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6.2.2.1 Sensor-Level Models Some of the more common types of sensor errors are illustrated in Fig. 6.1. These are (a) (b) (c) (c) (e) bias, which is any nonzero sensor output when the input is zero; scale factor error, often resulting from aging or manufacturing tolerances; nonlinearity, which is present in most sensors to some degree; scale factor sign asymmetry, often from mismatched push pull ampli ers; a dead zone, usually due to mechanical stiction or lock-in [for a ring laser gyroscope (RLG)]; and (f) quantization error, inherent in all digitized systems. Theoretically, one should be able to recover the input from the sensor output so long as the input=output relationship is known and invertible. Dead-zone errors and quantization errors are the only ones shown with this problem. The cumulative effects of both types (dead zone and quantization) often bene t from zero-mean input noise or dithering. Also, not all digitization methods have equal cumulative effects. Cumulative quantization errors for sensors with frequency outputs are bounded by 1 LSB, but the variance of cumulative errors from independent 2 sample-to-sample A=D conversion errors can grow linearly with time.
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4.6 Least-cost Dispatch in a Single Node with Spinning Reserve and Regulation
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4.5.6. Resource management COs may form a self-organizing network, in which COs arbitrarily join and leave or even move during this operation. Since system components are distributed, any
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