximum production of ozone is estimated about 61g/kWh.

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Power utilities must understand how customers use their products. This is essential to formulate any long range planning，forecasting or marketing strategies. However，only customer’s service level loads are available. Monitoring/survey analysis of customer usage patterns is the best way to understand how customers use electricity. The key problem is that collection and analysis of customer usage data is very time consuming and very costly. In this　research，we propose a method to estimate the power consumption of appliances used in a household from service level load by Artificial Neural Network （ANN）. However，as selection of optimum parameters of stand-alone ANN and learning weights in ANN is difficult，in this research，we apply Genetic Algorithm （GA） to ANN called as Neuro-Genetic for automatic finding of optimal ANN parameters such as learning rate，momentum，etc . and also optimal weights for better training. As the result，we obtained some findings that are at least as same of that with the trial-and-error methods. Ultimately，we could greatly shorten the learning time. This method is required for monitoring.

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Since the torsional oscillations of turbine shaft generators due to system disturbance is liable to cause fatigue life expenditure，it is necessary that evaluation be made for all the equipment including those for the operation of the power source which are required to have high reliability. This problem applies in not only a large-scale power generation unit but also a private generation system. This study proposes the torsional oscillation monitor for turbine-generator shaft using shaft torque observer in private generation system. This system provided with the functions of evaluation of fatigue damage and life prediction. This　paper discusses a cycle counter method and life prediction method for turbine-generator shaft that is appropriate for operating characteristic of private generation system.

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